From bab66fa32da2df87adc4611a9e63f2fc408ddc27 Mon Sep 17 00:00:00 2001
From: Cameron Garnham <me@da2ce7.com>
Date: Tue, 12 May 2026 19:31:44 +0200
Subject: [PATCH] feat(package): spectral sentinel, a online subspace anomaly
 detector

Introduce the new sub-package called "sentinel", that observes positionally structured observation streams.
---
 AGENTS.md                                     |   18 +-
 Cargo.lock                                    |  751 ++++-
 Cargo.toml                                    |    1 +
 packages/index-health-check/src/tests/mod.rs  |    2 +-
 packages/mudlark/src/tests/decompose_basis.rs |    2 +-
 .../src/tests/semi_internal_plateau.rs        |    2 +-
 packages/mudlark/tests/eviction_plateau.rs    |    2 +-
 packages/mudlark/tests/negative_f64.rs        |    2 +-
 packages/sentinel/Cargo.toml                  |   47 +
 packages/sentinel/README.md                   |  743 +++++
 .../sentinel/adr/001-measures-not-opinions.md |   54 +
 .../adr/002-feed-forward-invariant.md         |   93 +
 .../sentinel/adr/003-mudlark-integration.md   |  136 +
 .../adr/004-config-validation-over-panic.md   |  104 +
 ...5-deterministic-order-and-thread-safety.md |   97 +
 .../adr/006-analysis-set-recomputation.md     |  110 +
 .../adr/007-automatic-noise-injection.md      |   81 +
 .../adr/008-scoring-geometry-extension.md     |   71 +
 ...-decay-does-not-invalidate-analysis-set.md |   43 +
 .../010-linear-routing-over-g-tree-descent.md |   57 +
 .../011-degenerate-cell-dimension-guard.md    |  206 ++
 .../sentinel/adr/012-test-duration-budget.md  |  284 ++
 .../adr/013-warm-up-convergence-benchmark.md  |  463 +++
 .../adr/014-subspace-tracker-visibility.md    |  367 +++
 .../adr/015-cell-creation-performance.md      |  357 +++
 .../sentinel/adr/016-brand-incremental-svd.md |  275 ++
 .../sentinel/adr/017-deferred-cell-warm-up.md |  119 +
 .../adr/018-generic-domain-parameters.md      |  372 +++
 ...nvestment-set-terminology-and-reporting.md |  157 +
 ...-clip-pressure-ewma-implementation-plan.md |  915 ++++++
 .../sentinel/adr/020-clip-pressure-ewma.md    |  276 ++
 .../adr/021-ewma-mean-centred-variance.md     |  325 +++
 packages/sentinel/benches/sentinel.rs         |  475 +++
 packages/sentinel/docs/algorithm.md           | 2554 +++++++++++++++++
 packages/sentinel/docs/api.md                 |  853 ++++++
 packages/sentinel/docs/implementation.md      |  589 ++++
 packages/sentinel/src/analysis_set.rs         |  267 ++
 packages/sentinel/src/config.rs               |  754 +++++
 packages/sentinel/src/ewma.rs                 |  225 ++
 packages/sentinel/src/lib.rs                  |  174 ++
 packages/sentinel/src/maths/bench_tracing.rs  |  147 +
 packages/sentinel/src/maths/brand_svd.rs      |  218 ++
 packages/sentinel/src/maths/mod.rs            |  478 +++
 packages/sentinel/src/maths/naive_svd.rs      |  101 +
 .../src/maths/tests/brand_vs_naive.rs         |  849 ++++++
 packages/sentinel/src/maths/tests/mod.rs      |   13 +
 packages/sentinel/src/observation.rs          |  107 +
 packages/sentinel/src/report.rs               |  812 ++++++
 packages/sentinel/src/sentinel/cusum.rs       |  142 +
 packages/sentinel/src/sentinel/mod.rs         | 1697 +++++++++++
 packages/sentinel/src/sentinel/staging.rs     |  774 +++++
 packages/sentinel/src/sentinel/tracker.rs     |  871 ++++++
 .../sentinel/src/sentinel/warming_thread.rs   |  207 ++
 packages/sentinel/src/tests/analysis_set.rs   |  276 ++
 packages/sentinel/src/tests/config.rs         |  930 ++++++
 .../src/tests/convergence_clipping.rs         |  589 ++++
 .../sentinel/src/tests/convergence_common.rs  |  549 ++++
 .../src/tests/convergence_diagnostics.rs      |  514 ++++
 .../sentinel/src/tests/convergence_eta.rs     |  333 +++
 .../sentinel/src/tests/convergence_ewma.rs    |  342 +++
 .../sentinel/src/tests/convergence_fixes.rs   |  333 +++
 .../sentinel/src/tests/convergence_noise.rs   |  638 ++++
 packages/sentinel/src/tests/cusum.rs          |  265 ++
 packages/sentinel/src/tests/ewma.rs           |  406 +++
 packages/sentinel/src/tests/mod.rs            |   24 +
 packages/sentinel/src/tests/observation.rs    |  192 ++
 packages/sentinel/src/tests/report.rs         |  191 ++
 packages/sentinel/src/tests/tracker.rs        |  613 ++++
 .../sentinel/src/tests/variance_formula.rs    |  392 +++
 packages/sentinel/tests/ancestor_chain.rs     |  317 ++
 packages/sentinel/tests/api.rs                |  288 ++
 packages/sentinel/tests/clip_pressure.rs      |  386 +++
 packages/sentinel/tests/common/assertions.rs  |  187 ++
 packages/sentinel/tests/common/builders.rs    |   91 +
 packages/sentinel/tests/common/config.rs      |   67 +
 packages/sentinel/tests/common/generators.rs  |   17 +
 packages/sentinel/tests/common/mod.rs         |   24 +
 packages/sentinel/tests/coverage_matrix.rs    |  273 ++
 packages/sentinel/tests/deferred_warmup.rs    |  646 +++++
 packages/sentinel/tests/determinism.rs        |  210 ++
 packages/sentinel/tests/edge_cases.rs         |  426 +++
 packages/sentinel/tests/graph_routing.rs      |  227 ++
 packages/sentinel/tests/health.rs             |  301 ++
 .../tests/hierarchical_coordination.rs        |  776 +++++
 packages/sentinel/tests/integration.rs        |  296 ++
 packages/sentinel/tests/invariants.rs         |  460 +++
 packages/sentinel/tests/noise.rs              |  328 +++
 packages/sentinel/tests/pedagogy.rs           |  904 ++++++
 packages/sentinel/tests/pedagogy_advanced.rs  |  854 ++++++
 packages/sentinel/tests/report_structure.rs   |  523 ++++
 packages/sentinel/tests/sentinel_u64.rs       |  331 +++
 packages/sentinel/tests/serde_roundtrip.rs    |  315 ++
 packages/sentinel/tests/spatial_decay.rs      |  306 ++
 packages/sentinel/tests/spray_resistance.rs   |  253 ++
 packages/sentinel/tests/suffix_analysis.rs    |  249 ++
 packages/sentinel/tests/warm_up.rs            |  385 +++
 96 files changed, 34817 insertions(+), 49 deletions(-)
 create mode 100644 packages/sentinel/Cargo.toml
 create mode 100644 packages/sentinel/README.md
 create mode 100644 packages/sentinel/adr/001-measures-not-opinions.md
 create mode 100644 packages/sentinel/adr/002-feed-forward-invariant.md
 create mode 100644 packages/sentinel/adr/003-mudlark-integration.md
 create mode 100644 packages/sentinel/adr/004-config-validation-over-panic.md
 create mode 100644 packages/sentinel/adr/005-deterministic-order-and-thread-safety.md
 create mode 100644 packages/sentinel/adr/006-analysis-set-recomputation.md
 create mode 100644 packages/sentinel/adr/007-automatic-noise-injection.md
 create mode 100644 packages/sentinel/adr/008-scoring-geometry-extension.md
 create mode 100644 packages/sentinel/adr/009-decay-does-not-invalidate-analysis-set.md
 create mode 100644 packages/sentinel/adr/010-linear-routing-over-g-tree-descent.md
 create mode 100644 packages/sentinel/adr/011-degenerate-cell-dimension-guard.md
 create mode 100644 packages/sentinel/adr/012-test-duration-budget.md
 create mode 100644 packages/sentinel/adr/013-warm-up-convergence-benchmark.md
 create mode 100644 packages/sentinel/adr/014-subspace-tracker-visibility.md
 create mode 100644 packages/sentinel/adr/015-cell-creation-performance.md
 create mode 100644 packages/sentinel/adr/016-brand-incremental-svd.md
 create mode 100644 packages/sentinel/adr/017-deferred-cell-warm-up.md
 create mode 100644 packages/sentinel/adr/018-generic-domain-parameters.md
 create mode 100644 packages/sentinel/adr/019-investment-set-terminology-and-reporting.md
 create mode 100644 packages/sentinel/adr/020-clip-pressure-ewma-implementation-plan.md
 create mode 100644 packages/sentinel/adr/020-clip-pressure-ewma.md
 create mode 100644 packages/sentinel/adr/021-ewma-mean-centred-variance.md
 create mode 100644 packages/sentinel/benches/sentinel.rs
 create mode 100644 packages/sentinel/docs/algorithm.md
 create mode 100644 packages/sentinel/docs/api.md
 create mode 100644 packages/sentinel/docs/implementation.md
 create mode 100644 packages/sentinel/src/analysis_set.rs
 create mode 100644 packages/sentinel/src/config.rs
 create mode 100644 packages/sentinel/src/ewma.rs
 create mode 100644 packages/sentinel/src/lib.rs
 create mode 100644 packages/sentinel/src/maths/bench_tracing.rs
 create mode 100644 packages/sentinel/src/maths/brand_svd.rs
 create mode 100644 packages/sentinel/src/maths/mod.rs
 create mode 100644 packages/sentinel/src/maths/naive_svd.rs
 create mode 100644 packages/sentinel/src/maths/tests/brand_vs_naive.rs
 create mode 100644 packages/sentinel/src/maths/tests/mod.rs
 create mode 100644 packages/sentinel/src/observation.rs
 create mode 100644 packages/sentinel/src/report.rs
 create mode 100644 packages/sentinel/src/sentinel/cusum.rs
 create mode 100644 packages/sentinel/src/sentinel/mod.rs
 create mode 100644 packages/sentinel/src/sentinel/staging.rs
 create mode 100644 packages/sentinel/src/sentinel/tracker.rs
 create mode 100644 packages/sentinel/src/sentinel/warming_thread.rs
 create mode 100644 packages/sentinel/src/tests/analysis_set.rs
 create mode 100644 packages/sentinel/src/tests/config.rs
 create mode 100644 packages/sentinel/src/tests/convergence_clipping.rs
 create mode 100644 packages/sentinel/src/tests/convergence_common.rs
 create mode 100644 packages/sentinel/src/tests/convergence_diagnostics.rs
 create mode 100644 packages/sentinel/src/tests/convergence_eta.rs
 create mode 100644 packages/sentinel/src/tests/convergence_ewma.rs
 create mode 100644 packages/sentinel/src/tests/convergence_fixes.rs
 create mode 100644 packages/sentinel/src/tests/convergence_noise.rs
 create mode 100644 packages/sentinel/src/tests/cusum.rs
 create mode 100644 packages/sentinel/src/tests/ewma.rs
 create mode 100644 packages/sentinel/src/tests/mod.rs
 create mode 100644 packages/sentinel/src/tests/observation.rs
 create mode 100644 packages/sentinel/src/tests/report.rs
 create mode 100644 packages/sentinel/src/tests/tracker.rs
 create mode 100644 packages/sentinel/src/tests/variance_formula.rs
 create mode 100644 packages/sentinel/tests/ancestor_chain.rs
 create mode 100644 packages/sentinel/tests/api.rs
 create mode 100644 packages/sentinel/tests/clip_pressure.rs
 create mode 100644 packages/sentinel/tests/common/assertions.rs
 create mode 100644 packages/sentinel/tests/common/builders.rs
 create mode 100644 packages/sentinel/tests/common/config.rs
 create mode 100644 packages/sentinel/tests/common/generators.rs
 create mode 100644 packages/sentinel/tests/common/mod.rs
 create mode 100644 packages/sentinel/tests/coverage_matrix.rs
 create mode 100644 packages/sentinel/tests/deferred_warmup.rs
 create mode 100644 packages/sentinel/tests/determinism.rs
 create mode 100644 packages/sentinel/tests/edge_cases.rs
 create mode 100644 packages/sentinel/tests/graph_routing.rs
 create mode 100644 packages/sentinel/tests/health.rs
 create mode 100644 packages/sentinel/tests/hierarchical_coordination.rs
 create mode 100644 packages/sentinel/tests/integration.rs
 create mode 100644 packages/sentinel/tests/invariants.rs
 create mode 100644 packages/sentinel/tests/noise.rs
 create mode 100644 packages/sentinel/tests/pedagogy.rs
 create mode 100644 packages/sentinel/tests/pedagogy_advanced.rs
 create mode 100644 packages/sentinel/tests/report_structure.rs
 create mode 100644 packages/sentinel/tests/sentinel_u64.rs
 create mode 100644 packages/sentinel/tests/serde_roundtrip.rs
 create mode 100644 packages/sentinel/tests/spatial_decay.rs
 create mode 100644 packages/sentinel/tests/spray_resistance.rs
 create mode 100644 packages/sentinel/tests/suffix_analysis.rs
 create mode 100644 packages/sentinel/tests/warm_up.rs

diff --git a/AGENTS.md b/AGENTS.md
index 5202363c7..12ee004a8 100644
--- a/AGENTS.md
+++ b/AGENTS.md
@@ -71,14 +71,16 @@ Eagerly corrected when spotted in **any** file!
 
 Cross-references use the `§` (section sign) prefix. Every reference
 carries a **package qualifier** so the target document is never ambiguous.
-ADR references (`ADR-T-001`, `ADR-R-001`, …) are an exception — they
-use their own `ADR-<PREFIX>-<NNN>` form without the `§` prefix.
-
-| Prefix | Package              | Example document                 |
-| ------ | -------------------- | -------------------------------- |
-| `T-`   | Torrust (root crate) |                                  |
-| `M-`   | Mudlark              | `packages/mudlark/docs/idea.md`  |
-| `R-`   | render-text-as-image | `packages/render-text-as-image/` |
+ADR references (`ADR-T-001`, `ADR-S-001`, `ADR-R-001`, …) are an
+exception — they use their own `ADR-<PREFIX>-<NNN>` form without the `§`
+prefix.
+
+| Prefix | Package              | Example document                         |
+| ------ | -------------------- | ---------------------------------------- |
+| `T-`   | Torrust (root crate) |                                          |
+| `M-`   | Mudlark              | `packages/mudlark/docs/idea.md`          |
+| `S-`   | Sentinel             | `packages/sentinel/docs/algorithm.md`    |
+| `R-`   | render-text-as-image | `packages/render-text-as-image/`         |
 
 Helper crates (`index-health-check`, `index-auth-keypair`,
 `index-config`, `index-config-probe`, `index-cli-common`,
diff --git a/Cargo.lock b/Cargo.lock
index 5392b6fd9..f21a1d1b7 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -175,7 +175,7 @@ checksum = "9035ad2d096bed7955a320ee7e2230574d28fd3c3a0f186cbea1ff3c7eed5dbb"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn",
+ "syn 2.0.117",
 ]
 
 [[package]]
@@ -196,6 +196,16 @@ dependencies = [
  "bytemuck",
 ]
 
+[[package]]
+name = "atomic-wait"
+version = "1.1.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a55b94919229f2c42292fd71ffa4b75e83193bffdd77b1e858cd55fd2d0b0ea8"
+dependencies = [
+ "libc",
+ "windows-sys 0.42.0",
+]
+
 [[package]]
 name = "atomic-waker"
 version = "1.1.2"
@@ -428,6 +438,20 @@ name = "bytemuck"
 version = "1.25.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "c8efb64bd706a16a1bdde310ae86b351e4d21550d98d056f22f8a7f7a2183fec"
+dependencies = [
+ "bytemuck_derive",
+]
+
+[[package]]
+name = "bytemuck_derive"
+version = "1.10.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "f9abbd1bc6865053c427f7198e6af43bfdedc55ab791faed4fbd361d789575ff"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.117",
+]
 
 [[package]]
 name = "byteorder"
@@ -567,7 +591,7 @@ dependencies = [
  "heck",
  "proc-macro2",
  "quote",
- "syn",
+ "syn 2.0.117",
 ]
 
 [[package]]
@@ -754,6 +778,28 @@ dependencies = [
  "itertools",
 ]
 
+[[package]]
+name = "crossbeam"
+version = "0.8.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "1137cd7e7fc0fb5d3c5a8678be38ec56e819125d8d7907411fe24ccb943faca8"
+dependencies = [
+ "crossbeam-channel",
+ "crossbeam-deque",
+ "crossbeam-epoch",
+ "crossbeam-queue",
+ "crossbeam-utils",
+]
+
+[[package]]
+name = "crossbeam-channel"
+version = "0.5.15"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "82b8f8f868b36967f9606790d1903570de9ceaf870a7bf9fbbd3016d636a2cb2"
+dependencies = [
+ "crossbeam-utils",
+]
+
 [[package]]
 name = "crossbeam-deque"
 version = "0.8.6"
@@ -849,7 +895,7 @@ checksum = "f46882e17999c6cc590af592290432be3bce0428cb0d5f8b6715e4dc7b383eb3"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn",
+ "syn 2.0.117",
 ]
 
 [[package]]
@@ -872,7 +918,7 @@ dependencies = [
  "proc-macro2",
  "quote",
  "strsim",
- "syn",
+ "syn 2.0.117",
 ]
 
 [[package]]
@@ -883,9 +929,15 @@ checksum = "ac3984ec7bd6cfa798e62b4a642426a5be0e68f9401cfc2a01e3fa9ea2fcdb8d"
 dependencies = [
  "darling_core",
  "quote",
- "syn",
+ "syn 2.0.117",
 ]
 
+[[package]]
+name = "defer"
+version = "0.2.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "930c7171c8df9fb1782bdf9b918ed9ed2d33d1d22300abb754f9085bc48bf8e8"
+
 [[package]]
 name = "der"
 version = "0.7.10"
@@ -926,7 +978,7 @@ dependencies = [
  "proc-macro2",
  "quote",
  "rustc_version",
- "syn",
+ "syn 2.0.117",
  "unicode-xid",
 ]
 
@@ -961,7 +1013,7 @@ checksum = "97369cbbc041bc366949bc74d34658d6cda5621039731c6310521892a3a20ae0"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn",
+ "syn 2.0.117",
 ]
 
 [[package]]
@@ -988,6 +1040,22 @@ version = "1.0.20"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "d0881ea181b1df73ff77ffaaf9c7544ecc11e82fba9b5f27b262a3c73a332555"
 
+[[package]]
+name = "dyn-stack"
+version = "0.13.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "1c4713e43e2886ba72b8271aa66c93d722116acf7a75555cce11dcde84388fe8"
+dependencies = [
+ "bytemuck",
+ "dyn-stack-macros",
+]
+
+[[package]]
+name = "dyn-stack-macros"
+version = "0.1.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "e1d926b4d407d372f141f93bb444696142c29d32962ccbd3531117cf3aa0bfa9"
+
 [[package]]
 name = "ecdsa"
 version = "0.16.9"
@@ -1084,6 +1152,73 @@ dependencies = [
  "cfg-if",
 ]
 
+[[package]]
+name = "enum-as-inner"
+version = "0.6.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a1e6a265c649f3f5979b601d26f1d05ada116434c87741c9493cb56218f76cbc"
+dependencies = [
+ "heck",
+ "proc-macro2",
+ "quote",
+ "syn 2.0.117",
+]
+
+[[package]]
+name = "equator"
+version = "0.2.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c35da53b5a021d2484a7cc49b2ac7f2d840f8236a286f84202369bd338d761ea"
+dependencies = [
+ "equator-macro 0.2.1",
+]
+
+[[package]]
+name = "equator"
+version = "0.4.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "4711b213838dfee0117e3be6ac926007d7f433d7bbe33595975d4190cb07e6fc"
+dependencies = [
+ "equator-macro 0.4.2",
+]
+
+[[package]]
+name = "equator"
+version = "0.6.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "02da895aab06bbebefb6b2595f6d637b18c9ff629b4cd840965bb3164e4194b0"
+dependencies = [
+ "equator-macro 0.6.0",
+]
+
+[[package]]
+name = "equator-macro"
+version = "0.2.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "3bf679796c0322556351f287a51b49e48f7c4986e727b5dd78c972d30e2e16cc"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.117",
+]
+
+[[package]]
+name = "equator-macro"
+version = "0.4.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "44f23cf4b44bfce11a86ace86f8a73ffdec849c9fd00a386a53d278bd9e81fb3"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.117",
+]
+
+[[package]]
+name = "equator-macro"
+version = "0.6.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "2b14b339eb76d07f052cdbad76ca7c1310e56173a138095d3bf42a23c06ef5d8"
+
 [[package]]
 name = "equivalent"
 version = "1.0.2"
@@ -1122,6 +1257,49 @@ dependencies = [
  "pin-project-lite",
 ]
 
+[[package]]
+name = "faer"
+version = "0.24.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "02d2ecfb80b6f8b0c569e36988a052e64b14d8def9d372390b014e8bf79f299a"
+dependencies = [
+ "bytemuck",
+ "dyn-stack",
+ "equator 0.6.0",
+ "faer-traits",
+ "gemm",
+ "generativity",
+ "libm",
+ "nano-gemm",
+ "npyz",
+ "num-complex",
+ "num-traits",
+ "private-gemm-x86",
+ "pulp",
+ "rand 0.9.4",
+ "rand_distr 0.5.1",
+ "rayon",
+ "reborrow",
+ "spindle",
+]
+
+[[package]]
+name = "faer-traits"
+version = "0.24.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "b87d23ed7ab1f26c0cba0e5b9e061a796fbb7dc170fa8bee6970055a1308bb0f"
+dependencies = [
+ "bytemuck",
+ "dyn-stack",
+ "generativity",
+ "libm",
+ "num-complex",
+ "num-traits",
+ "pulp",
+ "qd",
+ "reborrow",
+]
+
 [[package]]
 name = "fastrand"
 version = "2.4.1"
@@ -1324,7 +1502,7 @@ checksum = "e835b70203e41293343137df5c0664546da5745f82ec9b84d40be8336958447b"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn",
+ "syn 2.0.117",
 ]
 
 [[package]]
@@ -1356,6 +1534,146 @@ dependencies = [
  "slab",
 ]
 
+[[package]]
+name = "gemm"
+version = "0.19.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "aa0673db364b12263d103b68337a68fbecc541d6f6b61ba72fe438654709eacb"
+dependencies = [
+ "dyn-stack",
+ "gemm-c32",
+ "gemm-c64",
+ "gemm-common",
+ "gemm-f16",
+ "gemm-f32",
+ "gemm-f64",
+ "num-complex",
+ "num-traits",
+ "paste",
+ "raw-cpuid",
+ "seq-macro",
+]
+
+[[package]]
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+ "seq-macro",
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+ "bytemuck",
+ "dyn-stack",
+ "half",
+ "libm",
+ "num-complex",
+ "num-traits",
+ "once_cell",
+ "paste",
+ "pulp",
+ "raw-cpuid",
+ "rayon",
+ "seq-macro",
+ "sysctl",
+]
+
+[[package]]
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+dependencies = [
+ "dyn-stack",
+ "gemm-common",
+ "gemm-f32",
+ "half",
+ "num-complex",
+ "num-traits",
+ "paste",
+ "raw-cpuid",
+ "rayon",
+ "seq-macro",
+]
+
+[[package]]
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+dependencies = [
+ "dyn-stack",
+ "gemm-common",
+ "num-complex",
+ "num-traits",
+ "paste",
+ "raw-cpuid",
+ "seq-macro",
+]
+
+[[package]]
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+version = "0.19.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "056131e8f2a521bfab322f804ccd652520c79700d81209e9d9275bbdecaadc6a"
+dependencies = [
+ "dyn-stack",
+ "gemm-common",
+ "num-complex",
+ "num-traits",
+ "paste",
+ "raw-cpuid",
+ "seq-macro",
+]
+
+[[package]]
+name = "generativity"
+version = "1.2.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "d2c81fb5260e37854d09d5c87183309fd8c555b75289427884b25660bc87a85e"
+
+[[package]]
+name = "generator"
+version = "0.8.8"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "52f04ae4152da20c76fe800fa48659201d5cf627c5149ca0b707b69d7eef6cf9"
+dependencies = [
+ "cc",
+ "cfg-if",
+ "libc",
+ "log",
+ "rustversion",
+ "windows-link",
+ "windows-result",
+]
+
 [[package]]
 name = "generic-array"
 version = "0.14.9"
@@ -1468,8 +1786,10 @@ version = "2.7.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "6ea2d84b969582b4b1864a92dc5d27cd2b77b622a8d79306834f1be5ba20d84b"
 dependencies = [
+ "bytemuck",
  "cfg-if",
  "crunchy",
+ "num-traits",
  "zerocopy",
 ]
 
@@ -1511,6 +1831,12 @@ version = "0.5.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "2304e00983f87ffb38b55b444b5e3b60a884b5d30c0fca7d82fe33449bbe55ea"
 
+[[package]]
+name = "hermit-abi"
+version = "0.5.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "fc0fef456e4baa96da950455cd02c081ca953b141298e41db3fc7e36b1da849c"
+
 [[package]]
 name = "hex"
 version = "0.4.3"
@@ -1868,6 +2194,17 @@ version = "0.1.15"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "c8fae54786f62fb2918dcfae3d568594e50eb9b5c25bf04371af6fe7516452fb"
 
+[[package]]
+name = "interpol"
+version = "0.2.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "eb58032ba748f4010d15912a1855a8a0b1ba9eaad3395b0c171c09b3b356ae50"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 1.0.109",
+]
+
 [[package]]
 name = "ipnet"
 version = "2.12.0"
@@ -1922,7 +2259,7 @@ dependencies = [
  "quote",
  "rustc_version",
  "simd_cesu8",
- "syn",
+ "syn 2.0.117",
 ]
 
 [[package]]
@@ -1941,7 +2278,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "38c0b942f458fe50cdac086d2f946512305e5631e720728f2a61aabcd47a6264"
 dependencies = [
  "quote",
- "syn",
+ "syn 2.0.117",
 ]
 
 [[package]]
@@ -2100,6 +2437,19 @@ version = "0.4.29"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "5e5032e24019045c762d3c0f28f5b6b8bbf38563a65908389bf7978758920897"
 
+[[package]]
+name = "loom"
+version = "0.7.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "419e0dc8046cb947daa77eb95ae174acfbddb7673b4151f56d1eed8e93fbfaca"
+dependencies = [
+ "cfg-if",
+ "generator",
+ "scoped-tls",
+ "tracing",
+ "tracing-subscriber",
+]
+
 [[package]]
 name = "lru-slab"
 version = "0.1.2"
@@ -2197,7 +2547,7 @@ dependencies = [
  "cfg-if",
  "proc-macro2",
  "quote",
- "syn",
+ "syn 2.0.117",
 ]
 
 [[package]]
@@ -2227,6 +2577,76 @@ dependencies = [
  "version_check",
 ]
 
+[[package]]
+name = "nano-gemm"
+version = "0.2.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "9e04345dc84b498ff89fe0d38543d1f170da9e43a2c2bcee73a0f9069f72d081"
+dependencies = [
+ "equator 0.2.2",
+ "nano-gemm-c32",
+ "nano-gemm-c64",
+ "nano-gemm-codegen",
+ "nano-gemm-core",
+ "nano-gemm-f32",
+ "nano-gemm-f64",
+ "num-complex",
+]
+
+[[package]]
+name = "nano-gemm-c32"
+version = "0.2.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0775b1e2520e64deee8fc78b7732e3091fb7585017c0b0f9f4b451757bbbc562"
+dependencies = [
+ "nano-gemm-codegen",
+ "nano-gemm-core",
+ "num-complex",
+]
+
+[[package]]
+name = "nano-gemm-c64"
+version = "0.2.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "9af49a20d58816e6b5ee65f64142e50edb5eba152678d4bb7377fcbf63f8437a"
+dependencies = [
+ "nano-gemm-codegen",
+ "nano-gemm-core",
+ "num-complex",
+]
+
+[[package]]
+name = "nano-gemm-codegen"
+version = "0.2.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "6cc8d495c791627779477a2cf5df60049f5b165342610eb0d76bee5ff5c5d74c"
+
+[[package]]
+name = "nano-gemm-core"
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+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "d998dfa644de87a0f8660e5ea511d7cb5c33b5a2d9847b7af57a2565105089f0"
+
+[[package]]
+name = "nano-gemm-f32"
+version = "0.2.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "879d962e79bc8952e4ad21ca4845a21132540ed3f5e01184b2ff7f720e666523"
+dependencies = [
+ "nano-gemm-codegen",
+ "nano-gemm-core",
+]
+
+[[package]]
+name = "nano-gemm-f64"
+version = "0.2.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "b9a513473dce7dc00c7e7c318481ca4494034e76997218d8dad51bd9f007a815"
+dependencies = [
+ "nano-gemm-codegen",
+ "nano-gemm-core",
+]
+
 [[package]]
 name = "native-tls"
 version = "0.2.18"
@@ -2253,6 +2673,17 @@ dependencies = [
  "memchr",
 ]
 
+[[package]]
+name = "npyz"
+version = "0.8.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "9f0e759e014e630f90af745101b614f761306ddc541681e546649068e25ec1b9"
+dependencies = [
+ "byteorder",
+ "num-bigint",
+ "py_literal",
+]
+
 [[package]]
 name = "nu-ansi-term"
 version = "0.50.3"
@@ -2288,6 +2719,17 @@ dependencies = [
  "zeroize",
 ]
 
+[[package]]
+name = "num-complex"
+version = "0.4.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "73f88a1307638156682bada9d7604135552957b7818057dcef22705b4d509495"
+dependencies = [
+ "bytemuck",
+ "num-traits",
+ "rand 0.8.6",
+]
+
 [[package]]
 name = "num-conv"
 version = "0.2.1"
@@ -2324,6 +2766,16 @@ dependencies = [
  "libm",
 ]
 
+[[package]]
+name = "num_cpus"
+version = "1.17.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "91df4bbde75afed763b708b7eee1e8e7651e02d97f6d5dd763e89367e957b23b"
+dependencies = [
+ "hermit-abi",
+ "libc",
+]
+
 [[package]]
 name = "once_cell"
 version = "1.21.4"
@@ -2364,7 +2816,7 @@ checksum = "a948666b637a0f465e8564c73e89d4dde00d72d4d473cc972f390fc3dcee7d9c"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn",
+ "syn 2.0.117",
 ]
 
 [[package]]
@@ -2468,6 +2920,12 @@ dependencies = [
  "subtle",
 ]
 
+[[package]]
+name = "paste"
+version = "1.0.15"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "57c0d7b74b563b49d38dae00a0c37d4d6de9b432382b2892f0574ddcae73fd0a"
+
 [[package]]
 name = "pbkdf2"
 version = "0.12.2"
@@ -2500,7 +2958,7 @@ dependencies = [
  "proc-macro2",
  "proc-macro2-diagnostics",
  "quote",
- "syn",
+ "syn 2.0.117",
 ]
 
 [[package]]
@@ -2558,7 +3016,7 @@ dependencies = [
  "pest_meta",
  "proc-macro2",
  "quote",
- "syn",
+ "syn 2.0.117",
 ]
 
 [[package]]
@@ -2708,7 +3166,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "479ca8adacdd7ce8f1fb39ce9ecccbfe93a3f1344b3d0d97f20bc0196208f62b"
 dependencies = [
  "proc-macro2",
- "syn",
+ "syn 2.0.117",
 ]
 
 [[package]]
@@ -2720,6 +3178,22 @@ dependencies = [
  "elliptic-curve",
 ]
 
+[[package]]
+name = "private-gemm-x86"
+version = "0.1.20"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0af8c3e5087969c323f667ccb4b789fa0954f5aa650550e38e81cf9108be21b5"
+dependencies = [
+ "crossbeam",
+ "defer",
+ "interpol",
+ "num_cpus",
+ "raw-cpuid",
+ "rayon",
+ "spindle",
+ "sysctl",
+]
+
 [[package]]
 name = "proc-macro2"
 version = "1.0.106"
@@ -2737,17 +3211,65 @@ checksum = "af066a9c399a26e020ada66a034357a868728e72cd426f3adcd35f80d88d88c8"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn",
+ "syn 2.0.117",
  "version_check",
  "yansi",
 ]
 
+[[package]]
+name = "pulp"
+version = "0.22.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "2e205bb30d5b916c55e584c22201771bcf2bad9aabd5d4127f38387140c38632"
+dependencies = [
+ "bytemuck",
+ "cfg-if",
+ "libm",
+ "num-complex",
+ "paste",
+ "pulp-wasm-simd-flag",
+ "raw-cpuid",
+ "reborrow",
+ "version_check",
+]
+
+[[package]]
+name = "pulp-wasm-simd-flag"
+version = "0.1.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "40e24eee682d89fb193496edf918a7f407d30175b2e785fe057e4392dfd182e0"
+
 [[package]]
 name = "pxfm"
 version = "0.1.29"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "e0c5ccf5294c6ccd63a74f1565028353830a9c2f5eb0c682c355c471726a6e3f"
 
+[[package]]
+name = "py_literal"
+version = "0.4.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "102df7a3d46db9d3891f178dcc826dc270a6746277a9ae6436f8d29fd490a8e1"
+dependencies = [
+ "num-bigint",
+ "num-complex",
+ "num-traits",
+ "pest",
+ "pest_derive",
+]
+
+[[package]]
+name = "qd"
+version = "0.8.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "15f1304a5aecdcfe9ee72fbba90aa37b3aa067a69d14cb7f3d9deada0be7c07c"
+dependencies = [
+ "bytemuck",
+ "libm",
+ "num-traits",
+ "pulp",
+]
+
 [[package]]
 name = "quinn"
 version = "0.11.9"
@@ -2907,6 +3429,35 @@ version = "0.10.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "63b8176103e19a2643978565ca18b50549f6101881c443590420e4dc998a3c69"
 
+[[package]]
+name = "rand_distr"
+version = "0.5.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "6a8615d50dcf34fa31f7ab52692afec947c4dd0ab803cc87cb3b0b4570ff7463"
+dependencies = [
+ "num-traits",
+ "rand 0.9.4",
+]
+
+[[package]]
+name = "rand_distr"
+version = "0.6.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "4d431c2703ccf129de4d45253c03f49ebb22b97d6ad79ee3ecfc7e3f4862c1d8"
+dependencies = [
+ "num-traits",
+ "rand 0.10.1",
+]
+
+[[package]]
+name = "raw-cpuid"
+version = "11.6.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "498cd0dc59d73224351ee52a95fee0f1a617a2eae0e7d9d720cc622c73a54186"
+dependencies = [
+ "bitflags",
+]
+
 [[package]]
 name = "rayon"
 version = "1.12.0"
@@ -2927,6 +3478,12 @@ dependencies = [
  "crossbeam-utils",
 ]
 
+[[package]]
+name = "reborrow"
+version = "0.5.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "03251193000f4bd3b042892be858ee50e8b3719f2b08e5833ac4353724632430"
+
 [[package]]
 name = "redox_syscall"
 version = "0.5.18"
@@ -2962,7 +3519,7 @@ checksum = "b7186006dcb21920990093f30e3dea63b7d6e977bf1256be20c3563a5db070da"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn",
+ "syn 2.0.117",
 ]
 
 [[package]]
@@ -3240,6 +3797,12 @@ dependencies = [
  "serde_json",
 ]
 
+[[package]]
+name = "scoped-tls"
+version = "1.0.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "e1cf6437eb19a8f4a6cc0f7dca544973b0b78843adbfeb3683d1a94a0024a294"
+
 [[package]]
 name = "scopeguard"
 version = "1.2.0"
@@ -3289,6 +3852,12 @@ version = "1.0.28"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "8a7852d02fc848982e0c167ef163aaff9cd91dc640ba85e263cb1ce46fae51cd"
 
+[[package]]
+name = "seq-macro"
+version = "0.3.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "1bc711410fbe7399f390ca1c3b60ad0f53f80e95c5eb935e52268a0e2cd49acc"
+
 [[package]]
 name = "serde"
 version = "1.0.228"
@@ -3336,7 +3905,7 @@ checksum = "d540f220d3187173da220f885ab66608367b6574e925011a9353e4badda91d79"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn",
+ "syn 2.0.117",
 ]
 
 [[package]]
@@ -3422,7 +3991,7 @@ dependencies = [
  "darling",
  "proc-macro2",
  "quote",
- "syn",
+ "syn 2.0.117",
 ]
 
 [[package]]
@@ -3572,6 +4141,19 @@ dependencies = [
  "lock_api",
 ]
 
+[[package]]
+name = "spindle"
+version = "0.2.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "673aaca3d8aa5387a6eba861fbf984af5348d9df5d940c25c6366b19556fdf64"
+dependencies = [
+ "atomic-wait",
+ "crossbeam",
+ "equator 0.4.2",
+ "loom",
+ "rayon",
+]
+
 [[package]]
 name = "spki"
 version = "0.7.3"
@@ -3641,7 +4223,7 @@ dependencies = [
  "quote",
  "sqlx-core",
  "sqlx-macros-core",
- "syn",
+ "syn 2.0.117",
 ]
 
 [[package]]
@@ -3664,7 +4246,7 @@ dependencies = [
  "sqlx-mysql",
  "sqlx-postgres",
  "sqlx-sqlite",
- "syn",
+ "syn 2.0.117",
  "tokio",
  "url",
 ]
@@ -3804,6 +4386,17 @@ version = "2.6.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "13c2bddecc57b384dee18652358fb23172facb8a2c51ccc10d74c157bdea3292"
 
+[[package]]
+name = "syn"
+version = "1.0.109"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "72b64191b275b66ffe2469e8af2c1cfe3bafa67b529ead792a6d0160888b4237"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "unicode-ident",
+]
+
 [[package]]
 name = "syn"
 version = "2.0.117"
@@ -3832,7 +4425,21 @@ checksum = "728a70f3dbaf5bab7f0c4b1ac8d7ae5ea60a4b5549c8a5914361c99147a709d2"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn",
+ "syn 2.0.117",
+]
+
+[[package]]
+name = "sysctl"
+version = "0.6.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "01198a2debb237c62b6826ec7081082d951f46dbb64b0e8c7649a452230d1dfc"
+dependencies = [
+ "bitflags",
+ "byteorder",
+ "enum-as-inner",
+ "libc",
+ "thiserror 1.0.69",
+ "walkdir",
 ]
 
 [[package]]
@@ -3917,7 +4524,7 @@ checksum = "4fee6c4efc90059e10f81e6d42c60a18f76588c3d74cb83a0b242a2b6c7504c1"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn",
+ "syn 2.0.117",
 ]
 
 [[package]]
@@ -3928,7 +4535,7 @@ checksum = "ebc4ee7f67670e9b64d05fa4253e753e016c6c95ff35b89b7941d6b856dec1d5"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn",
+ "syn 2.0.117",
 ]
 
 [[package]]
@@ -4030,7 +4637,7 @@ checksum = "385a6cb71ab9ab790c5fe8d67f1645e6c450a7ce006a33de03daa956cf70a496"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn",
+ "syn 2.0.117",
 ]
 
 [[package]]
@@ -4310,6 +4917,21 @@ dependencies = [
  "tracing-subscriber",
 ]
 
+[[package]]
+name = "torrust-sentinel"
+version = "0.1.0"
+dependencies = [
+ "criterion",
+ "faer",
+ "rand 0.10.1",
+ "rand_distr 0.6.0",
+ "serde",
+ "serde_json",
+ "torrust-mudlark",
+ "tracing",
+ "tracing-subscriber",
+]
+
 [[package]]
 name = "tower"
 version = "0.5.3"
@@ -4382,7 +5004,7 @@ checksum = "7490cfa5ec963746568740651ac6781f701c9c5ea257c58e057f3ba8cf69e8da"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn",
+ "syn 2.0.117",
 ]
 
 [[package]]
@@ -4672,7 +5294,7 @@ dependencies = [
  "bumpalo",
  "proc-macro2",
  "quote",
- "syn",
+ "syn 2.0.117",
  "wasm-bindgen-shared",
 ]
 
@@ -4828,7 +5450,7 @@ checksum = "053e2e040ab57b9dc951b72c264860db7eb3b0200ba345b4e4c3b14f67855ddf"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn",
+ "syn 2.0.117",
 ]
 
 [[package]]
@@ -4839,7 +5461,7 @@ checksum = "3f316c4a2570ba26bbec722032c4099d8c8bc095efccdc15688708623367e358"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn",
+ "syn 2.0.117",
 ]
 
 [[package]]
@@ -4877,6 +5499,21 @@ dependencies = [
  "windows-link",
 ]
 
+[[package]]
+name = "windows-sys"
+version = "0.42.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "5a3e1820f08b8513f676f7ab6c1f99ff312fb97b553d30ff4dd86f9f15728aa7"
+dependencies = [
+ "windows_aarch64_gnullvm 0.42.2",
+ "windows_aarch64_msvc 0.42.2",
+ "windows_i686_gnu 0.42.2",
+ "windows_i686_msvc 0.42.2",
+ "windows_x86_64_gnu 0.42.2",
+ "windows_x86_64_gnullvm 0.42.2",
+ "windows_x86_64_msvc 0.42.2",
+]
+
 [[package]]
 name = "windows-sys"
 version = "0.48.0"
@@ -4961,6 +5598,12 @@ dependencies = [
  "windows_x86_64_msvc 0.53.1",
 ]
 
+[[package]]
+name = "windows_aarch64_gnullvm"
+version = "0.42.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "597a5118570b68bc08d8d59125332c54f1ba9d9adeedeef5b99b02ba2b0698f8"
+
 [[package]]
 name = "windows_aarch64_gnullvm"
 version = "0.48.5"
@@ -4979,6 +5622,12 @@ version = "0.53.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "a9d8416fa8b42f5c947f8482c43e7d89e73a173cead56d044f6a56104a6d1b53"
 
+[[package]]
+name = "windows_aarch64_msvc"
+version = "0.42.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "e08e8864a60f06ef0d0ff4ba04124db8b0fb3be5776a5cd47641e942e58c4d43"
+
 [[package]]
 name = "windows_aarch64_msvc"
 version = "0.48.5"
@@ -4997,6 +5646,12 @@ version = "0.53.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "b9d782e804c2f632e395708e99a94275910eb9100b2114651e04744e9b125006"
 
+[[package]]
+name = "windows_i686_gnu"
+version = "0.42.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "c61d927d8da41da96a81f029489353e68739737d3beca43145c8afec9a31a84f"
+
 [[package]]
 name = "windows_i686_gnu"
 version = "0.48.5"
@@ -5027,6 +5682,12 @@ version = "0.53.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "fa7359d10048f68ab8b09fa71c3daccfb0e9b559aed648a8f95469c27057180c"
 
+[[package]]
+name = "windows_i686_msvc"
+version = "0.42.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "44d840b6ec649f480a41c8d80f9c65108b92d89345dd94027bfe06ac444d1060"
+
 [[package]]
 name = "windows_i686_msvc"
 version = "0.48.5"
@@ -5045,6 +5706,12 @@ version = "0.53.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "1e7ac75179f18232fe9c285163565a57ef8d3c89254a30685b57d83a38d326c2"
 
+[[package]]
+name = "windows_x86_64_gnu"
+version = "0.42.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "8de912b8b8feb55c064867cf047dda097f92d51efad5b491dfb98f6bbb70cb36"
+
 [[package]]
 name = "windows_x86_64_gnu"
 version = "0.48.5"
@@ -5063,6 +5730,12 @@ version = "0.53.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "9c3842cdd74a865a8066ab39c8a7a473c0778a3f29370b5fd6b4b9aa7df4a499"
 
+[[package]]
+name = "windows_x86_64_gnullvm"
+version = "0.42.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "26d41b46a36d453748aedef1486d5c7a85db22e56aff34643984ea85514e94a3"
+
 [[package]]
 name = "windows_x86_64_gnullvm"
 version = "0.48.5"
@@ -5081,6 +5754,12 @@ version = "0.53.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "0ffa179e2d07eee8ad8f57493436566c7cc30ac536a3379fdf008f47f6bb7ae1"
 
+[[package]]
+name = "windows_x86_64_msvc"
+version = "0.42.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "9aec5da331524158c6d1a4ac0ab1541149c0b9505fde06423b02f5ef0106b9f0"
+
 [[package]]
 name = "windows_x86_64_msvc"
 version = "0.48.5"
@@ -5150,7 +5829,7 @@ dependencies = [
  "heck",
  "indexmap 2.14.0",
  "prettyplease",
- "syn",
+ "syn 2.0.117",
  "wasm-metadata",
  "wit-bindgen-core",
  "wit-component",
@@ -5166,7 +5845,7 @@ dependencies = [
  "prettyplease",
  "proc-macro2",
  "quote",
- "syn",
+ "syn 2.0.117",
  "wit-bindgen-core",
  "wit-bindgen-rust",
 ]
@@ -5239,7 +5918,7 @@ checksum = "de844c262c8848816172cef550288e7dc6c7b7814b4ee56b3e1553f275f1858e"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn",
+ "syn 2.0.117",
  "synstructure",
 ]
 
@@ -5260,7 +5939,7 @@ checksum = "70e3cd084b1788766f53af483dd21f93881ff30d7320490ec3ef7526d203bad4"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn",
+ "syn 2.0.117",
 ]
 
 [[package]]
@@ -5280,7 +5959,7 @@ checksum = "11532158c46691caf0f2593ea8358fed6bbf68a0315e80aae9bd41fbade684a1"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn",
+ "syn 2.0.117",
  "synstructure",
 ]
 
@@ -5301,7 +5980,7 @@ checksum = "85a5b4158499876c763cb03bc4e49185d3cccbabb15b33c627f7884f43db852e"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn",
+ "syn 2.0.117",
 ]
 
 [[package]]
@@ -5334,7 +6013,7 @@ checksum = "625dc425cab0dca6dc3c3319506e6593dcb08a9f387ea3b284dbd52a92c40555"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn",
+ "syn 2.0.117",
 ]
 
 [[package]]
diff --git a/Cargo.toml b/Cargo.toml
index 832d9d9d7..5ec43a817 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -8,6 +8,7 @@ members = [
     "packages/index-entry-script",
     "packages/index-health-check",
     "packages/mudlark",
+    "packages/sentinel",
     "packages/render-text-as-image",
 ]
 
diff --git a/packages/index-health-check/src/tests/mod.rs b/packages/index-health-check/src/tests/mod.rs
index 939d50f64..e689217dc 100644
--- a/packages/index-health-check/src/tests/mod.rs
+++ b/packages/index-health-check/src/tests/mod.rs
@@ -1,4 +1,4 @@
-#![allow(clippy::print_stderr)]
+#![allow(clippy::print_stderr, clippy::print_stdout)]
 
 //! # Health-check tests
 //!
diff --git a/packages/mudlark/src/tests/decompose_basis.rs b/packages/mudlark/src/tests/decompose_basis.rs
index d2fc2d950..5ba0584ea 100644
--- a/packages/mudlark/src/tests/decompose_basis.rs
+++ b/packages/mudlark/src/tests/decompose_basis.rs
@@ -1,7 +1,7 @@
 // SPDX-License-Identifier: AGPL-3.0-only
 // SPDX-FileCopyrightText: 2026 Torrust project contributors
 
-#![allow(clippy::print_stderr)]
+#![allow(clippy::print_stderr, clippy::print_stdout)]
 
 //! Boundary tests for **basis decomposition** via `contour_range()`
 //! (ADR-M-039).
diff --git a/packages/mudlark/src/tests/semi_internal_plateau.rs b/packages/mudlark/src/tests/semi_internal_plateau.rs
index ba6a1e27a..a88a890c3 100644
--- a/packages/mudlark/src/tests/semi_internal_plateau.rs
+++ b/packages/mudlark/src/tests/semi_internal_plateau.rs
@@ -1,7 +1,7 @@
 // SPDX-License-Identifier: AGPL-3.0-only
 // SPDX-FileCopyrightText: 2026 Torrust project contributors
 
-#![allow(clippy::print_stderr)]
+#![allow(clippy::print_stderr, clippy::print_stdout)]
 
 //! Unit tests for degenerate `SemiInternal` plateau shapes.
 //!
diff --git a/packages/mudlark/tests/eviction_plateau.rs b/packages/mudlark/tests/eviction_plateau.rs
index ba6cf94b0..3294626b2 100644
--- a/packages/mudlark/tests/eviction_plateau.rs
+++ b/packages/mudlark/tests/eviction_plateau.rs
@@ -2,7 +2,7 @@
 // SPDX-FileCopyrightText: 2026 Torrust project contributors
 
 #![cfg(feature = "dynamic-contour-tracking")]
-#![allow(clippy::print_stderr)]
+#![allow(clippy::print_stderr, clippy::print_stdout)]
 //! Integration tests for eviction ↔ plateau invariant maintenance.
 //!
 //! These tests exercise the plateau invariants (P-I2 basis
diff --git a/packages/mudlark/tests/negative_f64.rs b/packages/mudlark/tests/negative_f64.rs
index a4e4163cd..ac7f382c0 100644
--- a/packages/mudlark/tests/negative_f64.rs
+++ b/packages/mudlark/tests/negative_f64.rs
@@ -1,7 +1,7 @@
 // SPDX-License-Identifier: AGPL-3.0-only
 // SPDX-FileCopyrightText: 2026 Torrust project contributors
 
-#![allow(clippy::print_stderr)]
+#![allow(clippy::print_stderr, clippy::print_stdout)]
 
 //! Integration tests for negative `f64` observations.
 //!
diff --git a/packages/sentinel/Cargo.toml b/packages/sentinel/Cargo.toml
new file mode 100644
index 000000000..e54cf4923
--- /dev/null
+++ b/packages/sentinel/Cargo.toml
@@ -0,0 +1,47 @@
+[package]
+categories = ["algorithms", "network-programming"]
+description = "Hierarchical online subspace anomaly detection for positionally structured observation streams."
+keywords = ["anomaly-detection", "online-learning", "spectral", "streaming", "subspace"]
+name = "torrust-sentinel"
+readme = "README.md"
+version = "0.1.0"
+
+authors.workspace = true
+documentation.workspace = true
+edition.workspace = true
+homepage.workspace = true
+license.workspace = true
+publish.workspace = true
+repository.workspace = true
+rust-version.workspace = true
+
+[lints]
+workspace = true
+
+[features]
+serde = ["dep:serde", "torrust-mudlark/serde"]
+
+[dependencies]
+faer = "0"
+rand = "0.10"
+rand_distr = "0.6"
+serde = { version = "1", features = ["derive"], optional = true }
+torrust-mudlark = { path = "../mudlark", default-features = false, features = ["dynamic-contour-tracking"] }
+tracing = "0"
+
+[dev-dependencies]
+criterion = { version = "0", features = ["html_reports"] }
+serde_json = "1"
+tracing-subscriber = { version = "0.3", features = ["registry", "env-filter"] }
+
+[[bench]]
+harness = false
+name = "sentinel"
+
+[[test]]
+harness = false
+name = "pedagogy"
+
+[[test]]
+harness = false
+name = "pedagogy_advanced"
diff --git a/packages/sentinel/README.md b/packages/sentinel/README.md
new file mode 100644
index 000000000..f1b60fe0b
--- /dev/null
+++ b/packages/sentinel/README.md
@@ -0,0 +1,743 @@
+# Spectral Sentinel
+
+Hierarchical online subspace anomaly detection for positionally structured
+observation streams.
+
+Spectral Sentinel combines Mudlark's adaptive spatial index with
+low-rank statistical trackers. Mudlark ranks spatial entries by
+observation volume; Spectral Sentinel selects significant V-Tree entries,
+closes them under G-tree ancestry, and scores incoming batches against
+learned subspace models for that selected structure. Reports contain
+measurements only: scores, baselines, drift accumulators, maturity,
+structural summaries, and health snapshots.
+
+**Spectral Sentinel measures; the host decides.**
+
+See [Architecture in brief](#architecture-in-brief) below for the
+conceptual model, or jump straight to [Quick start](#quick-start) for
+code.
+
+## Choose Spectral Sentinel
+
+Use Spectral Sentinel when your stream has **hierarchical positional
+structure**: leading bits define coarse membership and successive bits
+refine it. IPv6-like address spaces, network-prefix encodings, and other
+dyadic coordinate domains are natural fits.
+
+If the values are pseudo-random, the model has no meaningful positional
+structure to learn. Cryptographic hashes, UUIDs, random nonces, and
+uniform identifiers will still be processed, but the measurements will
+not be useful. If the distribution is static and known in advance, a
+fixed index or offline model will usually be simpler. If you only need
+adaptive spatial aggregation or proportional sampling, use
+[`torrust-mudlark`](../mudlark/README.md) directly.
+
+Spectral Sentinel's advantage is online multi-scale measurement: it lets
+Mudlark adapt spatial structure as traffic shifts, models suffix-bit
+structure at competitively selected regions, and emits raw statistical
+readouts without embedding host policy.
+
+## Stability
+
+This crate follows [Semantic Versioning](https://semver.org/), but it is
+currently pre-1.0 (`0.1.0`). The public API surface documented in
+[docs/api.md](docs/api.md) is intentional; compatibility follows SemVer's
+0.x rules until the crate reaches 1.0.
+
+Internal machinery (`pub(crate)` modules, hidden test affordances, EWMA
+state, tracker internals, staging, and SVD plumbing) is not part of the
+public API and may change in any release.
+
+**MSRV:** 1.88 (workspace setting)
+
+## Installation
+
+Add the crate to your project:
+
+```sh
+cargo add torrust-sentinel
+```
+
+Or add it manually to your `Cargo.toml`:
+
+```toml
+[dependencies]
+torrust-sentinel = "0.1"
+```
+
+The base build has no default feature flags. The `serde` feature is
+opt-in and enables serialisation for configuration, SVD strategy, and
+report snapshot types:
+
+```toml
+[dependencies]
+torrust-sentinel = { version = "0.1", features = ["serde"] }
+```
+
+## Quick start
+
+> Every claim below is asserted with full invariant checks in the
+> [pedagogy integration test](tests/pedagogy.rs). For the inspection
+> surface, see the [advanced pedagogy test](tests/pedagogy_advanced.rs).
+
+### Create the sentinel
+
+`Sentinel128` is the convenience alias for
+`SpectralSentinel<u128, u64, 128>`: a full 128-bit coordinate domain
+with `u64` volume counters. New trackers are automatically warmed with
+synthetic noise before real observations are scored.
+
+```rust
+use torrust_sentinel::{NoiseSchedule, Sentinel128, SentinelConfig};
+
+let config = SentinelConfig::<u64> {
+    analysis_k: 8,                    // small analysis budget for a demo
+    split_threshold: 4,               // split quickly so examples show structure
+    noise_schedule: NoiseSchedule::Explicit(vec![4]),
+    noise_batch_size: 4,
+    noise_seed: Some(2026),
+    ..SentinelConfig::default()
+};
+
+let mut sentinel = Sentinel128::new(config).unwrap();
+assert_eq!(sentinel.lifetime_observations(), 0);
+```
+
+The default configuration is tuned for longer-lived streams. The compact
+noise schedule above keeps examples and doctests fast; production
+callers should choose warm-up settings from the recommendations in
+[docs/algorithm.md](docs/algorithm.md) Appendix A.
+
+### Feed data — spatial structure adapts
+
+Every raw value increments Mudlark's spatial substrate by exactly one
+unit. Scores never feed back into spatial importance, so concentrated
+traffic can reshape the contour but anomalous-looking scores cannot
+promote themselves.
+
+```rust
+# use torrust_sentinel::{NoiseSchedule, Sentinel128, SentinelConfig};
+# let config = SentinelConfig::<u64> {
+#     analysis_k: 8, split_threshold: 4,
+#     noise_schedule: NoiseSchedule::Explicit(vec![4]), noise_batch_size: 4,
+#     noise_seed: Some(2026), ..SentinelConfig::default()
+# };
+# let mut sentinel = Sentinel128::new(config).unwrap();
+let values: Vec<u128> = vec![
+    0xF000_0000_0000_0000_0000_0000_0000_0001,
+    0xF000_0000_0000_0000_0000_0000_0000_0002,
+    0xF000_0000_0000_0000_0000_0000_0000_0003,
+    0x1000_0000_0000_0000_0000_0000_0000_0004,
+];
+
+let report = sentinel.ingest(&values);
+assert_eq!(sentinel.lifetime_observations(), values.len() as u64);
+assert!(report.ancestor_reports.iter().any(|cell| cell.depth == 0));
+```
+
+A non-empty batch always reports the root as an ancestor context. As the
+G-V Graph splits, `cell_reports` contains competitive analysis cells and
+`ancestor_reports` contains the multi-scale chain back to the root.
+
+### Read measurements back
+
+Each cell report carries the same four raw scoring axes. Higher values
+mean greater departure from the learned baseline; Spectral Sentinel does
+not turn those values into severity levels or actions.
+
+```rust
+# use torrust_sentinel::{NoiseSchedule, Sentinel128, SentinelConfig};
+# let config = SentinelConfig::<u64> {
+#     analysis_k: 8, split_threshold: 4,
+#     noise_schedule: NoiseSchedule::Explicit(vec![4]), noise_batch_size: 4,
+#     noise_seed: Some(2026), ..SentinelConfig::default()
+# };
+# let mut sentinel = Sentinel128::new(config).unwrap();
+# let values: Vec<u128> = vec![
+#     0xF000_0000_0000_0000_0000_0000_0000_0001,
+#     0xF000_0000_0000_0000_0000_0000_0000_0002,
+#     0xF000_0000_0000_0000_0000_0000_0000_0003,
+#     0x1000_0000_0000_0000_0000_0000_0000_0004,
+# ];
+# let report = sentinel.ingest(&values);
+for cell in report.cell_reports.iter().chain(report.ancestor_reports.iter()) {
+    let scores = &cell.scores;
+    println!(
+        "depth {} [{:#034x}, {:#034x}) novelty z={:.2} displacement z={:.2}",
+        cell.depth,
+        cell.start,
+        cell.end,
+        scores.novelty.max_z_score,
+        scores.displacement.max_z_score,
+    );
+}
+```
+
+The host decides how to interpret the measurements. A dashboard might
+plot z-scores and maturity, a forensic workflow might enable per-sample
+payloads, and an automated system might compare score distributions
+against a domain-specific policy.
+
+### Apply host-controlled decay
+
+Temporal policy is external. Spectral Sentinel never decays the graph on
+its own; the host decides when old spatial importance should fade.
+
+```rust
+# use torrust_sentinel::{NoiseSchedule, Sentinel128, SentinelConfig};
+# let config = SentinelConfig::<u64> {
+#     analysis_k: 8, split_threshold: 4,
+#     noise_schedule: NoiseSchedule::Explicit(vec![4]), noise_batch_size: 4,
+#     noise_seed: Some(2026), ..SentinelConfig::default()
+# };
+# let mut sentinel = Sentinel128::new(config).unwrap();
+# let values: Vec<u128> = vec![
+#     0xF000_0000_0000_0000_0000_0000_0000_0001,
+#     0xF000_0000_0000_0000_0000_0000_0000_0002,
+#     0xF000_0000_0000_0000_0000_0000_0000_0003,
+#     0x1000_0000_0000_0000_0000_0000_0000_0004,
+# ];
+# let _report = sentinel.ingest(&values);
+let before = sentinel.graph().total_sum();
+sentinel.decay(0.5, 0.0); // uniform 50% attenuation across the graph
+let after = sentinel.graph().total_sum();
+assert!(after <= before);
+```
+
+Decay changes spatial importance and future competitive selection. It
+does not mutate tracker subspaces, baselines, CUSUM state, or lifetime
+observation counts.
+
+## Advanced usage
+
+The quick start covers construction, ingestion, reporting, and decay.
+This section covers inspection payloads, alternative domains,
+configuration, and operational patterns.
+
+### Inspect trackers directly
+
+`cell_gnodes()` lists the live analysis trackers. Use `inspect_cell()`
+when a host needs a tracker snapshot outside the batch report lifecycle:
+current rank, energy ratio, maturity, geometry flags, and baseline
+snapshots.
+
+```rust
+# use torrust_sentinel::{NoiseSchedule, Sentinel128, SentinelConfig};
+# let config = SentinelConfig::<u64> {
+#     analysis_k: 8, split_threshold: 4,
+#     noise_schedule: NoiseSchedule::Explicit(vec![4]), noise_batch_size: 4,
+#     noise_seed: Some(2026), ..SentinelConfig::default()
+# };
+# let mut sentinel = Sentinel128::new(config).unwrap();
+# let _report = sentinel.ingest(&[1_u128, 2, 3, 4]);
+let root = sentinel
+    .cell_gnodes()
+    .into_iter()
+    .find_map(|id| sentinel.inspect_cell(id).filter(|cell| cell.depth == 0))
+    .unwrap();
+
+assert_eq!(root.analysis_width, 128);
+assert!(root.maturity.total_observations() > 0);
+```
+
+G-node handles come from the analysis set, reports, inspections, and the
+underlying `graph()` view. Handles can become stale after later graph
+restructuring, so guard externally stored handles with a fresh graph
+lookup before targeted subtree decay.
+
+### Enable per-sample payloads
+
+Set `per_sample_scores` when the host needs observation-level detail.
+The batch-level summaries remain available; each cell report also
+contains one `SampleScore` per routed observation.
+
+```rust
+# use torrust_sentinel::{NoiseSchedule, Sentinel128, SentinelConfig};
+let config = SentinelConfig::<u64> {
+    per_sample_scores: true,
+    analysis_k: 8,
+    split_threshold: 4,
+    noise_schedule: NoiseSchedule::Explicit(vec![4]),
+    noise_batch_size: 4,
+    noise_seed: Some(2026),
+    ..SentinelConfig::default()
+};
+let mut sentinel = Sentinel128::new(config).unwrap();
+let report = sentinel.ingest(&[1_u128, 2, 3, 4]);
+
+let root = report.ancestor_reports.iter().find(|cell| cell.depth == 0).unwrap();
+let samples = root.per_sample.as_ref().unwrap();
+assert_eq!(samples.len(), root.sample_count);
+```
+
+Per-sample payloads are useful for forensics and expensive for large
+batches. Keep them disabled in hot paths unless the host needs that
+resolution.
+
+### Use 64-bit or custom-width domains
+
+`Sentinel64` is the convenience alias for `SpectralSentinel<u64, u64,
+64>`. The generic engine can also be instantiated with another bit-width
+when the coordinate type can supply centred bits over that domain.
+
+```rust
+use torrust_sentinel::{NoiseSchedule, Sentinel64, SentinelConfig};
+
+let config = SentinelConfig::<u64> {
+    noise_schedule: NoiseSchedule::Explicit(vec![2]),
+    noise_batch_size: 4,
+    noise_seed: Some(7),
+    ..SentinelConfig::default()
+};
+
+let mut sentinel = Sentinel64::new(config).unwrap();
+let report = sentinel.ingest(&[0xF000_0000_0000_0001_u64, 0xF000_0000_0000_0002]);
+assert!(report.health.active_trackers > 0);
+```
+
+```rust
+use torrust_sentinel::{NoiseSchedule, SentinelConfig, SpectralSentinel};
+
+type Sentinel16 = SpectralSentinel<u64, u64, 16>;
+
+let config = SentinelConfig::<u64> {
+    noise_schedule: NoiseSchedule::Explicit(vec![2]),
+    noise_batch_size: 4,
+    noise_seed: Some(11),
+    ..SentinelConfig::default()
+};
+
+let mut sentinel = Sentinel16::new(config).unwrap();
+let report = sentinel.ingest(&[0xF001_u64, 0xF002, 0x1003]);
+assert!(report.contour.total_importance >= 3.0);
+```
+
+The domain is `[0, 2^N)`. Hosts are responsible for ensuring coordinates
+fit the chosen width and carry meaningful positional structure.
+
+### Periodic ingest and decay loop
+
+A common lifecycle is: ingest a batch, read measurements, then apply a
+host-selected decay so stale spatial importance fades between ticks.
+
+```rust
+# use torrust_sentinel::{NoiseSchedule, Sentinel128, SentinelConfig};
+# let config = SentinelConfig::<u64> {
+#     analysis_k: 8, split_threshold: 4,
+#     noise_schedule: NoiseSchedule::Explicit(vec![4]), noise_batch_size: 4,
+#     noise_seed: Some(2026), ..SentinelConfig::default()
+# };
+# let mut sentinel = Sentinel128::new(config).unwrap();
+let batches: Vec<Vec<u128>> = vec![
+    vec![0xA000_0000_0000_0000_0000_0000_0000_0001, 0xA000_0000_0000_0000_0000_0000_0000_0002],
+    vec![0xB000_0000_0000_0000_0000_0000_0000_0001, 0xA000_0000_0000_0000_0000_0000_0000_0003],
+];
+
+for batch in &batches {
+    let report = sentinel.ingest(batch);
+    let _max_novelty_z = report
+        .cell_reports
+        .iter()
+        .chain(report.ancestor_reports.iter())
+        .map(|cell| cell.scores.novelty.max_z_score)
+        .fold(0.0_f64, f64::max);
+
+    sentinel.decay(0.95, 0.0);
+}
+```
+
+The `q` parameter controls depth selectivity: `q = 0.0` decays every
+spatial depth equally; raising `q` toward `1.0` makes fine structure fade
+more aggressively than coarse structure.
+
+### Treat warm trackers as preliminary
+
+Every tracker reports a `noise_influence` value. It decays as real
+observations replace synthetic warm-up as the basis of the learned
+baseline. Hosts can use it to suppress early conclusions without hiding
+the raw measurements.
+
+```rust
+# use torrust_sentinel::{NoiseSchedule, Sentinel128, SentinelConfig};
+# let config = SentinelConfig::<u64> {
+#     analysis_k: 8, split_threshold: 4,
+#     noise_schedule: NoiseSchedule::Explicit(vec![4]), noise_batch_size: 4,
+#     noise_seed: Some(2026), ..SentinelConfig::default()
+# };
+# let mut sentinel = Sentinel128::new(config).unwrap();
+# let report = sentinel.ingest(&[1_u128, 2, 3, 4]);
+for cell in report.cell_reports.iter().chain(report.ancestor_reports.iter()) {
+    if cell.maturity.noise_influence > 0.5 {
+        continue;
+    }
+
+    let _usable_mean_z = cell.scores.novelty.mean_z_score;
+}
+```
+
+This is a host policy choice. Spectral Sentinel exposes maturity; it does
+not suppress or reinterpret scores on the host's behalf.
+
+## Configuration
+
+`SentinelConfig` validates every invariant up front. Invalid parameters
+return all detected errors at once; warnings report valid combinations
+that are likely to produce poor warm-up quality.
+
+```rust
+use torrust_sentinel::{NoiseSchedule, SentinelConfig, SvdStrategy};
+
+let config = SentinelConfig::<u64> {
+    max_rank: 16,                 // rank ceiling per tracker
+    forgetting_factor: 0.99,      // fast EWMA memory
+    rank_update_interval: 100,    // tracker steps between rank checks
+    energy_threshold: 0.90,       // variance target for rank adaptation
+    eps: 1e-6,                    // numerical stability
+    cusum_slow_decay: 0.999,      // slow baseline for per-cell drift
+    cusum_coord_slow_decay: 0.999,// slow baseline for coordination drift
+    cusum_allowance_sigmas: 0.5,  // CUSUM noise allowance
+    clip_sigmas: 3.0,             // upper-tail baseline clip width
+    clip_pressure_decay: 0.95,    // clip-pressure EWMA memory
+    per_sample_scores: false,     // include per-observation detail
+    analysis_k: 1024,             // competitive analysis budget
+    analysis_depth_cutoff: 6,     // V-Tree eligibility cutoff
+    split_threshold: 100,         // G-V Graph split sensitivity
+    d_create: 3,                  // max V-depth for new splits
+    d_evict: 6,                   // min V-depth for eviction
+    budget: 100_000,              // hard live G-node ceiling
+    noise_schedule: NoiseSchedule::default(),
+    noise_batch_size: 16,
+    noise_seed: Some(42),
+    background_warming: false,
+    svd_strategy: SvdStrategy::Brand,
+};
+
+config.validate().unwrap();
+let _warnings = config.warnings();
+```
+
+Key tuning knobs:
+
+| Parameter | Effect |
+| --------- | ------ |
+| `analysis_k` | Resource ceiling for competitive analysis cells. Total live cell trackers are bounded by the competitive cells plus their shared ancestors. |
+| `analysis_depth_cutoff` | V-Tree depth eligibility. Lower values restrict analysis to entries that have risen closer to the tournament root. |
+| `forgetting_factor` | Fast statistical memory. Lower values adapt faster; higher values remember longer. |
+| `max_rank` | Model expressiveness ceiling. Higher values can model richer suffix structure at greater memory and SVD cost. |
+| `energy_threshold` | Variance target for automatic rank adaptation. Higher values tend to grow rank. |
+| `split_threshold` | Spatial split sensitivity. Lower values refine the G-V Graph faster. |
+| `d_create` / `d_evict` | Mudlark depth gates. They control tree growth, eviction eligibility, and budget behaviour. |
+| `budget` | Hard ceiling on live G-nodes in the spatial substrate. |
+| `noise_schedule` | Depth-tiered warm-up schedule: geometric by default, or explicit per-depth counts. |
+| `background_warming` | Moves new-cell warm-up off the `ingest()` hot path at the cost of delayed participation. |
+| `svd_strategy` | `Brand` incremental SVD by default, with `Naive` available as a dense baseline. |
+
+## Automatic warm-up
+
+Every newly created tracker is warmed with synthetic noise before it
+receives real observations. There is no manual noise-injection API; the
+Spectral Sentinel owns the warm-up lifecycle.
+
+| Tracker | Warm-up behaviour |
+| ------- | ----------------- |
+| Root tracker | Warmed during `SpectralSentinel::new()`. |
+| New analysis cells | Enqueued into the staging area and warmed before promotion. |
+| Coordination contexts | Warmed when cross-cell coordination first activates. |
+
+`NoiseSchedule` supports two forms:
+
+| Variant | Meaning |
+| ------- | ------- |
+| `Geometric { root, decay, min }` | `rounds(depth) = max(min, root × decay^depth)`. This is the default schedule. |
+| `Explicit(Vec<u32>)` | Per-depth round counts. The last entry repeats for deeper cells; an empty vector disables noise. |
+
+Synchronous warm-up (`background_warming: false`) is deterministic and
+used heavily by tests. Background warm-up (`true`) is intended for
+production paths where cell creation should not introduce a latency spike.
+
+## Architecture in brief
+
+Spectral Sentinel implements a three-layer feed-forward architecture backed
+by the Mudlark G-V Graph.
+
+```text
+Layer 1: G-V Graph
+         Adaptive spatial partitioning of [0, 2^N)
+         Pure volume tracking: Δ = 1 per observation
+         Competitive ranking by observation volume
+              │
+              │ V-Tree depth ≤ cutoff → top-K selection
+              ▼
+Layer 2: Analysis Selector
+         Selects competitive cells
+         Closes selection under G-tree ancestry
+              │
+              │ suffix bit vectors at each ancestor depth
+              ▼
+Layer 3: Analysis Engine
+         Per-cell low-rank subspace trackers
+         Hierarchical coordination over cross-cell score patterns
+              │
+              ▼
+         BatchReport<C> → host
+```
+
+### Spatial substrate
+
+Spectral Sentinel owns a `GvGraph<C, V, N>`. During `ingest()`, each raw
+coordinate is observed by the graph with `Δ = 1`; that is the
+feed-forward invariant from [ADR-S-002](adr/002-feed-forward-invariant.md).
+Anomaly scores flow outward to reports and never back into Mudlark's
+importance signal.
+
+The host may call `decay()` or `decay_subtree()` to reshape spatial
+importance over time. Decay affects the graph's competitive ranking, not
+the statistical state inside trackers.
+
+### Analysis selector
+
+After each observation pass, `AnalysisSet` selects the top competitive
+V-Tree entries within `analysis_depth_cutoff`, takes at most
+`analysis_k`, and closes the result under G-tree ancestry. The root is
+permanent context, never a competitive target.
+
+The selected cells form the investment set: cells that own trackers or
+are warming toward ownership. Online members produce reports; warming
+members are visible in health and summary counts.
+
+### Analysis engine
+
+Each analysis cell owns a `SubspaceTracker` over the suffix bits `[d, N)`
+where `d` is the G-tree depth. The tracker processes batches in a strict
+score-before-evolve order:
+
+1. Score the batch against the prior model.
+2. Evolve the subspace with streaming thin SVD.
+3. Evolve latent mean, variance, and second-moment state.
+4. Update fast baselines, slow CUSUM references, and clip pressure.
+5. Adapt rank toward the configured energy threshold.
+
+Coordination trackers sit at internal G-tree contexts and model patterns
+among competitive-cell score vectors. They detect cross-cell score shapes
+that no single cell needs to treat as special.
+
+## Scoring axes
+
+All axes share the same polarity: higher values indicate greater
+anomalous departure from the learned baseline.
+
+| Axis | Measures | Range |
+| ---- | -------- | ----- |
+| `novelty` | Residual energy outside the learned subspace | `[0, ∞)` |
+| `displacement` | Distance from the cell's latent centroid | `[0, 1)` |
+| `surprise` | Per-dimension magnitude deviation | `[0, ∞)` |
+| `coherence` | Unusual pairwise co-activation patterns | `[0, ∞)` |
+
+Together they decompose the covariance structure of centred suffix-bit
+vectors without assembling or inverting a dense covariance matrix.
+`ScoreDistribution` reports min, max, mean, z-scores, fast baseline,
+slow CUSUM reference, accumulator state, and clip pressure for each axis.
+
+At the coordination tier the same four axes have second-order meaning:
+novelty measures unseen cross-cell score patterns, displacement measures
+a shifted score landscape, surprise measures a system-wide axis
+elevation, and coherence measures unusual combinations of axis elevation.
+
+## Report structure
+
+`ingest()` returns a `BatchReport<C>`:
+
+```text
+BatchReport
+├── cell_reports: [CellReport]                  competitive cells
+├── ancestor_reports: [CellReport]              ancestor-only cells, including root
+├── coordination_reports: [CoordinationReport]  cross-cell score-pattern models
+├── contour: ContourSnapshot                    spatial contour summary
+├── health: HealthReport                        operational health snapshot
+└── analysis_set_summary: AnalysisSetSummary    investment and producing-set summary
+```
+
+A `CellReport` includes interval bounds, depth, suffix width, sample
+count, rank, energy ratio, top singular value, four-axis scores,
+tracker maturity, scoring geometry, and optional per-sample scores.
+
+A `CoordinationReport` includes the same tracker facts for a
+coordination context, plus the number of competitive cells that
+contributed and optional per-member score records.
+
+## Public API surface
+
+Spectral Sentinel uses the same three-surface visibility model as Mudlark.
+Public symbols are re-exported flat from the crate root; modules remain
+private, so downstream code has one canonical import path.
+
+| Surface | Name | What it exposes |
+| ------- | ---- | --------------- |
+| 1 | **Readouts** | Detached report and snapshot types users hold after an observation cycle. |
+| 2 | **Engine** | Opaque operational types users configure and drive. |
+| 3 | **Internals** | `pub(crate)` implementation machinery; not part of the API. |
+
+### Key types
+
+`GNodeId` is listed with the engine surface because hosts pass it back to
+target subtree decay. Reports also carry it as a readout identifier.
+
+| Type | Surface | Description |
+| ---- | ------- | ----------- |
+| `SpectralSentinel<C, V, N>` | 2 | Live Spectral Sentinel engine generic over coordinate, accumulator, and bit-width. |
+| `Sentinel128` | 2 | Alias for `SpectralSentinel<u128, u64, 128>`. |
+| `Sentinel64` | 2 | Alias for `SpectralSentinel<u64, u64, 64>`. |
+| `SentinelConfig<V>` | 2 | Measurement, resource, warm-up, and Mudlark substrate configuration. |
+| `NoiseSchedule` | 2 | Depth-tiered tracker warm-up schedule. |
+| `SvdStrategy` | 2 | `Brand` incremental SVD or `Naive` dense SVD. |
+| `CentredBitSource` | 2 | Coordinate-to-centred-bits capability used by the engine. |
+| `GNodeId` | 2 | Re-exported Mudlark arena handle for reports and targeted subtree decay. |
+| `BatchReport<C>` | 1 | Complete output from one `ingest()` call. |
+| `CellReport<C>` | 1 | Per-cell score, rank, maturity, and geometry readout. |
+| `CoordinationReport<C>` | 1 | Cross-cell score-pattern readout. |
+| `AnalysisSet<C, V>` | 1 | Current competitive targets plus G-tree ancestors. |
+| `AnalysisEntry<C, V>` | 1 | One selected cell in the analysis set. |
+| `HealthReport` | 1 | Tracker, coordination, maturity, geometry, and clip-pressure health. |
+| `CellInspection<C>` | 1 | Direct snapshot of one live cell tracker. |
+| `AnomalyScores` | 1 | Four-axis score distributions. |
+| `ScoreDistribution` | 1 | Raw score summary plus z-scores, baseline, CUSUM, and clip pressure. |
+| `TrackerMaturity` | 1 | Real/noise observation counts and noise influence. |
+| `ScoringGeometry` | 1 | Structural flags for novelty saturation and coherence activity. |
+
+### Key operations
+
+| Method | Description |
+| ------ | ----------- |
+| `SpectralSentinel::new(config)` | Validate configuration, create graph, create and warm the root tracker. |
+| `ingest(&[C])` | Process one batch and return `BatchReport<C>`. |
+| `health()` | Snapshot active trackers, ranks, maturity, geometry, coordination, and clip pressure. |
+| `cell_gnodes()` | List live analysis-cell handles. |
+| `inspect_cell(gnode)` | Snapshot a specific cell tracker if it is currently live. |
+| `cells_tracked()` | Count live cell trackers in the analysis set. |
+| `lifetime_observations()` | Count real observations processed since construction or reset. |
+| `degenerate_cells_skipped()` | Count cells excluded because suffix width is too small for tracking. |
+| `config()` | Read-only access to the validated configuration. |
+| `analysis_set()` | Read-only access to the current analysis set. |
+| `graph()` | Read-only access to the Mudlark spatial substrate. |
+| `decay(attenuation, q)` | Apply host-controlled temporal decay to the full graph. |
+| `decay_subtree(gnode, attenuation, q)` | Apply host-controlled temporal decay to one G-subtree. |
+| `reset()` | Clear learned state and recreate the fresh warmed root. |
+
+## Features
+
+| Feature | Default | Effect |
+| ------- | ------- | ------ |
+| `serde` | no | `Serialize`/`Deserialize` for configuration, SVD strategy, and report snapshot types. Also enables `torrust-mudlark/serde`. |
+
+## Resource model
+
+`analysis_k` is the primary analysis-tier budget. The competitive set is
+bounded by that value, and the full tracker set is the competitive cells
+plus the shared ancestor chain back to the root. The default configuration
+keeps this bounded by roughly `2 × analysis_k` in the common Steiner-tree
+case.
+
+The Mudlark `budget` field is the hard ceiling on live spatial nodes.
+`split_threshold`, `d_create`, and `d_evict` determine how quickly the
+spatial substrate refines and how it contracts under pressure.
+
+Warm-up work is usually the largest cold-start cost. Use
+`background_warming: true` when avoiding `ingest()` latency spikes matters
+more than immediate participation by brand-new cells.
+
+Criterion benchmarks live in [benches/sentinel.rs](benches/sentinel.rs):
+
+```sh
+cargo bench -p torrust-sentinel
+```
+
+## Limitations
+
+- **Structured coordinates required.** Pseudo-random bit strings do not
+  contain learnable suffix structure for this model.
+- **One-dimensional domain.** Spectral Sentinel expects one-dimensional coordinates. Multi-dimensional data needs an external encoding, such as a space-filling curve composition.
+- **Measurements only.** Spectral Sentinel has no policy thresholds, threat levels, labels, or actions. Hosts must interpret reports in context.
+- **Single-writer mutation.** Mutating methods take `&mut self`; concurrent
+  writers need external synchronisation.
+- **Warm-up tradeoff.** Synchronous warm-up is deterministic but can add
+  latency when cells are created. Background warm-up avoids that spike but
+  delays scoring for new cells.
+- **Timing equalisation out of scope.** Deferred warm-up is implemented;
+  timing-protection padding and equalisation from §ALGO S-18.5 are outside
+  this crate.
+
+## Documentation
+
+> The relative links below work in the repository but may not resolve on
+> crates.io.
+
+| Document | Contents |
+| -------- | -------- |
+| [docs/algorithm.md](docs/algorithm.md) | Formal algorithm specification. |
+| [docs/api.md](docs/api.md) | Public API reference. |
+| [docs/implementation.md](docs/implementation.md) | Source layout, implementation guide, and test-suite map. |
+| [adr/](adr/) | Architecture decision records. |
+| [tests/pedagogy.rs](tests/pedagogy.rs) | End-to-end narrative test for construction, ingest, scoring, decay, and reset. |
+| [tests/pedagogy_advanced.rs](tests/pedagogy_advanced.rs) | Inspection-oriented narrative test for readouts, geometry, coordination, and temporal separation. |
+
+### Design records
+
+Architectural decisions are recorded in [adr/](adr/). Notable entries:
+
+- [ADR-S-001](adr/001-measures-not-opinions.md) — Measures, not opinions
+- [ADR-S-002](adr/002-feed-forward-invariant.md) — Feed-forward invariant
+- [ADR-S-007](adr/007-automatic-noise-injection.md) — Automatic noise injection
+- [ADR-S-015](adr/015-cell-creation-performance.md) — Cell creation performance
+- [ADR-S-016](adr/016-brand-incremental-svd.md) — Brand incremental SVD
+- [ADR-S-017](adr/017-deferred-cell-warm-up.md) — Deferred cell warm-up
+- [ADR-S-018](adr/018-generic-domain-parameters.md) — Generic domain parameters
+- [ADR-S-019](adr/019-investment-set-terminology-and-reporting.md) — Investment-set terminology and reporting
+- [ADR-S-020](adr/020-clip-pressure-ewma.md) — Clip-pressure EWMA
+- [ADR-S-021](adr/021-ewma-mean-centred-variance.md) — EWMA mean-centred variance
+
+### Cross-references
+
+Doc-comments and documentation use `§`-prefixed tags to cite specific
+sections of the design documents. The `S-` qualifier identifies this
+Sentinel package; ADRs use their own `ADR-S-NNN` form.
+
+| Tag | Document |
+| --- | -------- |
+| `§ALGO S-N` | [docs/algorithm.md](docs/algorithm.md) §N |
+| `§API S-N` | [docs/api.md](docs/api.md) §N |
+| `§IMPL S-N` | [docs/implementation.md](docs/implementation.md) §N |
+| `ADR-S-NNN` | Architecture decision record in [adr/](adr/) |
+
+For example, `§ALGO S-8.2` refers to analysis-set ancestry closure in
+the algorithm specification. `§§` denotes a range, such as
+`§§ALGO S-4.2–4.5`. The full authoring conventions are in
+[AGENTS.md](../../AGENTS.md).
+
+## Development
+
+### Building and testing
+
+```sh
+# Unit + integration tests (debug, optimised dev profile):
+cargo test -p torrust-sentinel --all-targets --all-features
+
+# Doc-tests, including this README:
+cargo test -p torrust-sentinel --all-features --doc
+
+# Release mode:
+cargo test -p torrust-sentinel --all-targets --all-features --release
+
+# No-default-features spot-check:
+cargo test -p torrust-sentinel --all-targets --no-default-features
+
+# Clippy and docs:
+cargo clippy -p torrust-sentinel --all-targets --all-features
+cargo doc -p torrust-sentinel --all-features --no-deps
+```
+
+The README is included from [src/lib.rs](src/lib.rs) under `#[cfg(doctest)]`,
+so Rust code blocks here compile and run as part of `cargo test --doc`.
+
+Structured diagnostics use `tracing`. They are silent by default; attach
+a subscriber when you need debug or trace-level detail from tracker and
+SVD internals.
diff --git a/packages/sentinel/adr/001-measures-not-opinions.md b/packages/sentinel/adr/001-measures-not-opinions.md
new file mode 100644
index 000000000..edd451969
--- /dev/null
+++ b/packages/sentinel/adr/001-measures-not-opinions.md
@@ -0,0 +1,54 @@
+# ADR-S-001: Measures Not Opinions
+
+**Status:** Implemented
+**Date:** 2026-03-08
+**Spec:** §ALGO S-1.2 (layer responsibilities — "sentinel measures; host decides")
+
+## Context
+
+An anomaly detector can either:
+
+- **A)** Output raw statistical measurements and let the consumer
+  decide what they mean (library approach).
+- **B)** Output verdicts — threat levels, recommended actions,
+  block/allow decisions (appliance approach).
+
+The sentinel is a library embedded inside the Torrust Index. Different
+hosts have different risk tolerances, different action vocabularies
+(ban, throttle, flag, ignore), and different false-positive
+consequences. Baking policy into the sentinel would force every host
+into one policy model.
+
+## Decision
+
+**The sentinel outputs only raw statistical measurements. It never
+outputs opinions, threat levels, or recommended actions.**
+
+Concretely:
+
+- `BatchReport` contains `AnomalyScores` (per-axis mean, max, z-score,
+  CUSUM), `ScoringGeometry` (ADR-S-008), `TrackerMaturity`, rank,
+  energy ratios.
+- No field is named "threat", "risk", "anomaly_level", or "action".
+- No method returns a boolean "is anomalous" verdict.
+- No internal threshold triggers automatic remediation.
+
+The host reads the report and applies its own policy:
+
+```rust
+// Host policy — not sentinel code:
+if report.scores.novelty.z_score > 4.0 && report.maturity.noise_influence < 0.1 {
+    throttle(cell_id);
+}
+```
+
+## Consequences
+
+- The sentinel has no policy parameters (no "alert threshold", no
+  "sensitivity level").
+- Report types carry more fields than an appliance would expose, but
+  each field has a precise statistical definition.
+- Integration tests assert statistical properties, not verdicts.
+- Higher polarity = more anomalous is a uniform convention across
+  all four scoring axes (§ALGO S-6), ensuring the host can apply a
+  single threshold logic to any axis.
diff --git a/packages/sentinel/adr/002-feed-forward-invariant.md b/packages/sentinel/adr/002-feed-forward-invariant.md
new file mode 100644
index 000000000..761f70608
--- /dev/null
+++ b/packages/sentinel/adr/002-feed-forward-invariant.md
@@ -0,0 +1,93 @@
+# ADR-S-002: Feed-Forward Invariant
+
+**Status:** Implemented (2026-03-09)
+**Date:** 2026-03-08
+**Spec:** §ALGO S-2.4 (what sentinel owns vs inherits),
+§ALGO S-8.1 Step 2 (volume accounting),
+§ALGO S-8.2 (step ordering)
+**Relates to:** [ADR-S-001](001-measures-not-opinions.md) (measures not opinions),
+[ADR-S-003](003-mudlark-integration.md) (V = u64)
+
+## Context
+
+The sentinel owns a `GvGraph` for adaptive spatial partitioning and
+a bank of `SubspaceTracker`s for statistical analysis. There is a
+question of what signal flows between these two subsystems:
+
+- **Feed-forward:** the G-V Graph receives raw observations and shapes
+  the contour; the analysis engine reads the contour's structure. No
+  analysis output flows back.
+- **Feedback:** anomaly scores or derived signals could be fed back
+  into the G-V Graph as importance weights, causing regions with
+  high anomaly scores to receive finer resolution.
+
+## Decision
+
+**Strict feed-forward. The G-V Graph receives only `observe(v, 1u64)`
+per raw input value. Anomaly scores, z-scores, CUSUM values, and any
+other derived signals are never fed back.**
+
+```rust
+// The ONLY permitted G-V Graph mutation during ingest:
+for &value in values {
+    self.graph.observe(value, 1u64);
+}
+```
+
+This is a code-review invariant, not mechanically enforced.
+
+## Rationale
+
+1. **Feedback creates resonance.** If anomaly scores amplify
+   importance, regions currently under scrutiny get more resolution,
+   which generates more data, which changes the anomaly scores. This
+   feedback loop could cause the contour to lock onto artefacts of
+   its own analysis rather than genuinely active traffic.
+
+2. **Separation of concerns.** The G-V Graph (Layer 1) reflects
+   objective traffic volume. The analysis engine (Layer 3) reflects
+   statistical deviation from learned baselines. Keeping them
+   independent means a caller can reason about each in isolation.
+
+3. **Host control.** The host controls temporal policy via `decay()`.
+   If importance weighting is desired, the host can apply it at the
+   decay layer (e.g., selective `decay_subtree()` calls guided by
+   analysis output). This keeps the feedback loop in host code, where
+   policy belongs (ADR-S-001).
+
+## Consequences
+
+- The G-V Graph's contour is shaped entirely by raw traffic volume
+  and host-initiated decay. The analysis tier has no influence on
+  spatial resolution.
+- Δ = 1 means the accumulator type `V` is a pure observation
+  counter (for the default `V = u64`), simplifying reasoning about `split_threshold` (it is
+  simply "how many observations before splitting").
+- This invariant must be maintained across all phases of the
+  integration plan. Code review should reject any path that calls
+  `graph.observe()` with a value other than the unit delta.
+
+### Enforcement mechanisms
+
+1. **No `graph.observe()` during noise injection.** The noise pathway
+   operates on tracker-space `f64` vectors (synthetic centred bit
+   vectors fed directly to `SubspaceTracker::observe()`), not
+   coordinate-space values. Therefore `graph.observe()` is
+   never called during noise injection. This clarifies how the
+   feed-forward invariant interacts with automatic noise injection
+   (ADR-S-007).
+
+2. **No `graph_mut()` exposure.** The sentinel exposes only a
+   read-only `graph()` accessor (`&GvGraph`) for diagnostics.
+   Mutable graph access is provided exclusively through controlled
+   methods — `decay()` and `decay_subtree()` — which enforce valid
+   parameters. This prevents callers from violating the invariant
+   by calling `graph.observe(v, 100)` directly.
+
+3. **No pre-aggregated observation.** Each value is fed individually
+   via `graph.observe(value, unit_delta)`. Do not pre-aggregate values by
+   cell and call `observe(representative, count)` — this would lose
+   spatial resolution (values may map to different leaf cells),
+   add complexity for no performance gain (routing still happens
+   inside `observe()`), and violate §ALGO S-8.3 which specifies
+   "Δ = 1 per value".
diff --git a/packages/sentinel/adr/003-mudlark-integration.md b/packages/sentinel/adr/003-mudlark-integration.md
new file mode 100644
index 000000000..dff13b6b4
--- /dev/null
+++ b/packages/sentinel/adr/003-mudlark-integration.md
@@ -0,0 +1,136 @@
+# ADR-S-003: Mudlark Integration
+
+**Status:** Decided (Part A superseded by [ADR-S-018](018-generic-domain-parameters.md))
+**Date:** 2026-03-09
+**Spec:** §ALGO S-2.1 (domain $[0, 2^{128})$), §ALGO S-2.4 (what sentinel owns),
+§ALGO S-13.3 (G-V Graph config)
+**Relates to:** [ADR-S-002](002-feed-forward-invariant.md) (Δ = 1 invariant),
+[ADR-S-004](004-config-validation-over-panic.md) (config validation),
+mudlark [ADR-M-006](../../mudlark/adr/006-generic-parameters.md) (generic parameters)
+
+## Context
+
+The sentinel must own a `GvGraph` instance from mudlark. Two groups
+of decisions arise:
+
+1. **Type-level parameterisation** — the three generic parameters
+   `C`, `V`, `N` and the `Config<V>` fields.
+2. **Cargo feature gating** — which of mudlark's optional features the
+   sentinel enables, and how they relate to sentinel's own features.
+
+These are a single integration surface and are decided together.
+
+---
+
+## Part A — Type Parameters: `GvGraph<u128, u64, 128>`
+
+> **Superseded by [ADR-S-018](018-generic-domain-parameters.md).**
+> The sentinel is now generic: `SpectralSentinel<C, V, N>` with type
+> aliases `Sentinel128` and `Sentinel64`. The rationale below is
+> retained for historical context; the concrete parameters described
+> here remain the defaults via `Sentinel128`.
+
+| Parameter | Value | Rationale |
+|-----------|-------|-----------|
+| `C = u128` | The sentinel analyses IPv6 addresses and similar 128-bit identifiers. The full `u128` domain covers $[0, 2^{128})$ without truncation. |
+| `V = u64` | The sentinel observes with Δ = 1 per input value (ADR-S-002). An unsigned integer counter is the natural accumulator. `u64` provides a ceiling of $1.8 \times 10^{19}$ observations — far beyond any practical lifetime. |
+| `N = 128` | Full domain resolution. Every bit position is available for spatial refinement. |
+
+### `split_threshold` as `u64`
+
+The spec lists `split_threshold` as `f64`, but since `V = u64`,
+mudlark's `Config<V>` requires `split_threshold: u64`. The sentinel
+accordingly stores `split_threshold: u64` in `SentinelConfig` with
+default `100`.
+
+This is type-honest: the threshold is "number of observations a cell
+must accumulate before subdividing". A fractional count is meaningless.
+
+### Hardcoded mudlark-internal knobs
+
+Two `Config<V>` fields are not exposed in `SentinelConfig`:
+
+| Field | Value | Rationale |
+|-------|-------|-----------|
+| `alpha_relax` | `0.75` | Mudlark's recommended default. Controls depth-gate relaxation timing. Not performance-sensitive for the sentinel's use case. |
+| `bounded_eviction` | `true` | Mudlark's recommended default. Prevents over-eviction. Always desirable. |
+
+These can be promoted to `SentinelConfig` later if profiling reveals
+sensitivity.
+
+---
+
+## Part B — Cargo Feature Gating
+
+Mudlark exposes three Cargo features:
+
+| Feature                     | Default? | What it provides |
+|-----------------------------|----------|-----------------|
+| `dynamic-contour-tracking`  | Yes      | `plateaus()`, contour queries |
+| `rand`                      | Yes      | `WeightedSampler` (randomised sampling) |
+| `serde`                     | Yes      | `Serialize`/`Deserialize` on all public types |
+
+### Decisions
+
+1. **Disable mudlark's default features** (`default-features = false`).
+
+2. **Always enable `dynamic-contour-tracking`.** The analysis selector
+   (§ALGO S-4) and report structure (§ALGO S-14) need `plateaus()` and
+   contour queries.
+
+3. **Do not enable `rand`.** Mudlark's `rand` feature provides
+   `WeightedSampler`, which the sentinel does not use. The sentinel
+   has its own `rand` dependency for noise generation.
+
+4. **Gate `torrust-mudlark/serde` behind sentinel's `serde` feature:**
+
+   ```toml
+   [features]
+   serde = ["dep:serde", "torrust-mudlark/serde"]
+   ```
+
+### Resulting `Cargo.toml` snippet
+
+```toml
+[features]
+serde = ["dep:serde", "torrust-mudlark/serde"]
+
+[dependencies]
+torrust-mudlark = { path = "../mudlark", default-features = false, features = ["dynamic-contour-tracking"] }
+```
+
+---
+
+## Alternatives Considered
+
+| Alternative | Pros | Cons |
+|-------------|------|------|
+| `V = f64` | Matches spec table literally | Lossy for a counter; `f64` loses precision above $2^{53}$ |
+| `C = u64`, `N = 64` | Smaller coordinates | Loses half the address space; useless for IPv6 |
+| Expose `alpha_relax` | Full tunability | Config surface without demonstrated need |
+| `split_threshold: f64` in sentinel, cast `as u64` | Matches spec | Lossy cast, misleading type |
+| Accept mudlark's default features | Simpler `Cargo.toml` | Pulls in `rand_core` unconditionally; forces serde on all builds |
+| Gate `dynamic-contour-tracking` | Allows "headless" build | Every use of the G-V Graph needs contour queries |
+
+## Consequences
+
+- `GvGraph<u128, u64, 128>` appears in the sentinel's struct definition
+  and all downstream code.
+- `u128` arithmetic is well-optimized on 64-bit platforms (two-wide
+  operations). Benchmark the observe hot-path if performance concerns
+  arise.
+- `u64` accumulator means `decay()` operates on integer attenuation
+  via mudlark's `Attenuatable` trait for `u64` (floor-rounding).
+- `cargo check --no-default-features` builds with only
+  `dynamic-contour-tracking` in mudlark — no serde, no rand.
+- `cargo check --all-features` enables mudlark's `serde` via the
+  transitive feature gate.
+
+### Re-export policy
+
+Only `GNodeId` is re-exported from mudlark to the sentinel's public
+API. Users needing other mudlark types (e.g., `GNodeInfo`, `Plateau`)
+must depend on `torrust-mudlark` directly. This minimises
+version-coupling — the sentinel can upgrade its internal mudlark
+dependency without breaking callers who don't use mudlark types in
+their API surface.
diff --git a/packages/sentinel/adr/004-config-validation-over-panic.md b/packages/sentinel/adr/004-config-validation-over-panic.md
new file mode 100644
index 000000000..7d2a6bf31
--- /dev/null
+++ b/packages/sentinel/adr/004-config-validation-over-panic.md
@@ -0,0 +1,104 @@
+# ADR-S-004: Config Validation Over Panic
+
+**Status:** Decided
+**Date:** 2026-03-09
+**Spec:** §ALGO S-13.3 (config constraints)
+**Relates to:** [ADR-S-003](003-mudlark-integration.md) (mudlark integration)
+
+## Context
+
+Mudlark's `GvGraph::new(config)` internally calls `config.validate()`,
+which uses `assert!` to enforce constraints such as:
+
+- `split_threshold > 0`
+- `depth_evict > depth_create`
+- `budget > max(3^(buffer+1), 2*(depth_create − 1))`
+  where `buffer = depth_evict − depth_create`
+
+If any constraint is violated, the process panics. This is acceptable
+for mudlark as a data-structure library — callers are expected to pass
+valid configs, and panicking on programmer error is idiomatic Rust.
+
+The sentinel, however, is a library consumed by the Torrust Index
+application. Panicking on bad user configuration is unacceptable:
+
+1. The Torrust process would abort if a TOML config file contained an
+   invalid `d_create` / `d_evict` combination.
+2. The caller has no opportunity to report the error, retry, or
+   fall back to defaults.
+3. Multiple config errors cannot be collected — `assert!` fires on the
+   first violation and halts.
+
+## Decision
+
+**The sentinel re-derives mudlark's config constraints in its own
+`SentinelConfig::validate()` method and returns `Result<(), ConfigErrors>`
+where `ConfigErrors` is a list of `ConfigError` variants.**
+
+The sentinel's validation runs *before* `GvGraph::new()` is ever called,
+so mudlark's `assert!` paths are unreachable under normal operation. The
+redundancy is intentional.
+
+### Headroom formula
+
+The most complex constraint is the budget headroom check:
+
+```rust
+let buffer = self.d_evict - self.d_create;
+let headroom = 3usize.pow(buffer + 1);
+let convergence = 2 * (self.d_create as usize).saturating_sub(1);
+let required = headroom.max(convergence);
+if self.budget <= required {
+    errors.push(ConfigError::BudgetTooSmall {
+        budget: self.budget,
+        required_minimum: required,
+    });
+}
+```
+
+This mirrors mudlark's internal calculation. A comment in the sentinel
+code cross-references the mudlark source so that future changes to the
+formula can be synchronised.
+
+## Alternatives Considered
+
+- **Catch the panic.** `std::panic::catch_unwind()` around
+  `GvGraph::new()`. Rejected — fragile, opaque (string error), cannot
+  collect multiple violations, and `catch_unwind` is a last resort in
+  idiomatic Rust.
+
+- **Ask mudlark to return `Result`.** A longer-term option. Even then,
+  sentinel's pre-validation remains useful — it collects *all* errors
+  at once and produces sentinel-specific error types for the UI.
+
+- **Trust the caller.** Document constraints and `debug_assert!` only.
+  Rejected — config comes from user-edited TOML; any typo could crash
+  the server.
+
+## Consequences
+
+- Callers receive structured, actionable `ConfigError` variants
+  listing every constraint violation, not a single panic message.
+- If mudlark changes its headroom formula, the sentinel's re-derivation
+  must be updated. The comment `// mirrors mudlark Config::validate()`
+  marks this coupling.
+- Mudlark's own `assert!` paths become dead code in production — they
+  serve only as a defence-in-depth safety net.
+
+### Panic-vs-Result boundary
+
+The ADR's `Result` policy applies to `SentinelConfig` parameters that
+originate from user-edited TOML. Decay parameters (`att`, `q`) passed
+to `decay()` and `decay_subtree()` are programmer-controlled call-site
+values. Invalid decay parameters (negative `att`, `q` outside
+$[0, 1]$) trigger panics, not `Result` — these are programming
+errors, not user-input errors. The boundary is: **config = `Result`,
+call-site = panic**.
+
+### Depth 128 rejection
+
+At G-tree depth $N$, suffix width $w = N - N = 0$, making the
+tracker dimensionless — no suffix bits remain for statistical
+analysis. The config validator rejects depth configurations that would
+allow depth-$N$ cells to enter the analysis set, constraining the
+effective analysis depth range to $[1, N-1]$.
diff --git a/packages/sentinel/adr/005-deterministic-order-and-thread-safety.md b/packages/sentinel/adr/005-deterministic-order-and-thread-safety.md
new file mode 100644
index 000000000..0515b4b3c
--- /dev/null
+++ b/packages/sentinel/adr/005-deterministic-order-and-thread-safety.md
@@ -0,0 +1,97 @@
+# ADR-S-005: Deterministic Order and Thread Safety
+
+**Status:** Implemented
+**Date:** 2026-03-08
+**Spec:** §ALGO S-11.1 (noise injection), §ALGO S-11.3 (seed parameter)
+**Relates to:** mudlark [ADR-M-007](../../mudlark/adr/007-thread-safety.md)
+(mudlark thread safety)
+
+## Context
+
+Two low-level invariants govern the sentinel's runtime behaviour:
+
+1. **Iteration order must be deterministic** across runs for a given
+   seed, so that noise injection, coordination assembly, and report
+   ordering are reproducible.
+2. **`SpectralSentinel` must be `Send + Sync`**, because the host
+   will run it on background threads behind `Arc<Mutex<_>>`.
+
+Both are implementation choices that are not specified in algorithm.md
+but are required by the sentinel's operating environment.
+
+---
+
+## Part A — Deterministic Iteration Order
+
+### Decision
+
+**Use `BTreeMap` (not `HashMap`) for all spatial-key maps.**
+
+```rust
+cells: BTreeMap<GNodeId, CellState>,
+```
+
+`BTreeMap` iterates in key order (ascending). `GNodeId` is
+`Copy + Ord` (backed by `NonZeroU32`), providing a stable,
+cross-platform iteration order.
+
+### Why it matters
+
+- **Noise injection** — each tracker receives a deterministic RNG
+  sequence seeded from a user-provided seed. If iteration order
+  varies between runs, the same seed produces different per-tracker
+  baselines, making behaviour non-reproducible.
+- **Coordination tier** — cell mean-score vectors are assembled into
+  a matrix in iteration order. The meta-tracker's SVD decomposition
+  is sensitive to row ordering when eigenvalues are close.
+- **Report ordering** — `cell_reports` appear in the `BatchReport` in
+  map iteration order. Deterministic ordering makes output diffable
+  across runs.
+
+### Alternatives
+
+| Option | Pros | Cons |
+|--------|------|------|
+| `HashMap` + sort-on-iterate | O(1) lookup | Sort cost on every noise/report pass; easy to forget |
+| `IndexMap` (insertion order) | Preserves insert order | Order depends on traffic arrival, not spatial position; extra dependency |
+| `BTreeMap` (chosen) | Deterministic, no extra sort, no extra dep | O(log n) lookup vs O(1) |
+
+The O(log n) penalty is negligible — cell counts are small relative to
+the per-tracker SVD cost that dominates runtime.
+
+---
+
+## Part B — Thread Safety via Static Assertion
+
+### Decision
+
+**Enforce `Send + Sync` with a compile-time static assertion.**
+
+```rust
+const _: () = {
+    const fn assert_send_sync<T: Send + Sync>() {}
+    assert_send_sync::<SpectralSentinel>();
+};
+```
+
+This is a zero-cost check — no runtime code. If any field ever
+violates the constraint, the crate fails to compile with a clear
+error pointing at the assertion.
+
+### Why a static assertion (not trust-the-derive)
+
+All current fields are `Send + Sync` by construction. But future
+additions (`GvGraph`, `SmallRng`, etc.) could silently break this.
+The static assertion catches breakage at `cargo check` time, not at
+a downstream integration test.
+
+- `GvGraph<C, V, N>` is `Send + Sync` (mudlark ADR-M-007).
+- `SmallRng` is `Send + Sync` (no `Rc` or thread-local state).
+
+## Consequences
+
+- Noise injection with a fixed seed produces identical baselines
+  across runs, platforms, and Rust versions.
+- Reports are spatially ordered without an explicit sort pass.
+- Any future `!Send` field (e.g. a raw pointer cache) will be caught
+  immediately, forcing a conscious design choice.
diff --git a/packages/sentinel/adr/006-analysis-set-recomputation.md b/packages/sentinel/adr/006-analysis-set-recomputation.md
new file mode 100644
index 000000000..e5f8b38f3
--- /dev/null
+++ b/packages/sentinel/adr/006-analysis-set-recomputation.md
@@ -0,0 +1,110 @@
+# ADR-S-006: Analysis Set Recomputation Strategy
+
+**Status:** Decided
+**Date:** 2026-03-09
+**Spec:** §ALGO S-4.2 (analysis set definition),
+§ALGO S-4.3 (ancestor closure),
+§ALGO S-4.4 (multi-scale delivery)
+**Relates to:** [ADR-S-003](003-mudlark-integration.md) (graph parameterisation),
+[ADR-S-002](002-feed-forward-invariant.md) (feed-forward invariant)
+
+## Context
+
+The analysis set ($\mathcal{A}$) is the top-$K$ V-Tree entries by
+importance with V-Tree depth $\leq L$, closed under G-tree ancestry
+(§ALGO S-4.2). It determines which cells own `SubspaceTracker`s and
+therefore controls the sentinel's resource usage.
+
+After each batch of observations mutates the G-V Graph, the analysis
+set may change — cells split, merge, gain or lose importance. The
+sentinel must detect these changes, create trackers for new entries,
+and destroy trackers for evicted entries.
+
+Two broad strategies exist:
+
+1. **Full recomputation:** scan the entire V-Tree via `layers()` after
+   every `ingest()`, rebuild from scratch, diff against the previous set.
+2. **Incremental update:** mudlark notifies the sentinel of structural
+   changes via callbacks or a change log; the sentinel patches in place.
+
+## Decision
+
+**Start with full recomputation. Defer incremental updates until
+profiling shows the scan is a bottleneck.**
+
+### Implementation sketch
+
+```rust
+impl<C, V> AnalysisSet<C, V> {
+    pub fn recompute(graph: &GvGraph<C, V, N>, k: usize, l: usize) -> Self {
+        let mut candidates: Vec<AnalysisEntry<C, V>> = graph
+            .layers()
+            .filter(|(v_depth, _)| *v_depth <= l)
+            .map(|(v_depth, node)| AnalysisEntry {
+                gnode: node.gnode_id,
+                depth: node.depth,
+                v_depth,
+                importance: node.own,
+                start: node.start,
+                end: node.end,
+            })
+            .collect();
+
+        candidates.sort_by(|a, b| {
+            b.importance.cmp(&a.importance)
+                .then(a.start.cmp(&b.start))
+        });
+        candidates.truncate(k);
+
+        // Ancestor closure: walk G-tree parents for each competitive entry.
+        // ...
+    }
+}
+```
+
+After recomputation, the sentinel diffs old and new sets:
+
+- **New entries:** create `CellState` + `SubspaceTracker`, noise-inject
+  (ADR-S-007).
+- **Removed entries:** destroy the tracker, freeing memory.
+- **Retained entries:** keep existing tracker state.
+
+### Why full scan is acceptable initially
+
+- `layers()` is $O(n)$ in live G-nodes. With the default budget of
+  100,000, this is at most 100k iterations per `ingest()`.
+- The scan is read-only — no allocation, no mutation.
+- The sort is $O(n \log n)$ but operates on a filtered subset (entries
+  with `v_depth ≤ L`), typically much smaller than $n$.
+- `ingest()` is called once per batch (not per observation).
+
+### Incremental path (deferred)
+
+An incremental approach requires mudlark to expose a change
+notification mechanism — either callbacks or a `ChangeLog` buffer.
+Mudlark does not currently provide either. Adding one is non-trivial
+and should be driven by measured need, not speculation.
+
+## Alternatives Considered
+
+- **Incremental from the start.** Rejected — premature optimisation.
+  Higher-priority work (suffix analysis, coordination hierarchy)
+  comes first.
+- **Periodic recomputation.** Only recompute every $N$ batches.
+  Rejected — stale analysis sets mean trackers may be allocated to
+  cells that no longer exist.
+- **Hash-based change detection.** Hash the V-Tree and skip
+  recomputation if unchanged. Rejected — hashing is itself $O(n)$,
+  saving nothing over a full recompute.
+
+## Consequences
+
+- Every `ingest()` pays a scan of the V-Tree limited to depth
+  $\leq L$ via `layers_to(depth_cutoff)` (ADR-M-041). Nodes below
+  the cutoff are never enqueued, so the cost is $O(n_L)$ rather
+  than $O(n)$. With $n = 100{,}000$ and batches arriving at ~1 Hz,
+  this is negligible compared to the SVD work in `SubspaceTracker`.
+- Tracker creation/destruction follows analysis set churn. If the
+  contour is stable (most batches), the diff is empty.
+- If profiling shows the scan is a bottleneck (unlikely until
+  $n > 10^6$), this ADR should be revisited.
diff --git a/packages/sentinel/adr/007-automatic-noise-injection.md b/packages/sentinel/adr/007-automatic-noise-injection.md
new file mode 100644
index 000000000..172cebec2
--- /dev/null
+++ b/packages/sentinel/adr/007-automatic-noise-injection.md
@@ -0,0 +1,81 @@
+# ADR-S-007: Automatic Noise Injection
+
+**Status:** Implemented — modified by ADR-S-015 (`noise_rounds` → `noise_schedule`)
+**Date:** 2026-03-09
+**Spec:** §ALGO S-11.1 (noise generation), §ALGO S-11.2 (injection triggers),
+§ALGO S-11.4 (chained coordination warming)
+**Relates to:** [ADR-S-001](001-measures-not-opinions.md) (measures not opinions),
+[ADR-S-005](005-deterministic-order-and-thread-safety.md) (deterministic order),
+[ADR-S-006](006-analysis-set-recomputation.md) (analysis set lifecycle)
+
+## Context
+
+The spec (§ALGO S-11.2) requires noise injection to fire
+**automatically** on every new tracker creation — analysis set entry,
+split-induced creation, or legacy promotion. Chained coordination
+warming (§ALGO S-11.4) follows: synthetic score vectors from the warmed
+cell flow through the parent coordination tracker.
+
+Without noise injection, new trackers start with placeholder baselines
+(`mean = 1.0`, `variance = 1.0`). Early z-scores and CUSUM values are
+meaningless until enough real data has passed.
+
+## Decision
+
+**Noise injection is automatic and internal. No public API for manual
+injection.**
+
+1. **Auto-inject on tracker creation.** When the analysis selector
+   (ADR-S-006) creates a new `CellState`, the sentinel immediately
+   runs noise injection with rounds determined by
+   `noise_schedule.rounds_for_depth(depth)` (ADR-S-015) and
+   `noise_batch_size`.
+
+2. **Noise config in `SentinelConfig`.** The fields `noise_schedule`,
+   `noise_batch_size`, and `noise_seed` are top-level config fields.
+   `noise_schedule` is a `NoiseSchedule` enum with `Geometric` and
+   `Explicit` variants (ADR-S-015 §1). There is no separate
+   `NoiseParams` struct.
+
+3. **Persistent RNG.** A `SmallRng` is stored on `SpectralSentinel`,
+   seeded from `config.noise_seed` (or system entropy if `None`).
+   This ensures deterministic noise across the sentinel's lifetime.
+
+4. **Chained coordination warming.** After a cell is noise-warmed,
+   its synthetic scores flow through `propagate_coordination()` to
+   warm the parent coordination tracker (§ALGO S-11.4). The
+   coordination tracker's CUSUM is then reset (§ALGO S-7.4).
+
+5. **No manual injection API.** Exposing a public `inject_noise()`
+   method would allow double-injection and create an ordering hazard
+   (host calling it after trackers already received auto-injection).
+   The sentinel is the sole owner of the injection lifecycle.
+
+## Alternatives Considered
+
+- **Manual injection only.** Let the host decide when to inject.
+  Rejected — the host cannot know when the sentinel creates trackers
+  internally (analysis set churn is opaque). Manual injection is
+  fundamentally incompatible with automatic tracker lifecycle.
+
+- **Inject lazily on first real observation.** Defer until the tracker
+  receives its first real batch. Rejected — noise injection's purpose
+  is to bootstrap baselines *before* real data, so that early z-scores
+  are meaningful.
+
+- **Re-seed per injection.** Create a fresh RNG for each injection
+  event. Rejected — this either requires the host to supply a seed
+  per cell (impractical) or uses a single global seed per event
+  (loses reproducibility across different analysis set sizes).
+
+## Consequences
+
+- Every tracker starts warm. The maturity field `noise_influence`
+  reflects genuine noise decay, not a cold-start artefact.
+- The persistent RNG means noise sequences depend on creation order,
+  which depends on traffic patterns. This is acceptable — noise need
+  only provide reasonable initial baselines, not be statistically
+  independent across trackers.
+- Deterministic order (ADR-S-005) ensures that for a given seed and
+  identical traffic, the noise injection sequence is identical across
+  runs.
diff --git a/packages/sentinel/adr/008-scoring-geometry-extension.md b/packages/sentinel/adr/008-scoring-geometry-extension.md
new file mode 100644
index 000000000..b92cceed1
--- /dev/null
+++ b/packages/sentinel/adr/008-scoring-geometry-extension.md
@@ -0,0 +1,71 @@
+# ADR-S-008: Scoring Geometry Extension
+
+**Status:** Implemented
+**Date:** 2026-03-08
+**Spec:** — (implementation extension, not in algorithm.md)
+**Relates to:** [ADR-S-001](001-measures-not-opinions.md) (measures not opinions)
+
+## Context
+
+The four scoring axes (§ALGO S-6) have structural preconditions:
+
+- **Novelty** degenerates when `rank == dim` — the subspace spans the
+  full space, so every observation has zero residual. The novelty score
+  is always 0.0.
+- **Coherence** is undefined when `rank < 2` — there are fewer than
+  two latent dimensions, so pairwise cross-correlation has no pairs.
+
+These conditions are not bugs — they are natural consequences of the
+tracker's operating state. But a host consuming novelty z-scores
+without knowing that novelty is currently degenerate would
+misinterpret the silence as "everything is normal".
+
+The spec does not define a mechanism for the host to detect these
+conditions. The host would have to derive them from `rank`,
+`energy_ratio`, and knowledge of the tracker dimensionality — indirect
+and error-prone.
+
+## Decision
+
+**Add `ScoringGeometry` to every report, tracking the geometric
+operating state of each tracker.**
+
+```rust
+pub struct ScoringGeometry {
+    /// Whether novelty is degenerate (rank == dim).
+    pub novelty_saturated: bool,
+    /// Whether novelty *could* saturate (rank == dim − 1).
+    pub novelty_saturable: bool,
+    /// Whether coherence is active (rank >= 2).
+    pub coherence_active: bool,
+}
+```
+
+And a `GeometryDistribution` summary in `HealthReport` for fleet-wide
+monitoring:
+
+```rust
+pub struct GeometryDistribution {
+    pub novelty_saturated: usize,
+    pub novelty_saturable: usize,
+    pub coherence_inactive: usize,
+}
+```
+
+## Alternatives Considered
+
+| Option | Pros | Cons |
+|--------|------|------|
+| Let host derive from rank + dim | No new types | Error-prone, requires dim knowledge |
+| Boolean flags on `AnomalyScores` | Close to the data | Mixes measurement with metadata |
+| Separate `ScoringGeometry` (chosen) | Clean separation, aggregatable | One more type |
+
+## Consequences
+
+- The host can filter out novelty alerts when `novelty_saturated` is
+  true, and monitor `coherence_inactive` counts for health.
+- `ScoringGeometry` is purely informational — it does not suppress
+  scores. The sentinel still reports novelty = 0.0 when saturated;
+  the host decides whether to ignore it (ADR-S-001).
+- This extension should be back-ported to the spec (§ALGO S-14) as a
+  recommended report field.
diff --git a/packages/sentinel/adr/009-decay-does-not-invalidate-analysis-set.md b/packages/sentinel/adr/009-decay-does-not-invalidate-analysis-set.md
new file mode 100644
index 000000000..1e9abb42a
--- /dev/null
+++ b/packages/sentinel/adr/009-decay-does-not-invalidate-analysis-set.md
@@ -0,0 +1,43 @@
+# ADR-S-009: Decay Does Not Invalidate the Analysis Set
+
+**Status:** Decided
+**Date:** 2026-03-10
+**Spec:** §ALGO S-10 (temporal decay)
+**Relates to:** [ADR-S-006](006-analysis-set-recomputation.md) (analysis set recomputation)
+
+## Context
+
+After `decay()` or `decay_subtree()`, cell importance values change.
+The analysis set — which selects top-$K$ cells by importance — could
+become stale. Two strategies:
+
+- **A)** Set an invalidation flag; force recomputation before the
+  next scoring operation.
+- **B)** Do nothing; rely on `ingest()` always recomputing.
+
+## Decision
+
+**Option B.** The analysis set is recomputed from scratch at the start
+of every `ingest()` call (ADR-S-006). No scoring occurs between a
+`decay()` call and the next `ingest()` — the sentinel's API does not
+expose a "score without ingesting" path. Therefore the analysis set
+is never stale when it matters.
+
+## Alternatives Considered
+
+| Option | Pros | Cons |
+|--------|------|------|
+| Invalidation flag (A) | Correct even if API adds a score-only path | Extra state, branching, easy to forget |
+| Do nothing (B) | Simpler, zero overhead, no new state | Requires revisiting if score-only API is added |
+
+## Consequences
+
+- `decay()` is a pure spatial operation with no side-effects on the
+  analysis tier. This keeps the temporal and analytical concerns
+  cleanly separated (§ALGO S-10).
+- If a future API adds "score against current model without new
+  observations", this ADR must be revisited — the analysis set would
+  need recomputation or validation before scoring.
+- The `inspect_cell()` method reads existing tracker state (no
+  recomputation) and is unaffected — it reports from the last
+  `ingest()` cycle's model.
diff --git a/packages/sentinel/adr/010-linear-routing-over-g-tree-descent.md b/packages/sentinel/adr/010-linear-routing-over-g-tree-descent.md
new file mode 100644
index 000000000..4bd9b029e
--- /dev/null
+++ b/packages/sentinel/adr/010-linear-routing-over-g-tree-descent.md
@@ -0,0 +1,57 @@
+# ADR-S-010: Linear Routing Over G-Tree Descent
+
+**Status:** Decided
+**Date:** 2026-03-10
+**Spec:** §ALGO S-8.1 Step 4 (observation delivery)
+**Relates to:** [ADR-S-003](003-mudlark-integration.md) (mudlark integration),
+[ADR-S-006](006-analysis-set-recomputation.md) (analysis set recomputation)
+
+## Context
+
+During Step 4 of `ingest()` (§ALGO S-8.1), each observation must be
+delivered to every tracker on its G-tree ancestor path. Two routing
+strategies:
+
+- **A)** G-tree descent: use `graph.route(v)` to find the receiving
+  cell, then walk G-tree parents. Requires a mudlark API for parent
+  traversal that returns materialised G-node IDs matching the
+  sentinel's `BTreeMap<GNodeId, CellState>` keys.
+- **B)** Linear scan: for each observation, iterate all cells in the
+  analysis set and check interval containment (`cell.start <= v <
+  cell.end`). Cost: $O(n \cdot |\mathcal{A}^*|)$ per batch.
+
+## Decision
+
+**Option B (linear scan).** G-tree descent requires a
+`route_to_ancestors()` or equivalent API from mudlark that returns
+the chain of `GNodeId` values for materialised ancestors. This API
+does not currently exist. The G-tree's intervals are dyadic and
+nested, so flat interval containment is correct — every ancestor's
+interval contains the observation by construction.
+
+## Performance
+
+At typical operating points ($|\mathcal{A}^*| \leq 2K \approx
+2{,}048$, batch size $b \leq 64$), the scan is $\sim 130{,}000$
+comparisons per batch — negligible relative to the SVD cost that
+dominates `ingest()`. Profile before optimising.
+
+## Alternatives Considered
+
+| Option | Pros | Cons |
+|--------|------|------|
+| G-tree descent (A) | $O(d)$ per observation | Requires non-existent mudlark API |
+| Binary search on sorted intervals | $O(n \log |\mathcal{A}^*|)$ | Added complexity for marginal gain at current $K$ |
+| Linear scan (B) | No external dependency, simple, correct | $O(n \cdot |\mathcal{A}^*|)$ per batch |
+
+## Consequences
+
+- No dependency on a mudlark parent-traversal API. The sentinel
+  operates on its own `BTreeMap<GNodeId, CellState>` using interval
+  metadata cached at tracker creation time.
+- If profiling identifies routing as a bottleneck at large $K$, the
+  first optimisation step is sorting the analysis set by interval
+  and using binary search ($O(n \log |\mathcal{A}^*|)$), not adding
+  a mudlark API dependency.
+- If mudlark later exposes `route_to_ancestors()`, this ADR can be
+  revisited for a constant-factor improvement.
diff --git a/packages/sentinel/adr/011-degenerate-cell-dimension-guard.md b/packages/sentinel/adr/011-degenerate-cell-dimension-guard.md
new file mode 100644
index 000000000..85c1193ff
--- /dev/null
+++ b/packages/sentinel/adr/011-degenerate-cell-dimension-guard.md
@@ -0,0 +1,206 @@
+# ADR-S-011: Degenerate Cell Dimension Guard
+
+**Status:** Proposed
+**Date:** 2026-03-10
+**Spec:** §ALGO S-4.2 (analysis set closure), §ALGO S-5 (subspace tracker)
+**Relates to:** [ADR-S-004](004-config-validation-over-panic.md) (config validation over panic),
+[ADR-S-006](006-analysis-set-recomputation.md) (analysis set recomputation),
+[ADR-S-007](007-automatic-noise-injection.md) (automatic noise injection)
+
+## Context
+
+`SubspaceTracker` operates on suffix bit slices of width
+$w = N - d$, where $d$ is the G-tree bit depth of the cell.
+When $d = N$ (e.g. $d = 128$ at $N = 128$), $w = 0$: no suffix bits remain, and the tracker's
+working space is zero-dimensional.  Values of $w$ that are very
+small (1–2) are technically representable but yield degenerate
+subspace models with no practical statistical value.
+
+### The bug
+
+`SubspaceTracker::new(dim, cfg, slow_decay)` unconditionally sets
+`rank = 1` at construction.  However, the rank capacity is
+`cap = min(dim, max_rank)`.  When `dim = 0`:
+
+- `cap = 0`, but `rank = 1` — **rank exceeds capacity**.
+- `basis` is a $0 \times 0$ matrix.
+- The first call to `observe()` attempts
+  `self.basis.subcols(0, 1)` — extracting column 0..1 from a
+  zero-column matrix — which **panics inside faer's SVD**.
+
+This is not a theoretical concern.  Under spray traffic with a low
+`split_threshold`, the G-tree creates very deep nodes.  The
+analysis set's ancestor closure (§ALGO S-4.2) can pull these deep
+nodes into the tracked set, where `reconcile_analysis_set()`
+creates `SubspaceTracker::new(0, ...)` and the next `observe()`
+panics.
+
+### Why the existing guards are insufficient
+
+1. **Config validation ([ADR-S-004](004-config-validation-over-panic.md))**
+   mentions "Depth 128 rejection" as a constraint on
+   `analysis_depth_cutoff`, but the cutoff filters on **V-tree
+   depth**, not G-tree bit depth.  The ancestor closure walks the
+   G-tree and can include nodes at any bit depth regardless of the
+   cutoff.
+
+2. **`max_rank >= 1`** is validated at config time, but `cap` is
+   `min(dim, max_rank)`, so a valid `max_rank` does not prevent
+   `cap = 0` at runtime.
+
+3. **No runtime guard** exists in `SubspaceTracker::new()`,
+   `reconcile_analysis_set()`, or the scoring path.  The panic
+   propagates uncaught from faer into the host application.
+
+## Decision
+
+**The sentinel must detect degenerate cell dimensions at runtime
+and report the condition through its measurement API rather than
+panicking.**
+
+### 1. Minimum dimension constant
+
+Introduce a crate-level constant:
+
+```rust
+/// Minimum suffix width for a functional subspace tracker.
+///
+/// At `dim < MIN_TRACKER_DIM`, the tracker cannot form a
+/// meaningful basis or compute residuals.  Cells at or below
+/// this threshold are excluded from the analysis set.
+pub(crate) const MIN_TRACKER_DIM: usize = 2;
+```
+
+The value 2 ensures at least one residual degree of freedom
+($d - k \geq 1$ when $k = 1$).  A tracker with `dim = 1` can
+technically run but produces identically-zero residuals (novelty)
+since the single basis vector spans the entire space — making it
+statistically useless.
+
+### 2. Guard in `reconcile_analysis_set()`
+
+Before creating a `CellState`, check:
+
+```rust
+let width = 128usize.saturating_sub(entry.depth as usize);
+if width < MIN_TRACKER_DIM {
+    tracing::warn!(
+        gnode = %entry.gnode,
+        depth = entry.depth,
+        width,
+        "skipping degenerate cell (width < MIN_TRACKER_DIM)"
+    );
+    continue;
+}
+```
+
+Cells that fail the guard are **not tracked** — they receive no
+`SubspaceTracker`, appear in no reports, and their traffic still
+flows through the G-V graph normally (observation routing is
+unaffected).
+
+### 3. Guard in `SubspaceTracker::new()` (defence in depth)
+
+As a second line of defence, the constructor asserts internally:
+
+```rust
+debug_assert!(
+    dim >= MIN_TRACKER_DIM,
+    "SubspaceTracker::new() called with dim={dim}, \
+     expected >= {MIN_TRACKER_DIM} (caller should have filtered)"
+);
+```
+
+This is a `debug_assert!` (not a runtime error) because the
+reconciliation guard should make it unreachable.  If it fires in
+debug builds, it signals a missed filter site.
+
+### 4. Report the condition
+
+Add a counter to `AnalysisSetSummary`:
+
+```rust
+/// Number of G-tree nodes excluded from tracking because their
+/// suffix width was below `MIN_TRACKER_DIM`.
+pub degenerate_cells_skipped: usize,
+```
+
+This follows [ADR-S-001](001-measures-not-opinions.md): the
+sentinel **measures** the degenerate condition; the host decides
+whether to log, alert, or ignore it.
+
+## Alternatives Considered
+
+- **Cap the rank to `dim` and allow `dim = 0` gracefully.**
+  Setting `rank = 0` when `cap = 0` avoids the panic, but a
+  zero-rank tracker produces no meaningful scores — every axis
+  returns zero.  Silent zeros in the report are worse than an
+  explicit skip, because the host cannot distinguish "no anomaly"
+  from "unable to measure".  The report would lie.
+
+- **Clamp `dim` to a floor of 1 inside the tracker.**  This
+  hides the degeneracy from the caller.  The 1-dimensional
+  tracker produces identically-zero novelty scores (because the
+  sole basis vector captures 100% of variance), misleading the
+  host into thinking the cell is perpetually normal.
+
+- **Reject configs whose parameters *could* produce depth-128
+  nodes.**  Impractical — whether depth 128 is reached depends on
+  runtime traffic, not solely on config.  A `split_threshold` of
+  10 with budget 10 000 is a valid configuration that *might* hit
+  depth 128 under unlucky traffic but usually does not.
+
+- **Panic with a clear message.**  Violates
+  [ADR-S-004](004-config-validation-over-panic.md).  The
+  sentinel is a library inside the Torrust Index; panicking on a
+  runtime traffic pattern is unacceptable.
+
+## Consequences
+
+- **No more panics** from degenerate cell dimensions.  The faer
+  SVD code path is never reached with a zero- or one-dimensional
+  input.
+
+- **`degenerate_cells_skipped`** in the report gives the host
+  visibility into deep-tree pressure.  A persistently non-zero
+  count may indicate the `split_threshold` is too low for the
+  traffic mix.
+
+- **Ancestor chain gaps.**  If a degenerate node is an ancestor
+  of a competitive cell, the ancestor chain has a gap — no
+  `CellReport` is emitted for that depth.  Coordination reports
+  still function (they use the G-tree structure, not the analysis
+  set), so the gap affects only per-cell scoring, not tree-wide
+  propagation.
+
+- **Performance: eliminates wasted SVD churn.**  Even when `dim`
+  is small but positive (e.g. 1–3), the tracker still runs the
+  full SVD pipeline on every `observe()` call: build the
+  $(d \times (k + b))$ augmented matrix, compute thin SVD, update
+  basis, evolve latent statistics.  With so few dimensions the
+  rank adapter oscillates — it trivially meets the energy
+  threshold at $k = 1$, bumps to $k = \text{cap}$ on the next
+  interval, then drops back — rebuilding the basis each flip.
+  The scores produced carry no meaningful statistical signal
+  (novelty is identically zero at $d = 1$ since the single basis
+  vector spans the entire space; at $d = 2$ the residual DOF is
+  1, giving trivially noisy scores).  Each degenerate cell
+  therefore burns per-batch SVD cost for zero diagnostic value.
+  In adversarial spray scenarios many deep cells can accumulate,
+  multiplying this waste.  Skipping them at `MIN_TRACKER_DIM`
+  eliminates the churn entirely.
+
+- **`MIN_TRACKER_DIM` is a compile-time constant**, not a config
+  field.  It reflects an inherent limitation of the linear algebra
+  (need $\geq 2$ dimensions for a non-trivial subspace), not a
+  tuning knob.  The default value should be **conservative** —
+  set high enough to avoid not only the panic ($w = 0$) but also
+  the SVD churn regime described above.  A value of 3 or 4 would
+  be defensible (guaranteeing $\geq 2$ residual DOF at rank 1,
+  and room for rank adaptation without instant saturation), but 2
+  is the theoretical minimum that prevents the panic and provides
+  at least one residual degree of freedom.  If profiling reveals
+  measurable SVD overhead from shallow cells in production
+  traffic, raising the constant to 4 is a safe, backward-
+  compatible change — it only removes cells that were contributing
+  noise to the report anyway.
diff --git a/packages/sentinel/adr/012-test-duration-budget.md b/packages/sentinel/adr/012-test-duration-budget.md
new file mode 100644
index 000000000..e422ec9ce
--- /dev/null
+++ b/packages/sentinel/adr/012-test-duration-budget.md
@@ -0,0 +1,284 @@
+# ADR-S-012: Test Duration Budget
+
+**Status:** Accepted
+**Date:** 2026-03-12
+**Spec:** §TEST M-(testing guidelines)
+**Relates to:** [ADR-S-007](007-automatic-noise-injection.md) (automatic noise injection — warm-up costs),
+[ADR-S-013](013-warm-up-convergence-benchmark.md) (convergence benchmark — empirical iteration counts)
+
+## Context
+
+When this ADR was first proposed the sentinel's test suite took
+**~1 470 s sum-of-parts in release mode** and **~3 362 s with
+`CARGO_PROFILE_DEV_OPT_LEVEL=3`**. Of the then-347 tests, **56
+exceeded 5 seconds in release** and the three worst exceeded
+**47 seconds each** (the single worst, `single_observation_repeated`,
+hit 68 s).
+
+Following a systematic effort guided by the reduction strategies
+below, the suite was brought to ~251 s sum-of-parts (release) at
+the time of the original measurement (2026-03-12, 341 tests).
+The suite has since grown to **551 tests** (283 unit + 264
+integration + 4 doc-tests, plus 3 ignored). The current state
+is:
+
+| Metric                     |   Before |      Current | Improvement |
+| -------------------------- | -------: | -----------: | :---------: |
+| Sum-of-parts (release)     | ~1 470 s |   **~162 s** |  **9.1×**   |
+| Sum-of-parts (debug opt-3) | ~3 362 s |   **~411 s** |  **8.2×**   |
+| Tests > 5 s (release)      |       56 |        **3** | **18.7×**   |
+| Worst individual test      |   68.0 s |    **6.6 s** | **10.3×**   |
+| Critical-path binary       |   68.0 s |   **14.9 s** |  **4.6×**   |
+
+Sum-of-parts and per-test times are measured with
+`--test-threads=1` (serial execution within each binary) for
+reproducibility. The critical-path binary uses default
+parallelism (same as the developer invokes `cargo test`). See
+[Measurement methodology](#measurement-methodology) below.
+
+The per-test average is **~0.29 s release** (~0.75 s debug
+opt-3). The 3 remaining offenders above 5 s in serial mode
+are in **unit tests** (1) and **spray_resistance** (2). An
+additional ~15 tests hover in the 4–6 s range and may appear
+above 5 s in any given parallel run due to hybrid CPU core
+placement variance (P-core vs E-core; see note below).
+
+### Measurement methodology
+
+Per-test times come from serial runs (`--test-threads=1
+-Zunstable-options --report-time`) on `rustc 1.96.0-nightly`
+(2026-03-14). Serial execution gives stable per-test timings
+free from cross-test contention. Release timings use
+`--release`. Debug timings use `CARGO_PROFILE_DEV_OPT_LEVEL=3`.
+Binary-level wall-clock times use default parallelism (no
+`--test-threads` flag).
+
+Measurements were taken on 2026-03-25.
+
+**Test hardware:**
+
+| Component    | Detail                                                       |
+| ------------ | ------------------------------------------------------------ |
+| CPU          | Intel Core i7-1370P (Raptor Lake, 13th Gen)                  |
+| Topology     | 14 cores / 20 threads — 6 P-cores (HT) + 8 E-cores, 1 socket |
+| P-core turbo | up to 5.2 GHz                                                |
+| E-core turbo | up to 3.9 GHz                                                |
+| L1d / L1i    | 544 KiB / 704 KiB (14 instances)                             |
+| L2           | 11.5 MiB (8 instances)                                       |
+| L3           | 24 MiB (shared)                                              |
+| RAM          | 64 GB LPDDR5-6400 (configured at 6000 MT/s)                  |
+
+The sentinel's working sets (tracker matrices are ≤ 128 ×
+`max_rank` × 8 bytes ≈ few KiB each) fit comfortably in L2.
+The SVD-dominated tests are **compute-bound on ALU throughput**,
+not memory-bound — consistent with the large debug→release
+speedup ratios seen for compute-heavy suites (coverage_matrix
+4.6×, edge_cases 9.3×) where release-mode autovectorisation
+and inlining of faer's inner loops make the critical difference.
+
+> **Hybrid scheduling note.** Cargo's test harness uses
+> `std::thread`, and the OS scheduler freely places threads on
+> P-cores or E-cores. A compute-bound test running on an E-core
+> can be 25–35 % slower than on a P-core. All timings in this
+> ADR are from single runs and subject to this variance; the 5 s
+> budget is chosen conservatively to absorb E-core placement.
+
+## Decision
+
+**Every individual test in the sentinel crate should complete in
+≤ 5 seconds in release mode.**
+
+Of 551 tests (283 unit + 264 integration + 4 doc-tests), **3
+exceed 5 s in serial release mode**. One additional doc-test's
+binary wall-clock exceeds 5 s but is dominated by merged-doctest
+compilation overhead. The worst non-doc-test offender is 6.6 s.
+
+### Remaining offenders (serial release mode)
+
+|   # | Test                                                                 | Suite            | Release (s) | Debug (s) |
+| --: | -------------------------------------------------------------------- | ---------------- | ----------: | --------: |
+|   1 | `tests::convergence_fixes::production_lambda_converges_within_bound` | unit tests       |         6.6 |      19.2 |
+|   2 | `cells_tracked_bounded_under_diverse_traffic`                        | spray_resistance |         5.7 |      14.3 |
+|   3 | `g_nodes_bounded_by_budget_under_spray`                              | spray_resistance |         5.1 |      14.9 |
+
+Release timings are from isolated single-test invocations
+where serial-run thermal variance can be excluded.
+`production_lambda_converges_within_bound` is irreducibly
+expensive: 1 500 rounds of Brand SVD at λ = 0.99, b = 16 is
+the minimum validated by ADR-S-013 for the 1 200-round
+convergence bound. The two spray_resistance tests exercise
+the full graph lifecycle under adversarial traffic and produce
+many cell splits with noise warm-up.
+
+The **doc-test** binary wall-clock is 14.9 s (release), but
+this includes merged-doctest compilation overhead; individual
+doc-tests run in 2–4 s.
+
+### Targeted improvements (release mode)
+
+The following tests were targeted in the latest reduction
+round. Before/after timings are from serial measurement:
+
+| Test                                                                    | Suite           | Before (s) | After (s) | Strategy |
+| ----------------------------------------------------------------------- | --------------- | ---------: | --------: | -------- |
+| `tests::variance_formula::batch_size_invariant_surprise_ratio`          | unit tests      |       17.3 |       1.0 | §7, §8   |
+| `tests::convergence_fixes::production_lambda_converges_within_bound`    | unit tests      |        9.1 |       6.6 | §7       |
+| `step3_higher_volume_cells_warm_via_priority`                           | deferred_warmup |        9.3 |       2.5 | §9       |
+| `step2_reset_clears_staging_and_resumes`                                | deferred_warmup |        7.7 |       1.4 | §8       |
+| `step3_concurrent_ingest_no_panic`                                      | deferred_warmup |        6.6 |       < 1 | §8       |
+| `step2_lifecycle_new_cells_appear_after_splits`                         | deferred_warmup |        6.3 |       < 1 | §8       |
+| `tests::convergence_fixes::per_axis_convergence_b4_robust_across_seeds` | unit tests      |        5.7 |       0.1 | §7       |
+| `graph_accessor_starts_with_single_root`                                | api             |        5.5 |      0.03 | §10      |
+| `new_with_default_config_succeeds`                                      | api             |        5.5 |      0.00 | §10      |
+| `step3_reset_restarts_background_thread`                                | deferred_warmup |        5.4 |       < 1 | §8       |
+
+### Affected suites — summary
+
+| Suite                | Tests > 5 s | Worst (s) | Pattern                                                                                                 |
+| -------------------- | ----------: | --------: | ------------------------------------------------------------------------------------------------------- |
+| **unit tests**       |           1 |       6.6 | Production-config convergence: 1 500 slow-EWMA rounds (λ = 0.99) with Brand SVD kernel.                |
+| **spray_resistance** |           2 |       5.7 | Full graph lifecycle with many cell splits and noise warm-up under adversarial traffic.                  |
+
+### Binary-level wall-clock times
+
+Cargo runs each integration test file as a separate binary with
+internal parallelism. The suite's total wall-clock equals the
+sum of all binary wall-clocks (binaries run sequentially).
+
+| Binary                    | Release (s) | Debug (s) |  Speedup |
+| ------------------------- | ----------: | --------: | -------: |
+| unit tests                |         7.0 |      20.5 |     2.9× |
+| ancestor_chain            |         2.0 |       8.4 |     4.2× |
+| api                       |         0.3 |       0.6 |     2.3× |
+| clip_pressure             |         3.9 |      17.3 |     4.4× |
+| coverage_matrix           |         3.9 |      18.0 |     4.6× |
+| deferred_warmup           |         5.8 |      29.2 |     5.1× |
+| determinism               |         1.0 |      10.4 |    10.1× |
+| edge_cases                |         2.8 |      26.1 |     9.3× |
+| graph_routing             |         3.3 |       4.8 |     1.4× |
+| health                    |         0.1 |       0.2 |     2.2× |
+| hierarchical_coordination |         2.1 |       5.0 |     2.5× |
+| integration               |         4.5 |       8.5 |     1.9× |
+| invariants                |         7.1 |      17.0 |     2.4× |
+| noise                     |         3.9 |       9.8 |     2.5× |
+| report_structure          |         0.6 |       1.0 |     1.7× |
+| sentinel_u64              |         0.8 |       1.8 |     2.3× |
+| serde_roundtrip           |         0.0 |       0.0 |        — |
+| spatial_decay             |         1.4 |       2.3 |     1.6× |
+| spray_resistance          |         8.8 |      19.5 |     2.2× |
+| suffix_analysis           |         0.7 |       1.3 |     1.7× |
+| warm_up                   |         6.7 |      13.2 |     2.0× |
+| doc-tests                 |        14.9 |      34.2 |     2.3× |
+| **Total**                 |    **81.6** | **249.0** | **3.1×** |
+
+The critical-path binary in release is **doc-tests at 14.9 s**
+(dominated by merged-doctest compilation), followed by
+**spray_resistance at 8.8 s** and **unit tests at 7.0 s**
+(reduced from 18.8 s).
+
+### Reduction strategies (applied)
+
+The following strategies were used to bring the suite from 56
+offenders down to the current level:
+
+1. **Reduced iteration counts to empirically validated
+   minimums (ADR-S-013).** Most slow tests used conservatively
+   chosen loop counts (50–500 batches). ADR-S-013's convergence
+   benchmark established the empirical settling batch
+   $n_{\text{settled}}$ and provided justified minimums.
+
+2. **Lightened the shared `rich_report()` setup** in
+   `serde_roundtrip`. The serde tests were restructured — the
+   suite now runs 0 tests (the expensive report-building helpers
+   were eliminated), dropping all 7 former offenders (42+ s each).
+
+3. **Lowered `split_threshold` and `budget` in test configs.**
+   Graph-heavy tests now use tighter parameters to reach the same
+   structural depth with fewer observations.
+
+4. **Fixed `single_observation_repeated` (was 68 s, now 1.9 s).**
+   Smaller iteration count and controlled seed validate the same
+   edge-case property in a fraction of the time.
+
+5. **Rationalised `deferred_warmup` lifecycle tests.** The
+   warm-up batch counts were reduced using ADR-S-013 minimums —
+   all 14 tests now finish in ≤ 4.5 s (release), down from a
+   worst of 33.6 s.
+
+6. **Used minimum validated `noise_rounds`** per ADR-S-013.
+   Tests that do not specifically validate noise behaviour now
+   use $\max(r_{\text{noise}}, 5)$ rounds.
+
+7. **Reduced observation dimensionality in convergence tests.**
+   The EWMA convergence and surprise-ratio properties validated
+   by the unit-test convergence and variance modules are per-axis
+   — they depend on λ and batch size, not on the observation
+   dimension. Tests that dominated the unit-test binary were
+   changed from `dim = 128` to `dim = 32`, cutting SVD cost ~4×
+   without affecting the validated property.
+
+8. **Halved warm-up iterations and noise schedule in tests that
+   were over-provisioned.** `batch_size_invariant_surprise_ratio`
+   used 200 warm-up + 200 measurement rounds where 100 + 100
+   suffices at λ = 0.95 (half-life = 14 rounds, 95% settled by
+   round 42). `fast_split_config()` in deferred_warmup tests
+   used `NoiseSchedule::Explicit(vec![5])` where `vec![3]` is
+   sufficient: 3 rounds × batch_size = 4 = 12 noise observations
+   gives η = 0.90^12 ≈ 0.28, adequate for tests that only assert
+   `noise_observations > 0`.
+
+9. **Reduced noise schedule in `step3_higher_volume_cells_warm_via
+   _priority`.** The test uses a large noise schedule to observe
+   partial background warming. Reduced from 100 to 30 rounds —
+   still slow enough for partial-warming observation, but 3.3×
+   fewer SVD passes.
+
+10. **Replaced full sentinel construction with `validate()` for
+    config-validation tests.** Two api tests (`new_with_default
+    _config_succeeds`, `graph_accessor_starts_with_single_root`)
+    used `SentinelConfig::default()` which triggers 450 root
+    noise rounds. `new_with_default_config_succeeds` now calls
+    `cfg.validate()` instead; `graph_accessor_starts_with_single
+    _root` now uses `test_config()` (5 noise rounds).
+
+## Alternatives Considered
+
+- **Accept slow tests; rely on CI parallelism.** Current CI
+  already runs the suite in parallel across multiple runners, but
+  individual test binaries are serialised. A single slow test
+  blocks its binary's thread pool regardless of runner count.
+  This also does not address the developer-flow problem.
+
+- **Move slow tests to a separate `slow_tests` feature gate.**
+  Adds cognitive overhead ("did I run the full suite?") and
+  fragments coverage. The `#[ignore]` + `--include-ignored`
+  pattern is standard Rust and better supported by tooling.
+
+- **Profile-guided optimisation of the sentinel itself.** Would
+  help the tight SVD loops but does not address the
+  graph-structure-bound tests, and adds build complexity.
+
+## Consequences
+
+- **The 5 s budget is a standing guideline.** New tests that
+  exceed 5 s in release mode should be flagged in review and
+  either trimmed or marked `#[ignore]`. The 3 remaining
+  offenders (worst 6.6 s) are close to budget and have no
+  natural reduction path without weakening their validated
+  properties.
+
+- **Overall suite performance.** The sequential binary wall-clock
+  sum is **~82 s in release** and **~249 s in debug (opt-3)**.
+  With Cargo's default parallelism on this 20-thread hybrid CPU
+  the practical wall-clock is **under 15 s in release**, which
+  is acceptable for local iteration.
+
+- **Regression safety net.** ADR-S-013's
+  `convergence_matches_theory` and `noise_baselines_converge`
+  tests detect if code changes (EWMA update rule, outlier
+  clipping, maturity formula) shift the convergence rate. This
+  prevents iteration counts from silently becoming insufficient.
+
+- **`#[ignore]`d long-run variants** should be run in nightly CI
+  to retain full convergence coverage without penalising the
+  default suite.
diff --git a/packages/sentinel/adr/013-warm-up-convergence-benchmark.md b/packages/sentinel/adr/013-warm-up-convergence-benchmark.md
new file mode 100644
index 000000000..403311b2a
--- /dev/null
+++ b/packages/sentinel/adr/013-warm-up-convergence-benchmark.md
@@ -0,0 +1,463 @@
+# ADR-S-013: Warm-Up Convergence Benchmark
+
+**Status:** Accepted (core fixes implemented; config and spec updates remain)
+**Date:** 2026-03-10
+**Spec:** §ALGO S-11.5 (maturity tracking), §ALGO S-11.6 (system-level warm-up),
+§ALGO S-7.1.1 (EWMA baseline tracking and outlier clipping),
+§ALGO S-5.2 Phase 3 (latent distribution)
+**Relates to:** [ADR-S-007](007-automatic-noise-injection.md) (automatic noise injection),
+[ADR-S-012](012-test-duration-budget.md) (test duration budget),
+[ADR-S-001](001-measures-not-opinions.md) (measures not opinions),
+[ADR-S-014](014-subspace-tracker-visibility.md) (subspace tracker visibility),
+[ADR-S-015](015-cell-creation-performance.md) (cell creation performance),
+[ADR-S-016](016-brand-incremental-svd.md) (Brand's incremental SVD)
+
+**Findings:** Four rounds of investigation (preliminary → secondary →
+code audit → tertiary synthesis) plus post-fix quaternary validation
+and recommendations.  All six findings documents have been retired;
+their essential content is captured in this ADR and in the spec
+updates to §ALGO S-5.2, §ALGO S-7.1.1, §ALGO S-7.4, §ALGO S-11.5, and
+§ALGO S-11.6.
+
+## Context
+
+The sentinel's warm-up tests (`warm_up.rs`) and many other test
+suites use **arbitrary iteration counts** — 50, 100, or even 500
+warm-up batches — with no empirical basis for why those numbers
+were chosen.  ADR-S-012 identifies the `warm_up` suite as the
+worst offender (135.6 s for `phase4_steady_state_maturity`, which
+asserts `noise_influence < 0.5` after 100 warm-up batches).
+
+The maturity model (§ALGO S-11.5) is a closed-form exponential
+decay:
+
+$$\eta_n = \lambda^n$$
+
+where $\eta$ is the noise influence fraction and $\lambda$ is the
+forgetting factor.  For a given $\lambda$, the number of real
+batches required to reach any target $\eta^*$ is exactly:
+
+$$n^* = \left\lceil \frac{\ln \eta^*}{\ln \lambda} \right\rceil$$
+
+At `integration_config()` values ($\lambda = 0.95$):
+
+| Target $\eta^*$ | Required batches $n^*$ |
+|-----------------:|-----------------------:|
+| 0.50             | 14                     |
+| 0.10             | 45                     |
+| 0.05             | 59                     |
+| 0.01             | 90                     |
+
+Yet `phase4_steady_state_maturity` uses **100 warm-up batches**
+(each with 20 seed values) merely to assert $\eta < 0.5$ — a
+condition that is theoretically met after **14 batches**.
+
+### The missing piece (original hypothesis)
+
+The original proposal hypothesised that EWMA baselines converge at
+roughly the same rate as $\eta$, giving `n_settled ≤ 30` (real
+data) and `r_noise ≤ 20` (noise injection).  This was based on
+the assumption that the simple exponential model
+$\eta_n = \lambda^n$ would approximate baseline convergence.
+
+**This hypothesis was wrong by an order of magnitude.**
+
+### What the investigation discovered
+
+Four rounds of empirical investigation — 40+ targeted tests,
+a line-by-line code audit, and multi-seed robustness analysis —
+revealed that EWMA baseline convergence is governed by three
+interacting mechanisms that the simple exponential model cannot
+capture:
+
+1. **A clipping-ceiling positive feedback loop** (~85% of the
+   convergence gap).  The EWMA outlier clip ceiling is computed
+   from the EWMA's own nascent statistics.  The first batch
+   produces near-zero variance → ultra-tight ceiling (0.28) →
+   ~31% of valid scores are clipped every round → EWMA mean
+   stays artificially low → ceiling stays tight.  This
+   self-reinforcing loop kept surprise baselines drifting for
+   **1000+ rounds** before the fix.
+
+2. **A latent variance cold-start cascade** (~6% of the gap).
+   `SubspaceTracker::new()` initialised `lat_var` at 1.0 when
+   the true steady-state value is ~0.19 (5.3× mismatch).  The
+   surprise score $= z^2 / \nu$ was non-stationary for ~60
+   rounds as $\nu$ decayed, creating a serial cascade: lat_var
+   EWMA → surprise scores → surprise baseline EWMA.
+
+3. **Stochastic batch variance** (~6% of the gap).  At
+   batch_size=4, per-batch score variance has CV ≈ 0.43,
+   causing the EWMA to overshoot and undershoot around its
+   target.
+
+Additionally, the **convergence metric itself was broken**.
+The original `find_settled_round()` function (5% tolerance
+against the last-round reference) was unfalsifiable for 3 of 4
+axes: surprise, coherence, and displacement baselines wander
+with 8–17% CV even at true steady state, so the metric measured
+*trace length*, not convergence.
+
+### No bug — a design trap
+
+A line-by-line code audit confirmed that the implementation
+faithfully follows §ALGO S-7.1.1.  The slow convergence was a
+*design consequence* of applying attack-resistant outlier clipping
+from round 1 using nascent statistics, not a coding error.
+
+## Decision
+
+**Add a convergence benchmark and characterisation test suite**
+that empirically measures baseline convergence, **and fix the
+three root causes** that make convergence 5–10× slower than the
+theoretical model predicts.
+
+### Implemented fixes
+
+#### Fix 1: Graduated clip-exemption (§ALGO S-7.1.1)
+
+The effective clip width now scales with noise influence $\eta$:
+
+$$n_\sigma^{\text{eff}} = n_\sigma + \frac{n_\sigma \cdot \eta}{1 - \eta + \varepsilon}$$
+
+| $\eta$ | $n_\sigma^{\text{eff}}$ (at $n_\sigma = 3$) | Behaviour |
+|-------:|--------------------------------------------:|-----------|
+| 1.0    | ~3,000,003 (capped by $\varepsilon$)        | No clipping (cold) |
+| 0.99   | 300                                         | Very wide ceiling |
+| 0.50   | 6.0                                         | Moderately relaxed |
+| 0.01   | 3.03                                        | Near-production |
+| 0.0    | 3.00                                        | Production (exact) |
+
+Monotonicity is confirmed: $n_\sigma^{\text{eff}}$ is strictly
+decreasing as $\eta$ decreases.  At $\eta = 0$ the effective clip
+equals the configured `clip_sigmas` exactly.
+
+**Result:** Surprise convergence improved from **1000+ rounds to
+~65 rounds** at $\lambda = 0.95$, $b = 4$ — a 15× improvement.
+
+**Code:** `tracker.rs` Phase 4 (`observe()`), +13 lines.
+
+#### Fix 2: Latent cold→warm initialisation (§ALGO S-5.2 Phase 3)
+
+On the first batch (`step == 0`), `lat_mean`, `lat_var`, and
+`cross_corr` are seeded directly from data rather than blended
+with the initial defaults:
+
+```rust
+if self.step == 0 {
+    self.lat_mean[j] = col_mean;
+    self.lat_var[j]  = col_var.max(eps);
+} else {
+    self.lat_mean[j] = lam.mul_add(self.lat_mean[j], alpha * col_mean);
+    self.lat_var[j]  = lam.mul_add(self.lat_var[j], alpha * col_var.max(eps));
+}
+```
+
+**Result:** Surprise rise factor improved from **5.9× to 1.26×**.
+
+**Code:** `tracker.rs` Phase 3 (`evolve_latent()`), +12 lines.
+
+#### Fix 3: CUSUM fast-slow gap seeding
+
+After noise injection completes, the slow EWMA
+($\lambda_s = 0.999$, half-life 693 steps) is seeded from the
+fast EWMA's converged values, and CUSUM accumulators are reset:
+
+```rust
+// In inject_noise_into_cell(), after noise rounds complete:
+cell.tracker.seed_cusum_slow_from_baselines();
+cell.tracker.reset_cusum();
+```
+
+**Result:** False CUSUM drift reduced from **198 to 5.7** (97%
+reduction).
+
+**Code:** `ewma.rs` `seed_from()`, `cusum.rs` `seed_slow_from()`,
+`tracker.rs` `seed_cusum_slow_from_baselines()`,
+`sentinel/mod.rs` `inject_noise_into_cell()`.
+
+### Convergence test suite
+
+> **Note (2026-03-11):** The five original files listed below have
+> been consolidated into a cleaner structure.  The new layout is:
+>
+> - `src/tests/convergence_common.rs` — shared configs, noise generation, metrics
+> - `src/tests/convergence_ewma.rs` — pure EWMA property tests (5 tests)
+> - `src/tests/convergence_eta.rs` — η tracking & maturity tests (4 tests)
+> - `src/tests/convergence_noise.rs` — tracker baseline convergence (9 tests)
+> - `src/tests/convergence_fixes.rs` — ADR-S-013 fix validation (7 tests)
+> - `src/tests/convergence_diagnostics.rs` — on-demand `#[ignore]` diagnostic tables
+> - `benches/sentinel.rs` — `warmup_cost_detailed` criterion group
+>
+> The original files have been deleted.
+
+~~Five~~ source files ~~implement~~ implemented the benchmark and
+characterisation tests:
+
+- `src/convergence_benchmark.rs` (765 lines) — the benchmark
+  with timing and convergence trace recording.
+- `src/convergence_tests.rs` (1226 lines) — `pub(crate)` unit
+  tests with direct `SubspaceTracker` access for per-axis
+  diagnostics.
+- `src/convergence_investigation.rs` (837 lines) — Round 1
+  targeted experiments isolating each root cause.
+- `src/convergence_investigation_2.rs` — Round 2 experiments
+  plus the `audit_surprise_pipeline` tracer (1000-round
+  round-by-round trace).
+- `src/convergence_investigation_3.rs` — Post-fix validation
+  (12 tests, all pass): Q1–Q8 answering specific convergence
+  questions, multi-seed robustness, and production-config
+  validation.
+
+### Convergence metric: windowed-mean comparison
+
+The original `find_settled_round()` (single-point 5% tolerance)
+was replaced by a **windowed-mean comparison** that absorbs
+per-round noise:
+
+```rust
+fn find_converged_round(
+    baselines: &[f64],
+    window: usize,    // e.g. 20 = 1/α at λ = 0.95
+    tolerance: f64,
+) -> usize
+```
+
+Convergence is declared when the rolling mean over a window of
+$W$ rounds is within tolerance of the reference window at the
+end of the trace.  Per-axis tolerances are mandatory:
+
+| Axis | Tolerance | Empirical CV ($b = 4$) | Empirical CV ($b = 16$) |
+|------|:---------:|:----------------------:|:-----------------------:|
+| Novelty | 1% | 0.13% | 0.06% |
+| Displacement | 10% | 5.9% | 3.0% |
+| Surprise | 20% | 9.0% | 3.6% |
+| Coherence | 20% | 19.9% | 11.5% |
+
+## Empirical Convergence Results
+
+### Post-fix convergence times
+
+**Test config ($\lambda = 0.95$, $b = 4$, seed = 42):**
+
+| Axis | Converged (rounds) | CV (last 100) |
+|------|-------------------:|:-------------:|
+| Novelty | 21 | 0.07% |
+| Displacement | 404 | 3.37% |
+| Surprise | **65** | 6.46% |
+| Coherence | **406** | 10.01% |
+
+**Test config ($\lambda = 0.95$, $b = 16$, seed = 42):**
+
+| Axis | Converged (rounds) | CV (last 100) |
+|------|-------------------:|:-------------:|
+| Novelty | 21 | 0.04% |
+| Displacement | **21** | 2.08% |
+| Surprise | **70** | 2.53% |
+| Coherence | **173** | 8.68% |
+
+**Production config ($\lambda = 0.99$, $b = 16$, seed = 42):**
+
+| Axis | Converged (rounds) | CV (last 200) |
+|------|-------------------:|:-------------:|
+| Novelty | 101 | 0.02% |
+| Displacement | 101 | 0.83% |
+| Surprise | **398** | 0.94% |
+| Coherence | 315 | 2.36% |
+
+### η-convergence matches theory exactly
+
+The maturity metric $\eta_n = \lambda^n$ matches the theoretical
+prediction to machine epsilon: max $|\eta - \lambda^{bn}| = 2.78
+\times 10^{-17}$.  The exponential model is trivially correct for
+η but **not** for EWMA baselines.
+
+### Per-axis convergence character
+
+| Axis | Character | Dominant bottleneck |
+|------|-----------|---------------------|
+| **Novelty** | Instant (round 21). CV = 0.07–0.13%. | None — constant-norm score is inherently stable. |
+| **Displacement** | Fast at $b \geq 16$ (21 rounds); bimodal at $b = 4$ (21–475). | Stochastic subspace evolution at small batch sizes. |
+| **Surprise** | 65 rounds ($\lambda = 0.95$); 398 rounds ($\lambda = 0.99$). | Cascaded lat_var → score EWMA; stochastic batch variance. The clipping feedback loop is eliminated. |
+| **Coherence** | Consistently slowest: 406 ($b = 4$), 173 ($b = 16$), 315 (production). | Rank-gating delay ($k < 2$ → score = 0) plus `cross_corr` convergence time. |
+
+### Multi-seed robustness ($\lambda = 0.95$, $b = 4$, 10 seeds)
+
+| Axis | Min | Max | Range | Mean |
+|------|----:|----:|------:|-----:|
+| Novelty | 21 | 21 | 0 | 21.0 |
+| Displacement | 21 | 475 | 454 | 260.0 |
+| Surprise | 61 | 392 | 331 | 131.8 |
+| Coherence | 365 | 477 | 112 | 411.1 |
+
+Novelty is deterministic.  Coherence is consistently slow but
+seed-stable (range 112).  Surprise and displacement have high
+seed variance at $b = 4$, driven by stochastic subspace
+evolution.  At $b = 16$, displacement becomes deterministic (21
+across all seeds) and surprise variance decreases substantially.
+
+## Revised Iteration-Count Guidance
+
+The original ADR proposed `n_settled ≤ 30` and `r_noise ≤ 20`.
+These targets were off by >10×.  Revised guidance:
+
+| Config | Worst-case axis | Convergence (rounds) |
+|--------|-----------------|---------------------:|
+| $\lambda = 0.95$, $b = 4$ | Coherence | 406 |
+| $\lambda = 0.95$, $b = 16$ | Coherence | 173 |
+| $\lambda = 0.99$, $b = 16$ | Surprise | 398 |
+
+**`noise_rounds` must be at least as large as the worst-case
+convergence time** for baselines to be settled before real
+traffic arrives.  The current defaults are insufficient:
+
+| Config | Current `noise_rounds` | Required minimum | Shortfall |
+|--------|:----------------------:|:----------------:|:---------:|
+| Test ($\lambda = 0.95$, $b = 4$) | 5 | ≥65 (surprise) | 13× |
+| Production ($\lambda = 0.99$, $b = 16$) | 50 | ≥400 | 8× |
+
+### Derived iteration-count table
+
+| Test need | Iterations | Justification |
+|-----------|:----------:|---------------|
+| Surprise baseline settled ($\lambda = 0.95$) | 65 | Empirical windowed-mean convergence |
+| Coherence baseline settled ($b = 4$) | 406 | Rank-gating delay + EWMA convergence |
+| Coherence baseline settled ($b = 16$) | 173 | Reduced by CLT at larger batch size |
+| Production worst-case ($\lambda = 0.99$) | 398 | Surprise at slower forgetting rate |
+| η < 0.05 | 59 | Exact: $\lceil \ln(0.05) / \ln(\lambda) \rceil$ |
+
+## Investigation History
+
+The path from hypothesis to validated fixes spanned four rounds:
+
+**Round 1 (Preliminary):** Identified the 5.1× convergence gap.
+Attributed it primarily to the `lat_var` cold-start cascade
+(deterministic model predicted 114 rounds).  Correct on
+mechanisms, wrong on dominance — the cascade accounts for only
+~6% of the gap.
+
+**Round 2 (Secondary):** Discovered the convergence metric was
+fundamentally broken (round-300 reference was 56% wrong for
+surprise; baselines wander 15.5% CV at true steady state).  The
+5% tolerance criterion was unfalsifiable for 3 of 4 axes.
+Incorrectly attributed 89% of the gap to score-level variance
+(conflated the measurement problem with the convergence problem).
+
+**Round 3 (Code Audit):** Line-by-line audit confirmed **no code
+bug** — the implementation faithfully follows §ALGO S-7.1.1.
+Discovered the **clipping-ceiling positive feedback loop**: the
+true dominant cause (~85% of the gap, adding 800+ rounds to
+surprise convergence).  Cold→warm variance ≈ $10^{-4}$ →
+ceiling ≈ 0.28 → perpetual clipping → slow mean/variance drift.
+
+**Round 4 (Quaternary — Post-fix Validation):** Implemented both
+fixes.  Surprise convergence improved from 1000+ to 65 rounds.
+Confirmed the CUSUM fast-slow gap as severe (198 false drift).
+Discovered coherence as the true production bottleneck (406
+rounds at $b = 4$) and displacement bimodality across seeds.
+
+### What each round got right and wrong
+
+| Finding | Preliminary | Secondary | Audit | Tertiary |
+|---------|:-----------:|:---------:|:-----:|:--------:|
+| lat_var cold-start exists | ✓ | ✓ | ✓ | ✓ |
+| lat_var cascade is PRIMARY | **✗** | corrected | — | rank 2 |
+| Criterion is broken for 3/4 axes | — | ✓ | ✓ | ✓ |
+| Clipping "adds ≤ 5 rounds" | — | **✗** | corrected | dominant |
+| Score-level variance is 89% of gap | — | **✗** | corrected | ~3% |
+| Clipping-ceiling feedback loop | — | — | ✓ | ✓ |
+| No code bug | — | — | ✓ | ✓ |
+
+## Downstream Consequences
+
+This investigation triggered three further ADRs:
+
+- **ADR-S-014** — Subspace Tracker Visibility.  Convergence tests
+  need direct `SubspaceTracker` access.  Decided to keep the
+  tracker `pub(crate)` and enrich `CellInspection` with baseline
+  snapshots rather than exposing the tracker publicly.
+
+- **ADR-S-015** — Cell Creation Performance.  The finding that
+  `noise_rounds` must increase to ≥400 raised hot-path cost
+  concerns: `inject_noise_into_cell()` runs on every new cell
+  in the analysis set, not just at construction.  Benchmarked
+  the cost and motivated ADR-S-016.
+
+- **ADR-S-016** — Brand's Incremental SVD.  Replaced dense thin
+  SVD in `evolve_subspace()` with Brand's incremental algorithm,
+  reducing per-round SVD cost and cell creation times by
+  ~1.9–2.9×.  This makes the higher `noise_rounds` affordable
+  on the hot path.
+
+## Remaining Work
+
+| Item | Priority | Status |
+|------|----------|--------|
+| Increase `noise_rounds` default (50 → ≥400 at $\lambda = 0.99$) | **HIGH** | TODO |
+| Promote windowed-mean convergence metric to production test suite | Medium | Validated in quaternary tests |
+| ~~Update §ALGO S-7.1.1 with graduated clip-exemption formula~~ | ~~Medium~~ | Done (2026-03-11) |
+| ~~Update §ALGO S-5.2 Phase 3 with cold→warm initialisation~~ | ~~Medium~~ | Done (2026-03-11) |
+| ~~Update §ALGO S-7.4 with slow-from-fast CUSUM seeding~~ | ~~Medium~~ | Done (2026-03-11) |
+| ~~Update §ALGO S-11.5–11.6 with empirical convergence data~~ | ~~Medium~~ | Done (2026-03-11) |
+| Address coherence as the production bottleneck (rank-gating delay) | Medium | Confirmed structural |
+| Per-axis test tolerances and displaced bimodality metric | Low | Characterised |
+| ~~Mark superseded findings documents~~ | ~~Low~~ | Done — files retired, content absorbed into spec (2026-03-11) |
+| ~~Update `convergence_tests.rs` module doc~~ | Low | Done — file removed in consolidation (2026-03-11) |
+
+## Alternatives Considered
+
+- **Just reduce iteration counts by the theoretical formula.**
+  The formula for $\eta$ is trivially exact, but EWMA baseline
+  convergence depends on the score distribution, outlier
+  clipping, and batch-mean aggregation.  The investigation
+  proved this approach would be off by 5–10×.
+
+- **Remove clipping outright during warm-up.**  The graduated
+  clip-exemption was chosen over binary on/off because it
+  provides proportional attack resistance at all maturity
+  levels.  At $\eta = 0.01$ the clip is 3.03σ — negligibly
+  wider than production.
+
+- **Accept longer noise injection without fixing the feedback
+  loop.**  Would require `noise_rounds > 1000` at $b = 4$.
+  Infeasible on the hot path (cell creation during live
+  traffic).
+
+- **Property-based testing across random configs.**  Worth
+  doing eventually, but the immediate need was concrete
+  empirical bounds for known configurations.
+
+## Consequences
+
+- **Empirically justified iteration counts** replace arbitrary
+  constants.  The investigation provides precise per-axis,
+  per-config convergence times rather than order-of-magnitude
+  estimates.
+
+- **Three code fixes** eliminate the dominant convergence
+  bottlenecks: the clipping feedback loop (Fix 1), the lat_var
+  cold-start cascade (Fix 2), and the CUSUM fast-slow gap
+  (Fix 3).
+
+- **Surprise convergence improved 15×** (1000+ → 65 rounds at
+  $\lambda = 0.95$), exceeding the original prediction of ~120
+  rounds.
+
+- **The bottleneck shifted** from surprise (fixed) to coherence
+  (structural: rank-gating delay + cross-correlation
+  convergence).  Coherence at $b = 4$ takes ~406 rounds — this
+  is the true system convergence time.
+
+- **`noise_rounds` defaults are insufficient.**  Production
+  needs ≥400 at $\lambda = 0.99$.  ADR-S-015 and ADR-S-016
+  ensure this increase is affordable on the hot path.
+
+- **The simple exponential model is only valid for η.**  EWMA
+  baselines converge at a rate determined by cascaded EWMA
+  interactions, clipping policy, batch size, and axis-specific
+  score distributions.  The spec (§ALGO S-11.5–11.6) must be
+  updated to reflect this.
+
+- **Regression detection.**  The convergence test suite catches
+  changes to EWMA update rules, clipping behaviour, or cold
+  start logic that would shift convergence times.
diff --git a/packages/sentinel/adr/014-subspace-tracker-visibility.md b/packages/sentinel/adr/014-subspace-tracker-visibility.md
new file mode 100644
index 000000000..92b5c17e7
--- /dev/null
+++ b/packages/sentinel/adr/014-subspace-tracker-visibility.md
@@ -0,0 +1,367 @@
+# ADR-S-014: SubspaceTracker Visibility
+
+**Status:** Decided
+**Date:** 2026-03-10
+**Spec:** §ALGO S-5 (subspace tracker), §ALGO S-11.1 (noise injection)
+**Relates to:** [ADR-S-007](007-automatic-noise-injection.md) (automatic noise injection),
+[ADR-S-013](013-warm-up-convergence-benchmark.md) (warm-up convergence benchmark),
+[ADR-S-001](001-measures-not-opinions.md) (measures not opinions)
+
+## Context
+
+`SubspaceTracker` is the statistical core of the sentinel — a
+low-rank online subspace model with four-axis scoring and EWMA
+baselines.  It is currently declared `pub struct` in a
+`pub(crate) mod tracker`, making it **visible within the crate
+but invisible to external consumers** (integration tests under
+`tests/`, Criterion benchmarks under `benches/`, and downstream
+crates).
+
+This visibility boundary has been adequate so far: every
+integration test, benchmark, and host interaction operates
+exclusively through the `SpectralSentinel` public API (`ingest`,
+`inspect_cell`, `health`, `decay`, `BatchReport`, etc.).
+
+ADR-S-013 introduces a new testing need that challenges this
+boundary.  The convergence benchmark's
+`noise_baselines_converge` test (§3) requires:
+
+1. **Direct tracker construction** — create a bare
+   `SubspaceTracker::new(dim, &cfg, slow_decay)` without
+   sentinel orchestration overhead (no G-V Graph, no analysis
+   set, no coordination).
+2. **Repeated `observe()` calls with `is_noise = true`** — feed
+   synthetic noise batches one at a time and inspect the EWMA
+   baselines after each round.
+3. **Access to `TrackerReport::scores.*.baseline.mean`** — read
+   the per-axis EWMA mean from the report returned by
+   `observe()`.
+
+The `SpectralSentinel` API is insufficient for this because:
+
+- `inspect_cell()` returns `CellInspection`, which exposes
+  `maturity` (including `noise_influence`) but **not** the
+  per-axis EWMA baseline means/variances.  These are only
+  available in `CellReport::scores.*.baseline` and
+  `TrackerReport::scores.*.baseline`, which come from
+  `ingest()` reports.
+- Using `ingest()` for this test adds sentinel orchestration
+  overhead (graph routing, analysis set reconciliation,
+  coordination) that obscures the measurement and slows the
+  test unnecessarily.
+- Noise injection is automatic and internal (ADR-S-007).
+  There is no way to call `observe(is_noise = true)` on a
+  tracker through the public API — the sentinel owns the
+  injection lifecycle.
+
+### The deeper question
+
+Should `SubspaceTracker` be part of the crate's public API?
+
+This is not just about one test.  It's about the crate's API
+philosophy and impacts:
+
+- **Host-side subspace analysis.**  Some hosts may want to run
+  a standalone tracker outside of sentinel orchestration —
+  e.g. for offline analysis, benchmarking specific workloads,
+  or building custom pipelines.
+- **Fuzz testing.**  External fuzzer harnesses can exercise the
+  tracker directly without paying graph construction costs.
+- **Benchmark granularity.**  Criterion benchmarks can isolate
+  tracker performance (SVD cost, EWMA update cost) without
+  sentinel overhead.
+- **Semver surface area.**  Any `pub` type is a commitment.
+  Changes to `SubspaceTracker`'s constructor signature, field
+  layout, or `observe()` return type become breaking changes.
+- **Encapsulation.**  ADR-S-007 establishes that the sentinel
+  owns the noise injection lifecycle.  Exposing `observe()`
+  with its `is_noise` parameter hands that control to external
+  callers, creating ordering hazards (double-injection, skipped
+  injection, interleaved noise/real traffic).
+- **`AxisBaseline` / `EwmaStats` visibility.**  Making the
+  tracker public is only useful if callers can also construct
+  configs and read results.  `SentinelConfig` and
+  `TrackerReport` are already public.  But `AxisBaseline` is
+  private, and `EwmaStats` is `pub` in `ewma.rs`.
+
+## Decision
+
+**Option E — hybrid: keep `SubspaceTracker` as `pub(crate)`,
+enrich `CellInspection` with baseline snapshots.**
+
+See [Recommendation](#recommendation) below for rationale.
+
+## Decision Options Considered
+
+### Option A: Keep `pub(crate)`, test inside the crate
+
+Leave `SubspaceTracker` as `pub(crate)`.  Place any test that
+needs direct tracker access inside the crate boundary:
+
+- **`noise_baselines_converge`** → `#[cfg(test)] mod` inside
+  `src/sentinel/tracker.rs`.
+- **Tracker-level benchmarks** → not possible via Criterion
+  (benches are external); would need to be approximated through
+  the sentinel API or use `cargo bench` with the `test` harness.
+
+**Pro:**
+- Zero semver surface change.
+- Encapsulation preserved: `SubspaceTracker`'s API can evolve
+  freely.
+- ADR-S-007's lifecycle invariant ("the sentinel owns injection")
+  is enforced by the type system — external code literally cannot
+  call `observe(is_noise = true)`.
+- No risk of misuse by downstream crates.
+- Unit tests in `src/` have full `pub(crate)` access with no
+  workarounds needed.
+
+**Con:**
+- Tests that need tracker access must live in `src/`, not in the
+  `tests/` folder.  This mixes test code with production code
+  (though `#[cfg(test)]` ensures it is stripped from release
+  builds).
+- Criterion benchmarks cannot isolate tracker-level performance
+  without a public API.  Sentinel-level benchmarks are a proxy
+  but include graph and analysis set overhead.
+- No path for hosts to use the tracker standalone.
+
+### Option B: Promote to `pub mod tracker`
+
+Change `pub(crate) mod tracker` → `pub mod tracker`, making
+`SubspaceTracker`, its constructor, `observe()`, `maturity()`,
+`rank()`, and `reset_cusum()` part of the crate's public API.
+
+**Pro:**
+- Integration tests and Criterion benches can construct trackers
+  directly.
+- Hosts can build custom pipelines with standalone trackers.
+- Benchmark isolation: measure SVD/EWMA costs without sentinel
+  overhead.
+- Fuzz testing can target the tracker in external harnesses.
+
+**Con:**
+- **Semver commitment.**  `SubspaceTracker::new()` signature,
+  `observe()` parameters, and `TrackerReport` fields all become
+  stable API surface.  Any internal refactor (e.g. changing the
+  basis representation, adding parameters to `observe()`) is a
+  breaking change.
+- **Breaks ADR-S-007's lifecycle invariant.**  External callers
+  can call `observe(is_noise = true)` arbitrarily, bypassing the
+  sentinel's controlled injection sequence.  While `SubspaceTracker`
+  is stateless w.r.t. injection ordering (it doesn't panic or
+  corrupt), the *host's interpretation* of maturity becomes
+  unreliable if injection was manual and uncontrolled.
+- **AxisBaseline exposure cascade.**  Making the module public
+  invites questions about `AxisBaseline`, `CusumAccumulator`,
+  and the internal scoring pipeline.  These are currently private
+  structs.
+- **Documentation burden.**  A public tracker needs doc-comments,
+  usage examples, and safety guidance ("do not mix `is_noise`
+  calls with real observations outside sentinel orchestration").
+
+### Option C: Test-only feature gate
+
+Add a Cargo feature `test-internals` (default off) that
+conditionally re-exports internal types:
+
+```rust
+#[cfg(feature = "test-internals")]
+pub mod test_internals {
+    pub use super::tracker::SubspaceTracker;
+    pub use super::cusum::CusumAccumulator;
+}
+```
+
+Integration tests and benches enable it via
+`[dev-dependencies]` features.
+
+**Pro:**
+- Tracker is accessible in tests and benches without being part
+  of the default public API.
+- No semver commitment to non-`test-internals` consumers.
+- ADR-S-007 lifecycle invariant is preserved for production
+  builds.
+- Clean separation: test authors explicitly opt in.
+
+**Con:**
+- Feature gates add conditional-compilation complexity.
+- `test-internals` is a social contract, not a hard boundary —
+  any downstream crate can enable it.
+- Documentation must explain the feature and its warranty
+  limitations.
+- `cargo doc` with `--all-features` exposes the internal types,
+  cluttering the generated docs unless `#[doc(hidden)]` is used,
+  which defeats discoverability for test authors.
+
+### Option D: Expose a limited inspection API on `SpectralSentinel`
+
+Instead of exposing the tracker, add a method like
+`inspect_cell_baselines(gnode) -> Option<BaselineInspection>`
+that returns per-axis EWMA means and variances.  Combined with
+`inspect_cell()` (maturity) and `ingest()` (reports), this
+covers the convergence test's needs without exposing the
+tracker.
+
+```rust
+pub struct BaselineInspection {
+    pub novelty: BaselineSnapshot,
+    pub displacement: BaselineSnapshot,
+    pub surprise: BaselineSnapshot,
+    pub coherence: BaselineSnapshot,
+}
+```
+
+**Pro:**
+- Narrow API addition — one method, one struct.
+- Encapsulation preserved: `SubspaceTracker` remains internal.
+- Fulfils the convergence test's data needs (`inspect_cell()` +
+  `inspect_cell_baselines()` after each `ingest()`).
+- Useful for hosts too (monitoring baseline drift).
+
+**Con:**
+- Does not address the "test tracker in isolation" need.  The
+  `noise_baselines_converge` test (ADR-S-013 §3) specifically
+  wants to measure noise-only convergence without real data.
+  Through the sentinel API, you cannot feed noise without also
+  triggering graph routing / analysis set / coordination.
+- Two-step inspection (`inspect_cell` + `inspect_cell_baselines`)
+  is slightly awkward; could be unified into a richer
+  `CellInspection` instead.
+
+### Option E: Hybrid — `pub(crate)` (Option A) + enriched inspection (Option D)
+
+Keep `SubspaceTracker` as `pub(crate)`.  Place the noise-only
+convergence test inside the crate (`#[cfg(test)]` in
+`tracker.rs`).  Additionally, enrich `CellInspection` with
+baseline snapshots so that the integration-level convergence test
+(`convergence_matches_theory`) doesn't need to hunt through
+`BatchReport` arrays:
+
+```rust
+pub struct CellInspection {
+    // ... existing fields ...
+    /// Per-axis EWMA baseline snapshots.
+    pub baselines: AxisBaselineSnapshots,
+}
+
+pub struct AxisBaselineSnapshots {
+    pub novelty: BaselineSnapshot,
+    pub displacement: BaselineSnapshot,
+    pub surprise: BaselineSnapshot,
+    pub coherence: BaselineSnapshot,
+}
+```
+
+**Pro:**
+- The noise-only test lives where it has full access (inside the
+  crate), matching the principle that unit tests belong near the
+  code they test.
+- The integration-level convergence test gets clean baseline
+  access through the public API without parsing `BatchReport`
+  cell/ancestor arrays.
+- No semver exposure of `SubspaceTracker`.
+- Enriched `CellInspection` is independently useful for hosts
+  monitoring baseline drift.
+- Clean separation of concerns: noise convergence tested at the
+  unit level, theory + EWMA convergence tested at the integration
+  level.
+
+**Con:**
+- Criterion benchmarks still cannot isolate tracker-level
+  performance.  (Mitigated: the existing benchmark suite already
+  measures sentinel-level throughput at various scales, and the
+  convergence benchmark measures batch-over-time cost through the
+  sentinel API.)
+- Adds a struct and 4 fields to `CellInspection` (minor API
+  growth).
+
+## Recommendation
+
+**Option E — hybrid.**
+
+The rationale:
+
+1. **`SubspaceTracker` is an implementation detail.**  Its
+   constructor takes a `slow_decay: f64` parameter that only
+   makes sense in the context of the sentinel's two-tier EWMA
+   architecture.  Its `observe()` method has an `is_noise: bool`
+   parameter whose correct usage depends on the injection
+   lifecycle described in ADR-S-007.  Exposing these to external
+   callers invites misuse without adding proportional value.
+
+2. **The one test that needs internal access is a unit test.**
+   `noise_baselines_converge` exercises a single tracker's EWMA
+   convergence — a textbook unit test.  Placing it in
+   `#[cfg(test)]` inside `tracker.rs` is idiomatic Rust.
+
+3. **Enriching `CellInspection` is the right public API
+   evolution.**  Baseline means and variances are legitimate
+   observable state.  Hosts monitoring the sentinel in production
+   will benefit from baseline visibility in `inspect_cell()`
+   without needing to parse `BatchReport` arrays.  This is
+   fully aligned with "the sentinel measures; the host decides"
+   (ADR-S-001).
+
+4. **Semver discipline.**  The sentinel crate is pre-1.0.  Even
+   so, keeping the internal machinery private now avoids painting
+   into a corner.  If a genuine need for standalone trackers
+   emerges (e.g. a host's custom analysis pipeline), a future ADR
+   can promote visibility with an intentional, documented API.
+
+5. **Benchmark isolation is a non-goal for now.**  The existing
+   Criterion suite measures ingest throughput at various scales.
+   The convergence benchmark (ADR-S-013 §1) measures
+   200-batch convergence wall-clock at the sentinel level, which
+   is the relevant integration point.  Tracker-level micro-
+   benchmarks would be useful but are not blocked by this
+   decision — they can live in `#[cfg(test)]`
+   `mod benches` inside `tracker.rs` using the `test::Bencher`
+   nightly API, or be added later if Option B is adopted.
+
+## Consequences
+
+1. **`noise_baselines_converge`** is implemented as a
+   `#[cfg(test)]` unit test in `src/sentinel/tracker.rs`.
+
+2. **`CellInspection`** gains a `baselines: AxisBaselineSnapshots`
+   field populated from the tracker's four `AxisBaseline` fast-
+   EWMA snapshots.
+
+3. **`AxisBaselineSnapshots`** is a new public type in
+   `report.rs`, containing four `BaselineSnapshot` fields.
+
+4. **`inspect_cell()`** in `SpectralSentinel` populates the new
+   field from `cell.tracker.axis_baselines()` (already a
+   `pub(crate)` method that returns the necessary data).
+
+5. **`convergence_matches_theory`** (integration test) reads
+   baselines from `inspect_cell().baselines` instead of parsing
+   `BatchReport` arrays.  This simplifies the test and removes
+   the dependency on knowing whether the root is in
+   `cell_reports` or `ancestor_reports`.
+
+6. **No change** to `SubspaceTracker`'s visibility
+   (`pub(crate)`).
+
+7. **Future reconsideration.**  If demand emerges for standalone
+   tracker usage (host custom pipelines, external fuzz harnesses),
+   revisit this ADR.  The migration path is straightforward:
+   change `pub(crate) mod tracker` → `pub mod tracker` and
+   document the `is_noise` lifecycle contract.
+
+## Files Changed
+
+| File | Change |
+|------|--------|
+| `src/report.rs` | Add `AxisBaselineSnapshots` struct; add `baselines` field to `CellInspection` |
+| `src/sentinel/mod.rs` | Populate `baselines` in `inspect_cell()` |
+| `src/sentinel/tracker.rs` | Add `#[cfg(test)] mod convergence_tests` (noise_baselines_converge) — now in `src/tests/convergence_noise.rs` |
+
+## Cross-References
+
+- §ALGO S-5 — Subspace tracker specification
+- §ALGO S-7.1 — EWMA baseline update rule
+- §ALGO S-11.1–11.4 — Noise injection lifecycle
+- ADR-S-001 — Measures not opinions (API philosophy)
+- ADR-S-007 — Automatic noise injection (lifecycle ownership)
+- ADR-S-013 — Warm-up convergence benchmark (motivating use case)
diff --git a/packages/sentinel/adr/015-cell-creation-performance.md b/packages/sentinel/adr/015-cell-creation-performance.md
new file mode 100644
index 000000000..d4eb00044
--- /dev/null
+++ b/packages/sentinel/adr/015-cell-creation-performance.md
@@ -0,0 +1,357 @@
+# ADR-S-015: Cell Creation Performance
+
+**Status:** Implemented (§1 `NoiseSchedule`)
+**Date:** 2026-03-10
+**Spec:** §ALGO S-8.2 (analysis set reconciliation), §ALGO S-11.2 (automatic noise injection)
+**Relates to:** [ADR-S-007](007-automatic-noise-injection.md) (automatic noise injection),
+[ADR-S-013](013-warm-up-convergence-benchmark.md) (warm-up convergence benchmark),
+[ADR-S-006](006-analysis-set-recomputation.md) (analysis set recomputation),
+[ADR-S-012](012-test-duration-budget.md) (test duration budget),
+[ADR-S-016](016-brand-incremental-svd.md) (Brand's incremental SVD)
+
+## Context
+
+Every time a new cell enters the analysis set — whether at sentinel
+construction, after `reset()`, or during live `reconcile_analysis_set()`
+— `inject_noise_into_cell()` runs `noise_rounds` batches of synthetic
+observations through a fresh `SubspaceTracker`.  This is the
+mechanism described in ADR-S-007.
+
+The critical realisation is that **cell creation is not a one-time
+startup cost**.  The G-V graph splits cells as traffic arrives.
+`reconcile_analysis_set()` runs on every `ingest()` call, and when
+the analysis set changes, new cells are created — each paying the
+full noise injection cost.
+
+### Call sites
+
+`inject_noise_into_cell()` is called from three places:
+
+1. **`SpectralSentinel::new()`** — root cell at construction.
+   One-time cost, acceptable.
+
+2. **`SpectralSentinel::reset()`** — root cell after reset.
+   Infrequent, acceptable.
+
+3. **`reconcile_analysis_set()`** — new cells entering the analysis
+   set during live traffic.  **This is the hot-path concern.**
+   Multiple cells may enter in a single `ingest()` call if the graph
+   has split since the last reconciliation.
+
+### Measured costs
+
+Criterion benchmarks (release profile, optimised) on the sentinel
+crate, 2026-03-10:
+
+| `noise_rounds` | Config | Construction time | Per-round marginal |
+|----------------:|--------|------------------:|-------------------:|
+| 10  | bench (rank=4, k=16, b=4)            |   38 ms |  ~2.5 ms |
+| 50  | bench                                |  319 ms |    ~7 ms |
+| 100 | bench                                |  161 ms |  ~2.5 ms |
+| 200 | bench                                | 1.28 s  |    ~5 ms |
+| 400 | bench                                | 3.27 s  |    ~7 ms |
+| 10  | realistic (rank=16, k=1024, b=16)    |   99 ms |      — |
+| 50  | realistic                            |  332 ms |    ~6 ms |
+| 100 | realistic                            |  164 ms |    ~2 ms |
+| 200 | realistic                            | 3.72 s  |   ~17 ms |
+| 400 | realistic                            | 4.92 s  |   ~10 ms |
+
+> **Update (post ADR-S-016):** Brand's incremental SVD reduces
+> construction times by **~1.9×** at b=4 and **~2.9×** at b=16.
+> The decision analysis below uses Brand's-adjusted figures.
+
+Per-round `ingest()` cost on a warmed sentinel (Criterion):
+
+| Config | batch_size | Per-ingest call |
+|--------|----------:|--------------:|
+| bench        |  4 |   232 µs |
+| bench        | 16 |  6.6 ms |
+| realistic    |  4 |  4.4 ms |
+| realistic    | 16 | 14.7 ms |
+
+> With Brand's SVD (ADR-S-016) these drop to ~122 µs (bench b=4),
+> ~2.3 ms (bench b=16), ~2.3 ms (realistic b=4), ~5.1 ms
+> (realistic b=16).
+
+### Convergence data
+
+From the convergence benchmark suite (formerly
+`convergence_benchmark.rs`, now `convergence_diagnostics.rs` /
+criterion `warmup_cost_detailed`, optimised debug):
+
+**Noise-only convergence (per-axis, rounds needed):**
+
+| noise_rounds | Novelty | Surprise | Displacement (b=16) | Coherence | All converged? |
+|---:|----|----|----|----|----|
+|   5 | 3  | 3  | 3  | 0  | NO |
+|  50 | 21 | 30 | 21 | 0  | NO |
+|  65 | 21 | 43 | 21 | 0  | YES (prod) |
+| 100 | 21 | 67 | 21 | 0  | YES |
+| 200 | 21 | 67 | 33 | 157 | YES |
+| 400 | 21 | 188 | 24 | 273 | YES |
+
+**Real-data phase convergence (after 200 noise rounds):**
+
+| Config | Worst axis | Converged at round | Wall-clock |
+|--------|-----------|---:|---:|
+| test (λ=0.95, b=4) | coherence | 233 | ~2.0 s |
+| production (λ=0.99, b=16) | coherence | 212 | ~5.1 s |
+
+**η (noise influence) matches theory exactly:** max $|\eta - \lambda^{bn}| = 2.78 \times 10^{-17}$.
+
+## Problem
+
+The current defaults are:
+
+| Config | `noise_rounds` | Per-cell cost (pre-Brand) | Per-cell cost (Brand) |
+|--------|---:|---:|---:|
+| Test | 5 | ~38 ms | ~13 ms |
+| Production | 50 | ~332 ms | ~115 ms |
+
+The test default of 5 is **13× too low** for surprise convergence
+(needs ≥65).  The production default of 50 is **6× too low** for
+η < 0.05 (needs ≥299 at λ = 0.99).
+
+However, raising `noise_rounds` to 300–400 is still **expensive
+on the hot path** even with Brand's incremental SVD (ADR-S-016).
+If the analysis set gains 5 new cells during a single `ingest()`:
+
+| `noise_rounds` | Per-cell (Brand) | 5 cells | Impact on `ingest()` |
+|---:|---:|---:|---|
+| 50 (current) | ~115 ms | ~575 ms | Tolerable |
+| 100 | ~57 ms | ~283 ms | **Well within budget** |
+| 200 | ~1.28 s | ~6.4 s | Blocking stall |
+| 300 | ~860 ms | ~4.3 s | Significant stall |
+| 400 | ~1.7 s | ~8.5 s | Blocking stall |
+
+At `analysis_k = 1024` and `analysis_depth_cutoff = 6`, the analysis
+set can contain up to 1024 cells.  Graph growth from zero to steady
+state may create hundreds of cells over time.  Each split that
+creates a new cell in the analysis set triggers noise injection.
+
+## Decision
+
+### 1. Depth-tiered `noise_schedule`
+
+Replace the single `noise_rounds` scalar with a **`noise_schedule`
+enum** that determines per-depth noise rounds.  Two variants:
+
+```rust
+enum NoiseSchedule {
+    /// Geometric decay: r(d) = max(min, floor(root * decay^d))
+    Geometric { root: u32, decay: f64, min: u32 },
+
+    /// Explicit per-depth array; last element repeats for all
+    /// deeper layers.
+    Explicit(Vec<u32>),  // non-empty, validated at construction
+}
+```
+
+Resolution at injection time:
+
+```rust
+fn rounds_for_depth(&self, depth: usize) -> u32 {
+    match self {
+        Geometric { root, decay, min } =>
+            (*min).max((*root as f64 * decay.powi(depth as i32)) as u32),
+        Explicit(v) =>
+            v[depth.min(v.len() - 1)],
+    }
+}
+```
+
+#### Default: `Geometric { root: 400, decay: 0.5, min: 100 }`
+
+$$r(d) = \max\!\bigl(100,\; \lfloor 400 \cdot 0.5^{\,d} \rfloor\bigr)$$
+
+Materialised:
+
+| Depth | Formula | Rounds |
+|---:|---:|---:|
+| 0 (root) | $400 \cdot 0.5^0$ | **400** |
+| 1 | $400 \cdot 0.5^1$ | **200** |
+| 2 | $400 \cdot 0.5^2 = 100$ | **100** |
+| 3+ | $\max(100, \ldots)$ | **100** (floor) |
+
+This is equivalent to `Explicit([400, 200, 100])`.
+
+#### Rationale per tier
+
+**Depth 0 (root) — 400 rounds.**  Created exactly once at
+`SpectralSentinel::new()` or `reset()`, never on the hot path.
+400 rounds guarantees **all axes converge**, including coherence:
+
+| Metric | At 400 rounds |
+|--------|---:|
+| Per-cell cost (realistic, Brand) | **~1.7 s** |
+| Surprise converged? | **Yes** (188 of 400) |
+| Displacement converged? | **Yes** (24 of 400) |
+| Coherence converged? | **Yes** (273 of 400) |
+| η at transition (λ=0.99, b=16) | 0.99^6400 ≈ 0 |
+| η at transition (λ=0.95, b=4) | 0.95^1600 ≈ 0 |
+
+The root sees *all* traffic before the graph splits, so full
+convergence here eliminates graduated exemptions and transition
+artefacts for the most-observed cell.
+
+**Depth 1 — 200 rounds.**  First-level splits are infrequent
+(typically a handful over the sentinel's lifetime) and each
+covers a large fraction of the address space.  200 rounds costs
+~1.28 s per cell (Brand), which is acceptable for an uncommon
+event.  Surprise, displacement, and partial coherence all
+converge.
+
+**Depth 2+ — 100 rounds (floor).**  These cells are created on
+the `reconcile_analysis_set()` hot path and may arrive in bursts.
+100 rounds costs ~57 ms per cell (Brand), keeping a 5-cell burst
+at ~283 ms:
+
+| Metric | At 100 rounds |
+|--------|---:|
+| Per-cell cost (realistic, Brand) | **~57 ms** |
+| 5 cells in one ingest | **~283 ms** |
+| Surprise converged? | **Yes** (67 of 100) |
+| η at transition (λ=0.99, b=16) | 0.99^1600 ≈ 1.2 × 10⁻⁷ |
+| η at transition (λ=0.95, b=4) | 0.95^400 ≈ 7.7 × 10⁻¹⁰ |
+| Coherence converged? | No — finishes during real traffic |
+
+100 rounds is sufficient for deep cells because:
+
+- **Surprise** (the hardest non-gated axis) converges by round 67.
+- **η** is already negligible (10⁻⁷ or below) — the maturity
+  model correctly reports the cell as fully warmed.
+- **The graduated clip-exemption** (ADR-S-013 §1) prevents false
+  scoring while baselines are still converging.
+- **The CUSUM slow-from-fast seeding** (ADR-S-013 §6) eliminates
+  false drift at the noise→real transition.
+- **Coherence** is gated by `rank_update_interval` anyway — it
+  doesn't produce scores until $k \geq 2$, which takes
+  `rank_update_interval` steps regardless of noise injection length.
+
+#### User configuration
+
+Operators choose whichever variant is most natural:
+
+```toml
+# Default — geometric decay (3 parameters)
+[sentinel.noise_schedule]
+type = "geometric"
+root = 400
+decay = 0.5
+min = 100
+
+# Same result, explicit array
+[sentinel.noise_schedule]
+type = "explicit"
+rounds = [400, 200, 100]
+
+# Converge everything at every depth (slow hot path)
+[sentinel.noise_schedule]
+type = "explicit"
+rounds = [400]
+
+# Slower decay — more rounds at shallow depths
+[sentinel.noise_schedule]
+type = "geometric"
+root = 400
+decay = 0.7
+min = 100
+# → [400, 280, 196, 137, 100, 100, ...]
+```
+
+**`Geometric`** is compact and self-documenting: the operator
+states intent (root quality, decay rate, floor) and the schedule
+scales automatically to any graph depth.  **`Explicit`** gives
+full control when specific per-layer tuning is needed; the last
+element repeats for all deeper layers.
+
+### 2. Document the per-cell cost in the spec
+
+§ALGO S-11.2 should note that noise injection runs per cell, not once
+globally.  The cost model is:
+
+$$T_{\text{noise}} = \sum_{\text{cells } c} r(d_c) \times t_{\text{round}}$$
+
+where $r(d_c)$ is the noise-schedule value for cell $c$ at depth
+$d_c$, and $t_{\text{round}}$ is the per-round observe cost (config
+and hardware dependent, ~2–17 ms in benchmarks).
+
+### 3. Consider future optimisations (not in this ADR)
+
+These are documented here for future reference but are **not**
+being implemented now:
+
+**a. Async / background noise injection.**  Move noise injection
+off the `ingest()` hot path.  New cells would be created with a
+"warming" flag and injected in a background task.  Scores from
+warming cells would be suppressed (η = 1.0) until injection
+completes.  This eliminates the hot-path stall entirely but adds
+concurrency complexity.
+
+**b. Adaptive schedule.**  Auto-tune `decay` or the explicit
+array based on observed cell-creation frequency per depth.  If
+depth-1 splits are rare, increase their rounds; if they burst,
+reduce.  Requires runtime telemetry that doesn't yet exist.
+
+**c. Shared noise cache.**  Pre-generate the noise batch matrix
+once and reuse it across cells (adjusting for dimension).  Saves
+RNG and allocation cost but not the `observe()` cost, which
+dominates.
+
+**d. Lazy injection.**  Defer noise injection until the cell
+actually receives real traffic.  Cells that enter the analysis set
+but never see observations (common during rapid graph churn) would
+skip injection entirely.  Risk: first real batch hits
+unconverged baselines.
+
+## Consequences
+
+- **Root cell starts fully converged** on all axes (400 rounds,
+  ~1.7 s one-time cost).  No graduated exemptions or transition
+  artefacts needed for the root.
+
+- **Depth-1 cells get 200 rounds** (~1.28 s each), converging
+  surprise and displacement fully.  These splits are infrequent.
+
+- **Depth-2+ cell creation cost is bounded to ~57 ms** at
+  realistic settings (with Brand's SVD, ADR-S-016).  A 5-cell
+  creation burst stalls `ingest()` for ~283 ms, well within
+  budget for a batch-oriented system.
+
+- **Surprise baselines are converged** at all depths before the
+  cell sees real traffic.
+
+- **Coherence baselines are NOT converged** on depth-2+ cells at
+  creation.  They finish converging during the real-data phase,
+  gated by η.  This is acceptable because coherence scoring
+  requires $k \geq 2$,
+  and the coherence EWMA cold→warms from the first real coherence
+  score (ADR-S-013 §2b).
+
+- **Test suite performance improves.**  Increasing test
+  `noise_rounds` from 5 to 100 adds ~57 ms per cell construction
+  (with Brand's SVD), but eliminates the ~500+ rounds of
+  "compensatory warm-up" that many tests currently run.  Net
+  effect depends on the test, but the convergence benchmark
+  suite (now in criterion `warmup_cost_detailed` and
+  `convergence_diagnostics.rs`) shows 100 rounds finishes well
+  under 1 second total for all four tests.
+
+- **The `noise_schedule` parameter is fully user-configurable.**
+  Operators supply an array `[root, layer_1, ..., floor]` where
+  the last element repeats for all deeper layers.  The geometric
+  default `[400, 200, 100]` is a measured compromise.  A
+  single-element `[400]` converges everything everywhere; a
+  single-element `[100]` minimises hot-path cost at the expense
+  of root convergence.
+
+## Appendix: Convergence vs Cost Tradeoff Table
+
+| `noise_rounds` | Per-cell, Brand (ms) | Surprise | Displ. | Coherence | η (λ=0.99,b=16) | Recommended? |
+|---:|---:|:---:|:---:|:---:|:---:|:---:|
+| 5 | ~13 | NO | NO | NO | 0.923 | NO — current test default, too low |
+| 50 | ~115 | NO | YES | NO | 0.449 | NO — current prod default, too low |
+| 65 | ~138 | MARGINAL | YES | NO | 0.353 | MARGINAL |
+| **100** | **~57** | **YES** | **YES** | **NO** | **1.2×10⁻⁷** | **YES — recommended default** |
+| 200 | ~1283 | YES | YES | PARTIAL | ~0 | NO — too expensive per cell |
+| 400 | ~1697 | YES | YES | YES | ~0 | NO — expensive on hot path |
diff --git a/packages/sentinel/adr/016-brand-incremental-svd.md b/packages/sentinel/adr/016-brand-incremental-svd.md
new file mode 100644
index 000000000..7ae99748b
--- /dev/null
+++ b/packages/sentinel/adr/016-brand-incremental-svd.md
@@ -0,0 +1,275 @@
+# ADR-S-016: Brand's Incremental SVD for Subspace Evolution
+
+**Status:** Implemented
+**Date:** 2026-03-10
+**Spec:** §ALGO S-5.2 Phase 2 (subspace evolution)
+**Relates to:** [ADR-S-015](015-cell-creation-performance.md) (cell creation performance),
+[ADR-S-007](007-automatic-noise-injection.md) (automatic noise injection),
+[ADR-S-013](013-warm-up-convergence-benchmark.md) (warm-up convergence benchmark)
+
+## Context
+
+### The hot loop
+
+Every call to `SubspaceTracker::observe()` invokes
+`evolve_subspace()`, which performs Phase 2 of the five-phase core
+loop (§ALGO S-5.2).  Convergence benchmarks (ADR-S-015) show that
+**`observe()` accounts for 99.9% of wall-clock time**, and Phase 2
+(the SVD) dominates `observe()`.
+
+### Previous implementation
+
+`evolve_subspace()` built the composite matrix
+
+$$M = \bigl[\;\sqrt{\lambda}\, U_k \operatorname{diag}(\sigma_{1..k}) \;\big|\; X^\top\;\bigr] \;\in\; \mathbb{R}^{d \times (k+b)}$$
+
+and computed a **full dense thin SVD** of $M$ via
+`faer::Mat::thin_svd()`.  This produced all $\min(d,\, k{+}b)$
+singular triplets, of which only the top $n = \min(k{+}b,\, d,\,
+\text{cap})$ were retained.
+
+### Cost analysis
+
+The thin SVD of a $d \times c$ matrix (where $c = k{+}b$) via
+bidiagonalisation + divide-and-conquer costs $O(d \, c^2)$ flops,
+plus $O(d \, c \, n)$ for back-transforming the $n$ left singular
+vectors from Householder form.
+
+At representative dimensions:
+
+| Config | d | k | b | M shape | SVD cost term | Per-round measured |
+|--------|---|---|---|---------|---:|---:|
+| bench | 128 | 2 | 4 | 128 × 6 | $128 \cdot 36$ | ~9 ms |
+| realistic | 128 | 2 | 16 | 128 × 18 | $128 \cdot 324$ | ~32 ms |
+| production (wide) | 128 | 16 | 16 | 128 × 32 | $128 \cdot 1024$ | est. ~90 ms |
+
+With `noise_rounds = 100` per cell and multiple cells created on
+the hot path (ADR-S-015), the cumulative SVD cost is the dominant
+throughput bottleneck.
+
+### What faer offers
+
+faer 0.24.0 provides:
+
+- **`Mat::thin_svd()`** — dense thin SVD (bidiag + D&C).  No
+  option to compute only the top-$k$ triplets.
+- **`matrix_free::eigen::partial_svd()`** — Lanczos-based partial
+  SVD for implicit operators (`&dyn BiLinOp<T>`).  Designed for
+  large sparse matrices; slower than direct decomposition at our
+  dimensions.
+- **`linalg::qr::no_pivoting::qr_in_place()`** — dense thin QR.
+- Dense matrix multiply via `&A * &B` operator overloads.
+
+faer does **not** provide an incremental/streaming SVD.  However,
+it provides all the primitives needed to implement one.
+
+## Decision
+
+Replace the naïve dense thin SVD in `evolve_subspace()` with
+**Brand's incremental SVD** (Brand 2002, 2006), using faer's
+existing QR and SVD primitives on a much smaller kernel matrix.
+
+### Algorithm
+
+Given the current rank-$k$ model $(U_k, \sigma_{1..k})$ and a new
+batch $X \in \mathbb{R}^{b \times d}$:
+
+**Step 1 — Project onto current basis:**
+
+$$P = U_k^\top X^\top \;\in\; \mathbb{R}^{k \times b}$$
+
+**Step 2 — Compute orthogonal residual:**
+
+$$Q = X^\top - U_k\, P \;\in\; \mathbb{R}^{d \times b}$$
+
+**Step 3 — Thin QR of residual:**
+
+$$Q = Q_\perp\, R_\perp, \quad Q_\perp \in \mathbb{R}^{d \times b},\; R_\perp \in \mathbb{R}^{b \times b}$$
+
+**Step 4 — Small-kernel SVD:**
+
+$$K = \begin{bmatrix} \sqrt{\lambda}\,\operatorname{diag}(\sigma_{1..k}) & P \\ 0 & R_\perp \end{bmatrix} \;\in\; \mathbb{R}^{(k+b) \times (k+b)}$$
+
+$$\hat{U}_K,\; \hat{\sigma},\; \hat{V}_K = \operatorname{thin\_svd}(K)$$
+
+This SVD is on a **$(k{+}b) \times (k{+}b)$** matrix — e.g. 18×18
+instead of 128×18.
+
+**Step 5 — Back-transform to full basis:**
+
+$$U_\text{new} = \bigl[\, U_k \;\big|\; Q_\perp \,\bigr] \;\hat{U}_K[:,\, :n] \;\in\; \mathbb{R}^{d \times n}$$
+
+where $n = \min(k{+}b,\, d,\, \text{cap})$.
+
+### Cost comparison
+
+| Operation | Current (naïve) | Brand's | Ratio |
+|-----------|---:|---:|---:|
+| Build $M$ | $O(d \cdot c)$ | — | — |
+| Projection $P = U_k^\top X^\top$ | — | $O(d \cdot k \cdot b)$ | new |
+| Residual $Q$ | — | $O(d \cdot k \cdot b)$ | new |
+| Thin QR of $Q$ | — | $O(d \cdot b^2)$ | new |
+| SVD kernel | $O(d \cdot c^2)$ | $O(c^3)$ | **$d/c$ ≈ 7×** |
+| Back-transform $U_\text{new}$ | $O(d \cdot c \cdot n)$ | $O(d \cdot c \cdot n)$ | same |
+
+### Phase 1 projection reuse
+
+Phase 1 of `observe()` already computes:
+
+```
+z     = X · U_k        // (b × k) — the latent coordinates
+x_hat = z · U_k^T      // (b × d)
+residual = X - x_hat    // (b × d)
+```
+
+Brand's Step 1 needs $P = U_k^\top X^\top = Z^\top$, and Step 2
+needs $Q = X^\top - U_k P = \text{residual}^\top$.  Both are
+already computed in Phase 1.  Passing `z` and `residual` into
+`evolve_subspace()` eliminates the redundant matrix builds entirely.
+
+## Implementation
+
+### Source layout
+
+| File | Purpose |
+|------|---------|
+| `maths/mod.rs` | `SvdStrategy` enum, `evolve()` dispatch + oracle |
+| `maths/brand_svd.rs` | Brand's incremental SVD (~130 lines) |
+| `maths/naive_svd.rs` | Naïve dense thin SVD (reference) |
+| `maths/bench_tracing.rs` | `SpanTimingLayer` for benchmark timing |
+| `maths/tests/brand_vs_naive.rs` | 14 oracle unit tests |
+| ~~`convergence_benchmark.rs`~~ | Removed — timing ported to criterion `warmup_cost_detailed`; diagnostics to `convergence_diagnostics.rs` |
+
+### Strategy pattern
+
+`SvdStrategy` is a runtime-selectable enum (`Naive` | `Brand`,
+default `Brand`) stored in `SentinelConfig`.  The `evolve()`
+function dispatches via `run_svd()`, which wraps each strategy
+call in a named `info_span!` (`"svd_brand"` or `"svd_naive"`) for
+timing capture.
+
+### Debug oracle
+
+When `cfg!(debug_assertions)` is true (debug builds) **or** a
+`DEBUG`-level tracing subscriber is attached (release with
+`RUST_LOG=debug`), `evolve()` runs **both** strategies and
+compares their outputs:
+
+- Singular values: relative tolerance $10^{-8}$
+- Basis columns: $|\cos \theta| > 1 - 10^{-6}$ (allowing SVD
+  sign ambiguity)
+
+A **thread-local `ORACLE_FLIP`** toggle alternates execution order
+on every call, so neither strategy consistently benefits from
+warmed caches or branch predictors.
+
+### Benchmark timing
+
+`SpanTimingLayer` (in `bench_tracing.rs`) accumulates per-span-name
+wall-clock durations without console output.  The benchmarks read
+timing programmatically via `timing.total_ns("svd_brand")`.  An
+`EnvFilter` (default INFO) controls whether the oracle fires —
+`RUST_LOG=debug` activates it in release mode.
+
+## Measured Results
+
+All measurements on d=128, 500 rounds, `convergence_matches_theory`
+benchmark.
+
+### Test config (λ=0.95, b=4)
+
+| Run mode | Brand (ms) | Naïve (ms) | Speedup |
+|----------|-----------|-----------|---------|
+| Debug (opt=3, oracle on) | 2188 | 4203 | **1.92×** |
+| Release (oracle off) | 1977 | — | — |
+| Release + RUST_LOG=debug | 1944 | 3731 | **1.92×** |
+
+### Production config (λ=0.99, b=16)
+
+| Run mode | Brand (ms) | Naïve (ms) | Speedup |
+|----------|-----------|-----------|---------|
+| Debug (opt=3, oracle on) | 4703 | 13998 | **2.98×** |
+| Release (oracle off) | 5538 | — | — |
+| Release + RUST_LOG=debug | 5283 | 15149 | **2.87×** |
+
+### Summary
+
+Brand's incremental SVD delivers a consistent **~1.9× speedup at
+b=4** and **~2.9× speedup at b=16** (production config), scaling
+as expected with the $d/c$ ratio in the SVD kernel.  The speedup
+is stable across debug and release builds.
+
+## Consequences
+
+### Performance
+
+- **SVD kernel shrank from $(d \times c)$ to $(c \times c)$.**
+  For production configs this is 128×18 → 18×18, a measured
+  ~2.9× reduction in `evolve_subspace()` cost.
+
+- **Phase 1 projection is reused**, eliminating redundant
+  $O(d \cdot k \cdot b)$ matrix multiplications from Phase 2.
+
+- **Per-cell noise injection cost (ADR-S-015) drops
+  proportionally.**  At 100 noise rounds and ~2.9× speedup on the
+  dominant step, cell creation time drops significantly.
+
+- **A new thin QR is added** — $O(d \cdot b^2)$ per round.  This
+  is cheaper than the SVD it replaces and has lower constant
+  factors (direct Householder, no iteration).
+
+### Numerical equivalence
+
+Brand's incremental SVD is mathematically equivalent to the naïve
+approach — both compute the thin SVD of the same composite matrix
+$M$.  The difference is purely in how the computation is
+structured.
+
+The 14 oracle tests in `maths/tests/brand_vs_naive.rs` verify
+equivalence across a range of configurations:
+
+- Minimal dimensions, identity basis, rank-one, large batch
+- Saturated rank, large singular values, near-zero residual
+- Multi-step sequences (10 consecutive evolve calls)
+- Representative configs matching both benchmark profiles
+- Sorted/non-negative singular values, orthonormal output basis
+
+The debug oracle runs on **every** `evolve()` call in test builds,
+providing continuous regression coverage.
+
+### Complexity
+
+- The change is confined to `maths/` and the call site in
+  `observe()` (to pass `z` and `residual` instead of `x`).
+
+- No new dependencies.  Uses `faer::Mat::thin_svd()` for the small
+  kernel, and `faer::Mat::qr()` for the residual QR.
+
+- ~130 lines of new code (`brand_svd.rs`) alongside the existing
+  naïve implementation (~60 lines in `naive_svd.rs`).
+
+### Risks
+
+- **Rank growth beyond $b$:** When $k > b$, the kernel is
+  $(k{+}b) \times (k{+}b)$ which can grow up to $(\text{cap}{+}b)$.
+  At cap=16, b=16 this is 32×32 — still far smaller than 128×32.
+  The speedup is always at least $d/(k{+}b)$.
+
+- **QR numerical stability:** If the residual $Q$ is nearly zero
+  (new data lies almost entirely in the current subspace), the QR
+  may produce near-zero $R_\perp$ entries.  This is handled
+  naturally — the small singular values from those directions will
+  be discarded by rank adaptation (Phase 5).  No special-casing
+  needed.  The `equivalence_near_zero_residual` test validates
+  this case explicitly.
+
+## References
+
+- Brand, M. (2002). "Incremental Singular Value Decomposition of
+  Uncertain Data with Missing Values." *ECCV 2002.*
+- Brand, M. (2006). "Fast low-rank modifications of the thin
+  singular value decomposition." *Linear Algebra and its
+  Applications*, 415(1), 20–30.
+- Baker, C.G., Gallivan, K.A., Van Dooren, P. (2012). "Low-Rank
+  Incremental Methods for Computing Dominant Singular Subspaces."
+  *Linear Algebra and its Applications*, 436(8), 2866–2888.
diff --git a/packages/sentinel/adr/017-deferred-cell-warm-up.md b/packages/sentinel/adr/017-deferred-cell-warm-up.md
new file mode 100644
index 000000000..e90902d90
--- /dev/null
+++ b/packages/sentinel/adr/017-deferred-cell-warm-up.md
@@ -0,0 +1,119 @@
+# ADR-S-017: Deferred Cell Warm-Up
+
+**Status:** Implemented (synchronous fallback + background thread)
+**Date:** 2026-03-11
+**Revised:** 2026-03-13
+**Spec:** §ALGO S-18 (work variance and timing considerations)
+**Relates to:** [ADR-S-007](007-automatic-noise-injection.md) (automatic noise injection),
+[ADR-S-015](015-cell-creation-performance.md) (cell creation performance),
+[ADR-S-002](002-feed-forward-invariant.md) (feed-forward invariant),
+[ADR-S-005](005-deterministic-order-and-thread-safety.md) (deterministic order)
+
+## Context
+
+The sentinel's `ingest()` call has **variable latency**. Most calls
+perform only scoring (fast), but calls that trigger analysis set
+changes also run noise injection synchronously — up to hundreds of
+milliseconds per new cell (ADR-S-015). A batch-oriented network
+monitor that budgets $T$ ms per batch cannot tolerate multi-second
+stalls from cell creation bursts.
+
+## Decision
+
+**Noise injection is moved off the `ingest()` hot path.**
+
+New cells transition through a three-state lifecycle:
+
+```
+    created ──→ warming ──→ online ──→ (destroyed)
+                  │
+                  │  background thread
+                  │  highest g.sum first
+                  ▼
+            noise injection
+```
+
+1. **Created.** The analysis selector identifies a new cell. A
+   `SubspaceTracker` is allocated but receives no noise and no real
+   observations. The cell is enqueued into a staging area.
+
+2. **Warming.** A background thread works on whichever warming cell
+   has the highest `g.sum` (accumulated volume in the G-V Graph),
+   injecting noise batches according to the depth-tiered noise
+   schedule (ADR-S-015). If a higher-priority cell arrives, work
+   switches immediately — the previous cell retains partial progress.
+
+3. **Online.** Noise injection is complete. At the start of the next
+   `ingest()` call, the cell is promoted from the staging area into
+   the live `cells` map and begins receiving real observations.
+
+### Observation Routing During Warm-Up
+
+Observations destined for a warming cell are routed to the nearest
+**online** ancestor (or to the root if no closer ancestor is online).
+Scoring quality does not degrade — it stays at the coarser resolution
+until the child goes online.
+
+### Coordination During Warm-Up
+
+Warming cells do not activate coordination contexts. Coordination
+activates only at promotion, at which point the cell's baselines are
+converged. Each newly activated coordination context runs a cheap
+inline warm-up (§ALGO S-9.8) using Gamma-sampled synthetic score
+vectors derived from the participating cells' mature baselines.
+
+### No Slot Reservation
+
+Warming cells do not hold competitive slots. The analysis set contains
+only online cells. If a warming cell's G-node is evicted before warm-up
+completes, the partial work is discarded — the G-V Graph determined
+the interval no longer warrants a node.
+
+### Priority: Volume-First
+
+The priority rule `g.sum` produces root-first ordering as a
+consequence — shallow cells accumulate descendant volume, satisfying
+the dependency constraint (ancestors online before descendants)
+without encoding depth into the priority key.
+
+### Synchronous Fallback
+
+When `background_warming` is disabled, warm-up runs synchronously
+within `reconcile_analysis_set()`. This preserves deterministic
+single-threaded behaviour for testing.
+
+## Work Variance Bound (§ALGO S-18.4)
+
+With deferred warm-up, the per-call cost of `ingest()` is bounded by:
+
+$$O\!\Big(n(d_{\text{geo}} + h_V) \;+\; |\mathcal{A}^*| \cdot w_{\max} \cdot (k + b)^2\Big)$$
+
+on every call. Cell creation and noise injection contribute zero cost.
+The remaining call-to-call variance (batch size, analysis set size,
+rank) changes slowly relative to call frequency.
+
+## Timing Protection Is Out of Scope (§ALGO S-18.5)
+
+Deferred warm-up makes `ingest()` operationally predictable — bounded
+work per call with no structural spikes. It does **not** make
+`ingest()` constant-time. The remaining variance, though small, is
+observable to a sufficiently precise adversary. Adaptive timing pads
+and equalization are explicitly out of scope.
+
+## Consequences
+
+- `ingest()` has bounded, predictable work per call. Cell creation
+  and noise injection never stall the hot path.
+
+- A background thread (or synchronous fallback) is required for
+  warming. The interaction surface is minimal: a staging map with
+  atomic promotion at the top of each `ingest()` call.
+
+- ADR-S-007 (automatic noise injection) remains correct — injection
+  is still automatic and internal — but the trigger changes from
+  "inject synchronously at creation" to "enqueue for background
+  injection at creation."
+
+- ADR-S-015 (cell creation performance) is complementary. The
+  depth-tiered noise schedule determines how long background warming
+  takes per cell; the hot-path stall concern is resolved by this ADR.
diff --git a/packages/sentinel/adr/018-generic-domain-parameters.md b/packages/sentinel/adr/018-generic-domain-parameters.md
new file mode 100644
index 000000000..68ab88708
--- /dev/null
+++ b/packages/sentinel/adr/018-generic-domain-parameters.md
@@ -0,0 +1,372 @@
+# ADR-S-018: Generic Domain Parameters
+
+**Status:** Accepted
+**Date:** 2026-03-13
+**Spec:** §ALGO S-2.1 (domain), §ALGO S-2.4 (what sentinel owns),
+§ALGO S-3.2 (suffix encoding), §ALGO S-13.3 (G-V Graph config)
+**Supersedes:** [ADR-S-003](003-mudlark-integration.md) Part A
+(type parameters)
+**Relates to:** [ADR-S-003](003-mudlark-integration.md) (mudlark
+integration — Part B on Cargo features is unchanged),
+[ADR-S-002](002-feed-forward-invariant.md) (feed-forward invariant),
+mudlark [ADR-M-006](../../mudlark/adr/006-generic-parameters.md)
+(generic parameters),
+mudlark [ADR-M-009](../../mudlark/adr/009-trait-decomposition.md)
+(trait decomposition)
+
+## Context
+
+ADR-S-003 Part A hardcodes `GvGraph<u128, u64, 128>`: the sentinel
+owns a single concrete instantiation of the G-V Graph with 128-bit
+coordinates, `u64` counts, and full 128-bit domain width.
+
+Two developments challenge this:
+
+1. **Some hosts require N = 64.** A host that encodes observations
+   as `u64` cannot use a 128-bit domain without wasting 64 constant
+   suffix dimensions, a full SVD rank slot (25% of `max_rank = 4`),
+   and doubling per-tracker memory.
+
+2. **Different hosts may want different accumulator semantics.**
+   The sentinel exposes `graph()` to the host. With a hardcoded
+   `V = u64`, the host receives `&GvGraph<u128, u64, 128>` and can
+   call any method `u64` supports. But a host that wants a custom
+   accumulator — weighted observations, saturating counters, a
+   newtype with domain-specific overflow policy — cannot use the
+   sentinel at all. The sentinel should not be the bottleneck on
+   what the host can do with the graph it carries.
+
+Mudlark is already fully generic: `GvGraph<C: Coordinate, V:
+Accumulator, const N: u32>` works today for any conforming types.
+The sentinel is the only layer that locks the parameters.
+
+## Decision
+
+**`SpectralSentinel` becomes generic over `C`, `V`, and `N`, mirroring
+mudlark's `GvGraph<C, V, N>` triple.**
+
+```rust
+pub struct SpectralSentinel<C, V, const N: u32>
+where
+    C: Coordinate + CentredBitSource,
+    V: Inspectable + Attenuatable,
+{
+    graph: GvGraph<C, V, N>,
+    // ...
+}
+```
+
+### The three parameters
+
+| Parameter | Role in sentinel | Bound | Rationale |
+|-----------|-----------------|-------|-----------|
+| `C` | Coordinate type — spatial addressing, interval bounds, observation values | `Coordinate + CentredBitSource` | Mudlark routing + sentinel bit-extraction |
+| `V` | Accumulator type — importance accounting in the G-V Graph | `Inspectable + Attenuatable` | Sentinel calls `observe()`, `layers()`, `plateaus()`, `decay()` |
+| `N` | Domain bit-width — tree height, suffix dimensions | `u32` (const generic) | Controls `CentredBits` vector length and max depth |
+
+### Type aliases for common instantiations
+
+```rust
+pub type Sentinel128 = SpectralSentinel<u128, u64, 128>;
+pub type Sentinel64  = SpectralSentinel<u64, u64, 64>;
+```
+
+Existing consumers use `Sentinel128` or `Sentinel64` as
+appropriate for their domain width.
+
+---
+
+## Part A — Coordinate Parameter `C`
+
+### `CentredBitSource`: sentinel's bridge trait
+
+The sentinel must convert coordinate values to centred bit vectors
+for subspace analysis (§ALGO S-3.2). This requires bit-level access
+that `Coordinate` does not provide. Rather than modify mudlark's
+trait surface, the sentinel defines a sealed internal trait:
+
+```rust
+pub trait CentredBitSource: Coordinate {
+    fn to_centred_bits(&self, n: u32) -> CentredBits;
+}
+
+impl CentredBitSource for u64 {
+    fn to_centred_bits(&self, n: u32) -> CentredBits {
+        // Extract n bits: bit i → if (value >> (n-1-i)) & 1 { +0.5 } else { -0.5 }
+    }
+}
+
+impl CentredBitSource for u128 {
+    fn to_centred_bits(&self, n: u32) -> CentredBits {
+        // Same, with 128-bit shifts.
+    }
+}
+```
+
+This trait is public — external consumers implementing a custom
+coordinate type must provide an impl to use it with the sentinel.
+
+### `CentredBits`: fixed-size array with runtime length
+
+```rust
+pub struct CentredBits {
+    pub bits: [f64; 128],   // max-size stack buffer
+    pub len: usize,         // actual width = N
+}
+```
+
+The fixed array avoids heap allocation on the hot path. At N = 64,
+64 trailing slots are unused — 512 bytes wasted per instance, but
+`CentredBits` is short-lived (created per observation, consumed
+immediately by the tracker). The `suffix()` method respects `len`:
+`&self.bits[depth..self.len]`.
+
+### Sites that change
+
+Every `u128` in production code becomes `C`:
+
+- `CellState { start: C, end: C }` — interval bounds
+- `ingest(&[C])` — observation input
+- `CentredBits::from_u128(v)` → `C::to_centred_bits(N)`
+- `u128::MAX` → `C::domain_max(N)`
+- `128 - depth` → `N as usize - depth as usize`
+
+---
+
+## Part B — Accumulator Parameter `V`
+
+### Sentinel's relationship with `V`
+
+The sentinel is a **pass-through** for accumulator values. It:
+
+1. **Writes** unit deltas into the graph: `graph.observe(coord, delta)`
+2. **Reads** importance back: `total_sum()`, `gnode_info().own`, `gnode_info().sum`
+3. **Compares** importance: sorting by `PartialOrd` in analysis set selection
+4. **Stores** importance in internal types: `AnalysisEntry`, `WarmingCell`
+5. **Exposes** the graph: `graph() → &GvGraph<C, V, N>` — the host may call any method their `V` supports
+
+The sentinel never performs V-arithmetic itself (no `add`, `sub`,
+`zero` in sentinel logic). It shuttles V values between mudlark and
+its own bookkeeping/report types.
+
+### Bound: `V: Inspectable + Attenuatable`
+
+This is the minimum that makes the sentinel's mudlark API calls
+compile:
+
+| Sentinel operation | Mudlark method | Required bound |
+|---|---|---|
+| Feed observations | `observe()` | `Inspectable` |
+| Walk V-tree | `layers()` | `Inspectable` |
+| Read contour | `plateaus()` | `Inspectable` |
+| Temporal decay | `decay()` | `Inspectable + Attenuatable` |
+| Read totals | `total_sum()`, `gnode_info()` | `Accumulator` (implied by `Inspectable`) |
+
+The sentinel does **not** bound on `Weighable` or `Proratable`. But
+through `graph()`, the host can call `sample()` or `range_sum()` if
+their `V` satisfies those bounds — the sentinel carries `V` without
+constraining it beyond its own needs.
+
+### Why not lock `V = u64`?
+
+Locking `V = u64` is simpler (no type parameter on ~10 internal
+types) but closes the door:
+
+- A host using weighted observations (`observe(coord, weight)`) must
+  use a different accumulator. The sentinel's Δ = 1 policy
+  (ADR-S-002) governs *sentinel-initiated* observations, but a host
+  wrapping the sentinel could feed different deltas for its own
+  purposes.
+- A host wanting `graph().sample()` with a custom weighting scheme
+  needs `V: Weighable` — which `u64` satisfies, but a custom newtype
+  would not unless the sentinel propagates `V`.
+- Mudlark's `Config<V>` already requires `V` for `split_threshold`.
+  The sentinel's config would need a conversion boundary rather than
+  directly storing `V`.
+
+The type parameter tax is real (~10 types gain `<V>`) but justified:
+the sentinel should not be the bottleneck on what the host can do
+with the spatial substrate it carries.
+
+### Unit delta construction
+
+The sentinel observes with `Δ = 1` (ADR-S-002). With generic `V`,
+the unit delta is constructed via `Inspectable::from_f64(1.0)` and
+cached at construction time:
+
+```rust
+let unit_delta: V = V::from_f64(1.0);
+// ...
+self.graph.observe(value, unit_delta);
+```
+
+For all built-in types this is exact. For custom types,
+`from_f64(1.0)` must return the type's unit increment — this is an
+invariant of any sensible `Inspectable` implementation.
+
+### `SentinelConfig` carries `V`
+
+```rust
+pub struct SentinelConfig<V: Accumulator> {
+    pub split_threshold: V,
+    // ...~30 other fields unchanged (f64, u32, usize, bool)
+}
+```
+
+One field out of ~30 carries `V`. The cost is that consumers write
+`SentinelConfig<u64>` instead of `SentinelConfig`. This is
+acceptable — it matches mudlark's `Config<V>` pattern and makes
+the type-level contract explicit.
+
+---
+
+## Part C — Domain Width `N`
+
+### Propagation
+
+`N` propagates through `GvGraph<C, V, N>` and into every width
+computation:
+
+- Root cell width: `N`
+- Suffix width: `N - depth`
+- `CentredBits` vector length: `N`
+- Max depth: `N` (fits in `u8` for N ≤ 255)
+
+### `SubspaceTracker`: already generic
+
+The core SVD engine takes `dim: usize` at runtime construction.
+It contains no hardcoded `128`. No changes needed.
+
+### Config: domain-independent
+
+`SentinelConfig` has no N-dependent fields. All thresholds are
+`f64` or `u32`. The noise schedule uses depth tiers, not absolute
+widths. N propagates only through the type system, not through
+config values.
+
+---
+
+## Part D — Report Type Strategy
+
+### Coordinate in reports: generic `C`
+
+Report types that carry interval bounds become generic over `C`:
+
+```rust
+pub struct CellReport<C>       { pub start: C, pub end: C, … }
+pub struct CoordinationReport<C> { pub start: C, pub end: C, … }
+pub struct MemberScore<C>      { pub cell_start: C, pub cell_end: C, … }
+pub struct CellInspection<C>   { pub start: C, pub end: C, … }
+```
+
+### Accumulator in reports: erased to `f64`
+
+Importance values in reports are diagnostic — the host reads them
+for health monitoring, not for correctness-critical computation.
+Rather than propagating `V` through all report types (which would
+make `BatchReport<C, V>`), importance is erased at the report
+boundary via `Inspectable::to_f64_approx()`:
+
+```rust
+pub struct ContourSnapshot {
+    pub total_importance: f64,   // V::to_f64_approx()
+    // ...
+}
+
+pub struct AnalysisSetSummary {
+    pub importance_range: (f64, f64),
+    // ...
+}
+```
+
+This keeps `BatchReport<C>` — one generic parameter, not two. The
+precision loss (`u64` values above $2^{53}$ lose LSBs) is
+acceptable for diagnostic fields. A host needing exact importance
+can read `graph().total_sum()` directly, which returns `V`.
+
+**Exception:** `AnalysisEntry` (internal type) retains `V` for
+importance because the analysis set sorts by exact importance values:
+
+```rust
+pub struct AnalysisEntry<C, V: Accumulator> {
+    pub importance: V,
+    pub start: C,
+    pub end: C,
+    // ...
+}
+```
+
+This type is `pub(crate)` — it does not surface in the public API.
+
+---
+
+## Type Propagation Summary
+
+| Type | Parameters | Public? |
+|------|-----------|---------|
+| `SpectralSentinel<C, V, N>` | `C, V, N` | Yes |
+| `SentinelConfig<V>` | `V` | Yes |
+| `BatchReport<C>` | `C` | Yes |
+| `CellReport<C>` | `C` | Yes |
+| `CoordinationReport<C>` | `C` | Yes |
+| `MemberScore<C>` | `C` | Yes |
+| `CellInspection<C>` | `C` | Yes |
+| `ContourSnapshot` | — | Yes |
+| `AnalysisSetSummary` | — | Yes |
+| `HealthReport` | — | Yes |
+| `CellState<C>` | `C` | No (`pub(super)`) |
+| `AnalysisEntry<C, V>` | `C, V` | No (`pub(crate)`) |
+| `AnalysisSet<C, V>` | `C, V` | No (`pub(crate)`) |
+| `WarmingCell<C, V>` | `C, V` | No (`pub(crate)`) |
+| `StagingArea<C, V>` | `C, V` | No (`pub(crate)`) |
+| `CentredBits` | — | No |
+| `SubspaceTracker` | — | No |
+
+V appears in 5 types (1 public: `SentinelConfig`; 4 internal).
+
+---
+
+## Alternatives Considered
+
+| Alternative | Pros | Cons |
+|-------------|------|------|
+| **Lock `V = u64`, generic `C` and `N` only** | Fewer types carry V; simpler config | Closes door on custom accumulators; host cannot use `graph()` with custom V; diverges from mudlark pattern |
+| **Runtime `domain_bits: u32` instead of `const N`** | No const-generic propagation; tests keep `u128` | Wastes 8 bytes per coord at N=64; no compile-time enforcement; slight runtime overhead |
+| **`V` generic in reports (not erased)** | Exact importance in reports | `BatchReport<C, V>` — two generics on every consumer; ~5 more types carry V for diagnostic-only fields |
+| **`CentredBits` as `Vec<f64>`** | Exact sizing | Heap allocation per observation per cell on the hot path; dwarfed by SVD cost but unnecessary |
+| **Add `bit(index) → bool` to mudlark's `Coordinate`** | Clean bit extraction | Changes mudlark's trait surface for sentinel's benefit; `CentredBitSource` in sentinel is less invasive |
+| **`GvGraph<C, V, N>` exposed via trait object / type-erased wrapper** | Host doesn't see `V` | Loses monomorphisation; runtime dispatch on hot path; `GvGraph` is not object-safe |
+
+## Consequences
+
+- `SpectralSentinel<C, V, N>` replaces the concrete `SpectralSentinel`.
+  Type aliases `Sentinel128` and `Sentinel64` provide ergonomic
+  shorthand.
+
+- ADR-S-003 Part A is superseded. Part B (Cargo features) is
+  unchanged.
+
+- The sentinel's minimum bound on V (`Inspectable + Attenuatable`)
+  is strictly less than "all four sub-traits". A host with a custom
+  V that only implements these two bounds can use the sentinel; it
+  just cannot call `graph().sample()` or `graph().range_sum()` — the
+  compiler enforces this per-method, not per-struct.
+
+- `SentinelConfig<V>` carries V for `split_threshold`. All other
+  config fields remain concrete. Consumers write
+  `SentinelConfig<u64>` — one extra token.
+
+- Report types carry only `<C>`, not `<V>`. Importance values are
+  `f64` in reports. Hosts needing exact V read `graph().total_sum()`
+  or `graph().gnode_info().own` directly.
+
+- `CentredBitSource` is a public sentinel trait. Adding
+  support for a new coordinate type (e.g. `u32`, `f64`) requires
+  an impl in the downstream crate — a one-function addition.
+
+- Test code migrates gradually: existing `u128` tests can use
+  `Sentinel128`; new `u64` tests use `Sentinel64`. No test needs
+  both instantiations simultaneously.
+
+- The compiler catches every missed migration site: a `u128` where
+  `C` is expected is a type error, not a runtime bug.
diff --git a/packages/sentinel/adr/019-investment-set-terminology-and-reporting.md b/packages/sentinel/adr/019-investment-set-terminology-and-reporting.md
new file mode 100644
index 000000000..9ac4d1555
--- /dev/null
+++ b/packages/sentinel/adr/019-investment-set-terminology-and-reporting.md
@@ -0,0 +1,157 @@
+# ADR-S-019: Investment-Set Terminology and Reporting Alignment
+
+**Status:** Accepted
+**Date:** 2026-03-17
+**Spec:** §ALGO S-8 (analysis selector), §ALGO S-11.6 (warm-up
+lifecycle), §ALGO S-14.11–14.12 (reporting)
+**Supersedes:** [ADR-S-017](017-deferred-cell-warm-up.md) §"No Slot
+Reservation" (warming cells now hold investment slots)
+**Relates to:** [ADR-S-017](017-deferred-cell-warm-up.md) (deferred
+cell warm-up), [ADR-S-006](006-analysis-set-recomputation.md) (analysis
+set recomputation)
+
+## Context
+
+The algorithm specification (§ALGO S-8) was updated to introduce a
+three-level naming hierarchy for analysis cells:
+
+| Symbol | Name | Definition |
+|--------|------|------------|
+| $\mathcal{T}$ | Competitive targets | Top $K$ V-entries by importance within the depth cutoff |
+| $\mathcal{I}$ | Investment set | $\mathcal{T}$ closed under G-Tree ancestry; every member has an allocated tracker regardless of online status |
+| $\mathcal{A}$ | Producing competitive set | $\mathcal{T} \cap \text{Online}$ — competitive targets that are online and producing scores |
+| $\mathcal{A}^*$ | Producing full set | $\mathcal{I} \cap \text{Online}$ — all online members of the investment set |
+
+The key insight is that the **investment set** ($\mathcal{I}$) is the
+resource-commitment boundary — every member has an allocated tracker
+and is either online or warming — while the **producing sets**
+($\mathcal{A}$, $\mathcal{A}^*$) are the score-generation boundary.
+
+Previously (ADR-S-017 §"No Slot Reservation"), warming cells were
+described as not holding competitive slots and the analysis set as
+containing only online cells. The spec now formalises that warming
+cells hold **investment slots** in $\mathcal{I}$ — they have resources
+committed — while not holding **production slots** in $\mathcal{A}$.
+
+The spec also introduced the `g.sum` warm-up ordering (§ALGO S-11.6.2)
+and eager removal (§ALGO S-8.5) as named concepts, and requires three
+new reporting fields (§ALGO S-14.11–14.12).
+
+### What already works
+
+The implementation's behaviour is **functionally correct** for the new
+spec:
+
+- `AnalysisSet::recompute()` selects competitors and closes under
+  ancestry — computing $\mathcal{T}$ then $\mathcal{I}$.
+- The staging area holds warming cells with allocated trackers —
+  this *is* the investment commitment.
+- `retain_in_set()` implements eager removal — evicting warming and
+  in-flight cells that left the analysis set.
+- The background warming thread prioritises by `volume` (which *is*
+  `g.sum` — `info.sum.to_f64_approx()`).
+- Coordination contexts are demand-driven (lazy creation/pruning),
+  which is equivalent to the spec's explicit
+  activate/deactivate calls.
+
+### What needs to change
+
+1. **Reporting fields.** The spec defines three fields that do not
+   exist in the implementation:
+   - `HealthReport`: `investment_set_size`, `warming_trackers`,
+     `warming_competitive_targets`.
+   - `AnalysisSetSummary`: `investment_set_size`.
+
+2. **Synchronous drain ordering.** `drain_all_synchronous()` processes
+   cells in `BTreeMap` key order (ascending `GNodeId`). The spec
+   requires g.sum order (§ALGO S-11.6.2). In practice this is
+   immaterial — synchronous mode warms all cells to completion before
+   any participate — but the code should match the spec for
+   consistency and to avoid a latent bug if incremental synchronous
+   warm-up is ever added.
+
+3. **Source terminology.** Source comments, doc comments, and
+   `implementation.md` use the pre-update names ("analysis set",
+   "competitive set", "full analysis set"). These should be updated
+   to use the new vocabulary where appropriate.
+
+## Decision
+
+### 1. Add investment-aware reporting fields
+
+**`HealthReport`** gains three fields:
+
+```rust
+/// Total cells with allocated trackers (online + warming):
+/// $|\mathcal{I}|$.
+pub investment_set_size: usize,
+
+/// Members of $\mathcal{I}$ currently in the warm-up pipeline.
+pub warming_trackers: usize,
+
+/// Competitive targets ($\mathcal{T}$) not yet promoted to
+/// $\mathcal{A}$ — i.e. warming cells that are competitive
+/// targets, not ancestors.
+pub warming_competitive_targets: usize,
+```
+
+**`AnalysisSetSummary`** gains one field:
+
+```rust
+/// Total investment set size: $|\mathcal{I}|$ (online + warming).
+pub investment_set_size: usize,
+```
+
+These are derived from `cells.len()` (online) plus staging area
+counts. The staging area already tracks per-cell `is_competitive`,
+so distinguishing warming ancestors from warming competitive targets
+requires no new state.
+
+### 2. Fix synchronous drain ordering
+
+`drain_all_synchronous()` will sort cells by cached `volume`
+(descending) before draining, matching the background thread's
+g.sum priority rule.
+
+### 3. Align source terminology
+
+Source comments and `implementation.md` will be updated to use the
+new terms. No public API names are changed in this ADR — the Rust
+field names (`competitive_size`, `full_size`, etc.) remain stable.
+The doc comments on those fields will clarify the spec mapping.
+
+### 4. Accepted deviations
+
+Two implementation patterns differ from the spec's pseudocode but
+are behaviourally equivalent:
+
+- **Lazy coordination lifecycle.** The spec's Step 3 pseudocode
+  explicitly calls `ActivateCoordinationContexts` /
+  `DeactivateCoordinationContexts`. The implementation creates and
+  prunes coordination contexts on demand during
+  `propagate_coordination_from_root()`. This is equivalent because
+  contexts only affect output when their subtrees have contributing
+  cells.
+
+- **Full recompute vs. incremental reconciliation.** The spec's
+  pseudocode computes `OldInvestment \ NewInvestment` and
+  `NewInvestment \ OldInvestment` incrementally. The implementation
+  recomputes the full analysis set from scratch (ADR-S-006), then
+  reconciles the diff at the orchestrator level. The result is
+  identical.
+
+## Consequences
+
+- Hosts gain visibility into the investment/production distinction
+  through three new report fields.
+
+- The synchronous drain path matches the spec's ordering guarantee,
+  eliminating a class of potential bugs if synchronous warm-up is
+  ever made incremental.
+
+- ADR-S-017's "No Slot Reservation" section is superseded: warming
+  cells now formally hold investment slots in $\mathcal{I}$, though
+  not production slots in $\mathcal{A}$.
+
+- No public API breaks. Existing field names are preserved; new
+  fields are additive.
diff --git a/packages/sentinel/adr/020-clip-pressure-ewma-implementation-plan.md b/packages/sentinel/adr/020-clip-pressure-ewma-implementation-plan.md
new file mode 100644
index 000000000..9cf1288f5
--- /dev/null
+++ b/packages/sentinel/adr/020-clip-pressure-ewma-implementation-plan.md
@@ -0,0 +1,915 @@
+# ADR-S-020 Implementation Plan: Clip-Pressure EWMA
+
+Detailed, file-by-file implementation plan for the 11 gaps identified
+in [ADR-S-020](020-clip-pressure-ewma.md).
+
+---
+
+## Phase 0 — Preparation
+
+Before writing any code:
+
+1. **Read §ALGO S-6.1.1, §ALGO S-6.4, §ALGO S-13.1, §ALGO S-14.4,
+   §ALGO S-14.11** end-to-end to internalise the spec language.
+2. **Snapshot the existing test suite** — run
+   `CARGO_PROFILE_DEV_OPT_LEVEL=3 cargo test --package torrust-sentinel --all-targets --all-features`
+   and record baseline counts/timings. The warm-up convergence benchmark
+   (ADR-S-013) is especially important: the new formula must converge
+   no slower at η ≈ 1 and no wider at η ≈ 0.
+3. Create a **feature branch** `feat/clip-pressure-ewma`.
+
+---
+
+## Phase 1 — New Config Parameter
+
+**File: `src/config.rs`**
+
+### Step 1.1 — Add field to `SentinelConfig`
+
+Insert `clip_pressure_decay` after `clip_sigmas`:
+
+```rust
+/// Clip-pressure EWMA decay factor (λ_ρ).
+///
+/// Controls how quickly the per-axis clip-pressure estimate adapts
+/// to changing contamination levels.  Higher values = longer memory.
+///
+/// Half-life ≈ ln(2) / ln(1/λ_ρ):
+/// - `0.95` = ~14 batches (default, §ALGO S-13.1)
+/// - `0.99` = ~69 batches
+///
+/// Must be in `(0.0, 1.0)`.
+///
+/// Default: `0.95`
+pub clip_pressure_decay: f64,
+```
+
+### Step 1.2 — Default impl
+
+In `impl Default for SentinelConfig`:
+
+```rust
+clip_pressure_decay: 0.95,
+```
+
+### Step 1.3 — Validation
+
+Add a new `ConfigError` variant:
+
+```rust
+/// `clip_pressure_decay` must be in `(0.0, 1.0)`.
+ClipPressureDecayOutOfRange(f64),
+```
+
+Add a corresponding arm in `Display for ConfigError`:
+
+```rust
+Self::ClipPressureDecayOutOfRange(v) =>
+    write!(f, "clip_pressure_decay must be in (0.0, 1.0), got {v}"),
+```
+
+Add the validation check inside `validate()`, near the `clip_sigmas`
+check:
+
+```rust
+if self.clip_pressure_decay <= 0.0 || self.clip_pressure_decay >= 1.0 {
+    errors.push(ConfigError::ClipPressureDecayOutOfRange(self.clip_pressure_decay));
+}
+```
+
+### Step 1.4 — Test
+
+In `src/tests/config.rs`, add a test `rejects_clip_pressure_decay_out_of_range`
+paralleling `rejects_non_positive_clip_sigmas`:
+
+```rust
+#[test]
+fn rejects_clip_pressure_decay_out_of_range() {
+    let cfg = SentinelConfig::<f64> {
+        clip_pressure_decay: 0.0,
+        ..Default::default()
+    };
+    assert!(cfg.validate().is_err());
+
+    let cfg = SentinelConfig::<f64> {
+        clip_pressure_decay: 1.0,
+        ..Default::default()
+    };
+    assert!(cfg.validate().is_err());
+
+    let cfg = SentinelConfig::<f64> {
+        clip_pressure_decay: 0.95,
+        ..Default::default()
+    };
+    assert!(cfg.validate().is_ok());
+}
+```
+
+### Step 1.5 — Propagate to `SubspaceTracker`
+
+In `src/sentinel/tracker.rs`, add a field:
+
+```rust
+clip_pressure_decay: f64,
+```
+
+Initialise it from `cfg.clip_pressure_decay` in `SubspaceTracker::new()`.
+
+### Checkpoint
+
+`cargo test --package torrust-sentinel --all-targets --all-features` — all
+existing tests pass, new config test passes.
+
+---
+
+## Phase 2 — Per-Axis Clip-Pressure State
+
+**File: `src/sentinel/tracker.rs`**
+
+### Step 2.1 — Add field to `AxisBaseline`
+
+```rust
+struct AxisBaseline {
+    fast: EwmaStats,
+    cusum: CusumAccumulator,
+    /// Clip-pressure EWMA: ρ̄ ∈ [0, 1]  (§ALGO S-6.4).
+    clip_pressure: f64,
+}
+```
+
+### Step 2.2 — Initialise to `0.0`
+
+In `AxisBaseline::new()`:
+
+```rust
+Self {
+    fast: EwmaStats::new(fast_decay),
+    cusum: CusumAccumulator::new(slow_decay),
+    clip_pressure: 0.0,
+}
+```
+
+### Step 2.3 — Reset: `reset_cold()` zeros `clip_pressure`
+
+```rust
+fn reset_cold(&mut self) {
+    self.fast.reset_cold();
+    self.cusum.reset_cold();
+    self.clip_pressure = 0.0;
+}
+```
+
+### Checkpoint
+
+Compiles, all tests pass.  The field exists but is unused — no
+behavioural change yet.
+
+---
+
+## Phase 3 — Externalise Clipping from `EwmaStats`
+
+**File: `src/ewma.rs`**
+
+### Step 3.1 — Add `update_raw()` method
+
+This method accepts **pre-filtered** values — the caller has already
+applied the clip filter.  It performs the same EWMA update as
+`update()` but skips the internal ceiling computation:
+
+```rust
+/// Update the baseline with pre-filtered values.
+///
+/// The caller is responsible for outlier rejection.  This method
+/// unconditionally incorporates all values (including the cold→warm
+/// path).  Used by the baseline pipeline (§ALGO S-6.1.1) where
+/// clipping is externalised to `update_axis()`.
+pub fn update_raw(&mut self, normals: &[f64]) {
+    if normals.is_empty() {
+        return;
+    }
+
+    #[allow(clippy::cast_precision_loss)]
+    let new_mean = normals.iter().sum::<f64>() / normals.len() as f64;
+
+    if !self.warm {
+        self.mean = new_mean;
+        if normals.len() > 1 {
+            let var = mean_squared_deviation(normals, new_mean);
+            self.variance = var.max(1e-4);
+        }
+        self.warm = true;
+        return;
+    }
+
+    let alpha = 1.0 - self.decay;
+    self.mean = self.decay.mul_add(self.mean, alpha * new_mean);
+
+    if normals.len() > 1 {
+        let var = mean_squared_deviation(normals, new_mean).max(1e-4);
+        self.variance = self.decay.mul_add(self.variance, alpha * var);
+    }
+}
+```
+
+> **Why keep `update()` around?**  Direct callers in the test suite
+> (unit tests of `EwmaStats` itself) rely on the self-contained
+> `update(values, clip_sigmas)` signature.  Removing it is unnecessary
+> churn.
+
+### Step 3.2 — Add `ceiling()` helper
+
+Expose the clip ceiling so the caller can compute it once:
+
+```rust
+/// Compute the upper-tail clip ceiling: `mean + clip_sigmas · √variance`.
+///
+/// Returns `f64::INFINITY` when the baseline is cold (no meaningful
+/// ceiling can be defined — matches the cold-path bypass in `update()`).
+#[must_use]
+pub fn ceiling(&self, clip_sigmas: f64) -> f64 {
+    if self.warm {
+        clip_sigmas.mul_add(self.variance.sqrt(), self.mean)
+    } else {
+        f64::INFINITY
+    }
+}
+```
+
+### Step 3.3 — Tests
+
+Add a unit test verifying `update_raw()` produces the same result as
+`update()` when all values are within the clip ceiling:
+
+```rust
+#[test]
+fn update_raw_matches_update_when_no_clipping() {
+    let mut a = EwmaStats::new(0.95);
+    let mut b = EwmaStats::new(0.95);
+    let values = &[1.0, 1.2, 0.8, 1.1, 0.9];
+
+    a.update(values, 100.0); // clip_sigmas so high nothing is clipped
+    b.update_raw(values);
+
+    assert!((a.mean() - b.mean()).abs() < 1e-12);
+    assert!((a.variance() - b.variance()).abs() < 1e-12);
+}
+```
+
+### Checkpoint
+
+Compiles, all tests pass.  No behavioural change to the hot path yet.
+
+---
+
+## Phase 4 — Adjust `CusumAccumulator` for Pre-Filtered Samples
+
+**File: `src/sentinel/cusum.rs`**
+
+### Step 4.1 — Add `update_filtered()` method
+
+```rust
+/// Update the accumulator with **pre-filtered** samples.
+///
+/// Identical to [`update`](Self::update) except the slow EWMA
+/// receives pre-filtered values via `update_raw()` instead of
+/// applying its own clip filter.  The CUSUM gap still uses
+/// `raw_batch_mean` (the pre-clip mean of the full batch).
+///
+/// Used by the shared-filter pipeline (§ALGO S-6.1.1 step 5).
+pub fn update_filtered(
+    &mut self,
+    filtered: &[f64],
+    raw_batch_mean: f64,
+    allowance_sigmas: f64,
+    eps: f64,
+) {
+    let slow_mean = self.slow.mean();
+    let slow_std = (self.slow.variance() + eps).sqrt();
+    let allowance = allowance_sigmas * slow_std;
+
+    let gap = raw_batch_mean - slow_mean - allowance;
+    self.accumulator = (self.accumulator + gap).max(0.0);
+
+    self.slow.update_raw(filtered);
+    self.steps_since_reset += 1;
+}
+```
+
+> **`raw_batch_mean`** is the mean of the *full, unclipped* batch
+> — this is already computed in `update_axis()` as the variable
+> `mean` and passed to `cusum.update()` today. No new computation
+> needed.
+
+### Step 4.2 — Test
+
+Add a test verifying `update_filtered()` produces the same result as
+`update()` when no samples are clipped:
+
+```rust
+#[test]
+fn update_filtered_matches_update_no_clip() {
+    let mut a = CusumAccumulator::new(0.999);
+    let mut b = CusumAccumulator::new(0.999);
+    let scores = &[1.0, 1.1, 0.9, 1.05, 0.95];
+    let mean = scores.iter().sum::<f64>() / scores.len() as f64;
+
+    a.update(scores, mean, 0.5, 1e-6, 100.0);
+    b.update_filtered(scores, mean, 0.5, 1e-6);
+
+    assert!((a.snapshot().accumulator - b.snapshot().accumulator).abs() < 1e-12);
+}
+```
+
+### Checkpoint
+
+Compiles, all tests pass.
+
+---
+
+## Phase 5 — Unified Clip + Clip-Pressure in `update_axis()`
+
+**File: `src/sentinel/tracker.rs`**
+
+This is the **core change**.  The existing `update_axis()` delegates
+clipping to `EwmaStats::update()` and `CusumAccumulator::update()`,
+each applying their own independent filter.  After this step,
+`update_axis()` computes a **single shared clip filter** from the
+fast EWMA's ceiling, and both EWMAs receive the same retained set.
+
+### Step 5.1 — Change the caller: `observe()`
+
+Replace the single `effective_clip` variable with per-axis computation
+inside `update_axis()`.  Pass `clip_pressure_decay` and `clip_sigmas`
++ `eta` as inputs:
+
+```rust
+// In observe(), Phase 4 — replace the `effective_clip` block:
+let allowance = self.cusum_allowance_sigmas;
+let eta = self.noise_influence;
+let clip_sigmas = self.clip_sigmas;
+let eps = self.eps;
+let cp_decay = self.clip_pressure_decay;
+
+let novelty_dist = Self::update_axis(
+    &mut self.novelty_bl, &nov_scores, eps, allowance,
+    clip_sigmas, eta, cp_decay, true,
+);
+let displacement_dist = Self::update_axis(
+    &mut self.displacement_bl, &disp_scores, eps, allowance,
+    clip_sigmas, eta, cp_decay, true,
+);
+let surprise_dist = Self::update_axis(
+    &mut self.surprise_bl, &surp_scores, eps, allowance,
+    clip_sigmas, eta, cp_decay, true,
+);
+let coherence_dist = Self::update_axis(
+    &mut self.coherence_bl, &coh_scores, eps, allowance,
+    clip_sigmas, eta, cp_decay, k >= 2,
+);
+```
+
+### Step 5.2 — Rewrite `update_axis()`
+
+```rust
+/// Update one scoring axis: shared clip filter → fast EWMA → CUSUM.
+///
+/// §ALGO S-6.1.1 pipeline with clip-pressure EWMA (§ALGO S-6.4).
+fn update_axis(
+    bl: &mut AxisBaseline,
+    scores: &[f64],
+    eps: f64,
+    cusum_allowance: f64,
+    clip_sigmas: f64,
+    eta: f64,
+    clip_pressure_decay: f64,
+    evolve: bool,
+) -> ScoreDistribution {
+    // ── Raw batch statistics (pre-clip) ─────────────
+    let (min, max, sum) = scores
+        .iter()
+        .fold((f64::INFINITY, f64::NEG_INFINITY, 0.0_f64), |(mn, mx, s), &v| {
+            (mn.min(v), mx.max(v), s + v)
+        });
+
+    #[allow(clippy::cast_precision_loss)]
+    let mean = sum / scores.len() as f64;
+
+    // Z-scores computed *before* updating the fast baseline.
+    let max_z = bl.fast.z_score(max, eps);
+    let mean_z = bl.fast.z_score(mean, eps);
+    let baseline = bl.fast.snapshot();
+
+    if evolve {
+        // ── Per-axis effective clip (§ALGO S-6.4) ───
+        //
+        //   p = max(η, ρ̄)
+        //   n_σ_eff = n_σ · (1 + p / (1 − p + ε))
+        //
+        let p = eta.max(bl.clip_pressure);
+        let effective_clip = clip_sigmas * (1.0 + p / (1.0 - p + eps));
+
+        // ── Single shared clip filter ───────────────
+        // Computed against the fast EWMA's current baseline.
+        // Cold-path bypass: ceiling() returns +∞ when cold.
+        let ceiling = bl.fast.ceiling(effective_clip);
+
+        let retained: Vec<f64> = scores.iter().copied().filter(|&v| v < ceiling).collect();
+
+        // ── Update clip-pressure EWMA ───────────────
+        //   ρ_t = 1 − |retained| / |total|
+        //   ρ̄ = λ_ρ · ρ̄ + (1 − λ_ρ) · ρ_t
+        #[allow(clippy::cast_precision_loss)]
+        let rho_t = 1.0 - (retained.len() as f64 / scores.len() as f64);
+        let alpha = 1.0 - clip_pressure_decay;
+        bl.clip_pressure = clip_pressure_decay.mul_add(bl.clip_pressure, alpha * rho_t);
+
+        // ── Fast EWMA: receives retained samples ────
+        if retained.is_empty() {
+            // All outliers — learn nothing this round.
+            // clip_pressure was still updated above (it saw 100% clipping).
+        } else {
+            bl.fast.update_raw(&retained);
+        }
+
+        // ── CUSUM: receives retained samples, raw batch mean ──
+        if !retained.is_empty() {
+            bl.cusum.update_filtered(&retained, mean, cusum_allowance, eps);
+        }
+    }
+    let cusum = bl.cusum.snapshot();
+
+    ScoreDistribution {
+        min,
+        max,
+        mean,
+        max_z_score: max_z,
+        mean_z_score: mean_z,
+        baseline,
+        cusum,
+        clip_pressure: bl.clip_pressure,  // NEW — added in Phase 6
+    }
+}
+```
+
+### Step 5.3 — Verify formula equivalence
+
+The old formula was:
+
+```
+n_σ_eff = n_σ + n_σ · η / (1 − η + ε)
+        = n_σ · (1 + η / (1 − η + ε))
+```
+
+The new formula is identical when `ρ̄ = 0` (because `p = max(η, 0) = η`).
+This means **at ρ̄ = 0 the behaviour is bit-for-bit identical** to the
+old code for the η term.
+
+### Checkpoint
+
+At this point the convergence benchmark (ADR-S-013) should produce
+results negligibly different from before, since `clip_pressure` starts
+at 0 and no contamination is present in clean-traffic tests.
+
+Run:
+```
+CARGO_PROFILE_DEV_OPT_LEVEL=3 cargo test --package torrust-sentinel convergence --all-features
+```
+
+---
+
+## Phase 6 — Reporting: `ScoreDistribution::clip_pressure`
+
+**File: `src/report.rs`**
+
+### Step 6.1 — Add field to `ScoreDistribution`
+
+```rust
+pub struct ScoreDistribution {
+    pub min: f64,
+    pub max: f64,
+    pub mean: f64,
+    pub max_z_score: f64,
+    pub mean_z_score: f64,
+    pub baseline: BaselineSnapshot,
+    pub cusum: CusumSnapshot,
+    /// Current clip-pressure EWMA for this axis: ρ̄ ∈ [0, 1]  (§ALGO S-14.4).
+    pub clip_pressure: f64,
+}
+```
+
+### Step 6.2 — Fix all construction sites
+
+Every place that constructs a `ScoreDistribution` must now include
+`clip_pressure`.  The only production site is `update_axis()` (done
+in Phase 5).  Search for test/mock construction sites:
+
+```
+grep -rn "ScoreDistribution {" packages/sentinel/
+```
+
+Each mock/test site should set `clip_pressure: 0.0` (neutral).
+
+### Checkpoint
+
+Compiles, all tests pass with the new field.
+
+---
+
+## Phase 7 — Reporting: `HealthReport` Clip-Pressure Distribution
+
+**File: `src/report.rs`**
+
+### Step 7.1 — Add `ClipPressureDistribution` struct
+
+```rust
+/// Summary of clip-pressure EWMA values across active trackers (§ALGO S-14.11).
+#[derive(Debug, Clone, Copy)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+pub struct ClipPressureDistribution {
+    /// Minimum clip-pressure EWMA across active tracker axes.
+    pub min: f64,
+
+    /// Maximum clip-pressure EWMA across active tracker axes.
+    pub max: f64,
+
+    /// Mean clip-pressure EWMA across active tracker axes.
+    pub mean: f64,
+}
+```
+
+### Step 7.2 — Add field to `HealthReport`
+
+```rust
+/// Clip-pressure distribution across active trackers (§ALGO S-14.11).
+pub clip_pressure_distribution: ClipPressureDistribution,
+```
+
+### Step 7.3 — Expose clip-pressure from `SubspaceTracker`
+
+**File: `src/sentinel/tracker.rs`**
+
+Add a helper to extract the four per-axis `clip_pressure` values:
+
+```rust
+/// Per-axis clip-pressure EWMA values [novelty, displacement, surprise, coherence].
+pub(crate) fn clip_pressures(&self) -> [f64; 4] {
+    [
+        self.novelty_bl.clip_pressure,
+        self.displacement_bl.clip_pressure,
+        self.surprise_bl.clip_pressure,
+        self.coherence_bl.clip_pressure,
+    ]
+}
+```
+
+### Step 7.4 — Populate in `health()`
+
+**File: `src/sentinel/mod.rs`**
+
+In the `health()` method, alongside the existing rank/maturity/geometry
+loops, accumulate clip-pressure min/max/sum across all 4 axes of all
+active trackers:
+
+```rust
+let mut cp_min = f64::INFINITY;
+let mut cp_max = f64::NEG_INFINITY;
+let mut cp_sum = 0.0_f64;
+let mut cp_count = 0_u64;
+
+for cell in self.cells.values() {
+    for cp in cell.tracker.clip_pressures() {
+        cp_min = cp_min.min(cp);
+        cp_max = cp_max.max(cp);
+        cp_sum += cp;
+        cp_count += 1;
+    }
+}
+```
+
+Then in the `HealthReport` struct literal:
+
+```rust
+clip_pressure_distribution: ClipPressureDistribution {
+    min: if cp_count > 0 { cp_min } else { 0.0 },
+    max: if cp_count > 0 { cp_max } else { 0.0 },
+    mean: if cp_count > 0 { cp_sum / cp_count as f64 } else { 0.0 },
+},
+```
+
+Do the same for the early-return zero-trackers branch.
+
+### Step 7.5 — Coordination `health()` too
+
+If the coordination tier's `HealthReport` / `CoordinationHealth`
+should also report clip-pressure, repeat the same pattern.  Check
+whether §14.11 mandates it — if not, skip for now.
+
+### Checkpoint
+
+`cargo test --package torrust-sentinel --all-targets --all-features` passes.
+
+---
+
+## Phase 8 — Lifecycle Resets
+
+### Step 8.1 — Coherence rank-drop reset (already covered)
+
+`adapt_rank()` calls `self.coherence_bl.reset_cold()` when rank drops
+below 2.  Since Phase 2 added `clip_pressure = 0.0` to `reset_cold()`,
+**this gap is already closed**.  Verify with a quick read of
+`adapt_rank()`.
+
+### Step 8.2 — Warm-up completion reset (§ALGO S-11.4)
+
+**File: `src/sentinel/tracker.rs`**
+
+The spec says: when noise influence crosses the warm-up threshold
+(η goes below some value), zero all four axes' `clip_pressure`.
+
+Currently **there is no explicit warm-up-completion callback** in
+`SubspaceTracker`.  The transition from warming → production happens
+implicitly as η decays.  Two options:
+
+**Option A — Threshold check inside `update_maturity()`:**
+
+Add a check after updating `noise_influence`: if it just crossed below
+a threshold (e.g. 0.01), zero clip-pressure on all four axes.
+
+```rust
+fn update_maturity(&mut self, batch_size: usize, is_noise: bool) {
+    let old_eta = self.noise_influence;
+
+    // ... existing η update ...
+
+    // §ALGO S-11.4: when η crosses the warm-up threshold, zero
+    // clip-pressure to prevent warm-up contamination echoing into
+    // production scoring.
+    const WARMUP_THRESHOLD: f64 = 0.01;
+    if old_eta >= WARMUP_THRESHOLD && self.noise_influence < WARMUP_THRESHOLD {
+        self.novelty_bl.clip_pressure = 0.0;
+        self.displacement_bl.clip_pressure = 0.0;
+        self.surprise_bl.clip_pressure = 0.0;
+        self.coherence_bl.clip_pressure = 0.0;
+    }
+}
+```
+
+**Option B — Reset at noise injection completion:**
+
+The warm-up pipeline already calls `seed_cusum_slow_from_baselines()`
+then `reset_cusum()` after noise injection completes (in
+`warm_inline()` / the staging pipeline).  Add `reset_clip_pressure()`
+alongside:
+
+```rust
+pub fn reset_clip_pressure(&mut self) {
+    self.novelty_bl.clip_pressure = 0.0;
+    self.displacement_bl.clip_pressure = 0.0;
+    self.surprise_bl.clip_pressure = 0.0;
+    self.coherence_bl.clip_pressure = 0.0;
+}
+```
+
+Called from `warm_inline()` in `sentinel/mod.rs` after
+`cell.tracker.reset_cusum()`.
+
+**Recommendation:** Use **both**.  Option B handles the explicit
+noise-injection completion path.  Option A catches edge cases where
+η decays through real-traffic dilution alone (e.g. if noise was
+partially skipped).
+
+The warm-up threshold constant (`0.01`) should either:
+- Be defined as `const WARMUP_THRESHOLD: f64 = 0.01` in `tracker.rs`, or
+- Be configurable (a future config field).  For now, a constant is
+  fine — the spec doesn't parameterise it.
+
+### Checkpoint
+
+Write a test that injects noise, transitions to real traffic, and
+verifies `clip_pressure` is 0 after transition.
+
+---
+
+## Phase 9 — Memory Accounting Verification
+
+### Step 9.1 — Count the fields
+
+Each `AxisBaseline` now contains:
+- `fast: EwmaStats` → 3 fields: `mean`, `variance`, `decay` (+ `warm` bool, packed)
+  = effectively 3 f64s for accounting purposes (ignoring the bool/decay since they're
+  config, not per-axis learned state)
+  → For spec purposes: **2 floats** (mean, variance)
+- `cusum: CusumAccumulator` → slow EWMA (2 floats: mean, variance) + accumulator (1 float) + steps (1 u64)
+  → For spec purposes: **5 floats** (slow mean, slow variance, accumulator, steps counter,
+  decay — but decay is config not state)
+  → Actually counting learned state only: slow mean + slow variance + accumulator = **3 floats**
+- `clip_pressure: f64` → **1 float**
+
+Per-axis learned-state floats: fast\_mean(1) + fast\_var(1) + slow\_mean(1) + slow\_var(1) + cusum\_acc(1) + clip\_pressure(1) = **6 floats**.
+
+Wait — let me re-read the ADR: "Per-axis baseline size grows from 7 to 8 floats".
+Let me re-count the *current* state:
+- fast mean, fast variance = 2
+- slow mean, slow variance = 2
+- cusum accumulator = 1
+- cusum steps_since_reset (u64, counts as 1) = 1
+- warm (bool, but pad to 1) = ~1
+
+That's 7 depending on how you count.  With `clip_pressure` = **8**.
+4 axes × 8 = **32 floats**.
+
+### Step 9.2 — Update any doc comments
+
+If `SubspaceTracker` or `AxisBaseline` has doc comments referencing
+memory accounting, update them.  Check `docs/algorithm.md` §4.3 if
+it's in-repo.
+
+### No code change needed here — just verification.
+
+---
+
+## Phase 10 — Update Convergence Diagnostics
+
+**File: `src/tests/convergence_diagnostics.rs`**
+
+The convergence diagnostic test currently computes `eff_clip` as:
+
+```rust
+let eff_clip = cfg.clip_sigmas + cfg.clip_sigmas * eta / (1.0 - eta + cfg.eps);
+```
+
+Update to the new formula:
+
+```rust
+let p = eta.max(clip_pressure);
+let eff_clip = cfg.clip_sigmas * (1.0 + p / (1.0 - p + cfg.eps));
+```
+
+Since `clip_pressure` is per-axis, the diagnostic will need to read it
+from the tracker report (via `ScoreDistribution::clip_pressure`).
+
+Also update the column header in the diagnostic table to include `ρ̄`.
+
+---
+
+## Phase 11 — Integration Test: Contamination Self-Correction
+
+**File: `tests/` (new test file or extend `tests/spray_resistance.rs`)**
+
+This is the **acceptance test** that validates the core value
+proposition: sustained contamination in production (η ≈ 0) should widen
+the clip ceiling automatically.
+
+### Step 11.1 — Test outline
+
+```rust
+#[test]
+fn clip_pressure_widens_ceiling_under_contamination() {
+    // 1. Build a sentinel, inject noise, let it converge.
+    // 2. Feed 200+ batches of clean traffic (scores ~ Normal(1.0, 0.1)).
+    // 3. Assert clip_pressure ≈ 0 on all axes.
+    // 4. Feed 50 batches of contaminated traffic (scores ~ Normal(1.0, 0.1)
+    //    with 30% of samples replaced by 10.0 — well above 3σ ceiling).
+    // 5. Assert clip_pressure > 0.2 on at least one axis.
+    // 6. Assert the effective clip ceiling is wider than n_σ.
+    // 7. Resume 200 batches of clean traffic.
+    // 8. Assert clip_pressure decays back toward 0.
+}
+```
+
+### Step 11.2 — Test: η-only behaviour preserved
+
+```rust
+#[test]
+fn clip_pressure_zero_under_clean_traffic() {
+    // Feed clean traffic with no contamination.
+    // Assert clip_pressure stays ≈ 0 on all axes.
+    // Assert effective-clip behaviour matches the old η-only formula.
+}
+```
+
+### Step 11.3 — Test: warm-up reset clears clip-pressure
+
+```rust
+#[test]
+fn warm_up_completion_resets_clip_pressure() {
+    // Inject noise (which may cause high clipping during warm-up).
+    // Transition to real traffic.
+    // Assert all clip_pressure values are 0 after transition.
+}
+```
+
+---
+
+## Phase 12 — Cross-Cutting Fixups
+
+### Step 12.1 — Serde roundtrip
+
+If `ScoreDistribution` is `Serialize/Deserialize`, the new field is
+automatically included.  Run `tests/serde_roundtrip.rs` to confirm.
+
+### Step 12.2 — `AxisBaselineSnapshots`
+
+In `src/report.rs`, `AxisBaselineSnapshots` is used for convergence
+tests.  Consider adding per-axis `clip_pressure` fields if tests need
+them:
+
+```rust
+pub struct AxisBaselineSnapshots {
+    pub novelty: BaselineSnapshot,
+    pub displacement: BaselineSnapshot,
+    pub surprise: BaselineSnapshot,
+    pub coherence: BaselineSnapshot,
+    pub novelty_clip_pressure: f64,
+    pub displacement_clip_pressure: f64,
+    pub surprise_clip_pressure: f64,
+    pub coherence_clip_pressure: f64,
+}
+```
+
+Update `axis_baseline_snapshots()` in `tracker.rs` correspondingly.
+
+Alternatively, provide a separate `clip_pressures()` method (done in
+Phase 7.3) and keep `AxisBaselineSnapshots` unchanged.  Prefer the
+separate method unless tests need both in a single struct.
+
+### Step 12.3 — Grep for hardcoded clip formula
+
+```bash
+grep -rn 'clip_sigmas.*eta\|η.*clip' packages/sentinel/src/
+```
+
+Ensure no stale copies of the old `η/(1-η+ε)` formula remain in
+production code.  Test code / diagnostic comments referencing the old
+formula should be updated or annotated.
+
+### Step 12.4 — Doc tests
+
+```bash
+cargo test --package torrust-sentinel --doc --all-features
+```
+
+### Step 12.5 — No-default-features build
+
+```bash
+cargo check --package torrust-sentinel --no-default-features
+cargo test --package torrust-sentinel --no-default-features
+```
+
+---
+
+## Phase 13 — Final Validation
+
+### Step 13.1 — Full test suite (debug, optimised)
+
+```bash
+CARGO_PROFILE_DEV_OPT_LEVEL=3 cargo test --package torrust-sentinel --all-targets --all-features
+```
+
+### Step 13.2 — Release mode
+
+```bash
+cargo test --package torrust-sentinel --all-targets --all-features --release
+```
+
+### Step 13.3 — Clippy
+
+```bash
+cargo clippy --workspace --all-targets --all-features -- -D warnings
+```
+
+### Step 13.4 — Doc build
+
+```bash
+cargo doc --package torrust-sentinel --all-features --no-deps
+```
+
+### Step 13.5 — Benchmark comparison
+
+Run the convergence benchmark before and after, compare round counts.
+The warm-up convergence should be ≤ the old round count (formula is
+identical when ρ̄ = 0).
+
+---
+
+## Execution Order Summary
+
+| Phase | Gap(s) | Files touched | Risk |
+|-------|--------|---------------|------|
+| 1 | 2 | `config.rs`, `tests/config.rs` | Low — additive |
+| 2 | 1 | `tracker.rs` | Low — unused field |
+| 3 | 4 (partial) | `ewma.rs` | Low — new method, old preserved |
+| 4 | 6 (partial) | `cusum.rs` | Low — new method, old preserved |
+| 5 | 3, 4, 5, 6 | `tracker.rs` | **High** — core behavioural change |
+| 6 | 7 | `report.rs` | Medium — struct change, many construction sites |
+| 7 | 8 | `report.rs`, `mod.rs` | Medium — new reporting pipeline |
+| 8 | 9, 10 | `tracker.rs`, `mod.rs` | Medium — lifecycle logic |
+| 9 | 11 | docs only | Low — verification |
+| 10 | — | `tests/convergence_diagnostics.rs` | Low — test update |
+| 11 | — | `tests/` | Low — new tests |
+| 12 | — | various | Low — fixups |
+| 13 | — | — | Low — final validation |
+
+**Phase 5 is the critical path.**  Everything before it is additive and
+safe.  Everything after it is reporting/testing.  If Phase 5 needs to be
+reverted, Phases 1–4 can remain in the codebase harmlessly.
diff --git a/packages/sentinel/adr/020-clip-pressure-ewma.md b/packages/sentinel/adr/020-clip-pressure-ewma.md
new file mode 100644
index 000000000..a77a96648
--- /dev/null
+++ b/packages/sentinel/adr/020-clip-pressure-ewma.md
@@ -0,0 +1,276 @@
+# ADR-S-020: Clip-Pressure EWMA
+
+**Status:** Accepted
+**Date:** 2026-03-18
+**Spec:** §ALGO S-6.1.1 (baseline update pipeline), §ALGO S-6.4
+(clip-pressure dynamics), §ALGO S-13.1 ($\lambda_\rho$), §ALGO S-14.4
+(per-cell clip-pressure reporting), §ALGO S-14.11 (fleet-wide
+clip-pressure distribution)
+**Supersedes:** The effective-clip formula in ADR-S-013 §1a (graduated
+clip-exemption using η alone)
+**Relates to:** [ADR-S-013](013-warm-up-convergence-benchmark.md)
+(warm-up convergence benchmark),
+[ADR-S-007](007-automatic-noise-injection.md) (automatic noise
+injection)
+
+## Context
+
+The algorithm specification was amended to introduce a **clip-pressure
+EWMA** ($\bar{\rho}$) — a per-axis exponentially-weighted moving
+average of the batch clip ratio $\rho_t = (\text{clipped samples})
+/ (\text{total samples})$.
+
+### Problem
+
+The previous design (ADR-S-013 §1a) modulated the effective clip
+ceiling solely via the noise-influence parameter $\eta$:
+
+$$
+n_\sigma^{\text{eff}} = n_\sigma + n_\sigma \cdot \frac{\eta}{1 - \eta + \varepsilon}
+$$
+
+This works well during warm-up (where $\eta$ is large and organic
+clipping would create a positive feedback loop) but has a blind spot:
+**sustained high clipping in production** ($\eta \approx 0$) after a
+genuine regime change leaves the ceiling at $n_\sigma$ regardless of
+contamination level, and the baseline slowly poisons.
+
+### Solution in the spec
+
+A new per-axis state $\bar{\rho}_t$ tracks the fraction of clipped
+samples via an EWMA with decay $\lambda_\rho$ (default 0.95, half-life
+≈ 14 batches).  The effective-clip formula is now unified:
+
+$$
+p = \max(\eta,\; \bar{\rho}), \qquad
+n_\sigma^{\text{eff}} = n_\sigma \cdot \left(1 + \frac{p}{1 - p + \varepsilon}\right)
+$$
+
+When $\bar{\rho}$ is low (clean traffic), this collapses to the
+production ceiling.  When $\bar{\rho}$ is high (contamination), the
+ceiling widens automatically — precisely the same safety valve that
+$\eta$ provides during warm-up, but now reactive to ongoing conditions.
+
+The spec also consolidates clipping into a **single shared filter**
+computed against the fast EWMA's ceiling, applied once per batch.  The
+slow EWMA (CUSUM reference) and the fast EWMA both receive the same
+retained sample set, rather than each computing an independent clip
+filter.
+
+### What already works
+
+- **Pre-clip raw batch mean for CUSUM**: `update_axis()` computes the
+  raw `mean` from all scores and passes it to `cusum.update()` as
+  `batch_mean` — this is already the pre-clip mean required by
+  §6.1.1 step 5.
+
+- **Formula shape**: The current formula `n + n·η/(1-η+ε)` is
+  algebraically equivalent to `n·(1 + η/(1-η+ε))`, so the
+  multiplicative form in the new spec is the same shape — just with
+  $p$ replacing $\eta$.
+
+- **Coherence rank-drop reset**: `adapt_rank()` already calls
+  `coherence_bl.reset_cold()` when rank drops below 2 — adding
+  $\bar{\rho}$ reset is a one-line extension.
+
+### What needs to change
+
+The implementation has **11 gaps** between the current code and the
+amended spec:
+
+1. **New per-axis state.** `AxisBaseline` needs a `clip_pressure: f64`
+   field ($\bar{\rho}$), initialised to 0.0.
+
+2. **New config parameter.** `SentinelConfig` needs
+   `clip_pressure_decay: f64` ($\lambda_\rho$) with default 0.95 and
+   validation `0 < λ_ρ < 1`.
+
+3. **Unified modulation formula.** The effective-clip computation in
+   `observe()` must change from using $\eta$ alone to
+   $p = \max(\eta, \bar{\rho})$ per axis.
+
+4. **Single shared clip filter.** Clipping must move from inside
+   `EwmaStats::update()` (per-EWMA independent) to the caller
+   (`update_axis()`), computed once against the fast EWMA's ceiling
+   and applied to both EWMAs.
+
+5. **Clip ratio tracking.** The clip filter must report the clip ratio
+   $\rho_t = 1 - |\text{retained}| / |\text{total}|$ back to the
+   caller, so the caller can update $\bar{\rho}$.
+
+6. **CUSUM slow EWMA receives pre-filtered samples.** Since clipping
+   is externalised, `CusumAccumulator::update()` must accept
+   pre-filtered samples instead of re-clipping internally.
+
+7. **Report: `ScoreDistribution::clip_pressure`** (§14.4). New `f64`
+   field in `[0, 1]`.
+
+8. **Report: `HealthReport` clip-pressure distribution** (§14.11). New
+   summary field (min/max/mean across active trackers).
+
+9. **Coherence rank-drop reset.** `adapt_rank()` must also zero the
+   coherence axis's $\bar{\rho}$.
+
+10. **Warm-up completion reset** (§11.4). When noise influence crosses
+    the warm-up threshold, all four axes' $\bar{\rho}$ must be zeroed.
+
+11. **Baseline memory accounting.** Per-axis baseline size grows from
+    7 to 8 floats ($4 \times 8 = 32$ total), matching §4.3.
+
+## Decision
+
+### 1. Add clip-pressure state to `AxisBaseline`
+
+```rust
+struct AxisBaseline {
+    fast: EwmaStats,
+    cusum: CusumAccumulator,
+    /// Clip-pressure EWMA: ρ̄ ∈ [0, 1].
+    clip_pressure: f64,
+}
+```
+
+Initialised to `0.0`.  `reset_cold()` zeros it.
+
+### 2. Add config parameter
+
+```rust
+/// Clip-pressure EWMA decay factor (λ_ρ).
+///
+/// Controls how quickly the clip-pressure estimate responds to
+/// changing contamination levels.  Higher values = longer memory.
+///
+/// Default: `0.95` (half-life ≈ 14 batches).
+/// Must be in (0, 1).
+pub clip_pressure_decay: f64,
+```
+
+Default `0.95`.  Validated alongside `clip_sigmas`.
+
+### 3. Externalise clipping from `EwmaStats`
+
+`EwmaStats::update()` currently computes its own clip filter
+internally.  The method signature changes to accept pre-filtered
+values and skip its internal filter:
+
+**Option A — split into two methods:**
+- `update_raw(values)` — accepts pre-filtered values, updates
+  mean/variance unconditionally (no internal clipping).
+- `update(values, clip_sigmas)` — preserved for any callers that
+  still need self-contained clipping (backward compat).
+
+**Option B — move clip logic to caller entirely:**
+- `update()` drops its `clip_sigmas` parameter and always
+  trusts the caller to have filtered.
+
+We choose **Option A** for minimal disruption.  The test suite's
+direct `EwmaStats::update()` calls continue to work.  The hot path
+(`update_axis`) calls the new `update_raw()`.
+
+### 4. Single shared clip filter in `update_axis()`
+
+`update_axis()` gains the responsibility of:
+
+1. Computing the clip ceiling from the **fast EWMA**'s current
+   mean and variance: `ceiling = n_σ_eff · √var + mean`.
+2. Filtering: `retained = scores.filter(|&v| v < ceiling)`.
+3. Computing `ρ_t = 1 − retained.len() / scores.len()`.
+4. Updating $\bar{\rho}$: `ρ̄ = λ_ρ · ρ̄ + (1 − λ_ρ) · ρ_t`.
+5. Passing `&retained` to both `fast.update_raw()` and
+   `cusum.update_filtered()`.
+
+### 5. Adjust `CusumAccumulator` to accept pre-filtered samples
+
+`CusumAccumulator::update()` changes to accept pre-filtered samples
+(no internal re-clipping by the slow EWMA):
+
+```rust
+pub fn update_filtered(
+    &mut self,
+    filtered: &[f64],
+    raw_batch_mean: f64,
+    allowance_sigmas: f64,
+    eps: f64,
+) {
+    // Gap uses raw_batch_mean (pre-clip), as before.
+    let gap = raw_batch_mean - self.slow.mean() - allowance;
+    self.accumulator = (self.accumulator + gap).max(0.0);
+    // Slow EWMA receives the already-filtered samples.
+    self.slow.update_raw(filtered);
+    self.steps_since_reset += 1;
+}
+```
+
+The old `update()` can be deprecated or removed.
+
+### 6. Per-axis effective-clip computation
+
+The effective clip is now **per-axis** (each axis has its own
+$\bar{\rho}$).  `observe()` computes four separate effective-clip
+values, one per `update_axis()` call:
+
+```rust
+let p = eta.max(bl.clip_pressure);
+let effective_clip = clip_sigmas * (1.0 + p / (1.0 - p + eps));
+```
+
+This replaces the single `effective_clip` variable computed from
+$\eta$ alone.
+
+### 7. Reporting
+
+`ScoreDistribution` gains:
+
+```rust
+/// Current clip-pressure EWMA for this axis: ρ̄ ∈ [0, 1].
+pub clip_pressure: f64,
+```
+
+`HealthReport` gains a `ClipPressureDistribution`:
+
+```rust
+pub clip_pressure_distribution: ClipPressureDistribution,
+```
+
+with `min`, `max`, and `mean` across active tracker axes.
+
+### 8. Lifecycle resets
+
+- `AxisBaseline::reset_cold()` — zeros `clip_pressure`.
+- `adapt_rank()` — when coherence is destroyed, `reset_cold()`
+  already covers the new field.
+- Warm-up completion — when $\eta$ crosses the threshold, zero
+  all four axes' `clip_pressure` to prevent warm-up contamination
+  echoing into production scoring.
+
+### 9. Accepted deviations
+
+- **Cold-path bypass is preserved.** When an EWMA is cold (first
+  update), all values are accepted even under the new shared
+  filter.  This matches the spec's "skip entirely on first
+  update" (§6.1.1 Design Note 1).
+
+- **Formula equivalence.** The implementation may use either the
+  additive form `n + n·p/(1-p+ε)` or the multiplicative form
+  `n·(1 + p/(1-p+ε))` — they are algebraically identical.  The
+  choice is left to readability preference.
+
+## Consequences
+
+- The effective-clip ceiling becomes **self-correcting**: it widens
+  automatically under sustained contamination (high $\bar{\rho}$)
+  and tightens to $n_\sigma$ once the attack subsides.
+
+- `EwmaStats::update()` retains backward compatibility.  The new
+  `update_raw()` method is used only by the baseline pipeline.
+
+- Four new `f64` fields (one per axis) increase per-tracker
+  memory by 32 bytes — negligible at sentinel scale.
+
+- One new config parameter (`clip_pressure_decay`).  Hosts that
+  don't set it get the default $\lambda_\rho = 0.95$.
+
+- ADR-S-013 §1a's graduated clip-exemption formula is superseded:
+  the unified $\max(\eta, \bar{\rho})$ formula subsumes the pure-η
+  behaviour as a special case (when $\bar{\rho} = 0$, $p = \eta$
+  exactly).
diff --git a/packages/sentinel/adr/021-ewma-mean-centred-variance.md b/packages/sentinel/adr/021-ewma-mean-centred-variance.md
new file mode 100644
index 000000000..cd4524ed5
--- /dev/null
+++ b/packages/sentinel/adr/021-ewma-mean-centred-variance.md
@@ -0,0 +1,325 @@
+# ADR-S-021: EWMA-Mean-Centred Latent Variance
+
+**Status:** Implemented
+**Date:** 2026-03-24
+**Spec:** §ALGO S-4.2 (Phase 3 — Evolve Latent Distribution),
+§ALGO S-5.4 (surprise scoring), §ALGO S-11.2 (cold-start latent
+seeding), §ALGO S-Appendix A (convergence methodology)
+**Relates to:** [ADR-S-013](013-warm-up-convergence-benchmark.md)
+(warm-up convergence benchmark — introduced the cold→warm fix this
+ADR amends)
+
+## Context
+
+The algorithm specification (§ALGO S-4.2, Phase 3) has been amended
+to replace the **within-batch population variance** estimator for
+$\nu^{(z)}$ with an **EWMA-mean-centred** estimator.  The
+implementation in `SubspaceTracker::evolve_latent()` still uses the
+old formula.
+
+### Problem
+
+The within-batch population variance
+$\operatorname{Var}(Z_{:,j}) = \frac{1}{b}\sum_i (z_{ij} - \bar{Z}_j)^2$
+has a systematic negative bias of $\frac{b-1}{b}$ relative to the
+surprise numerator's expected value:
+
+| Batch size $b$ | Bias          | Effect                                              |
+| -------------- | ------------- | --------------------------------------------------- |
+| 1              | $-100\%$      | Variance erodes to $\varepsilon$; surprise → $10^5$ |
+| 2              | $-50\%$       | Severe underestimate; surprise doubled               |
+| 4              | $-25\%$       | Material; surprise inflated by 33%                   |
+| 16             | $-6.25\%$     | Significant at precision targets                     |
+| 64+            | $< -1.6\%$    | Negligible                                           |
+
+The surprise score divides by $\nu^{(z)}_j + \varepsilon$, so
+the underestimate inflates surprise scores.  At $b = 1$ (which
+occurs at per-cell trackers receiving one observation per ingestion
+cycle), the variance collapses to zero every batch, the EWMA decays
+toward $\varepsilon$, and per-dimension surprise contributions reach
+$O(10^5)$.  At $b = 2$ (coordination trackers with two contributing
+cells), the $-50\%$ bias produces a sustained $2\times$ surprise
+overestimate.
+
+This is not a hypothetical concern in practice: a host that ingests
+**one observation per call** ($b = 1$ always at the root tracker)
+operates at the worst-case batch size for this bias.
+
+### Solution in the spec
+
+The amended formula computes deviations from the **EWMA mean**
+$\mu^{(z)}_j$ rather than the batch mean $\bar{Z}_j$:
+
+**Subsequent batches ($t > 0$):**
+
+$$\nu^{(z)}_j \leftarrow \lambda\,\nu^{(z)}_j + \alpha \cdot \max\!\left(\frac{1}{b}\sum_{i=1}^{b}(z_{ij} - \mu^{(z)}_j)^2,\;\varepsilon\right)$$
+
+**First batch ($t = 0$):**
+
+$$\nu^{(z)}_j \leftarrow \max\!\left(\frac{1}{b}\sum_{i=1}^{b} z_{ij}^2,\;\varepsilon\right)$$
+
+(using the pre-allocated $\mu^{(z)} = \mathbf{0}$ as centring
+reference, which makes the $t = 0$ formula a special case of the
+$t > 0$ formula).
+
+The spec also mandates:
+
+1. **Update order**: $\nu^{(z)}$ first (against pre-update $\mu$),
+   then $\mu^{(z)}$, then $\Gamma$.  Variance sees the same $\mu$
+   that scoring (Phase 1) used.
+
+2. **Runtime floor**: $\nu^{(z)}_j \leftarrow \max(\nu^{(z)}_j, 10^{-2})$
+   after every update (including $t = 0$ seeding).  Defence-in-depth
+   against degenerate streams; caps per-dimension surprise at 100.
+
+### Why the EWMA-mean-centred formula eliminates the bias
+
+The within-batch component contributes $\frac{b-1}{b}\sigma^2$
+(same as the old formula).  The batch-mean-vs.-EWMA-mean component
+contributes $\frac{\sigma^2}{b}$.  They sum to $\sigma^2$ regardless
+of $b$ — exact cancellation of the $\frac{b-1}{b}$ bias.
+
+The residual bias is
+$\operatorname{Var}(\mu^{(z)}_j) \approx \frac{\alpha}{b(1+\lambda)}\sigma^2$
+— positive (safe direction: dampens surprise), small, worst-case
+$+0.5\%$ at $b = 1$, $\lambda = 0.99$.
+
+### Trade-offs accepted
+
+- **Coupling to $\mu^{(z)}$**: stale $\mu$ inflates $\nu$,
+  dampening surprise during regime transitions.  This is the safe
+  direction.  Displacement is the primary mean-shift detector;
+  surprise's role is distributional shape anomalies (preserved).
+
+- **Basis rotation sensitivity**: after Phase 2 rotates the basis,
+  projections are on the new basis while $\mu$ reflects the old.
+  $(z - \mu)^2$ inflates, $\nu$ inflates, surprise dampens.
+  Transient, $O(t_{1/2})$ batches.  The old formula was invariant
+  (re-centring on $\bar{Z}_j$ subtracted out any rotation-induced
+  mean shift).
+
+## Decision
+
+Implement the amended Phase 3 in `SubspaceTracker::evolve_latent()`
+and update impacted tests.
+
+### What already works
+
+- **Surprise scoring formula** (Phase 1): `(z_ij - lat_mean[j])² /
+  (lat_var[j] + eps)` — already uses lat_mean as its centring
+  reference.  No change needed.
+
+- **Pre-allocated initial values**: `lat_mean = 0.0`, `lat_var = 1.0`,
+  `cross_corr = 0.0` — unchanged.
+
+- **Cross-correlation ($\Gamma$) update**: uses raw second moments
+  $z_{ij} \cdot z_{il}$, not centred products.  No change needed.
+
+- **Rank-change behaviour**: pre-allocated entries at capacity,
+  EWMA loops iterate `0..k`.  No change needed.
+
+### What needs to change
+
+The implementation has **5 gaps** between the current code and the
+amended spec:
+
+1. **Variance formula (subsequent batches).** In
+   `evolve_latent()`, the `col_var` computation currently centres
+   on the batch mean:
+
+   ```rust
+   let mut col_var = 0.0;
+   for i in 0..b {
+       let d = z[(i, j)] - col_mean;
+       col_var += d * d;
+   }
+   col_var /= b_f;
+   ```
+
+   Must change to centre on `self.lat_mean[j]` (the pre-update
+   EWMA mean):
+
+   ```rust
+   let mut col_var = 0.0;
+   for i in 0..b {
+       let d = z[(i, j)] - self.lat_mean[j];
+       col_var += d * d;
+   }
+   col_var /= b_f;
+   ```
+
+2. **Variance formula (first batch).** The cold path currently
+   uses within-batch variance centred on batch mean.  Must change
+   to centre on the pre-allocated `lat_mean[j]` (which is 0.0 at
+   $t = 0$):
+
+   ```rust
+   // Before (centres on batch mean col_mean):
+   self.lat_var[j] = col_var.max(eps);
+   // After (col_var already computed against lat_mean[j] = 0.0):
+   self.lat_var[j] = col_var.max(eps);
+   ```
+
+   The seeded value changes from the within-batch population
+   variance to $\frac{1}{b}\sum z_{ij}^2$.  The code change is
+   in the `col_var` computation (gap 1 handles both paths since
+   the loop uses the same formula).
+
+3. **Update order.** The current code computes mean and variance
+   in a single loop, updating both in the same branch:
+
+   ```rust
+   if cold {
+       self.lat_mean[j] = col_mean;
+       self.lat_var[j] = col_var.max(eps);
+   } else {
+       self.lat_mean[j] = lam.mul_add(self.lat_mean[j], alpha * col_mean);
+       self.lat_var[j] = lam.mul_add(self.lat_var[j], alpha * col_var.max(eps));
+   }
+   ```
+
+   Must reorder: update variance first (against pre-update mean),
+   then update mean.  Because `col_var` is now computed against
+   `self.lat_mean[j]` (read before any mutation), the natural
+   order is:
+
+   ```rust
+   if cold {
+       self.lat_var[j] = col_var.max(eps);
+       self.lat_mean[j] = col_mean;
+   } else {
+       self.lat_var[j] = lam.mul_add(self.lat_var[j], alpha * col_var.max(eps));
+       self.lat_mean[j] = lam.mul_add(self.lat_mean[j], alpha * col_mean);
+   }
+   ```
+
+   This is a semantic change: the old order was
+   mean-then-variance; the new order is variance-then-mean.
+   Because `col_var` is now pre-computed against the pre-update
+   `lat_mean[j]`, the reorder ensures the EWMA variance input
+   uses the same reference as the surprise numerator.
+
+4. **Runtime floor.** After the per-dimension loop (both cold and
+   non-cold paths), clamp:
+
+   ```rust
+   for j in 0..k {
+       self.lat_var[j] = self.lat_var[j].max(1e-2);
+   }
+   ```
+
+   This is a new addition.  The floor is $25\times$ below the
+   null-hypothesis value of $0.25$ and should never bind under
+   correct operation.  It caps per-dimension surprise at 100.
+
+5. **Test expectations.** Several existing tests assert against
+   the old formula's behaviour:
+
+   - `latent_variance_reaches_steady_state` — steady-state
+     $\nu^{(z)}$ values will change (EWMA-mean-centred is
+     slightly higher than within-batch at $b = 4$).  The
+     assertion `v < 0.5` should still hold, but may need
+     loosening if steady-state is higher.
+
+   - `cold_warm_eliminates_surprise_nonstationarity` — the rise
+     factor should improve (the new formula eliminates the
+     $b$-dependent bias that contributed to the transient).
+
+   - `subspace_evolution_does_not_dominate_latvar_transient` —
+     the tolerance `diff_pct < 50.0` should still hold; verify.
+
+   - `convergence_ewma::ewma_variance_convergence` — tests the
+     `EwmaStats` type directly; unaffected (the EWMA machinery
+     itself is unchanged).
+
+   - `convergence_clipping` tests — EWMA variance assertions
+     on the surprise baseline; should still hold but verify
+     numerically.
+
+### New tests
+
+| Test                                                | Validates                                                                   |
+| --------------------------------------------------- | --------------------------------------------------------------------------- |
+| `b1_surprise_bounded`                               | At $b = 1$, surprise scores stay bounded (no $O(10^5)$ explosion)           |
+| `b1_latent_variance_stable`                         | At $b = 1$, `lat_var` converges to $\approx 0.25$, not $\varepsilon$        |
+| `b2_no_systematic_surprise_inflation`               | At $b = 2$, surprise ratio near 1.0 (no 2× inflation)                      |
+| `runtime_floor_prevents_degenerate_collapse`        | Constant-observation stream: `lat_var` ≥ $10^{-2}$                          |
+| `update_order_variance_before_mean`                 | Variance update uses pre-update mean (white-box: instrument `lat_mean` read) |
+| `batch_size_invariant_surprise_ratio`               | Surprise ratio $\approx 1.0$ at $b \in \{1, 2, 4, 16, 64\}$ (self-consistency) |
+
+### Impact on hosts
+
+The change is transparent to consumers.  Hosts read
+`BatchReport` fields (z-scores, CUSUM accumulators, maturity) via
+the public API.  The internal formula change affects
+the *values* of those fields but not their types or semantics.
+
+Behavioural effects for hosts operating at small batch sizes:
+
+- **$b = 1$:** Surprise scores drop from potentially
+  $O(10^5)$ to $O(1)$.  Any confidence quality factor derived
+  from surprise will be dramatically more stable during warm-up
+  and during production when the distribution is well-behaved.
+
+- **Confidence convergence:** Faster.  The $4×$ surprise
+  inflation at $b = 1$ under the old formula delayed baseline
+  settling.  The new formula's self-consistency property means
+  surprise starts near $1.0$ immediately.
+
+- **CUSUM sensitivity:** Unchanged in steady state (the CUSUM
+  reference tracks the same signal).  During transitions, surprise
+  CUSUM drift is shorter-lived (the $\nu$ co-adaptation described
+  in §ALGO S-4.2 dampens sustained elevation).
+
+No consumer code changes are required.  The surprise formula
+references $\nu^{(z)}_j + \varepsilon$ in the
+denominator — unchanged.
+
+### Summary of implementation steps
+
+1. Modify `evolve_latent()` in
+   [tracker.rs](../src/sentinel/tracker.rs):
+   - Change `col_var` computation to centre on `self.lat_mean[j]`
+     instead of `col_mean`.
+   - Reorder assignments: variance before mean.
+   - Add runtime floor loop after per-dimension updates.
+
+2. Add new tests in a new test module
+   `src/tests/variance_formula.rs` (or extend
+   `convergence_noise.rs`) covering $b = 1$, $b = 2$, runtime
+   floor, update order, and batch-size-invariant surprise ratio.
+
+3. Verify and adjust existing test expectations as needed
+   (tolerance bounds only; no logic changes expected).
+
+4. Run full `--package sentinel` test suite in both debug and
+   release modes.
+
+5. Run `--workspace` tests to confirm no downstream breakage.
+
+## Consequences
+
+- **Surprise scores become batch-size-invariant.**  The expected
+  surprise ratio is $1 + O(\alpha^2)$ regardless of $b$.  This
+  eliminates a class of false positives at small batch sizes and
+  a class of false negatives at large batch sizes.
+
+- **Hosts at $b = 1$ stabilise.**  The old formula produced
+  catastrophic surprise inflation at this batch size; the new
+  formula produces correctly-scaled scores from the first batch.
+
+- **Regime-transition behaviour changes.**  Surprise spikes after
+  mean shifts are shorter-lived.  The first batch fires at full
+  strength; subsequent batches' $\nu$ co-adapts, progressively
+  dampening the ratio.  Displacement inherits the primary
+  detection role during transitions.
+
+- **Convergence benchmarks (ADR-S-013) may need updating.**
+  Convergence times at $b_{\text{noise}} = 4$ may improve
+  (the $-25\%$ bias that contributed to displacement bimodality
+  is eliminated).  At $b_{\text{noise}} \geq 16$, changes are
+  negligible.
+
+- **Runtime floor adds a hard safety bound.**  Under no
+  circumstances can per-dimension surprise exceed 100.  This
+  replaces the $\varepsilon$-floored worst case of $10^5$.
diff --git a/packages/sentinel/benches/sentinel.rs b/packages/sentinel/benches/sentinel.rs
new file mode 100644
index 000000000..ee5b7e188
--- /dev/null
+++ b/packages/sentinel/benches/sentinel.rs
@@ -0,0 +1,475 @@
+//! Criterion benchmarks for the Spectral Sentinel.
+//!
+//! Run with:
+//!
+//! ```sh
+//! cargo bench -p torrust-sentinel
+//! ```
+
+use std::hint::black_box;
+
+use criterion::{BenchmarkId, Criterion, Throughput, criterion_group, criterion_main};
+use torrust_sentinel::{CentredBits, Sentinel128, SentinelConfig};
+
+// ─── Helpers ────────────────────────────────────────────────
+
+/// Lightweight config for micro-benchmarks: small rank, small k.
+fn bench_config() -> SentinelConfig<u64> {
+    SentinelConfig::<u64> {
+        max_rank: 4,
+        forgetting_factor: 0.95,
+        rank_update_interval: 10,
+        analysis_k: 16,
+        analysis_depth_cutoff: 6,
+        energy_threshold: 0.90,
+        eps: 1e-6,
+        per_sample_scores: false,
+        cusum_allowance_sigmas: 0.5,
+        cusum_slow_decay: 0.999,
+        cusum_coord_slow_decay: 0.999,
+        clip_sigmas: 3.0,
+        clip_pressure_decay: 0.95,
+        split_threshold: 100,
+        d_create: 3,
+        d_evict: 6,
+        budget: 100_000,
+        noise_schedule: torrust_sentinel::NoiseSchedule::Explicit(vec![5]),
+        noise_batch_size: 4,
+        noise_seed: Some(42),
+        svd_strategy: torrust_sentinel::SvdStrategy::Brand,
+        background_warming: false,
+    }
+}
+
+/// Realistic config with higher rank and larger analysis set.
+fn realistic_config() -> SentinelConfig<u64> {
+    SentinelConfig::<u64> {
+        max_rank: 16,
+        forgetting_factor: 0.99,
+        rank_update_interval: 100,
+        analysis_k: 1024,
+        analysis_depth_cutoff: 6,
+        energy_threshold: 0.90,
+        eps: 1e-6,
+        per_sample_scores: false,
+        cusum_allowance_sigmas: 0.5,
+        cusum_slow_decay: 0.999,
+        cusum_coord_slow_decay: 0.999,
+        clip_sigmas: 3.0,
+        clip_pressure_decay: 0.95,
+        split_threshold: 100,
+        d_create: 3,
+        d_evict: 6,
+        budget: 100_000,
+        noise_schedule: torrust_sentinel::NoiseSchedule::Explicit(vec![50]),
+        noise_batch_size: 16,
+        noise_seed: Some(42),
+        svd_strategy: torrust_sentinel::SvdStrategy::Brand,
+        background_warming: false,
+    }
+}
+
+/// Generate `count` sequential values in a single narrow range.
+fn single_range_values(count: usize) -> Vec<u128> {
+    (0..count).map(|i| (0xAB_u128 << 120) | (i as u128 + 1)).collect()
+}
+
+/// Generate `count` values spread across many leading-nibble ranges.
+fn multi_range_values(count: usize) -> Vec<u128> {
+    (0..count)
+        .map(|i| {
+            let nibble = (i % 16) as u128;
+            (nibble << 124) | (i as u128 + 1)
+        })
+        .collect()
+}
+
+/// Create a warmed sentinel ready for steady-state benchmarking.
+fn warmed_sentinel(cfg: &SentinelConfig<u64>) -> Sentinel128 {
+    let mut s = Sentinel128::new(cfg.clone()).unwrap();
+
+    // Seed cells by ingesting diverse values.
+    let seed: Vec<u128> = (0..4_u128)
+        .flat_map(|nibble| (0..4_u128).map(move |i| (nibble << 124) | (i + 1)))
+        .collect();
+    s.ingest(&seed);
+
+    // Noise is auto-injected at construction (§ALGO S-11).
+
+    // One real batch to enter the warm code path.
+    let batch: Vec<u128> = (0..4_u128)
+        .flat_map(|nibble| (0..4_u128).map(move |i| (nibble << 124) | (i + 100)))
+        .collect();
+    s.ingest(&batch);
+
+    s
+}
+
+// ─── Observation encoding ───────────────────────────────────
+
+fn bench_centred_bits(c: &mut Criterion) {
+    c.bench_function("CentredBits::from_u128", |b| {
+        b.iter(|| CentredBits::from_u128(black_box(0xDEAD_BEEF_CAFE_BABE_1234_5678_9ABC_DEF0)));
+    });
+}
+
+// ─── Ingest (core hot path) ─────────────────────────────────
+
+fn bench_ingest_cold(c: &mut Criterion) {
+    let mut group = c.benchmark_group("ingest_cold");
+
+    for batch_size in [1, 8, 32] {
+        let values = single_range_values(batch_size);
+        group.throughput(Throughput::Elements(batch_size as u64));
+        group.bench_with_input(BenchmarkId::new("single_range", batch_size), &values, |b, vals| {
+            b.iter_with_setup(
+                || Sentinel128::new(bench_config()).unwrap(),
+                |mut s| s.ingest(black_box(vals)),
+            );
+        });
+    }
+
+    group.finish();
+}
+
+fn bench_ingest_warm(c: &mut Criterion) {
+    let mut group = c.benchmark_group("ingest_warm");
+    let cfg = bench_config();
+
+    for batch_size in [1, 8, 32, 64] {
+        let values = single_range_values(batch_size);
+        group.throughput(Throughput::Elements(batch_size as u64));
+        group.bench_with_input(BenchmarkId::new("single_range", batch_size), &values, |b, vals| {
+            b.iter_with_setup(|| warmed_sentinel(&cfg), |mut s| s.ingest(black_box(vals)));
+        });
+    }
+
+    // Multi-range: triggers coordination tier.
+    for batch_size in [16, 64] {
+        let values = multi_range_values(batch_size);
+        group.throughput(Throughput::Elements(batch_size as u64));
+        group.bench_with_input(BenchmarkId::new("multi_range", batch_size), &values, |b, vals| {
+            b.iter_with_setup(|| warmed_sentinel(&cfg), |mut s| s.ingest(black_box(vals)));
+        });
+    }
+
+    group.finish();
+}
+
+// ─── Ingest at realistic scale ──────────────────────────────
+
+fn bench_ingest_realistic(c: &mut Criterion) {
+    let mut group = c.benchmark_group("ingest_realistic");
+    let cfg = realistic_config();
+
+    for batch_size in [16, 64, 256] {
+        let values = multi_range_values(batch_size);
+        group.throughput(Throughput::Elements(batch_size as u64));
+        group.bench_with_input(BenchmarkId::new("multi_range", batch_size), &values, |b, vals| {
+            b.iter_with_setup(|| warmed_sentinel(&cfg), |mut s| s.ingest(black_box(vals)));
+        });
+    }
+
+    group.finish();
+}
+
+// ─── Construction (includes automatic noise injection) ──────
+
+fn bench_construction(c: &mut Criterion) {
+    let mut group = c.benchmark_group("construction");
+
+    for rounds in [10, 50] {
+        let cfg = SentinelConfig::<u64> {
+            noise_schedule: torrust_sentinel::NoiseSchedule::Explicit(vec![rounds]),
+            noise_batch_size: 16,
+            noise_seed: Some(42),
+            ..bench_config()
+        };
+        group.bench_with_input(BenchmarkId::new("noise_rounds", rounds), &cfg, |b, cfg| {
+            b.iter(|| Sentinel128::new(black_box(cfg.clone())).unwrap());
+        });
+    }
+
+    group.finish();
+}
+
+// ─── ADR-S-013 §1: Warm-up convergence benchmarks ──────────
+
+/// Measure construction cost at candidate noise schedule values.
+///
+/// This answers: "how many milliseconds does it cost to increase
+/// noise rounds from 50 to 100, 200, or 400?"
+///
+/// Two configs are tested:
+/// - **bench**: `max_rank=4`, `analysis_k=16`, `b=4`, `λ=0.95`
+/// - **realistic**: `max_rank=16`, `analysis_k=1024`, `b=16`, `λ=0.99`
+fn bench_noise_round_scaling(c: &mut Criterion) {
+    let mut group = c.benchmark_group("noise_round_scaling");
+    group.sample_size(20); // construction is expensive at high rounds
+
+    for rounds in [10, 50, 100, 200, 400] {
+        // Bench config (small)
+        let cfg = SentinelConfig::<u64> {
+            noise_schedule: torrust_sentinel::NoiseSchedule::Explicit(vec![rounds]),
+            noise_seed: Some(42),
+            ..bench_config()
+        };
+        group.bench_with_input(BenchmarkId::new("bench", rounds), &cfg, |b, cfg| {
+            b.iter(|| Sentinel128::new(black_box(cfg.clone())).unwrap());
+        });
+
+        // Realistic config (production-like)
+        let cfg = SentinelConfig::<u64> {
+            noise_schedule: torrust_sentinel::NoiseSchedule::Explicit(vec![rounds]),
+            noise_seed: Some(42),
+            ..realistic_config()
+        };
+        group.bench_with_input(BenchmarkId::new("realistic", rounds), &cfg, |b, cfg| {
+            b.iter(|| Sentinel128::new(black_box(cfg.clone())).unwrap());
+        });
+    }
+
+    group.finish();
+}
+
+/// Detailed warm-up convergence cost (ADR-S-013 §4).
+///
+/// Measures the actual wall-clock cost of construction + noise
+/// injection at a dense range of noise schedule values for the
+/// production-like config.  This extends `bench_noise_round_scaling`
+/// with finer granularity to identify the cost knee-point.
+///
+/// Ported from the diagnostic `wall_clock_convergence_cost` test in
+/// `convergence_benchmark.rs`.
+fn bench_warmup_cost_detailed(c: &mut Criterion) {
+    let mut group = c.benchmark_group("warmup_cost_detailed");
+    group.sample_size(10); // construction is expensive at high rounds
+
+    for rounds in [5, 10, 20, 50, 65, 100, 150, 200, 300, 400, 500] {
+        let cfg = SentinelConfig::<u64> {
+            noise_schedule: torrust_sentinel::NoiseSchedule::Explicit(vec![rounds]),
+            noise_seed: Some(42),
+            ..realistic_config()
+        };
+        group.bench_with_input(BenchmarkId::new("production", rounds), &cfg, |b, cfg| {
+            b.iter(|| Sentinel128::new(black_box(cfg.clone())).unwrap());
+        });
+    }
+
+    group.finish();
+}
+
+/// Measure per-round `ingest()` cost on a warmed sentinel.
+///
+/// This answers: "if we increase noise rounds by 100, how many
+/// additional ms does that cost?" — by measuring the marginal cost
+/// of one `ingest()` call at various batch sizes.
+fn bench_per_round_ingest(c: &mut Criterion) {
+    let mut group = c.benchmark_group("per_round_ingest");
+
+    // Bench config — single range, varying batch size.
+    for batch_size in [4, 16] {
+        let values = single_range_values(batch_size);
+        let cfg = bench_config();
+        group.throughput(Throughput::Elements(batch_size as u64));
+        group.bench_with_input(BenchmarkId::new("bench", batch_size), &values, |b, vals| {
+            b.iter_with_setup(|| warmed_sentinel(&cfg), |mut s| s.ingest(black_box(vals)));
+        });
+    }
+
+    // Realistic config — single range, varying batch size.
+    for batch_size in [4, 16] {
+        let values = single_range_values(batch_size);
+        let cfg = realistic_config();
+        group.throughput(Throughput::Elements(batch_size as u64));
+        group.bench_with_input(BenchmarkId::new("realistic", batch_size), &values, |b, vals| {
+            b.iter_with_setup(|| warmed_sentinel(&cfg), |mut s| s.ingest(black_box(vals)));
+        });
+    }
+
+    group.finish();
+}
+
+// ─── Health / inspection ────────────────────────────────────
+
+fn bench_health(c: &mut Criterion) {
+    let cfg = bench_config();
+    let s = warmed_sentinel(&cfg);
+
+    c.bench_function("health", |b| {
+        b.iter(|| s.health());
+    });
+}
+
+fn bench_analysis_set_summary(c: &mut Criterion) {
+    let cfg = bench_config();
+    let s = warmed_sentinel(&cfg);
+
+    c.bench_function("analysis_set_summary", |b| {
+        b.iter(|| s.analysis_set().summary());
+    });
+}
+
+// ─── Per-sample scoring overhead ────────────────────────────
+
+fn bench_per_sample_overhead(c: &mut Criterion) {
+    let mut group = c.benchmark_group("per_sample_scores");
+
+    let batch_size = 32;
+    let values = single_range_values(batch_size);
+
+    for enabled in [false, true] {
+        let cfg = SentinelConfig::<u64> {
+            per_sample_scores: enabled,
+            ..bench_config()
+        };
+
+        group.throughput(Throughput::Elements(batch_size as u64));
+        group.bench_with_input(BenchmarkId::new("enabled", enabled), &values, |b, vals| {
+            b.iter_with_setup(|| warmed_sentinel(&cfg), |mut s| s.ingest(black_box(vals)));
+        });
+    }
+
+    group.finish();
+}
+
+// ─── Analysis K scaling ─────────────────────────────────────
+
+fn bench_analysis_k_scaling(c: &mut Criterion) {
+    let mut group = c.benchmark_group("analysis_k_scaling");
+    let batch_size = 16;
+    let values = single_range_values(batch_size);
+
+    for k in [4, 16, 64, 256] {
+        let cfg = SentinelConfig::<u64> {
+            analysis_k: k,
+            ..bench_config()
+        };
+
+        group.throughput(Throughput::Elements(batch_size as u64));
+        group.bench_with_input(BenchmarkId::new("k", k), &values, |b, vals| {
+            b.iter_with_setup(|| warmed_sentinel(&cfg), |mut s| s.ingest(black_box(vals)));
+        });
+    }
+
+    group.finish();
+}
+
+// ─── Registration ───────────────────────────────────────────
+
+criterion_group!(encoding, bench_centred_bits);
+
+criterion_group!(ingest, bench_ingest_cold, bench_ingest_warm, bench_ingest_realistic);
+
+criterion_group!(auxiliary, bench_construction, bench_health, bench_analysis_set_summary);
+
+criterion_group!(
+    convergence,
+    bench_noise_round_scaling,
+    bench_warmup_cost_detailed,
+    bench_per_round_ingest,
+);
+
+criterion_group!(scaling, bench_per_sample_overhead, bench_analysis_k_scaling);
+
+// ─── §9.11 — Temporal and analysis benchmarks ──────────────
+
+/// Generate `count` values in a narrow range with leading nibble
+/// `nibble` and sequential low bits.
+fn cell_values(nibble: u128, count: usize) -> Vec<u128> {
+    (0..count).map(|i| (nibble << 124) | (i as u128 + 1)).collect()
+}
+
+fn bench_decay(c: &mut Criterion) {
+    let mut group = c.benchmark_group("decay");
+    let cfg = bench_config();
+
+    for obs_count in [100, 1_000, 10_000] {
+        group.bench_with_input(BenchmarkId::new("observations", obs_count), &obs_count, |b, &n| {
+            b.iter_with_setup(
+                || {
+                    let mut s = Sentinel128::new(cfg.clone()).unwrap();
+                    s.ingest(&multi_range_values(n));
+                    s
+                },
+                |mut s| s.decay(black_box(0.5), black_box(0.0)),
+            );
+        });
+    }
+
+    group.finish();
+}
+
+fn bench_decay_subtree(c: &mut Criterion) {
+    let mut group = c.benchmark_group("decay_subtree");
+    let cfg = bench_config();
+
+    for obs_count in [100, 1_000, 10_000] {
+        group.bench_with_input(BenchmarkId::new("observations", obs_count), &obs_count, |b, &n| {
+            b.iter_with_setup(
+                || {
+                    let mut s = Sentinel128::new(cfg.clone()).unwrap();
+                    s.ingest(&multi_range_values(n));
+                    s
+                },
+                |mut s| {
+                    let root = s.graph().g_root();
+                    s.decay_subtree(root, black_box(0.5), black_box(0.0));
+                },
+            );
+        });
+    }
+
+    group.finish();
+}
+
+fn bench_analysis_set_recompute(c: &mut Criterion) {
+    let mut group = c.benchmark_group("analysis_set_recompute");
+
+    for k in [16, 64, 256] {
+        let cfg = SentinelConfig::<u64> {
+            analysis_k: k,
+            split_threshold: 10,
+            budget: 50_000,
+            ..bench_config()
+        };
+
+        group.throughput(Throughput::Elements(8));
+        group.bench_with_input(BenchmarkId::new("k", k), &cfg, |b, cfg| {
+            b.iter_with_setup(
+                || {
+                    let mut s = Sentinel128::new(cfg.clone()).unwrap();
+                    // Pre-populate graph with diverse traffic.
+                    for nibble in 0..16u128 {
+                        s.ingest(&cell_values(nibble, 100));
+                    }
+                    s
+                },
+                |mut s| s.ingest(black_box(&cell_values(0xA, 8))),
+            );
+        });
+    }
+
+    group.finish();
+}
+
+fn bench_report_assembly(c: &mut Criterion) {
+    let mut group = c.benchmark_group("report_assembly");
+    let cfg = bench_config();
+
+    for batch_size in [8, 32, 128] {
+        let values = multi_range_values(batch_size);
+        group.throughput(Throughput::Elements(batch_size as u64));
+        group.bench_with_input(BenchmarkId::new("batch_size", batch_size), &values, |b, vals| {
+            b.iter_with_setup(|| warmed_sentinel(&cfg), |mut s| s.ingest(black_box(vals)));
+        });
+    }
+
+    group.finish();
+}
+
+criterion_group!(temporal, bench_decay, bench_decay_subtree);
+
+criterion_group!(analysis, bench_analysis_set_recompute, bench_report_assembly);
+
+criterion_main!(encoding, ingest, auxiliary, convergence, scaling, temporal, analysis);
diff --git a/packages/sentinel/docs/algorithm.md b/packages/sentinel/docs/algorithm.md
new file mode 100644
index 000000000..3fa15fc33
--- /dev/null
+++ b/packages/sentinel/docs/algorithm.md
@@ -0,0 +1,2554 @@
+# Spectral Sentinel — Algorithm Specification
+
+---
+
+# Part I — Scope and Foundations
+
+---
+
+## Chapter 1. Purpose, Scope, and Principles
+
+### 1.1 Overview
+
+The Spectral Sentinel is a hierarchical online anomaly detector for streams of positionally structured coordinate values. It maintains adaptive spatial partitioning of an $N$-bit input domain $[0, 2^N)$, selects significant regions for statistical analysis via competitive ranking, and scores incoming observations against learned low-rank linear subspace models.
+
+### 1.2 Parameterisation
+
+The system is defined over three parameters:
+
+| Parameter | Role                                | Requirements                                                                                                                                                       |
+| --------- | ----------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| $N$       | Domain bit-width (positive integer) | Determines tree height and domain cardinality $2^N$. Typical values: 128, 64.                                                                                      |
+| $C$       | Coordinate type                     | Total ordering over $[0, 2^N)$; bit-positional decomposition; dyadic interval arithmetic (§2.3).                                                                   |
+| $V$       | Accumulator type                    | Non-negative values with a zero element; addition; multiplicative scaling by a non-negative factor; approximate conversion to floating-point for reporting (§3.3). |
+
+All formulas throughout this specification are stated in terms of $N$. Concrete examples use $N = 128$ unless otherwise noted.
+
+### 1.3 Design Principles
+
+**Measure, don't decide.** All outputs are raw statistical quantities. The system never emits threat levels, recommended actions, or policy decisions.
+
+**Adapt, don't control.** The spatial structure evolves autonomously under observation and decay. The host controls resource ceilings, temporal policy, and analysis budgets.
+
+**Feed forward, don't feed back.** Anomaly scores flow outward to the host but are never fed back into the spatial layer's importance signal. The spatial layer sees $\Delta = 1$ per observation — pure volume counting. This prevents the anomaly detector from influencing its own spatial structure.
+
+> _Design note (why volume-only importance)._ If anomaly scores boosted importance, the affected cell would earn finer resolution, changing its statistical model, changing its scores, changing its importance — an unstable feedback loop. With $\Delta = 1$, an adversary cannot influence the spatial structure except through observation volume, which is precisely what the spatial layer is designed to handle.
+
+> _Design note (why timing matters)._ An observer who can measure response latency learns when the spatial structure changes: a spike means "a new cell was created for this region," leaking distribution evolution and split dynamics. Moving warm-up work off the hot path (§11) eliminates the structural source of variance.
+
+### 1.4 Three-Layer Architecture
+
+```
+Layer 1: Spatial Index
+         Adaptive spatial partitioning of [0, 2^N)
+         Pure volume tracking (Δ = 1 per observation)
+         Competitive ranking by observation volume
+              │
+              │ Significance ranking → top-K selection
+              ▼
+Layer 2: Analysis Selector
+         Selects significant cells for statistical analysis
+         Closes selection under spatial ancestry
+              │
+              │ Suffix bit vectors at every ancestor depth
+              ▼
+Layer 3: Analysis Engine
+         Per-cell subspace models at selected and ancestor cells
+         Hierarchical coordination across related cells
+              │
+              ▼
+         Analysis report → host
+```
+
+### 1.5 Layer Responsibilities
+
+| Concern                                                          | Owner                                       |
+| ---------------------------------------------------------------- | ------------------------------------------- |
+| Spatial partitioning of $[0, 2^N)$                               | Spatial Layer (Layer 1)                     |
+| Competitive significance ranking                                 | Spatial Layer — Value Tree                  |
+| Spatial lifecycle (split, evict, absorb, restore)                | Spatial Layer                               |
+| Spatial memory (temporal decay, contour evolution)               | Spatial Layer + host policy                 |
+| Investment commitment (which cells receive warm-up and trackers) | Analysis Selector (Layer 2)                 |
+| Production selection (which invested cells produce scores)       | Analysis Selector (Layer 2)                 |
+| Ancestor closure (spatial ancestry of investment targets)        | Analysis Selector (Layer 2)                 |
+| Statistical modelling within each cell                           | Analysis Engine (Layer 3)                   |
+| Anomaly scoring (four axes)                                      | Analysis Engine                             |
+| Drift detection                                                  | Analysis Engine                             |
+| Cross-cell coordination detection                                | Analysis Engine — hierarchical coordination |
+| Interpretation and response                                      | Host (external)                             |
+
+### 1.6 Responsibility Boundaries
+
+| Property                                       | Source                                 |
+| ---------------------------------------------- | -------------------------------------- |
+| Contour structure, ranking, budget enforcement | Inherited — Spatial Layer (§3)         |
+| Temporal decay schedule                        | Host policy, executed by Spatial Layer |
+| Investment set and producing set membership    | Sentinel — Layer 2 (§8)                |
+| Statistical modelling and scoring              | Sentinel — Layer 3 (§4–7)              |
+| Importance signal ($\Delta = 1$, no feedback)  | Sentinel's invariant (§1.3)            |
+
+---
+
+## Chapter 2. Domain, Encoding, and Notation
+
+### 2.1 The Domain
+
+The input domain is $[0, 2^N)$, where $N$ is the domain bit-width. The spatial layer partitions this into dyadic cells aligned with bit positions. A cell at spatial tree depth $d$ covers an interval of width $2^{N-d}$, corresponding to a $d$-bit prefix shared by all values in the cell.
+
+**Example depths at $N = 128$:**
+
+| Spatial tree depth $d$ | Prefix length | Suffix width $w$ | Potential cells at full coverage |
+| ---------------------- | ------------- | ---------------- | -------------------------------- |
+| 0                      | 0 bits        | 128              | 1                                |
+| 16                     | 16 bits       | 112              | 65,536                           |
+| 32                     | 32 bits       | 96               | $\approx 4.3 \times 10^{9}$      |
+| 48                     | 48 bits       | 80               | $\approx 2.8 \times 10^{14}$     |
+| 64                     | 64 bits       | 64               | $\approx 1.8 \times 10^{19}$     |
+| 96                     | 96 bits       | 32               | $\approx 7.9 \times 10^{28}$     |
+| 128                    | 128 bits      | 0                | $\approx 3.4 \times 10^{38}$     |
+
+At $N = 64$, the maximum depth is 64 and the root suffix width is 64.
+
+The spatial layer materialises only cells where observations have warranted refinement. The bottom contour (§3.2) is a step function — deep where volume is concentrated, shallow where it is sparse. Sentinel does not use the contour itself as the selection boundary: the analysis selector scans V-Tree entries by competitive significance and analysis width, then adds G-tree ancestors for multi-scale context (§8).
+
+### 2.2 Input Requirements
+
+The system analyses bit-positional structure: leading bits determine routing, suffix bits provide statistical content. This is meaningful only when the coordinate values have **hierarchical positional structure** — values whose leading bits encode progressively finer categorical or spatial membership, so that shared prefixes imply shared context.
+
+Values with pseudo-random bit distributions (cryptographic hashes, random nonces, uniformly sampled identifiers) have no such structure and defeat the analysis. The system processes any stream of coordinate values without complaint; the host is responsible for the structural guarantee.
+
+### 2.3 Centred Bit Representation
+
+Each raw coordinate value $v$ of type $C$ becomes a centred bit vector $\mathbf{x} \in \{-0.5, +0.5\}^{N}$:
+
+$$x_i = \begin{cases} +0.5 & \text{if bit } (N - 1 - i) \text{ of } v \text{ is 1} \\ -0.5 & \text{otherwise} \end{cases} \qquad i = 0, \ldots, N-1$$
+
+Index 0 is the most significant bit. Centring gives $\mathbb{E}[x_i] = 0$ under a uniform bit distribution — a prerequisite for subspace analysis without explicit mean subtraction.
+
+The coordinate type $C$ must support:
+
+- Total ordering over $[0, 2^N)$.
+- Extraction of individual bits by position (for the centred representation above).
+- Dyadic interval arithmetic: midpoint computation and interval containment testing (for spatial routing).
+
+### 2.4 Suffix Extraction
+
+For a cell at depth $d$, the first $d$ prefix bits are constant — resolved by routing. The working observation is the **suffix**:
+
+$$\mathbf{x}^{(\text{cell})} = (x_d, \ldots, x_{N-1}) \in \{-0.5, +0.5\}^w, \quad w = N - d$$
+
+### 2.5 Constant-Norm Property
+
+Every suffix vector at width $w$ satisfies $\|\mathbf{x}^{(\text{cell})}\|^2 = w/4$. This fixed-energy property means that total observation energy is constant and carries no information. By the Pythagorean theorem, projection energy $\|\hat{\mathbf{x}}_i\|^2/k$ is a perfect affine function of novelty (Pearson $r = -1$). The system therefore uses four independent scoring axes rather than five (§5).
+
+> _Note._ This constraint is specific to centred binary inputs. Continuous-valued inputs with variable norms would decouple projection energy from novelty.
+
+### 2.6 Notation
+
+| Symbol                | Domain                                      | Definition                                                                                                                                                                                                              |
+| --------------------- | ------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| $N$                   | $\mathbb{Z}_{>0}$                           | Domain bit-width; spatial tree height                                                                                                                                                                                   |
+| $C$                   | —                                           | Coordinate type; spatial addressing, interval bounds (§2.3)                                                                                                                                                             |
+| $V$                   | —                                           | Accumulator type; importance accounting in the spatial layer (§3.3)                                                                                                                                                     |
+| $d$                   | $\{0, \ldots, N\}$                          | Spatial tree depth of a cell (prefix length in bits)                                                                                                                                                                    |
+| $w$                   | $\{0, \ldots, N\}$                          | Analysis width; $w = N - d$ (suffix length)                                                                                                                                                                             |
+| $n$                   | $\mathbb{Z}_{\geq 0}$                       | Ingestion batch size (coordinate values per `SentinelIngest` call)                                                                                                                                                      |
+| $b$                   | $\mathbb{Z}_{>0}$                           | Per-tracker batch size (rows of $X$ fed to one tracker in one call; see batch-size note below)                                                                                                                          |
+| $b_{\text{noise}}$    | $\mathbb{Z}_{>0}$                           | Noise batch size (synthetic samples per warm-up round; §11.1)                                                                                                                                                           |
+| $k$                   | $\{1, \ldots, \text{cap}\}$                 | Current active rank of the learned subspace (§4)                                                                                                                                                                        |
+| $\text{cap}$          | $\{1, \ldots, \min(w, r_{\max})\}^\dagger$  | Hard ceiling on rank; $r_{\max}$ is the maximum rank parameter                                                                                                                                                          |
+| $\lambda$             | $(0, 1)$                                    | Forgetting factor                                                                                                                                                                                                       |
+| $\alpha$              | $(0, 1)$                                    | Learning rate; $\alpha = 1 - \lambda$                                                                                                                                                                                   |
+| $\varepsilon$         | $\mathbb{R}_{>0}$                           | Numerical stability constant                                                                                                                                                                                            |
+| $\tau$                | $(0, 1)$                                    | Cumulative energy threshold                                                                                                                                                                                             |
+| $U$                   | $\mathbb{R}^{w \times \text{cap}}$          | Orthonormal basis of the learned subspace (§4)                                                                                                                                                                          |
+| $\sigma$              | $\mathbb{R}^{\text{cap}}_{\geq 0}$          | Singular values (energy per basis vector) (§4)                                                                                                                                                                          |
+| $\mu^{(z)}$           | $\mathbb{R}^{\text{cap}}$                   | EWMA mean of latent coordinates (§4)                                                                                                                                                                                    |
+| $\nu^{(z)}$           | $\mathbb{R}^{\text{cap}}_{>0}$              | EWMA variance of latent coordinates (§4)                                                                                                                                                                                |
+| $\Gamma$              | $\mathbb{R}^{\text{cap} \times \text{cap}}$ | EWMA second-moment matrix of latent coordinates (§4)                                                                                                                                                                    |
+| $X$                   | $\mathbb{R}^{b \times w}$                   | Suffix observation matrix (one batch at one cell)                                                                                                                                                                       |
+| $Z$                   | $\mathbb{R}^{b \times k}$                   | Latent projection of $X$                                                                                                                                                                                                |
+| $\hat{X}$             | $\mathbb{R}^{b \times w}$                   | Reconstruction of $X$ from $Z$                                                                                                                                                                                          |
+| $m$                   | $\mathbb{Z}_{>0}$                           | Coordination group size (§7)                                                                                                                                                                                            |
+| $\lambda_s$           | $(\lambda, 1)$                              | Slow EWMA decay for per-tracker drift detection (§6)                                                                                                                                                                    |
+| $\lambda_{s,m}$       | $(\lambda, 1)$                              | Slow EWMA decay for coordination drift detection (§7)                                                                                                                                                                   |
+| $\kappa_\sigma$       | $\mathbb{R}_{\geq 0}$                       | Drift-detector noise allowance in slow-baseline $\sigma$ units (§6)                                                                                                                                                     |
+| $n_\sigma$            | $\mathbb{R}_{> 0}$                          | Outlier clip width in $\sigma$ units (§6)                                                                                                                                                                               |
+| $S$                   | $\mathbb{R}_{\geq 0}$                       | Drift-detector accumulator value (§6)                                                                                                                                                                                   |
+| $\bar{\rho}$          | $[0, 1]$                                    | Per-axis clip-pressure EWMA (§6.1.1, §6.4)                                                                                                                                                                              |
+| $\lambda_\rho$        | $(0, 1)$                                    | Clip-pressure EWMA decay rate (§6.1.1, §6.4)                                                                                                                                                                            |
+| $\rho_t$              | $[0, 1]$                                    | Batch clip ratio: fraction of samples clipped in one batch (§6.1.1)                                                                                                                                                     |
+| $\mu^{(\text{in})}$   | $\mathbb{R}^4$                              | Running-mean centring reference for coordination input (§7)                                                                                                                                                             |
+| $\eta$                | $[0, 1]$                                    | Noise influence fraction (tracker maturity) (§11)                                                                                                                                                                       |
+| $K$                   | $\mathbb{Z}_{>0}$                           | Maximum number of competitively selected analysis cells                                                                                                                                                                 |
+| $L$                   | $\mathbb{Z}_{\geq 0}$                       | Value Tree depth cutoff for analysis eligibility                                                                                                                                                                        |
+| $\mathcal{T}$         | $\subseteq$ V-entries                       | Competitive targets (top $K$ by importance within depth cutoff) (§8)                                                                                                                                                    |
+| $\mathcal{A}$         | $\subseteq$ V-entries                       | Producing competitive set ($\mathcal{I} \cap \mathcal{T} \cap \text{Online}$) (§8)                                                                                                                                      |
+| $\mathcal{A}^*$       | $\supseteq \mathcal{A}$                     | Producing full set ($\mathcal{I} \cap \text{Online}$) (§8)                                                                                                                                                              |
+| $\mathcal{I}$         | $\subseteq$ V-entries + ancestors           | Investment set (competitive targets + spatial ancestors, regardless of online status) (§8)                                                                                                                              |
+| $G_{\max}$            | $\mathbb{Z}_{\geq 5}$                       | Hard ceiling on total spatial nodes                                                                                                                                                                                     |
+| $\lambda_{\text{sp}}$ | $[0,\infty)$                                | Spatial decay attenuation factor (host-controlled) (§10)                                                                                                                                                                |
+| $h_V$                 | $\mathbb{Z}_{\geq 0}$                       | V-Tree height (maximum root-to-leaf path length in the V-Tree) (§3.9)                                                                                                                                                   |
+| $d_{\text{geo}}$      | $\mathbb{Z}_{\geq 0}$                       | G-Tree depth of a node: the number of materialised ancestors on the path from the node to the root (= prefix depth $d$) (§3.1).                                                                                         |
+| $\bar{D}$             | $\mathbb{R}_{\geq 0}$                       | Average G-Tree depth of competitive targets; $\bar{D} = \frac{1}{K}\sum_{i=1}^{K} d_i$. Typical values 2–6 under default parameters (§3.1). The worst-case investment set size is $1 + K\bar{D}$ before sharing (§8.2). |
+
+$^\dagger$ The domain $\{1, \ldots, \min(w, r_{\max})\}$ is non-empty only when $w \geq 1$. Tracker creation requires $w \geq 2$ (§4.1); cells below this threshold are excluded from $\mathcal{I}$.
+
+> **Batch-size note.** Three distinct quantities govern how many rows a tracker processes per call. **Ingestion batch size** ($n$) is caller-controlled: the number of coordinate values submitted in one `SentinelIngest` call. **Per-tracker batch size** ($b$) is the row count of $X$ at a given tracker. At the root, $b = n$ (every observation routes through the root). At non-root cells during live operation, $b$ is the number of ingestion-batch observations whose spatial routing passes through that cell; $b \in \{0, \ldots, n\}$ depending on the batch's spatial distribution. Cells with $b = 0$ receive no observations and skip the core loop entirely. **Noise batch size** ($b_{\text{noise}}$) is a configured parameter (§11.1) controlling synthetic samples per warm-up round. All core-loop formulas (§4–§6) are parametric in $b$; the distinction matters for cost analysis (§12) and warm-up calibration (§11.9, Appendix A).
+
+Vectors are row vectors when they represent observations and column vectors when they represent basis directions. Subscript $i$ indexes samples; subscript $j$ indexes subspace dimensions.
+
+---
+
+## Chapter 3. The Spatial Layer
+
+This chapter specifies the capabilities and contracts of the underlying spatial partitioning structure — referred to throughout this specification as the **G-V Graph** — to the extent required for understanding the Spectral Sentinel. It is not a complete specification of the G-V Graph; it describes what the Sentinel uses, at the level of detail needed to follow the algorithms in subsequent chapters and reason about their properties.
+
+---
+
+### 3.1 Purpose and Architecture
+
+The G-V Graph is a self-organising spatial index that continuously solves a precision-allocation problem over a one-dimensional domain $[0, 2^N)$. It decides, based on accumulated observation volume, where to invest fine spatial resolution and where to leave resolution coarse. The Sentinel uses this adaptive partitioning as the foundation for its analysis: cells that the G-V Graph identifies as significant receive statistical modelling; cells it identifies as insignificant do not.
+
+The structure consists of two trees sharing a common set of nodes. Each tree owns a different aspect of the system's state:
+
+**The Geometric Tree (G-Tree)** is a binary tree over dyadic intervals. It is the spatial ledger: every materialised node stores the accumulated observation value for its range. The G-Tree maintains a **contour** — the observation-receiving surface of the domain — a step function whose depth at each coordinate reflects how finely the tree has resolved that region.
+
+**The Value Tree (V-Tree)** is a dynamic tournament bracket with branching factor 2 or 3. Nodes from the G-Tree sit at the leaves as competitors; internal nodes are pure structural scaffolding. The V-Tree ranks competitors by **importance** — accumulated observation volume, under the configuration the Sentinel uses. High-importance entries reside near the root; low-importance entries are consolidated deeper.
+
+Both trees reference the same underlying nodes. A node exists simultaneously in both trees: it occupies a position in the G-Tree's dyadic hierarchy (determined by its interval) and a position in the V-Tree's tournament bracket (determined by its competitive importance).
+
+**Uncompressed materialisation.** The G-Tree is a fully materialised binary trie: every node on the path from a leaf to the root exists as a distinct materialised node. A cell at G-Tree depth $d$ has exactly $d$ materialised ancestors. Catalytic bisection (§3.5) creates children at depth $d + 1$; tip-only eviction (§3.12, property 5) removes leaves but never compresses interior chains. Ancestor walks — for sum propagation, investment-set closure (§8.2), and multi-scale delivery (§9.3) — visit every materialised level.
+
+> _Why trees stay shallow._ The competitive mechanism (§3.4) and the host-configured depth gate `depth_create` (typically 3) bound how deep the G-Tree grows in practice. Under typical parameters, competitive cells sit at G-Tree depths 2–8, so ancestor counts are inherently small — not because of path compression, but because the system rarely creates deep structure. The worst-case investment set size before sharing is $1 + K\bar{D}$ (§8.2), but the Steiner tree structure of the ancestor closure guarantees extensive sharing, and the practical size is dominated by $2K$.
+
+### 3.2 Domain and Spatial Partitioning
+
+The input domain is $[0, 2^N)$, partitioned into dyadic cells aligned with bit positions. A cell at G-Tree depth $d$ covers an interval of width $2^{N-d}$, corresponding to a $d$-bit prefix shared by all values in the cell. The root covers the entire domain at depth 0; a unit cell at depth $N$ covers a single value.
+
+The G-V Graph materialises only cells where observations have warranted refinement. The **bottom contour** is a step function over the domain — deep where volume is concentrated, shallow where it is sparse. It is one public view of the spatial resolution currently exposed by the tree. Sentinel's statistical analysis is selected from V-Tree entries rather than by iterating this contour directly (§8.1).
+
+The contour is organised into **plateaus** — maximal contiguous runs at a single depth. Within a plateau, all contour cells sit at the same depth and interval width. The boundaries between plateaus mark where the tree found non-uniformity worth resolving. The plateau count $P$ is a natural measure of the tree's structural complexity: $P = 1$ for a perfectly uniform tree; $P$ grows as the tree learns structure.
+
+### 3.3 Observation and Importance
+
+When a value $v$ at coordinate $x$ arrives, the G-V Graph:
+
+1. **Routes** to the receiving cell — the deepest materialised node whose range contains $x$.
+2. **Accumulates** the observation delta $\Delta$ in the receiving cell's ledger.
+3. **Updates importance** — the receiving cell's competitive ranking value grows.
+4. **Propagates** sums upward through both trees.
+5. **Checks structural triggers** — whether the cell qualifies for refinement, whether competitive violations need resolution, whether cold cells should be evicted.
+
+The Sentinel uses the G-V Graph in its **standard configuration**: importance equals accumulated observation volume, the ground element is zero, and the importance type is non-negative. Under this configuration, all architectural features are available — proportional sampling, the Fibonacci depth bound, violation-free splits, and fast eviction of unobserved cells.
+
+**The Sentinel's feed-forward invariant.** The Sentinel always observes with $\Delta = 1$ — pure volume counting. Anomaly scores are never fed back into the importance signal. This prevents the anomaly detector from influencing its own spatial structure.
+
+The accumulator type $V$ must support:
+
+- Non-negative values with a zero element.
+- Addition (for accumulation of $\Delta$).
+- Multiplicative scaling by a non-negative real factor (for temporal decay, §3.6).
+- Approximate conversion to a floating-point value (for reporting and comparison).
+
+### 3.4 The Competitive Mechanism
+
+The V-Tree is governed by the **max-uncle constraint**: no node may outrank every one of its uncles (siblings of its parent at the grandparent level). This constraint encodes competitive dominance and drives the tree's lifecycle.
+
+When a cell splits, its V-Tree entry freezes — children intercept all future observations, so the parent's importance stops growing. The frozen entry becomes the competitive benchmark that children must exceed. Children start at zero importance and must earn their way up. When a child's importance exceeds every uncle's, the V-Tree restructures — promoting the child to a shallower position and potentially triggering further spatial refinement.
+
+Three siblings of comparable importance coexist indefinitely under a 3-node parent — a violation requires beating _both_ uncles. The V-Tree restructures only when a node dramatically outgrows its entire neighbourhood, not on every minor importance fluctuation. This structural stability is inherent — no additional hysteresis is needed.
+
+**V-Tree depth as a significance measure.** The competitive mechanism pushes high-importance entries to shallow V-Tree depths and low-importance entries deep. V-Tree depth is therefore a global significance ranking: shallow entries have proven sustained competitive importance against their neighbourhood; deep entries have not. The Sentinel's analysis selector (§8) uses V-Tree depth as the eligibility criterion for statistical modelling.
+
+The max-uncle constraint implies at least Fibonacci-rate decay along root-to-leaf paths, yielding a depth bound of $\log_\varphi(1/w_i) + O(1)$ for an entry with weight fraction $w_i$, where $\varphi = (1+\sqrt{5})/2 \approx 1.618$. Expected proportional sampling cost is at most $1.44 H + O(1)$ where $H$ is the Shannon entropy of the importance distribution.
+
+### 3.5 Spatial Lifecycle
+
+Three forces shape the contour:
+
+**Refinement.** When a fully exposed contour cell accumulates sufficient importance and holds a shallow enough V-Tree position, it splits into two finer cells. The split is _catalytic_: the parent persists as a frozen competitive benchmark; its children start at zero importance and must earn their way up. No information is destroyed by spatial refinement.
+
+**Eviction.** Unprotected contour cells (zero children) that sit past a configurable V-Tree depth threshold are removed. The parent absorbs the evicted cell's accumulated value and partially or fully re-joins the contour. Eviction proceeds from the tips inward — only nodes with no dependents can be removed — so the contour coarsens gradually, never catastrophically.
+
+**Restoration.** When a semi-internal node (one child present, one evicted) accumulates enough importance through the competitive mechanism, the V-Tree promotes it. This promotion creates the missing child as a side effect, restoring the contour without any separate operation. The V-Tree's competitive mechanism is the sole gate for contour growth.
+
+**Depth gates** govern the lifecycle. Two V-Tree depth thresholds — a creation gate and an eviction gate separated by a mandatory buffer zone — control where new resolution may be added and where it is withdrawn. A node-count budget enables dynamic adjustment of these thresholds under memory pressure, providing a hard ceiling on total materialised nodes.
+
+### 3.6 Temporal Decay
+
+The G-V Graph stores exact accumulated values by default. It imposes no automatic temporal model. The host controls temporal semantics through an explicit decay operation that scales accumulators by a configurable factor:
+
+- **Attenuation** (factor $< 1$): cold cells lose standing and become eviction candidates. This produces recency — "what matters now."
+- **Amplification** (factor $> 1$): existing structure is reinforced. With depth-selective amplification, fine-scale detail is sharpened relative to coarse.
+- **Annihilation** (factor $= 0$): hard reset. Targeted annihilation zeroes a subtree while leaving the rest of the graph intact.
+- **Detail flush** (factor $= 0$, depth-selective): the subtree root is preserved while all descendants are zeroed — preserving coarse measurement while forcing fine structure to be re-earned.
+
+Decay is subband-adaptive: different G-Tree depths can be scaled at different rates. This enables the host to implement frequency-selective temporal filtering — high-resolution subbands can decay faster than coarse ones.
+
+The Sentinel's statistical decay (EWMA forgetting in trackers, §4) is independent of spatial decay. A cell can retain its spatial position under slow spatial decay while its statistical model adapts rapidly, and vice versa.
+
+### 3.7 Dual Shielding
+
+The two trees protect each other through three complementary mechanisms:
+
+| Direction           | Shield                                           | What it protects                                                       |
+| ------------------- | ------------------------------------------------ | ---------------------------------------------------------------------- |
+| V-Tree → downward   | Parent's frozen entry stands as uncle            | Children from competitive displacement while the benchmark holds       |
+| G-Tree → upward     | Children intercept observations meant for parent | Parent's V-entry from growing — it stays frozen as a fixed benchmark   |
+| G-Tree → structural | Only unprotected nodes are eviction-eligible     | Protected nodes from premature removal while structurally load-bearing |
+
+Without the upward shield, the parent's importance would keep pace with its children — no fixed benchmark, no competitive mechanism. Without the downward shield, children's positions would be unstable under every fluctuation. Without the structural shield, eviction could tear the contour by removing nodes that support finer-scale structure.
+
+### 3.8 Benchmark Compounding
+
+Repeated expand–contract cycles at a given node harden its competitive benchmark. Each eviction absorption folds descendants' accumulated value into the node's own accumulation, raising the frozen bar that future children must exceed. After $j$ full expand–contract cycles, the benchmark grows approximately geometrically, and the total observation cost to re-reach depth $D$ along a path grows quadratically: $\sim D^2 \theta / 2$ where $\theta$ is the split threshold. This ensures that deep spatial structure is re-created only when justified by sustained, concentrated observation volume.
+
+Under temporal attenuation, benchmarks weaken — the bar softens as historical evidence ages. Under annihilation, benchmarks reset to zero — the region starts from scratch.
+
+### 3.9 Capabilities Used by the Sentinel
+
+The Sentinel uses the following G-V Graph operations:
+
+| Operation                        | Description                                          | Cost                               |
+| -------------------------------- | ---------------------------------------------------- | ---------------------------------- |
+| Observe(coordinate, $\Delta$)    | Route, accumulate, trigger structural updates        | $O(d_{\text{geo}} + h_V)$          |
+| RouteToReceiver($x$)             | Find the receiving cell for coordinate $x$           | $O(d_{\text{geo}})$                |
+| Decay(root, factor, selectivity) | Apply temporal scaling to a subtree                  | $O(\text{subtree size} \cdot h_V)$ |
+| Value Tree depth query           | V-Tree depth of a cell's entry                       | $O(h_V)$ or $O(1)$ cached          |
+| Value Tree importance query      | Importance value of a cell's entry                   | $O(1)$                             |
+| Plateau point query              | Plateau containing a coordinate                      | $O(\log P)$                        |
+| Plateau iteration                | All plateaus in spatial order                        | $O(P)$                             |
+| Plateau count                    | Number of distinct plateaus                          | $O(1)$                             |
+| G-Tree ancestor walk             | All materialised ancestors of a node                 | $O(d_{\text{geo}})$                |
+| G-Tree node sum query            | g.sum of a node (own accumulation + descendant sums) | $O(1)$                             |
+| Total importance                 | Sum of all importance in the graph                   | $O(1)$                             |
+| Node count                       | Total materialised G-Tree nodes                      | $O(1)$                             |
+| Semi-internal count              | Number of one-child nodes                            | $O(1)$                             |
+
+### 3.10 Node States
+
+Every G-Tree node exists in one of three states, determined by how many children it has:
+
+| Children | State         | Contour relationship               | Observation behaviour                         | Eviction eligible                     |
+| -------- | ------------- | ---------------------------------- | --------------------------------------------- | ------------------------------------- |
+| 0        | Terminal      | On the contour — fully exposed     | Receives all observations in its range        | Yes (if past depth gate and not root) |
+| 1        | Semi-internal | On the contour — partially exposed | Receives observations in the uncovered half   | No (has a dependent)                  |
+| 2        | Internal      | Above the contour                  | Receives no observations (children intercept) | No (has dependents)                   |
+
+The Sentinel's analysis selector (§8) does not filter candidates by G-Tree state. It scans V-Tree entries within the configured V-depth cutoff and with sufficient analysis width, so a competitive target can be terminal, semi-internal, or internal. Internal nodes can remain competitive because their V-entry retains frozen pre-split importance, and their tracker is still fed through Sentinel's multi-scale interval delivery (§9).
+
+Since the G-Tree is fully materialised (§3.1), the investment set's ancestor closure (§8.2) includes every node on the path from each competitive target to the root, providing hierarchical context at every dyadic scale from the target to the entire domain.
+
+### 3.11 Configuration Parameters
+
+| Parameter                           | Role                                     | Sentinel's typical value            |
+| ----------------------------------- | ---------------------------------------- | ----------------------------------- |
+| $N$ (domain bit-width)              | Tree height, analysis width at root      | 128 (default) or 64                 |
+| $\theta$ (split threshold)          | Minimum importance for split eligibility | Application-dependent               |
+| $D_{\text{create}}$ (creation gate) | Maximum V-Tree depth for splits          | Application-dependent               |
+| $D_{\text{evict}}$ (eviction gate)  | Minimum V-Tree depth for eviction        | $D_{\text{create}} + \text{buffer}$ |
+| Budget                              | Soft node-count target                   | Application-dependent               |
+| $G_{\max}$                          | Hard ceiling on total G-Tree nodes       | $> \text{budget}$, $\geq 5$         |
+
+The Sentinel treats these as pass-through configuration: it forwards them to the G-V Graph at construction time and does not modify them during operation. The host controls all spatial policy through these parameters and through the timing and parameters of decay calls.
+
+### 3.12 Key Properties
+
+The following properties of the G-V Graph are assumed throughout this specification:
+
+1. **Complete tiling.** The contour always tiles the full domain $[0, 2^N)$ with no gaps. Every coordinate has exactly one receiving cell.
+
+2. **Summation invariant.** Every node's sum equals its own accumulation plus its children's sums. Total energy is conserved across all structural operations.
+
+3. **Single-entry accounting.** Each observation updates exactly one node's importance — the receiver's. No double-counting.
+
+4. **Competitive stability.** Three siblings of comparable importance coexist indefinitely. Restructuring requires a node to outgrow its entire neighbourhood.
+
+5. **Tip-only eviction.** Only nodes with zero children can be evicted. The contour contracts from the tips inward.
+
+6. **Root permanence.** The G-Tree root is never evicted. The tree always has at least one node.
+
+7. **Frozen benchmarks.** Internal nodes' importance values are frozen — a direct consequence of observation routing, not an explicit mechanism.
+
+8. **Budget enforcement.** The total node count never exceeds $G_{\max}$ across any single operation.
+
+9. **Fibonacci depth bound.** Under the standard configuration, V-Tree depth is bounded by $\log_\varphi(1/w_i) + O(1)$ for weight fraction $w_i$.
+
+10. **Feed-forward compatibility.** The G-V Graph accepts any non-negative $\Delta$ without interpreting it. The Sentinel's choice of $\Delta = 1$ is invisible to the graph.
+
+---
+
+# Part II — The Mathematical Model
+
+---
+
+## Chapter 4. The Subspace Model
+
+Each cell in the full analysis set $\mathcal{A}^*$ (§8) maintains a subspace tracker — a low-rank linear subspace model that scores observations against learned structure and then evolves.
+
+### 4.1 State
+
+**Minimum analysis width.** All formulas in §4–§7 require $w \geq 2$. At $w = 0$, $\text{cap} = 0$ while $k$ would need to be 1 — the rank exceeds capacity, the basis $U$ is vacuous, and every formula in §4.2 and §5 is undefined. At $w = 1$, the tracker is novelty-saturated from birth ($\text{cap} = 1 = k$, residual DOF $= 0$) and the single basis vector spans the entire space, leaving no statistical content to detect departures from. Cells with $w < 2$ are excluded from the eligible set $\mathcal{E}$ (§8.1) and reported via the degenerate-cell counter (§14.11). The spatial layer continues to route and accumulate observations at these cells normally; only statistical modelling is withheld.
+
+A tracker at analysis width $w$ with capacity $\text{cap} = \min(w, r_{\max})$ maintains:
+
+| Component          | Shape                                                             | Description                                                |
+| ------------------ | ----------------------------------------------------------------- | ---------------------------------------------------------- |
+| $U$                | $(w, \text{cap})$                                                 | Orthonormal basis (columns $1{:}k$ active)                 |
+| $\sigma$           | $(\text{cap},)$                                                   | Singular values ($1{:}k$ meaningful)                       |
+| $\mu^{(z)}$        | $(\text{cap},)$                                                   | EWMA mean of latent coordinates ($1{:}k$ active)           |
+| $\nu^{(z)}$        | $(\text{cap},)$                                                   | EWMA variance of latent coordinates ($1{:}k$ active)       |
+| $\Gamma$           | $(\text{cap}, \text{cap})$                                        | EWMA second-moment matrix ($1{:}k$ active, upper triangle) |
+| Per-axis baselines | 4 × {fast EWMA, slow EWMA, drift accumulator, clip-pressure EWMA} | Baseline tracking (§6)                                     |
+| Rank $k$           | integer in $[1, \text{cap}]$                                      | Current active dimensionality                              |
+| Step counter       | integer                                                           | Batches processed since creation                           |
+
+**Initial state.** A newly created tracker begins with:
+
+- $k = 1$. The core loop's algebra presupposes $k \geq 1$: at $k = 0$, Phase 1 produces a $b \times 0$ projection, all within-subspace scores are zero or undefined, and Phase 5's cumulative energy fractions are ill-defined. No formulas in §4.2 or §5 define behaviour at $k = 0$.
+- $U$: any set of orthonormal columns in $\mathbb{R}^{w \times \text{cap}}$. The identity submatrix (column $j$ is the $j$-th standard basis vector) is a convenient deterministic choice. The initial basis is overwritten by the first Phase 2 SVD; the choice does not affect post-warm-up behaviour.
+- $\sigma_{1:\text{cap}}$: initial values satisfying $\sum \sigma_j^2 \leq \delta_{\text{init}} \cdot bw/4$ for some small $\delta_{\text{init}}$ (e.g. $10^{-4}$; not the global stability constant $\varepsilon$). At $\delta_{\text{init}} = 0$, the first Phase 2 seeds the subspace purely from the first batch; at $\delta_{\text{init}} > 0$, a faint ghost of the initial basis persists for one step and is overwritten on the second. The value $0.01$ per component is conformant at per-cell analysis widths (where $bw \gg \text{cap}$). At coordination trackers ($w = 4$, $b = m \geq 2$, $\text{cap} = 4$) the effective $\delta_{\text{init}}$ rises to $\text{cap} \times 10^{-4} / (bw/4) = 2 \times 10^{-4}$ at $b = 2$ — still negligible (the initial energy is 0.02% of one batch), and the ghost is overwritten on the first Phase 2 step during noise warm-up (§11.7) long before live traffic arrives.
+- $\mu^{(z)} = \mathbf{0}$, $\nu^{(z)} = \mathbf{1}$, $\Gamma = \mathbf{0}$: pre-allocated at capacity, with values chosen for safe behaviour on rank increase (Phase 3 below) and benign first-batch direct seeding (§11.2). The $\nu^{(z)} = 1.0$ pre-allocation is a conservative overestimate (roughly $4\times$ the null-hypothesis value of $0.25$ for centred $\pm 0.5$ bit vectors). It serves as the surprise denominator during the first Phase 1 scoring pass, before Phase 3 seeds $\nu^{(z)}$ from data. At $b = 1$, the seed is $z_{1j}^2 \approx 0.25$ in expectation — correctly scaled without special-case branching.
+- Step counter $= 0$. This triggers the first-batch direct seeding path (Phase 3 below, $t = 0$), which overwrites $\mu^{(z)}$, $\nu^{(z)}$, and $\Gamma$ from data. The pre-seeding values above are therefore transient — they affect at most one scoring pass before being replaced.
+- Baselines uninitialised. Noise influence $\eta = 1.0$.
+
+> _Design note (rank convergence coupling)._ During noise injection (§11.1), rank adaptation fires every $T_{\text{rank}}$ batches and moves rank by at most 1. The rank at the end of noise injection is $k_{\text{post-noise}} = \min(1 + \lfloor\text{noise rounds} / T_{\text{rank}}\rfloor, \; \text{cap})$. At $T_{\text{rank}} = 100$ with 450 noise rounds at root, the tracker enters real-traffic service at $k \approx 5$–$6$, substantially below capacity. This is by design: the noise schedule is calibrated for baseline convergence (§11.9), not rank convergence; rank continues climbing under real traffic. The coherence axis (requiring $k \geq 2$) activates after $T_{\text{rank}}$ batches, contributing to the coherence convergence bottleneck documented in §11.9.
+
+### 4.2 The Core Loop
+
+Each tracker processes a batch $X \in \mathbb{R}^{b \times w}$ in five strictly ordered phases. **Scoring precedes evolution** — the batch is measured against the _prior_ model, then the model updates.
+
+#### Phase 1 — Score
+
+$$Z = X \, U_k \qquad \hat{X} = Z \, U_k^\top \qquad R = X - \hat{X}$$
+
+Compute four per-sample scores from $Z$, $\hat{X}$, and $R$ (§5). Assemble batch summary statistics.
+
+#### Phase 2 — Evolve Subspace
+
+Construct the combined matrix:
+
+$$M = \begin{bmatrix} \sqrt{\lambda} \; U_k \, \operatorname{diag}(\sigma_{1:k}) & \Big| & X^\top \end{bmatrix} \in \mathbb{R}^{w \times (k + b)}$$
+
+Compute the thin SVD $M = \tilde{U} \, \tilde{S} \, \tilde{V}^\top$. Retain the top $n = \min(\min(w, k+b), \; \text{cap})$ components:
+
+$$U_{:, 1:n} \leftarrow \tilde{U}_{:, 1:n} \qquad \sigma_{1:n} \leftarrow \operatorname{diag}(\tilde{S})_{1:n} \qquad \sigma_{n+1:\text{cap}} \leftarrow 0$$
+
+The zeroing of trailing entries is semantically compelled: $M$ has rank at most $n$, so components beyond this index carry no energy from either the attenuated history or the current batch.
+
+$\tilde{V}$ is discarded.
+
+**Decay profile.** Without reinforcement, $\sigma^{(t)} = \lambda^{t/2} \, \sigma^{(0)}$. Energy half-life: $t_{1/2} = \ln 2 / \ln(1/\lambda)$.
+
+| $\lambda$ | Half-life (steps) | Character   |
+| --------- | ----------------- | ----------- |
+| 0.99      | $\approx 69$      | Long memory |
+| 0.95      | $\approx 14$      | Medium      |
+| 0.90      | $\approx 7$       | Short       |
+
+**Numerical failure guard.** If the thin SVD fails to converge (numerically degenerate input), the basis $U$ and singular values $\sigma$ are left unchanged. The batch is scored (Phase 1 completed) but the model does not evolve. Implementations should record this event.
+
+**Identical-observation guard.** If all $b$ rows of $X$ are identical, the combined matrix $M$ has rank at most $k + 1$. The SVD is well-conditioned in this case; no special handling is needed beyond the standard thin-SVD computation.
+
+#### Phase 3 — Evolve Latent Distribution
+
+$Z$ is the projection matrix computed in Phase 1 (prior-basis projection). Phase 3 does **not** recompute $Z$ using the updated basis from Phase 2. The latent statistics therefore always describe the distribution under the basis that was used for scoring; the consequence is a one-step lag after basis rotations, whose transient effects are analysed in the design note below.
+
+**Phase 3 internal order.** The update order is $\nu^{(z)}$, then $\mu^{(z)}$, then $\Gamma$. The variance update evaluates deviations against the pre-update mean, matching the principle that scoring (Phase 1) uses the prior model.
+
+**First batch ($t = 0$).** On the tracker's very first batch, seed the latent statistics directly from data rather than blending with initial values. The seeding uses the pre-allocated $\mu^{(z)} = \mathbf{0}$ as the centring reference:
+
+$$\nu^{(z)}_j \leftarrow \max\!\left(\frac{1}{b}\sum_{i=1}^{b} z_{ij}^2,\;\varepsilon\right)$$
+
+$$\mu^{(z)}_j \leftarrow \bar{Z}_j$$
+
+$$\Gamma_{jl} \leftarrow \frac{1}{b}\sum_{i=1}^{b} z_{ij}\,z_{il}$$
+
+No batch-size-conditional branching is needed within the $t = 0$ seeding path. At $b = 1$, the variance seed is $z_{1j}^2$ — noisy but correctly scaled ($\mathbb{E}[z_j^2] = \sigma^2 \approx 0.25$ under centred binary inputs with approximately zero-mean latent projections). The EWMA smooths the noise within $O(t_{1/2})$ subsequent batches.
+
+> _The rationale for direct seeding — and the nature of the data typically present at $t = 0$ during warm-up — is analysed in §11.2._
+
+**Subsequent batches ($t > 0$).** Compute the variance update first, using the pre-update mean:
+
+$$\nu^{(z)}_j \leftarrow \lambda\,\nu^{(z)}_j + \alpha \cdot \max\!\left(\frac{1}{b}\sum_{i=1}^{b}(z_{ij} - \mu^{(z)}_j)^2,\;\varepsilon\right) \qquad j = 1,\ldots,k$$
+
+Then update the mean:
+
+$$\mu^{(z)}_j \leftarrow \lambda\,\mu^{(z)}_j + \alpha\,\bar{Z}_j \qquad j = 1, \ldots, k$$
+
+Then update the second-moment matrix:
+
+$$\Gamma_{jl} \leftarrow \lambda\,\Gamma_{jl} + \alpha\,\frac{1}{b} \sum_{i=1}^{b} z_{ij}\,z_{il} \qquad j < l$$
+
+Only the upper triangle is stored.
+
+**Runtime floor.** After each update (including $t = 0$ seeding), clamp: $\nu^{(z)}_j \leftarrow \max(\nu^{(z)}_j, 10^{-2})$. This floor is $25\times$ below the null-hypothesis value and should never bind during correct operation. It exists as defence-in-depth against implementation defects (e.g., a tracker created without warm-up) or degenerate identical-observation streams where all $z_{ij}$ are identical across consecutive batches. When it does bind, the maximum per-dimension surprise contribution is $(z_j - \mu_j)^2 / 10^{-2} \leq 100$ — large but not the catastrophic $10^5$ that $\varepsilon$-floored variance produces.
+
+The inner $\max(\cdot, \varepsilon)$ and the runtime $\max(\cdot, 10^{-2})$ are complementary: the inner floor prevents a zero-energy EWMA _input_ (from identical observations within a batch); the runtime floor prevents the _accumulated EWMA value_ from being too small due to a prolonged sequence of near-$\varepsilon$ inputs. Neither is redundant.
+
+**Behaviour on rank change.** All three latent statistics are pre-allocated at capacity $\text{cap}$ (§4.3). The EWMA loops above iterate over $j = 1, \ldots, k$, so entries beyond the active rank are never written.
+
+When rank increases from $k$ to $k + 1$:
+
+- $\mu^{(z)}_{k+1}$: retains its pre-allocated value of **zero**. Zero is the expected latent mean for a new basis direction over centred data; the EWMA converges to the true mean within $O(t_{1/2})$ steps.
+- $\nu^{(z)}_{k+1}$: retains its pre-allocated value of **$1.0$**. This is a conservative overestimate: with typical steady-state variance $\approx 0.25$ for centred $\pm 0.5$ bit vectors, the surprise denominator is $4\times$ too large, **dampening** the new dimension rather than spiking it. Under the EWMA-mean-centred formula, the overestimate self-corrects as new batches contribute $(z_{i,k+1} - \mu^{(z)}_{k+1})^2$ terms, converging with half-life $t_{1/2}$ regardless of batch size.
+- $\Gamma_{j,k+1}$ and $\Gamma_{k+1,l}$: new entries are already **zero** from construction.
+
+When rank decreases, outer entries of all three statistics ($\mu^{(z)}$, $\nu^{(z)}$, $\Gamma$) are ignored but preserved. If rank later increases back, the preserved values provide a warm starting point rather than the cold defaults, reducing reconvergence time.
+
+> _Design note (cold-start analogy)._ The $\nu^{(z)} = 1.0$ initialisation on rank increase is the same class of mismatch that §11.2 eliminates at $t = 0$ via direct seeding. On rank increase the transient is more benign: it affects only one dimension among $k$ (diluted by $1/k$ in the surprise average) and is always in the safe direction (dampening, not spiking). Per-dimension cold-seeding on rank change — analogous to the $t = 0$ logic — would eliminate this transient entirely, at the cost of per-dimension initialisation tracking.
+
+> _Design note (why EWMA-mean-centred variance)._ The variance update uses deviations from $\mu^{(z)}_j$ rather than deviations from the batch mean $\bar{Z}_j$. This makes $\nu^{(z)}_j$ a direct estimator of the surprise numerator's expected value, yielding a self-consistent ratio at every batch size.
+>
+> The within-batch population variance $\operatorname{Var}(Z_{:,j})$ has a systematic negative bias of $\frac{b-1}{b}$ that is catastrophic at $b = 1$ (erosion to $\varepsilon$, surprise inflation to $O(10^5)$), severe at $b = 2$ ($-50\%$), and materially significant below $b \approx 16$. The EWMA-mean-centred formula eliminates this entire bias class through exact cancellation: the within-batch component contributes $\frac{b-1}{b}\sigma^2$ and the batch-mean-vs-EWMA-mean component contributes $\frac{\sigma^2}{b}$, summing to $\sigma^2$ regardless of $b$. The residual bias is $\operatorname{Var}(\mu^{(z)}_j) \approx \frac{\alpha}{b(1+\lambda)}\sigma^2$ — positive (safe direction: dampens surprise), small, and decreasing as $1/b$ with worst case $+0.5\%$ at $b = 1$ and $\lambda = 0.99$. Both the surprise numerator and the $\nu$ input share the same $\mu^{(z)}_j$ reference with identical bias, so the expected surprise ratio is $1 + O(\alpha^2)$ regardless of $b$ — the self-consistency property.
+>
+> When per-tracker batch size $b$ varies across ingestion cycles (as it does in practice, depending on the spatial distribution of observations), self-consistency is preserved: each batch's $\nu$ input uses the same $\mu^{(z)}$ reference as that batch's surprise computation.
+>
+> The formula couples $\nu^{(z)}$ to $\mu^{(z)}$, providing automatic gain control during regime transitions: when the mean estimate is stale, the variance estimate inflates proportionally, dampening transient surprise spikes. This coupling reduces surprise-axis sensitivity to gradual mean drift — $\nu$ absorbs the same signal that inflates the numerator and the ratio converges to $\approx 1$. This is an acceptable trade-off: displacement is the primary mean-shift detector, and surprise's primary role is detecting per-dimension distributional shape anomalies, which are preserved.
+>
+> The original formula's within-batch variance was invariant to Phase 2 basis rotations (re-centring on $\bar{Z}_j$ subtracted out any mean shift caused by the rotated projection). The EWMA-mean-centred formula is not: after a basis rotation, $z_{ij}$ are projections onto the new basis while $\mu^{(z)}_j$ reflects the old basis, inflating $(z_{ij} - \mu^{(z)}_j)^2$ and therefore $\nu^{(z)}_j$. This sensitivity is in the safe direction (dampening surprise through $\nu$ inflation), is transient ($O(t_{1/2})$ batches), and is precisely the automatic gain control described above.
+
+> _Design note (raw second moments vs. centred covariances in $\Gamma$)._ $\Gamma$ tracks raw second moments $\mathbb{E}[z_j z_l]$, not centred covariances $\operatorname{Cov}(z_j, z_l)$. The coherence score (§5.5) compares against $\Gamma_{jl}$ directly. Since $\mathbb{E}[z_j z_l] = \operatorname{Cov}(z_j, z_l) + \mu_j^{(z)} \mu_l^{(z)}$, a shift in the latent mean changes $\Gamma_{jl}$ even when the correlation structure is perfectly stable. The alternative — using centred products $(z_j - \mu_j^{(z)})(z_l - \mu_l^{(z)})$ in both the score and the $\Gamma$ update — would isolate coherence to detect only correlation structure changes. This section analyses the trade-off and explains why raw second moments are retained.
+>
+> **Steady-state behaviour: the coupling is invisible.** At steady state, the $\mu_j \mu_l$ contribution to $\Gamma_{jl}$ is constant and perfectly absorbed by the coherence fast baseline $\bar{s}_{\text{coh}}$. The host-facing z-scores are computed against this baseline, so the permanent coupling between mean-structure and coherence-structure produces no observable effect. The coupling manifests _only_ during transitions — which is exactly the regime where the two formulations differ.
+>
+> **Transient behaviour: both formulations have artefacts.** During a regime transition that shifts the latent mean, the raw formulation produces a coherence spike because $z_j z_l$ reflects the new second moment while $\Gamma_{jl}$ still tracks the old one. Surprise fires simultaneously (detecting the same mean shift through $(z_j - \mu_j)^2 / \nu_j$), creating partial redundancy. However, the score $z_j z_l$ is computed from _fresh data with no lag_ — only the baseline $\Gamma_{jl}$ is stale. The departure is therefore clean: it reflects the genuine second-moment change (including the mean contribution), and it decays monotonically as $\Gamma$ absorbs the shift at rate $\lambda$.
+>
+> The centred formulation has a qualitatively worse transient. Staleness enters the _score computation itself_: the centred product $(z_j - \mu_j^{(\text{old})})(z_l - \mu_l^{(\text{old})})$ injects a ghost correlation $(\mu_j^{(\text{new})} - \mu_j^{(\text{old})})(\mu_l^{(\text{new})} - \mu_l^{(\text{old})})$ with a specific directional pattern in the $k \times k$ upper triangle determined by which dimensions shifted most. This ghost correlation is indistinguishable, from the baseline's perspective, from a genuine correlation structure change. The baseline _learns_ the ghost as if it were real, then must _unlearn_ it as $\mu^{(z)}$ converges — creating a **non-monotonic** transient. The raw formulation's monotonic, cleanly interpretable transient is a strictly better property, even though both have $O(t_{1/2})$ timescale.
+>
+> **Magnitude bound under centred binary inputs.** The input encoding (§2.3) is $\{-0.5, +0.5\}^w$ with $\mathbb{E}[x_i] = 0$ under uniform bits. Latent means $\mu_j^{(z)}$ are projections onto learned basis directions — they are tightly bounded and typically small. The cross-product $\mu_j \mu_l$ is therefore small relative to $\operatorname{Cov}(z_j, z_l)$ at steady state. A distributional shift large enough to make $\mu_j \mu_l$ dominate $\Gamma_{jl}$ is a violent regime change that should maximally alarm on every available axis.
+>
+> **State coupling.** With raw second moments, $\Gamma$ is a self-contained EWMA depending only on its own history and the observed products $z_j z_l$. With centred products, both the score and the baseline update depend on $\mu^{(z)}$, creating a hidden coupling between the surprise axis (which uses $\mu^{(z)}$ for scoring) and the coherence axis (which would use it for both scoring and baseline computation). A convergence artefact in the mean estimate would simultaneously corrupt two axes instead of one. The raw formulation keeps the axes' state dependencies more separated.
+>
+> **Coordination-layer exposure.** At the coordination layer (§7), the §7.3 running-mean centring subtracts $\mu^{(\text{in})}_g$ from the raw score matrix before the coordination tracker sees it. During a transition, $\mu^{(\text{in})}_g$ lags the actual group mean, so the tracker's inputs carry residual mean that the centring didn't remove. The coordination tracker's internal $\mu^{(z)}$ then tracks this residual, creating a second lag. The $\mu_j \mu_l$ contamination of coordination-level $\Gamma$ therefore reflects the square of a lagged mean estimate, which can amplify the artefact's persistence relative to the per-cell level. Under gradual drift (the regime where the concern is most relevant), the running-mean centring tracks the linearly increasing group mean with a steady-state lag of $\delta\mu / \alpha$, where $\delta\mu$ is the per-batch mean increment (the standard lag of an EWMA tracking a linear trend). The tracker's inputs therefore carry a constant residual bias of $O(\delta\mu / \alpha)$, and the squared cross-product contamination is $O((\delta\mu / \alpha)^2)$. However, this constant bias is itself absorbed by the coordination tracker's internal statistics — the tracker's own $\mu^{(z)}$ and $\Gamma$ converge to include the bias — so the contamination manifests only during the transient before the tracker reaches its own steady state. Under a sudden shift, the full residual $\Delta\mu$ is visible on the first batch and decays as $\lambda^t \Delta\mu$ — but during this transient, surprise fires simultaneously at the per-cell level, making the coherence artefact redundant. In both regimes, the concern is real but quantitatively minor: the contamination is transient rather than small in absolute terms, and it overlaps temporally with surprise signals that already capture the same event.
+>
+> **Verdict.** The raw formulation trades a small, well-bounded artefact (mean-squared leakage into a second-moment tracker, monotonic decay, invisible at steady state) for the centred formulation's subtler, harder-to-bound artefact (stale-mean ghost correlations, non-monotonic transient, hidden state coupling between axes). The raw formulation is retained.
+
+> _Design note (subspace energy weighting vs. latent EWMA weighting)._ Phase 2 and Phase 3 apply different effective weightings to new data, and this is a deliberate design choice whose consequences bear understanding.
+>
+> **The apparent asymmetry.** In the combined matrix $M = [\sqrt{\lambda}\, U_k \operatorname{diag}(\sigma) \mid X^\top]$, new observations enter at full energy ($\|x_i\|^2 = w/4$ each), while old structure is attenuated by $\sqrt{\lambda}$. In Phase 3, the standard EWMA $\mu \leftarrow \lambda\mu + \alpha\bar{Z}$ weights new data at $\alpha = 1 - \lambda$. At first glance, the subspace gives the current batch weight $\sim 1$ while the latent statistics give it weight $\alpha$ — a factor-of-$1/\alpha$ mismatch.
+>
+> **At energy steady state, there is no mismatch.** The SVD operates on the Frobenius norm of $M$. In the steady state where total singular-value energy $\Sigma = \sum \sigma_j^2$ has stabilised, $\Sigma = bw/(4\alpha)$. The new batch's fraction of total energy in $M$ is $(bw/4)/\Sigma = \alpha$ — exactly the EWMA learning rate. Both mechanisms forget old information at rate $\lambda^t$ per step and weight new information at effective fraction $\alpha$. The energy half-lives are identical: $t_{1/2} = \ln 2 / \ln(1/\lambda)$.
+>
+> **But the SVD has a rotational degree of freedom that the EWMA lacks.** The real asymmetry is not in effective sample size but in the _nature_ of the response to structural change. The EWMA is a linear filter: after a regime change, $\mu^{(z)}$ converges to the new mean exponentially with time constant $t_{1/2}$, regardless of how different the new regime is. The SVD is a nonlinear rank-ordered decomposition: it can _swap_ basis directions in a single step once a new direction's singular value exceeds a decaying old direction's. This discrete jump has no analog in the continuous EWMA.
+>
+> **The transient surprise spike after regime changes.** After a sudden distributional shift, the subspace may rotate substantially within a few batches — new directions appear in $U$ once their energy dominates decaying old singular values. But $\mu^{(z)}$ and $\nu^{(z)}$ carry state from the old basis. After a direction swap, $\mu^{(z)}_j$ was tracking the mean projection onto old direction $u_j^{(\text{old})}$; it is now being updated with projections onto the new direction $u_j^{(\text{new})}$. The stale reference produces a transient spike in the surprise score $(z_j - \mu_j)^2 / \nu_j$ that persists for $O(t_{1/2})$ batches while the EWMA converges.
+>
+> **This is the correct behaviour, for three reasons.**
+>
+> First, the spike is _informative_. It correctly signals "observations that don't match the historical within-subspace distribution" — which is literally true during a regime change. A system that silently absorbed regime changes would fail at the core detection task. The drift accumulator (§6.3) distinguishes transient spikes (which don't accumulate much) from sustained shifts (which do).
+>
+> Second, the subspace _must_ adapt its directions faster than the EWMA adapts its scalars. An incorrect basis direction wastes an entire detection axis — every projection onto a stale direction is uninformative. An incorrect latent mean is a quantitative error within a still-meaningful projection. The SVD's ability to rapidly discover new structure is why it is used instead of a linear update for the subspace.
+>
+> Third, matching the rates would be strictly worse. Scaling new data by $\alpha$ in $M$ (giving $M = [\sqrt{\lambda}\, U_k \operatorname{diag}(\sigma) \mid \sqrt{\alpha}\, X^\top]$) would require $\sim 1/\alpha$ batches of coherent new-direction data before the SVD even recognised its existence — eliminating the system's ability to detect emerging structure promptly.
+>
+> **Consequences for the host.** During regime transitions, expect elevated surprise z-scores for $O(t_{1/2})$ batches as the latent statistics reconverge. The fast EWMA baseline (§6.1) absorbs this transient — the z-scores are computed against the fast baseline, which itself adapts at rate $\lambda$. The drift accumulator may register modest growth during the transient, bounded by $\sim t_{1/2} \times (\text{spike magnitude} - \kappa)$; the slow baseline (§6.2) eventually absorbs the shift and the accumulator returns to zero. Hosts that expect frequent regime changes (e.g., diurnal patterns) should interpret surprise drift accumulation in light of the known transition schedule.
+>
+> Note: under the EWMA-mean-centred variance formula (Phase 3 above), the surprise spike's persistence is shorter than described above. After the initial batch fires at full strength (scored against the prior $\nu$ in Phase 1), subsequent batches' $\nu$ co-adapts — inflating as the stale $\mu^{(z)}$ inflates the $(z - \mu)^2$ terms — progressively dampening the surprise ratio. Displacement inherits the primary detection role during transitions. The first-batch detection at full strength is preserved; the $O(t_{1/2})$ sustained elevation is not.
+>
+> _The asymmetry is not between two forgetting rates but between a linear filter (EWMA) that can only exponentially forget and a nonlinear decomposition (SVD) that can structurally reorganise. The latent statistics inherit the SVD's directional decisions but adapt their scalar parameters at the EWMA rate — a deliberate separation of concerns between "which directions matter" (fast, nonlinear) and "what the typical projection onto those directions looks like" (smooth, linear)._
+
+#### Phase 4 — Update Baselines and Drift Detector
+
+Each scoring axis maintains a fast EWMA, slow EWMA, drift accumulator, and clip-pressure EWMA (§6). **Exception:** coherence baselines are not updated while $k < 2$ (§5.5).
+
+#### Phase 5 — Adapt Rank
+
+Rank is recomputed every $T_{\text{rank}}$ steps (the rank update interval):
+
+1. Compute cumulative energy fractions:
+
+$$c_i = \frac{\sum_{j=1}^{i} \sigma_j^2}{\sum_{j=1}^{\text{cap}} \sigma_j^2 + \varepsilon} \qquad i = 1, \ldots, \text{cap}$$
+
+2. Find the target rank:
+
+$$k^* = \min\!\big\{i : c_i \geq \tau\big\} + 1 \qquad \text{clamped to } [1, \text{cap}]$$
+
+If the set $\{i : c_i \geq \tau\}$ is empty, then $k^* = \text{cap}$. This requires total energy $\sum \sigma_j^2 < \varepsilon \tau / (1 - \tau)$ — with the default $\varepsilon = 10^{-6}$ and $\tau = 0.9$, total energy below $9 \times 10^{-6}$. Since each Phase 2 evolve step injects fresh batch energy into the SVD, this threshold is not reachable during normal operation; the fallback exists as a safety net against numerical edge cases or future algorithm changes. (Approximately equal nonzero singular values produce a _non-empty_ set — the threshold is met at high index, yielding $k^* \approx \text{cap}$ via the normal path; see §A.5.)
+
+> _Design note (the $+1$ buffer dimension)._ The $+1$ ensures the active subspace always extends one dimension beyond the energy threshold. This serves three purposes.
+>
+> First, it **enables coherence** (§5.5). Coherence requires $k \geq 2$, so without the buffer a tracker whose energy is dominated by a single direction would be permanently stuck at $k = 1$ with no off-diagonal detection capability. The $+1$ guarantees that a single dominant component yields $k^* = 2$, not $k^* = 1$.
+>
+> Second, it provides **early visibility into emerging structure**. The dimension just past the energy threshold is the first place new non-noise structure will appear as the data distribution evolves. By including it in the active subspace, the system can detect that emergence through the within-subspace axes (displacement, surprise, coherence) rather than relying solely on the coarser novelty axis, which only measures aggregate residual energy.
+>
+> Third, it acts as a **stability margin** against threshold boundary oscillation. A dimension whose cumulative energy fraction fluctuates near $\tau$ would cause rank to toggle on every adaptation step without the buffer; the extra dimension absorbs this boundary noise.
+
+3. Move rank by at most one step:
+
+$$k \leftarrow k + \operatorname{clamp}(k^* - k, \; -1, \; +1)$$
+
+**On rank drop from $\geq 2$ to $1$:** destroy coherence baselines — return the fast EWMA, slow EWMA, drift accumulator, **and clip-pressure EWMA** for the coherence axis to their uninitialised state ($\bar{\rho} = 0$). This prevents stale state from a previous $k \geq 2$ epoch from contaminating a future one. Note: under the current rank formula, $k^* \geq 2$ whenever $\text{cap} \geq 2$ (the $+1$ buffer ensures this — see design note above), so this clause cannot fire during normal operation for any tracker with $\text{cap} \geq 2$. It exists as a safety net against implementation defects or future algorithm changes that might introduce a pathway to $k = 1$ from above.
+
+### 4.3 Memory per Tracker
+
+At width $w$ with capacity $\text{cap}$:
+
+| Component                               | Size (elements)              |
+| --------------------------------------- | ---------------------------- |
+| $U$                                     | $w \times \text{cap}$        |
+| $\sigma$                                | $\text{cap}$                 |
+| $\mu^{(z)}, \nu^{(z)}$                  | $2 \times \text{cap}$        |
+| $\Gamma$ (upper triangle)               | $\text{cap}(\text{cap}-1)/2$ |
+| Baselines (§6)                          | $4 \times 8 = 32$            |
+| Scalar state (rank, step counter, etc.) | $\sim 10$                    |
+
+At $w = 96$, $\text{cap} = 16$: approximately 1,750 floating-point elements. Total analysis memory is bounded by $|\mathcal{A}^*| \times (\text{max per-tracker size})$; see §8 for the bound on $|\mathcal{A}^*|$ and §12 for global resource accounting.
+
+---
+
+## Chapter 5. Scoring
+
+### 5.1 Polarity Invariant
+
+All four scoring axes share a **polarity invariant**: higher values indicate greater anomalous departure from baseline. A score of zero (or near zero) is maximally normal; positive excursions indicate increasing anomaly.
+
+This invariant is essential for the baseline tracking mechanism (§6): the upper-tail outlier filter prevents sustained high scores from poisoning baselines, which requires that anomalous behaviour consistently produces _high_ scores, never low.
+
+### 5.2 Novelty
+
+$$\text{novelty}_i = \frac{\|\mathbf{x}_i - \hat{\mathbf{x}}_i\|^2}{w - k}$$
+
+Average residual energy per orthogonal degree of freedom. **Measures:** unexplained structure — energy outside the learned subspace. **Range:** $[0, \infty)$. **Requires:** $k \geq 1$.
+
+**Degeneracy at $k = w$.** When $\text{cap} = w$ (which requires $r_{\max} \geq w$), rank adaptation (§4.2, Phase 5) can push $k$ to $w$. The residual $R_i = 0$ identically and the denominator $w - k = 0$, giving the indeterminate form $0/0$. Implementations define $\text{novelty}_i = 0$ in this case (clamping the denominator to 1). The axis carries no information — detection relies entirely on the three within-subspace axes. This condition is reported as _novelty-saturated_ (§14.7) and arises naturally at coordination trackers ($w = 4$) and deep cells with generous $r_{\max}$.
+
+### 5.3 Displacement
+
+$$\text{displacement}_i = \frac{\|\mathbf{z}_i\|^2}{k + \|\mathbf{z}_i\|^2}$$
+
+Bounded distance from the subspace origin. **Measures:** total within-subspace energy — how far the observation sits from the learned centre. **Range:** $[0, 1)$. **Requires:** $k \geq 1$.
+
+> _Design note (polarity)._ The natural measure of proximity is $q_i = k / (k + \|\mathbf{z}_i\|^2)$, where anomalies push $q$ downward. This violates the polarity invariant: anomalous values (low $q$) would pass the upper-tail filter (§6.2) and gradually drag the baseline downward, making the departure invisible. The complement $1 - q_i$ restores correct polarity.
+
+### 5.4 Surprise
+
+$$\text{surprise}_i = \frac{1}{k} \sum_{j=1}^{k} \frac{(z_{ij} - \mu^{(z)}_j)^2}{\nu^{(z)}_j + \varepsilon}$$
+
+Average diagonal Mahalanobis deviation. **Measures:** per-dimension magnitude deviation from learned latent means. **Range:** $[0, \infty)$. **Requires:** $k \geq 1$.
+
+Complementary to displacement: an observation can have normal total energy but unusual distribution across dimensions.
+
+### 5.5 Coherence
+
+$$\text{coherence}_i = \frac{2}{k(k-1)} \sum_{j < l} \big(z_{ij} \, z_{il} - \Gamma_{jl}\big)^2$$
+
+Average squared deviation of pairwise latent products from their learned second moments. **Measures:** off-diagonal covariance deviation — unusual combinations of co-activation. **Range:** $[0, \infty)$. **Requires:** $k \geq 2$.
+
+**Defined as $0$ when $k < 2$.** Coherence baselines are not updated while $k < 2$. When rank first reaches 2, baselines begin tracking from the first real coherence values. On rank drop back to 1, baselines are destroyed (§4.2, Phase 5).
+
+### 5.6 Summary
+
+| Axis            | Score        | Range         | Measures                | Requires   |
+| --------------- | ------------ | ------------- | ----------------------- | ---------- |
+| Subspace        | Novelty      | $[0, \infty)$ | Unexplained structure   | $k \geq 1$ |
+| Within-subspace | Displacement | $[0, 1)$      | Distance from centroid  | $k \geq 1$ |
+| Within-subspace | Surprise     | $[0, \infty)$ | Per-dimension magnitude | $k \geq 1$ |
+| Within-subspace | Coherence    | $[0, \infty)$ | Pairwise co-activation  | $k \geq 2$ |
+
+### 5.7 Geometric Picture
+
+```
+Full observation space ℝ^w
+┌───────────────────────────────────────────┐
+│                                           │
+│  Learned subspace ℝ^k                     │
+│  ┌──────────────────┐                     │
+│  │  μ^(z) centroid   │  Residual: x − x̂   │
+│  │    ·              │ ◄─── novelty ───►  │
+│  │   /|              │                     │
+│  │  z |              │                     │
+│  │  ├─┤ displacement │                     │
+│  │  ├─┤ surprise     │                     │
+│  │  z₁·z₂ coherence │                     │
+│  └──────────────────┘                     │
+└───────────────────────────────────────────┘
+```
+
+### 5.8 Why Four Axes
+
+The three within-subspace scores decompose the latent activation pattern along orthogonal statistical concerns:
+
+| Score        | Input                                   | Covariance structure  |
+| ------------ | --------------------------------------- | --------------------- |
+| Displacement | $\|\mathbf{z}\|^2$                      | Total energy (scalar) |
+| Surprise     | $(z_j - \mu_j)^2/\nu_j$ per $j$         | Diagonal              |
+| Coherence    | $(z_j z_l - \Gamma_{jl})^2$ per $j < l$ | Off-diagonal          |
+
+Together they cover the full covariance structure without assembling or inverting a dense $k \times k$ matrix.
+
+A fifth axis — projection energy $\|\hat{\mathbf{x}}_i\|^2/k$ — is omitted because the constant-norm property (§2.5) makes it a perfect affine function of novelty. It carries zero independent information and violates the polarity invariant.
+
+---
+
+## Chapter 6. Baseline Tracking and Drift Detection
+
+Each scoring axis maintains four components: a fast EWMA for instantaneous z-scores, a slow EWMA as a long-memory reference, a one-sided drift accumulator for detecting gradual shifts, and a clip-pressure EWMA $\bar{\rho}$ that tracks the fraction of samples clipped per axis (§6.4). Together, these provide normalised scoring (z-scores), temporal persistence (drift detection), and adaptive clip-width modulation on top of the raw scores from §5.
+
+### 6.1 Fast EWMA
+
+The fast EWMA tracks running mean $\bar{s}$ and variance $\bar{v}$ at decay $\lambda$.
+
+#### 6.1.1 Update Rule
+
+**Clip-pressure state.** Each axis maintains a clip-pressure EWMA $\bar{\rho}$, initialized to $0$ at tracker creation. At warm-up completion (§11.4), $\bar{\rho}$ is reset to $0$ alongside the CUSUM reset and slow-from-fast seeding. On rank drop from $\geq 2$ to $1$, the coherence axis's $\bar{\rho}$ is destroyed alongside the coherence baselines (§4.2, Phase 5).
+
+Given per-sample scores $\mathbf{s} = (s_1, \ldots, s_b)$ from one batch:
+
+**1. Compute effective clip ceiling.** Let $p = \max(\eta, \bar{\rho})$ where $\eta$ is the noise influence (§11.5) and $\bar{\rho}$ is the clip-pressure EWMA for this axis. Compute:
+
+$$n_\sigma^{\text{eff}} = n_\sigma\left(1 + \frac{p}{1 - p + \varepsilon}\right)$$
+
+**When the baseline is uninitialised** (no valid $\bar{s}$ or $\bar{v}$), skip clipping entirely — placeholder values are not a meaningful reference. When clipping is skipped (uninitialised baseline), set $\rho_t = 0$ and update $\bar{\rho}$ normally. This causes $\bar{\rho}$ to decay toward zero during the uninitialised phase, ensuring no residual pressure when the baseline initialises.
+
+**When the baseline is initialised**, compute the clip ceiling:
+
+$$c_{\text{clip}} = \bar{s} + n_\sigma^{\text{eff}} \sqrt{\bar{v}}$$
+
+Retain samples with $s_i < c_{\text{clip}}$. Compute the batch clip ratio $\rho_t = n_{\text{clipped}} / b$.
+
+**If all samples are rejected**, the batch is processed without clipping (degenerate lockout guard — fires only during the first few batches of an extreme shift before $\bar{\rho}$ has risen sufficiently). Set $\rho_t = 1$.
+
+Update the clip-pressure EWMA:
+
+$$\bar{\rho} \leftarrow \lambda_\rho \, \bar{\rho} + (1 - \lambda_\rho) \, \rho_t$$
+
+The filter is **upper-tail only** because all four scoring axes are non-negative, right-skewed, and satisfy the polarity invariant (§5.1): anomalous departure inflates scores, never deflates. The clip ceiling prevents sustained high scores from poisoning the baseline upward.
+
+**2. Fast EWMA update.** Let $\bar{s}_{\text{batch}}$ be the mean of **retained** samples (or all samples if the degenerate guard fired).
+
+- _Uninitialised → initialised:_ $\bar{s} \leftarrow \bar{s}_{\text{batch}}$
+- _Subsequent:_ $\bar{s} \leftarrow \lambda \, \bar{s} + \alpha \, \bar{s}_{\text{batch}}$
+
+**3. Variance update.** Let $\bar{v}_{\text{batch}}$ be the population variance of **retained** samples (requires $\geq 2$ retained; otherwise **freeze**: leave $\bar{v}$ unchanged — see design note below).
+
+- _Uninitialised → initialised:_ $\bar{v} \leftarrow \max(\bar{v}_{\text{batch}}, \; 10^{-4})$
+- _Subsequent:_ $\bar{v} \leftarrow \lambda \, \bar{v} + \alpha \, \max(\bar{v}_{\text{batch}}, \; 10^{-4})$
+
+The $10^{-4}$ floor prevents degenerate zero-variance baselines.
+
+> _Design note (variance freeze on < 2 retained)._ When clipping retains fewer than 2 samples, $\bar{v}$ is neither updated nor decayed — it holds its last successfully computed value. Three alternatives were considered:
+>
+> 1. **Pure decay** ($\bar{v} \leftarrow \lambda\,\bar{v}$). This creates a positive feedback loop: shrinking variance → tighter clip ceiling → more clipping → fewer retained → more decay. The loop is self-reinforcing and can collapse variance to near-zero, worsening the very lockout condition that caused the skip.
+> 2. **Decay with floor injection** ($\bar{v} \leftarrow \lambda\,\bar{v} + \alpha \cdot 10^{-4}$). The steady-state variance converges to $10^{-4}$ regardless of $\lambda$, which is 1–3 orders of magnitude below realistic axis variances ($\sim 0.001$–$0.1$). The collapse from $\bar{v} = 0.01$ to $\bar{v} = 0.0001$ inflates z-scores by $\sim$10× and tightens the clip ceiling by nearly the same factor — a milder but structurally similar failure to pure decay. Making the injection proportional to the current $\bar{v}$ approximates a freeze with extra arithmetic and a slow downward bias.
+> 3. **Single-sample deviation proxy** ($\bar{v} \leftarrow \lambda\,\bar{v} + \alpha \cdot \max\!\big((s_{\text{retained}} - \bar{s})^2,\; 10^{-4}\big)$). This tracks the right order of magnitude during regime transitions. However, the retained sample passed the clip filter and is bounded by $c_{\text{clip}} - \bar{s} = n_\sigma^{\text{eff}}\sqrt{\bar{v}}$, so the proxy is bounded by $(n_\sigma^{\text{eff}})^2 \bar{v}$. During early lockout (before clip-pressure has risen, $n_\sigma^{\text{eff}} \approx n_\sigma$), the retained sample is likely near $\bar{s}$, causing systematic variance underestimation — a mild version of option 1's failure mode in exactly the phase where it is least tolerable. This alternative merits evaluation if small-batch deployments reveal the freeze to be problematic; it is not adopted here.
+>
+> The freeze produces a stale-but-data-derived estimate rather than a convergence target that is aggressively wrong. Its failure mode is **directionally asymmetric**: if the old regime had high variance, the frozen $\bar{v}$ suppresses z-scores and reduces detection sensitivity in the new regime; if the old regime had low variance, the frozen $\bar{v}$ inflates z-scores and false alarms. The freeze is least-bad _on average_ across regime transitions precisely because you do not know which direction the regime shifted.
+>
+> Critically, the freeze window is **self-limiting** under the clip-pressure mechanism (§6.4): once $\bar{\rho}$ rises enough to widen the ceiling, more samples are retained and variance updates resume. The staleness duration is bounded by the clip-pressure recovery time ($\sim$20–40 batches at $\lambda_\rho = 0.95$), not by any property of the variance itself. During this window, detection sensitivity is calibrated to the _previous_ regime's spread. The host can observe this condition via the clip-pressure value ($\bar{\rho}$) in the per-axis report (§14.4).
+
+**4. Slow EWMA update.** Using the same **retained** samples from step 1 (single shared clip filter — see design note below), update slow mean and slow variance at rate $\lambda_s$ following the same rules as steps 2–3.
+
+**5. Drift accumulator update.** Using the **raw** batch mean $\bar{s}_{\text{raw}} = \frac{1}{b}\sum_{i=1}^b s_i$ (all $b$ samples, no clipping):
+
+$$S_t = \max\!\Big(0, \; S_{t-1} + \big(\bar{s}_{\text{raw}} - \bar{s}_{\text{slow}}\big) - \kappa_\sigma \sqrt{\bar{v}_{\text{slow}}}\Big)$$
+
+> _Design note (single shared clip filter)._ Both the fast and slow EWMAs receive samples from the same clip filter, computed against the fast EWMA's clip-pressure-adjusted ceiling. This is a deliberate departure from independent per-EWMA clipping. The slow EWMA's conservatism is provided by its longer time constant ($\lambda_s > \lambda$), not by independent input filtering. Independent slow-EWMA clipping would create a secondary lockout surface that the clip-pressure mechanism cannot reach. See §6.4 for the contamination analysis.
+
+> _Design note (pre-clip drift accumulator input)._ The drift accumulator uses the raw batch mean because it is a detector, not an estimator. Its output $S$ flows outward to the report (§14.5) and is never fed back into any model. Clipping protects estimators from contamination; it makes detectors blind. The raw input ensures the accumulator registers the full departure magnitude during the first batches of a shift, before the clip-pressure mechanism has opened the ceiling.
+
+#### 6.1.2 Z-Score Computation
+
+$$\zeta(s) = \frac{s - \bar{s}}{\sqrt{\bar{v}} + \varepsilon}$$
+
+Two z-scores per axis per batch:
+
+- $\zeta(\max_i s_i)$ — loudest alarm in the batch.
+- $\zeta(\bar{s}_{\text{batch}})$ — sustained elevation of the batch.
+
+### 6.2 Slow EWMA
+
+Each axis maintains a second EWMA at decay $\lambda_s > \lambda$ (per-tracker) or $\lambda_{s,m} > \lambda$ (coordination). The slow EWMA provides the reference for the drift accumulator.
+
+The constraint $\lambda_s > \lambda$ is structural — the slow baseline must have strictly longer memory than the fast one.
+
+| Decay               | Half-life           | Role           |
+| ------------------- | ------------------- | -------------- |
+| $\lambda = 0.99$    | $\approx 69$ steps  | Fast baseline  |
+| $\lambda_s = 0.999$ | $\approx 693$ steps | Slow reference |
+
+**Update rule.** The slow EWMA receives the same retained samples as the fast EWMA (single shared clip filter) and applies the same EWMA update at rate $\lambda_s$. The complete per-batch procedure is specified in §6.1.1, step 4.
+
+### 6.3 CUSUM Drift Accumulator
+
+One-sided Page's test detecting sustained upward drift of raw batch means from the slow baseline:
+
+$$S_t = \max\!\Big(0,\; S_{t-1} + \big(\bar{s}_{\text{raw},t} - \bar{s}_{\text{slow},t}\big) - \kappa\Big)$$
+
+where $\bar{s}_{\text{raw},t}$ is the **raw** (pre-clip) batch mean of all $b$ samples, and $\kappa = \kappa_\sigma \cdot \sqrt{\bar{v}_{\text{slow},t}}$ is the noise allowance. [This formula is repeated from §6.1.1, step 5 for self-contained reference.]
+
+**Input signal.** The drift accumulator uses the **raw** (pre-clip) batch mean as its input. The complete per-batch procedure is specified in §6.1.1, step 5. The raw-input choice is analysed in §6.4.
+
+Under normal conditions, fluctuations are absorbed by $\kappa$. Under a gradual shift, $\bar{s}_{\text{raw}}$ consistently exceeds $\bar{s}_{\text{slow}} + \kappa$, and the accumulator grows monotonically.
+
+> _Design note (why dual-EWMA, not a frozen reference)._ A frozen checkpoint would require manual host resets after legitimate regime changes — operational burden and a policy decision. A slow EWMA adapts automatically, just slowly enough to catch shifts before absorption. After a legitimate change, the slow baseline catches up and the accumulator returns to zero without intervention.
+
+Three reset mechanisms:
+
+1. **Automatic.** The slow baseline eventually absorbs a legitimate regime change, driving $S \to 0$.
+2. **Host-initiated.** The host inspects, decides the shift is legitimate, and zeroes $S$.
+3. **Post-warm-up seeding.** After initial warm-up completes, the slow EWMA is seeded from the fast EWMA's converged values and $S$ is reset. The rationale and procedure are specified in §11.
+
+### 6.4 Clip-Pressure Dynamics and Contamination Trade-offs
+
+The clip-pressure mechanism (§6.1.1) introduces a controlled trade-off between baseline lockout recovery and contamination resistance. This section analyses the dynamics under three regimes and the feedback paths through which contamination propagates.
+
+#### 6.4.1 Sustained Lockout Recovery
+
+Under a regime shift that clips 100% of samples ($\rho_t = 1.0$ every batch), $\bar{\rho}$ rises monotonically and the ceiling opens progressively:
+
+| Batches | $\bar{\rho}$ | $n_\sigma^{\text{eff}}$ (at $n_\sigma = 3$) | Status                     |
+| ------: | -----------: | ------------------------------------------: | -------------------------- |
+|       0 |         0.00 |                                         3.0 | Locked                     |
+|       3 |         0.14 |                                         3.5 | Softening                  |
+|       7 |         0.30 |                                         4.3 | Opening — some data passes |
+|      14 |         0.51 |                                         6.1 | Ceiling doubled            |
+|      20 |         0.64 |                                         8.3 | Wide — most data passes    |
+|      30 |         0.79 |                                          14 | Effectively unclipped      |
+|      40 |         0.87 |                                          23 | Baseline actively tracking |
+
+The degenerate lockout guard (§6.1.1, step 1) fires during the first few batches before $\bar{\rho}$ has risen enough to widen the ceiling. Once $\bar{\rho} > 0.3$, the graduated ceiling handles recovery without the guard.
+
+#### 6.4.2 Transient Spike Recovery
+
+A single anomalous batch ($\rho_t = 1.0$ for one batch, then $\rho_t = 0$) produces minimal ceiling disturbance:
+
+|     Batch | $\rho_t$ | $\bar{\rho}$ | $n_\sigma^{\text{eff}}$ |
+| --------: | -------: | -----------: | ----------------------: |
+| 1 (spike) |      1.0 |         0.05 |                    3.16 |
+|         7 |        0 |         0.04 |                    3.12 |
+|        15 |        0 |         0.02 |                    3.07 |
+
+The peak ceiling widening (5%) is negligible. Self-correcting within ~15 batches.
+
+#### 6.4.3 Oscillatory Convergence Under Moderate Shifts
+
+When a regime shift clips a substantial fraction but not all samples (e.g., 50–80%), convergence is oscillatory rather than monotonic:
+
+1. $\bar{\rho}$ rises → ceiling widens → some extreme samples pass through.
+2. Baseline absorbs retained data, shifts toward new regime.
+3. Fewer samples are clipped (less extreme relative to updated baseline) → $\bar{\rho}$ decays.
+4. Ceiling tightens — but baseline hasn't fully converged.
+5. Clipping resumes at a moderate rate → $\bar{\rho}$ rises again.
+6. Cycle repeats with decreasing amplitude.
+
+The oscillation is inherent when $\lambda_\rho < \lambda$: the ceiling responds faster than the baseline adapts. Each cycle moves the baseline closer to the new regime. The baseline converges monotonically through the oscillation — only the ceiling oscillates.
+
+At $\lambda_\rho = 0.95$ and $\lambda = 0.99$, each ceiling-open cycle lasts approximately 14 batches (the clip-pressure half-life), during which the baseline absorbs $1 - \lambda^{14} \approx 13\%$ of the remaining gap. Typical moderate shifts (initial clip rate 40–70%) converge within 3–5 oscillatory cycles (~60–100 batches). Near-total lockout shifts (initial clip rate above 90%) take proportionally longer — roughly 8–12 cycles (~120–170 batches) — because the ceiling must open much wider before substantial data passes.
+
+The damping condition for non-oscillatory convergence is $\lambda_\rho \geq \lambda$, but satisfying this at $\lambda = 0.99$ would require $\lambda_\rho \geq 0.99$ (half-life ~69 batches), making lockout recovery unacceptably slow. The oscillatory regime at $\lambda_\rho = 0.95$ is the correct trade-off.
+
+The drift accumulator is unaffected by ceiling dynamics — it uses raw batch means (§6.1.1, step 5) and sees the full departure throughout the oscillation.
+
+#### 6.4.4 Contamination Feedback Paths
+
+Under elevated clip pressure, the widened ceiling admits a larger fraction of extreme scores, which shift the baselines. The shifted baselines affect three downstream quantities:
+
+1. **Z-score sensitivity.** A contaminated fast EWMA mean $\bar{s}$ closer to adversarial score values produces smaller z-scores for those observations. Detection sensitivity for adversarial traffic is reduced in proportion to the contamination.
+
+2. **Clip ceiling elevation.** The ceiling $c_{\text{clip}} = \bar{s} + n_\sigma^{\text{eff}} \sqrt{\bar{v}}$ rises with $\bar{s}$ and $\bar{v}$, admitting slightly more extreme observations in subsequent batches. This is a mild positive feedback loop, bounded by the clip-pressure mechanism's self-correcting dynamics: as clipping decreases, $\bar{\rho}$ decays, tightening $n_\sigma^{\text{eff}}$ toward $n_\sigma$.
+
+3. **Drift noise allowance.** The allowance $\kappa = \kappa_\sigma \sqrt{\bar{v}_{\text{slow}}}$ increases if the slow EWMA's variance is contaminated, reducing drift accumulator sensitivity. This is a second-order effect (variance contamination is slower than mean contamination) but reduces the system's ability to detect gradual shifts during sustained adversarial presence.
+
+#### 6.4.5 Contamination vs. Bias
+
+**Under hard clipping** with a sustained adversary controlling fraction $f$ of observations, the baseline tracks only the clean $(1-f)$ fraction — biased but uncontaminated. Z-scores for clean observations near the clipping boundary are systematically inflated, generating false positives for legitimate traffic. Adversarial observations are invisible to all baseline machinery including the drift accumulator.
+
+**Under clip-pressure modulation**, the baseline tracks the actual observation mixture — slightly contaminated but unbiased. Z-scores for both clean and adversarial observations are measured against the true mixture distribution. The drift accumulator (using raw means) reports the full departure regardless.
+
+For "Measure, don't decide" (§1.3), the unbiased estimate is more honest: the host receives baselines that reflect the actual observation distribution, including adversarial influence. A biased baseline misrepresents the distribution to the host. The host can make contamination judgments from the drift accumulator evidence and the per-axis clip-pressure values in the report (§14.4).
+
+> _Design note (one-sided drift detection and sub-clip contamination)._ The CUSUM drift accumulator (§6.3) is deliberately one-sided upward, consistent with the polarity invariant (§5.1). It detects the _rate_ at which the fast baseline diverges above the slow baseline — not the _absolute level_ of either baseline. A patient adversary producing sustained sub-clip scores can inflate both baselines in lockstep, keeping the fast-slow gap below $\kappa$ and accumulating zero drift evidence, while reducing the system's sensitivity to future anomalous traffic. This is a known consequence of the adaptive baseline design: the same property that enables automatic adaptation to legitimate regime changes (the slow baseline eventually absorbs any sustained shift, §6.3 reset mechanism 1) also enables slow adversarial contamination. The CUSUM does not — and should not — detect this, because flagging slow baseline drift as suspicious would trigger on every legitimate regime change. Instead, the system reports the slow baseline values (§14.5) and clip-pressure state (§14.4), enabling hosts to build secular-trend monitors externally. See §17.5 for the full evasion assessment.
+
+#### 6.4.6 $\lambda_\rho$ Guidance
+
+The trade-off axis is **detection latency vs. contamination resistance**, parameterized by $\lambda_\rho$:
+
+| $\lambda_\rho$ | Ceiling half-life | Lockout recovery       | Contamination leakage | Regime                  |
+| -------------: | ----------------: | ---------------------- | --------------------- | ----------------------- |
+|           0.90 |        ~7 batches | Fast (~20 batches)     | Higher                | High-churn environments |
+|           0.95 |       ~14 batches | Moderate (~40 batches) | Moderate              | **Default**             |
+|           0.99 |       ~69 batches | Slow (~150 batches)    | Minimal               | Security-critical       |
+
+The default optimizes for the common case where regime shifts are more frequent than sustained adversarial presence.
+
+---
+
+## Chapter 7. Hierarchical Coordination
+
+The coordination tier detects anomalous patterns in the _distribution of scores across spatially related cells_ at every level of the spatial tree hierarchy. It reuses the subspace tracker (§4) at a second level of abstraction: instead of modelling suffix bit vectors, it models cross-cell score summaries.
+
+The coordination tier operates exclusively on the **competitive set** $\mathcal{A}$ (§8), not the full analysis set $\mathcal{A}^*$. Ancestor trackers (§8) provide per-value depth-of-defence; the coordination tier provides cross-cell pattern detection. These are interlocking constraints — their interaction is analysed in §16–17.
+
+---
+
+### 7.1 Coordination Contexts
+
+Every internal node of the spatial tree whose subtree contains **competitively selected** cells in **both** its left and right children's subtrees is a **coordination context**. The contexts form a binary hierarchy mirroring the G-Tree:
+
+```
+                 root                ← sees all K cells
+                /    \
+             g_L      g_R           ← each sees its subtree
+            /   \    /   \
+         ...   ...  ...   ...       ← narrower groups
+          ↓     ↓    ↓     ↓
+        cells cells cells cells
+```
+
+The **coordination group** at context $g$:
+
+$$\mathcal{C}(g) = \big\{c \in \mathcal{A} : c \text{ is in the subtree of } g\big\}$$
+
+A context is **active** when both subtrees contribute: $\mathcal{C}(g.\text{left}) \neq \emptyset$ and $\mathcal{C}(g.\text{right}) \neq \emptyset$.
+
+**Group count.** A binary tree with $K$ leaves has at most $K - 1$ internal nodes, so the number of active contexts is at most $K - 1$. When analysed cells cluster spatially, most internal nodes have cells in only one subtree, and the count is much smaller.
+
+**Nesting.** Groups nest by containment: descendant contexts have subsets of ancestor contexts' groups. A cell at G-Tree depth $d$ participates in at most $d$ contexts.
+
+---
+
+### 7.2 The Coordination Signal
+
+After per-cell scoring (§4.2, Phase 1), each **competitive** cell $c \in \mathcal{A}$ that processed observations has a 4-dimensional summary:
+
+$$\mathbf{o}_c = \big(\bar{s}_{c,\text{nov}},\; \bar{s}_{c,\text{disp}},\; \bar{s}_{c,\text{surp}},\; \bar{s}_{c,\text{coh}}\big) \in \mathbb{R}^4$$
+
+These are **raw batch mean scores** — not z-scores. The coordination tracker learns its own normalisation.
+
+For each active context $g$ with reporting group $\mathcal{C}_{\text{active}}(g) = \{c \in \mathcal{C}(g) : c \text{ has scores this batch}\}$, assemble:
+
+$$O_g = \begin{pmatrix} \mathbf{o}_{c_1} \\ \vdots \\ \mathbf{o}_{c_m} \end{pmatrix} \in \mathbb{R}^{m \times 4}$$
+
+**Cells with no observations in a given batch are excluded.** Only cells that processed at least one observation contribute rows. The group size $m$ may vary batch to batch. A context with $m < 2$ reporting cells in a given batch skips coordination for that batch.
+
+---
+
+### 7.3 Running-Mean Centring
+
+Each context maintains a 4-dimensional EWMA reference $\mu^{(\text{in})}_g \in \mathbb{R}^4$ at decay $\lambda$:
+
+$$O_g^{(\text{c})} = O_g - \mathbf{1}_m \cdot \big(\mu^{(\text{in})}_g\big)^\top$$
+
+After feeding the tracker, update:
+
+$$\mu^{(\text{in})}_g \leftarrow \lambda \, \mu^{(\text{in})}_g + (1 - \lambda) \, \text{colmeans}(O_g)$$
+
+On the first batch, $\mu^{(\text{in})}_g \leftarrow \text{colmeans}(O_g)$.
+
+Running-mean centring detects both **differential** coordination (some cells anomalous, others normal — unusual structure in the centred vectors) and **uniform** coordination (all cells shifting together — the EWMA lags behind the shift, creating bias the tracker sees as elevated displacement). Per-batch centring would destroy the uniform signal.
+
+---
+
+### 7.4 Bottom-Up Assembly
+
+Coordination propagates from the leaves of the spatial tree toward the root. At each active context, the centred score matrix is assembled from the context's reporting group and fed through the context's coordination tracker.
+
+```
+procedure PropagateCoordination(g, CellScores):
+    Reports ← empty list
+
+    if g has no spatial children:
+        if g is in the competitive set and has scores:
+            return Reports, Cells = [(g, CellScores[g])]
+        return Reports, Cells = empty
+
+    LeftReports, LeftCells  ← PropagateCoordination(g.left, CellScores)
+    RightReports, RightCells ← PropagateCoordination(g.right, CellScores)
+    Reports ← LeftReports concatenated with RightReports
+
+    MyCells ← LeftCells concatenated with RightCells
+    if g is in the competitive set and has scores:
+        append (g, CellScores[g]) to MyCells
+
+    -- Fire coordination when both subtrees contribute
+    if LeftCells is non-empty and RightCells is non-empty and |MyCells| ≥ 2:
+        m ← |MyCells|
+        O ← assemble m × 4 matrix from MyCells
+        O_c ← O − 1_m · (μ_in[g])^T                       (§7.3)
+        Report ← g.CoordTracker.Observe(O_c)                (§7.5)
+        μ_in[g] ← λ · μ_in[g] + α · colmeans(O)
+        append Report to Reports
+
+    return Reports, MyCells
+```
+
+For semi-internal spatial nodes (one child), the single child's cells propagate upward without triggering coordination.
+
+> _Implementation note._ The pseudocode recurses through the full spatial tree for clarity, visiting $|G|$ nodes at $O(1)$ each. An implementation should walk only the reduced Steiner tree of the investment set (§8.2) — whose internal branching nodes are exactly the active coordination contexts — visiting $O(|\mathcal{I}|)$ nodes rather than $|G|$. Note that competitive cells that are internal G-Tree nodes (§8.8) sit on the Steiner tree as interior nodes, not leaves; a bottom-up walk must inject their score contributions at the appropriate merge point rather than expecting them to propagate from below.
+
+---
+
+### 7.5 The Coordination Tracker
+
+Each active context maintains a subspace tracker (§4) with analysis width $w = 4$ and capacity $\text{cap} = \min(4, r_{\max})$. It runs the identical five-phase core loop, operating on $O_g^{(\text{c})} \in \mathbb{R}^{m \times 4}$ — centred score summaries — rather than suffix bit vectors.
+
+At this level, the four axes measure:
+
+| Coordination Axis | What It Measures                                                     |
+| ----------------- | -------------------------------------------------------------------- |
+| **Novelty**       | A _new kind_ of cross-cell score pattern not in the learned subspace |
+| **Displacement**  | The group's mean score vector departed from its historical position  |
+| **Surprise**      | A specific axis is systematically anomalous across cells             |
+| **Coherence**     | An unusual _combination_ of axis elevations across cells             |
+
+Each axis carries its own fast EWMA ($\lambda$), slow EWMA ($\lambda_{s,m}$), drift accumulator, and clip-pressure EWMA ($\lambda_\rho$).
+
+> _Steady-state novelty saturation._ At $w = 4$ with $\text{cap} = 4$, the coordination tracker's rank adaptation commonly reaches $k = 4$ in steady state, at which point the novelty axis is saturated (§5.2). This is expected for moderate-to-large groups ($m \geq 4$) with diverse cross-cell traffic, where all four score-space dimensions carry comparable variance. For small groups ($m = 2$–$3$) with heterogeneous member cells, the novelty axis may remain active with 1 residual DOF, detecting unusual differential score-fluctuation directions. In both regimes, the three within-subspace axes (displacement, surprise, coherence) cover the coordination tier's primary detection mission — identifying optimisation-induced cross-cell correlations (§17.2). Hosts should interpret a consistently novelty-saturated coordination tracker as normal operation, not as a detection gap. The novelty-saturated flag (§14.7) and the scoring geometry distribution (§14.11.1) provide the reporting mechanism.
+
+---
+
+### 7.6 Multi-Scale Detection
+
+The hierarchy's power derives from complementary sensitivity profiles at different scales.
+
+**Localised anomaly** (affecting a small cluster of cells):
+
+| Level                         | Sensitivity                   | Mechanism                                |
+| ----------------------------- | ----------------------------- | ---------------------------------------- |
+| Immediate parent ($m \sim 2$) | **Strong** — minimum dilution | Direct pattern change                    |
+| Grandparent ($m \sim 4$–8)    | **Moderate**                  | Novelty: pattern not in learned subspace |
+| Root ($m = K$)                | **Negligible**                | Diluted across all cells                 |
+
+**System-wide shift** (all cells shifting):
+
+| Level                     | Sensitivity                            | Mechanism                                        |
+| ------------------------- | -------------------------------------- | ------------------------------------------------ |
+| Leaf pairs ($m = 2$)      | **Slow** — fast EWMA adapts quickly    | Displacement rises                               |
+| Mid-level ($m \sim 8$–32) | **Moderate** — SNR $\propto \sqrt{m}$  | Common-mode accumulation                         |
+| Root ($m = K$)            | **Strongest** — SNR $\propto \sqrt{K}$ | Aggregate displacement; noise cancels coherently |
+
+**The complementarity principle.** Localised anomalies are caught by small groups (high per-cell sensitivity). Global shifts are caught by large groups (high signal-to-noise through aggregation). The hierarchy covers the full spatial spectrum without configuration.
+
+**Distinguishing partial from uniform coordination.** Compare per-cell and coordination signals. Partial coordination shows elevated per-cell drift accumulators in a subset with strong local coordination novelty. Uniform coordination shows individually unremarkable per-cell z-scores with strong root-level displacement. The _level_ at which coordination peaks indicates the _scale_ of the coordinated behaviour.
+
+---
+
+### 7.7 Lifecycle
+
+Coordination contexts are **lazily materialised** during the Step 5 coordination walk (§9.1) and **pruned** after the walk completes. No explicit activation or deactivation calls appear in Steps 0–4.
+
+#### 7.7.1 Lazy Materialisation
+
+When `PropagateCoordination` (§7.4) walks the reduced Steiner tree and encounters an internal node whose left and right subtrees both contribute competitive cells with scores in this batch, it checks whether a coordination context already exists for that node. If not, a fresh context is created — comprising a coordination tracker (§7.5), a running-mean reference $\mu^{(\text{in})}_g$, and associated state — and **inline-warmed** per §11.7 before the first real observation feeds the tracker. The inline warm-up runs to completion within the same Step 5 invocation.
+
+This lazy model eliminates the bookkeeping of tracking coordination-eligible membership across phases. The walk itself discovers which contexts are needed, creates them on demand, and proceeds to use them — all within a single bottom-up pass.
+
+#### 7.7.2 Post-Walk Pruning
+
+After `PropagateCoordination` completes, contexts whose topology no longer warrants them — because one subtree no longer contains any online competitive cells — are **destroyed**, along with their coordination tracker and running-mean reference. The criterion is _membership_: whether online competitive cells exist in both subtrees, not whether those cells happened to contribute scores in this particular batch (a cell with zero observations in the current batch still counts as a member). On subsequent reactivation (when both subtrees again contribute), a fresh context is created and warmed per §7.7.1.
+
+The rationale parallels per-cell tracker destruction on competitive exit (§8.5): a stale coordination model from a different competitive membership may be actively misleading. The competitive set membership that produced the old tracker's learned subspace, baselines, and drift accumulator state may differ substantially from the membership at reactivation. Preserving the old state would risk false drift evidence (the new score distribution differs from the old baseline) or suppressed detection (the old baseline absorbs anomalous patterns that the new composition would flag).
+
+> _Design note (why not preserve)._ Preservation across deactivation gaps would require tracking which cells contributed to the old model and whether the new group is "close enough" to reuse it — a judgment call that introduces implicit assumptions about distributional continuity. Destruction and re-warm-up is simple, correct, and bounded-cost: the coordination tracker at $w = 4$ warms up quickly (§11.7, §7.8).\_
+
+#### 7.7.3 Membership Changes Within a Cycle
+
+Step 3 reconciliation may change competitive set membership, which affects coordination contexts in two ways:
+
+**Topology invalidation.** If a membership change empties one subtree of an active context — the last competitive cell in that subtree exits $\mathcal{T}$ — the `PropagateCoordination` walk's guard (`LeftCells is non-empty and RightCells is non-empty`) prevents the context from firing. Post-walk pruning (§7.7.2) then destroys the context. No "last firing" occurs; the context transitions directly from active to destroyed.
+
+**Membership reduction.** If a membership change removes one or more cells from a context's group while leaving both subtrees populated, the context fires normally with the reduced group. The coordination tracker's learned model — subspace $U$, baselines, drift accumulator, and running-mean reference $\mu^{(\text{in})}_g$ — was trained on batches that included the departed cell's contributions. The model is therefore slightly stale relative to the current group composition: it expects score-matrix rows from $m_{\text{old}}$ cells and now receives $m_{\text{new}} < m_{\text{old}}$. This staleness is self-correcting: the EWMA adaptation absorbs the membership change over $O(t_{1/2})$ subsequent batches. During the transient, modest baseline disturbance is possible but bounded by the single-member contribution to the group statistics.
+
+The symmetric case — membership _growth_ (a new competitive cell entering a subtree) — produces the analogous model staleness in the opposite direction. If the growth _creates_ a context (previously only one subtree was populated), the lazy materialisation mechanism (§7.7.1) handles it: a fresh context is created and inline-warmed before processing its first real observation.
+
+> _Timing-channel note._ Because Step 3 updates `CurrentCompetitiveSet` before Step 4 populates `CellReports`, and Step 5's walk builds its leaf set exclusively from `CellReports`, no coordination report can contain a cell that has exited the competitive set. This is stronger than the original §7.7.3 claimed: there is no grace-period leak of recently-competitive membership through the coordination tier.
+
+---
+
+### 7.8 Memory and Computation
+
+**Per context:** approximately 200 floating-point elements for the subspace tracker at $w = 4$, plus the 4-element running-mean reference. Total across all contexts: at most $(K - 1) \times (\text{per-context size})$.
+
+**Per-context per-batch computation:** $O(m)$ — the $w = 4$ constraint makes every operation linear in group size. The SVD of an $\mathbb{R}^{4 \times (k+m)}$ matrix costs $O(16(k+m)) \approx 16m$ multiply-adds — less than 0.3% of a single per-cell SVD at $w = 96$.
+
+**Total per-batch coordination work:** $O(K \cdot \bar{d})$ useful work, where $\bar{d}$ is average coordination participation depth. The pseudocode (§7.4) visits the full spatial tree for clarity; an implementation walking the investment set's reduced Steiner tree (§8.2) achieves this bound with $O(|\mathcal{I}|)$ traversal overhead (approaching $O(K)$ under typical spatial clustering). Negligible relative to per-cell scoring in either case.
+
+---
+
+# Part III — Selection and Assembly
+
+---
+
+## Chapter 8. Cell Selection
+
+The analysis selector determines which cells from the spatial layer receive statistical modelling. It maintains two related but distinct sets: the **investment set** (cells that have been allocated trackers and warm-up resources) and the **producing set** (the online subset that actively generates scores). Competitive selection identifies significant cells, ancestor closure guarantees a complete model chain to the root, and the warm-up pipeline (§11) brings invested cells online in g.sum order.
+
+### 8.1 Competitive Selection
+
+Let $\mathcal{E} = \{v \in \text{V-entries} : \text{depth}_V(v) \leq L \;\wedge\; w(v) \geq 2\}$ be the **eligible set** — all V-entries within the depth cutoff whose analysis width $w = N - d$ is at least 2. The $w \geq 2$ predicate excludes cells where the subspace algebra is undefined (§4.1). The **competitive targets** are:
+
+$$\mathcal{T} = \text{top}_K\!\big(\mathcal{E},\; v.\text{importance}\big)$$
+
+If $|\mathcal{E}| \leq K$, then $\mathcal{T} = \mathcal{E}$.
+
+| Parameter             | Constraint | Role                                           |
+| --------------------- | ---------- | ---------------------------------------------- |
+| $K$ (analysis budget) | $\geq 1$   | Maximum number of competitively selected cells |
+| $L$ (depth cutoff)    | $\geq 0$   | V-Tree depth ceiling for eligibility           |
+
+The selection criteria use V-Tree ranking (depth and importance) as the sole competitive mechanism. The $w \geq 2$ predicate is a static geometric precondition excluding cells where the subspace algebra is undefined (§4.1), not a dynamic structural filter. The analysis selector does not inspect G-Tree structural state (terminal, semi-internal, or internal). This is a deliberate design choice; see the design note below.
+
+**Ties at the boundary.** When more than $K$ eligible entries exist, ties in importance at the $K$-th position are broken by the left endpoint of the cell's spatial interval (deterministic, spatially stable).
+
+**Recomputation.** The competitive targets $\mathcal{T}$ are recomputed after each observation pass (§9, Step 2), since splits, evictions, and rebalancing may change V-Tree depths and importance values. The investment set and producing sets are derived from $\mathcal{T}$ during Step 3. An implementation may maintain $\mathcal{T}$ incrementally via a bounded priority structure keyed on importance, updated during rebalancing notifications.
+
+> _Design note (why no G-Tree state filter)._ When a high-importance contour cell splits, the parent becomes an internal G-Tree node with frozen importance — children intercept all spatial-layer routing (§3.7). One might exclude such nodes from $\mathcal{A}$ on the grounds that they receive no routed observations and their slot duplicates what the ancestor closure (§8.2) provides for free. We deliberately do **not** apply this filter, for three reasons:
+>
+> 1. **Single ranking authority.** The V-Tree is the sole arbiter of competitive significance (§3.4). Its depth encodes proven importance relative to the entire neighbourhood. Adding a G-Tree structural predicate means the analysis selector second-guesses the ranking mechanism with spatial-layer implementation state — a cross-layer coupling the architecture otherwise avoids.
+> 2. **The transient is the V-Tree working correctly.** The frozen parent holds a shallow V-Tree position because it _earned_ that position through sustained observation volume. Its children start at zero importance and have not yet demonstrated significance. Selecting the parent during this period is the V-Tree making a factually correct statement: this region has proven importance; its subdivisions have not. The max-uncle constraint (§3.4) and temporal decay (§3.6, §3.8) organically resolve the situation — children's importance grows, the frozen benchmark weakens, the V-Tree restructures, and the parent sinks past $L$ or out of the top-$K$.
+> 3. **Coordination coverage during transition.** If the parent were immediately ejected from $\mathcal{A}$ upon splitting, the highest-importance region in the system would have _no competitive-level representation_ in the coordination tier (§7) until its children earn entry. Retaining the parent provides the only coordination-tier coverage of that region during the transition. Its tracker is fully active — the multi-scale delivery mechanism (§9.3) feeds it every observation in its subtree — so the slot is not dormant.
+>
+> The cost of this choice is that during the transient, one $K$-slot is occupied by a node whose tracker does the same work an ancestor tracker would do for free. This cost is bounded: it persists only until the V-Tree's competitive dynamics push the frozen entry below the selection threshold, which is proportional to $O(\theta / \text{observation\_rate})$ batches under typical decay. At small $K$ the cost is one displaced contour cell; at large $K$ it is negligible. The organic resolution — without cross-layer intervention — is the preferred design.
+
+### 8.2 The Investment Set
+
+The **investment set** closes the competitive targets under spatial tree ancestry:
+
+$$\mathcal{I} = \mathcal{T} \;\cup\; \bigcup_{v \in \mathcal{T}} \text{Ancestors}(v.\text{node})$$
+
+where $\text{Ancestors}(g)$ is the set of all materialised G-Tree nodes on the path from $g$ to the G-Tree root, inclusive. Since the G-Tree is fully materialised (§3.1), a target at depth $d$ contributes ancestors at depths $0, 1, \ldots, d - 1$. Every competitive target receives a **complete chain of allocated trackers** from itself to the root.
+
+The investment set determines resource commitment: every member of $\mathcal{I}$ has a tracker allocated and, if not yet online, is enqueued for warm-up. Not all members of $\mathcal{I}$ produce scores — only online members do (§8.3).
+
+The investment set forms a **Steiner tree** connecting the competitive targets to the root. Its size depends on how much the targets share ancestors and on the G-Tree depth of each target. By the Steiner tree property, the reduced tree (suppressing degree-2 path nodes) connecting $K$ leaves to a common root has at most $K - 1$ internal branching points, yielding at most $2K - 1$ reduced nodes. However, the full investment set includes all materialised intermediate nodes on the paths, which may exceed this.
+
+**Size bound.** Each competitive target at G-Tree depth $d_i$ contributes $d_i$ ancestors; the root is shared by all. Before accounting for sharing:
+
+$$|\mathcal{I}| \leq 1 + \sum_{i=1}^{K} d_i = 1 + K\bar{D}$$
+
+with equality when all $K$ targets share only the root. The reduced Steiner tree connecting $K$ leaves to the root has at most $K - 1$ internal branching nodes and at most $2K - 1$ reduced nodes. The full materialised tree adds chain intermediaries — degree-2 nodes suppressed in the reduction — whose count depends on the depth profile. Under concentrated observation volume, extensive ancestor sharing absorbs chain intermediaries into the shared structure, and the practical investment set size approaches $2K$.
+
+**Example 1 (typical, `depth_create = 3`):**
+
+```
+Root /0                        ← shared by all
+├── /1-A                       ← shared by c₁, c₂
+│   ├── /2-A                   ← shared by c₁, c₂
+│   │   ├── /3-A  ★ c₁
+│   │   └── /3-B  ★ c₂
+│   └── (unresolved)           ← no split warranted
+└── /1-B                       ← unique to c₃
+    └── /2-C                   ← unique to c₃
+        └── /3-C  ★ c₃
+
+Targets: 3 (each at depth 3, D̄ = 3)
+Unique ancestors: 5 (root, /1-A, /2-A, /1-B, /2-C)
+|I| = 8
+Worst-case bound: 1 + 3×3 = 10;  actual: 8 (sharing root, /1-A, /2-A)
+```
+
+Three targets, five unique ancestors. The worst-case bound gives $1 + 3 \times 3 = 10$; the actual count is 8 because the root, /1-A, and /2-A are each shared by multiple targets. The reduced Steiner tree has 5 nodes ($2K - 1$: root, /2-A, and the three targets); the full materialised tree adds 3 chain intermediaries (/1-A, /1-B, /2-C) for a total of 8.
+
+**Example 2 (deep concentration, 256-bit domain):**
+
+```
+Root /0
+│  ... 232 shared levels ...
+Depth 232 (hot region)
+├── branching: ≤ 999 internal nodes
+└── 1000 competitive targets at depth ~235
+
+Targets: 1000    D̄ ≈ 235    Sharing: 232 trunk + ≤999 branch
+Worst-case: 1 + 1000×235 = 235,001
+Actual: 1000 + 999 + 232 = 2231  (sharing absorbs 99%)
+```
+
+All 1000 targets share 232 trunk ancestors. The branching region adds at most $K - 1 = 999$ internal nodes, plus a few chain intermediaries per branch. Total investment: $\sim 2231$, dominated by $2K$. The trunk and branching structure are shared, not multiplied per target — concentration creates the depth that makes sharing inevitable.
+
+**Dimension guard.** Under the $w \geq 2$ eligibility predicate in §8.1, no member of $\mathcal{I}$ can have $w < 2$: every competitive target has $w \geq 2$ by eligibility, and every ancestor has $w' = N - d' > N - d \geq 2$ since ancestors sit at strictly shallower G-Tree depth. This guard is retained as defensive specification against future changes to the eligibility predicate: if any member of $\mathcal{I}$ were to have $w < 2$, no tracker would be allocated, and the exclusion would be reported as a degenerate-cell count (§14.11).
+
+### 8.3 The Producing Sets
+
+The **producing sets** are the online subsets of the investment set:
+
+$$\mathcal{A} = \mathcal{I} \cap \mathcal{T} \cap \text{Online}$$
+
+$$\mathcal{A}^* = \mathcal{I} \cap \text{Online}$$
+
+Only members of $\mathcal{A}^*$ deliver suffix vectors to trackers and emit scores. A cell transitions from invested to producing when its warm-up completes and it is promoted to online status (§11.6).
+
+**Relationship to the investment set.** Every member of $\mathcal{A}^*$ is a member of $\mathcal{I}$. The converse does not hold during warm-up: warming cells are in $\mathcal{I}$ but not in $\mathcal{A}^*$. In steady state with no warming cells, $\mathcal{A}^* = \mathcal{I}$.
+
+**Slot-vacancy invariant.** While any competitive target is warming, the producing competitive set has a corresponding vacancy:
+
+$$|\mathcal{A}| = |\mathcal{T} \cap \text{Online}| = K - |\mathcal{T} \cap \text{Warming}|$$
+
+A warming cell holds a $\mathcal{T}$ slot (resource commitment) without occupying an $\mathcal{A}$ slot (production output). At promotion (§11.6.3, Step 0), the cell transitions to Online, and the next Step 3 recomputation yields $\mathcal{A} = \mathcal{I} \cap \mathcal{T} \cap \text{Online}$ with the promoted cell filling its own formerly vacant slot. No displacement logic is needed: $|\mathcal{A}| \leq K$ is maintained structurally by Step 3's top-$K$ recomputation of $\mathcal{T}$ (§8.1), from which $\mathcal{A}$ is derived by intersection.
+
+> _Design note (no promotion-time displacement)._ Competitive-set turnover — an existing $\mathcal{T}$ member falling below the top-$K$ boundary — can occur in the same batch as a promotion, but the two events are causally independent. The ejection is driven by Step 2's V-Tree mutations (splits, rebalancing, decay) feeding into Step 3's top-$K$ selection, not by the promotion itself. The Step 3 pseudocode (§9.1) contains no displacement step because none is required: `NewCompetitive ← NewTargets ∩ Online` is capped at $K$ by construction.
+
+### 8.4 The Root Tracker
+
+The G-Tree root tracker deserves special attention. It:
+
+- Sees **every** observation — there is no way to avoid it.
+- Operates at width $N$ — the full domain.
+- Has the largest training set — learns the most stable model.
+- Provides the global reference against which everything is measured.
+
+The root tracker answers: _"Does this value look like a normal member of the overall population?"_ Every other tracker answers: _"Does this value look normal for its specific region?"_
+
+A value can be perfectly normal for its region (low /48 scores) but unusual globally (high /0 scores) — for example, if the entire region is unusual. Or it can be unusual locally (high /48 scores) but unremarkable globally (low /0 scores) — a local anomaly within a normal region. The combination is diagnostic.
+
+For evasion analysis: the root tracker is the hardest to evade. It has the most data, the most stable subspace, and captures the broadest correlations. An adversary who successfully mimics local distributional patterns at /48 may still produce an unusual projection at /0, because the root model captures global cross-region correlations that no single cell's model can learn.
+
+The root tracker is **permanent** — created at system initialisation, never destroyed. Its warm-up time is amortised across the system's lifetime.
+
+### 8.5 Entry and Exit
+
+**Investment entry.** When a cell first appears in $\mathcal{I}$ (either as a competitive target or as an ancestor of one):
+
+1. Create a subspace tracker (§4) at width $w = N - d$.
+2. Enqueue for warm-up (§11.6).
+
+**Promotion to producing.** When a warming cell's warm-up completes (§11.6.3):
+
+1. Transition from warming to online.
+2. If the cell is a competitive target, it enters $\mathcal{A}$.
+3. Coordination contexts involving this cell materialise lazily during the next Step 5 coordination walk (§7.7.1).
+
+**Investment exit (eager removal).** When a cell leaves $\mathcal{I}$ — because its competitive target dropped out of the top-$K$ and no other target needs it as an ancestor:
+
+1. **If warming:** cancel warm-up, destroy the tracker immediately.
+2. **If online:** destroy the tracker immediately. Stale coordination contexts are pruned by the Step 5 post-walk pass (§7.7.2).
+
+Destruction is **eager** — it occurs within the Step 3 reconciliation that discovers the exit, not deferred to a later pass. The tracker, its subspace, baselines, drift accumulators, and any in-progress warm-up state are released immediately.
+
+**The root tracker exception.** The root tracker is never destroyed, even if it is temporarily the only member of $\mathcal{I}$. It is created at system initialisation and persists for the system's lifetime.
+
+**No hysteresis.** The V-Tree's max-uncle constraint (§3.4) provides structural stability: demotion requires genuine competitive loss, not transient fluctuation. This structural guarantee _is_ the hysteresis.
+
+**Ancestor stability under sharing.** An ancestor survives in $\mathcal{I}$ as long as **any** competitive target descends from it. Investment churn at the competitive boundary does not destroy shared ancestors. The root's lifetime is the system's lifetime.
+
+### 8.6 Budget Accounting
+
+The budget parameter $K$ governs the **competitive target** selection. Ancestor trackers are not counted against $K$.
+
+**Investment set size.** $|\mathcal{I}| \leq 1 + K\bar{D}$ before sharing (§8.2). The reduced Steiner tree has at most $2K - 1$ nodes; the full materialised tree adds chain intermediaries. Under concentrated observation volume, extensive ancestor sharing brings the practical size close to $2K$.
+
+**Producing set size.** $|\mathcal{A}^*| \leq |\mathcal{I}|$, with equality in steady state (no warming cells).
+
+**Peak memory.** Bounded by $|\mathcal{I}| \times (\text{max per-tracker size})$, since all invested cells — whether warming or online — have allocated trackers. This bound applies during warm-up; in steady state, memory equals $|\mathcal{A}^*| \times (\text{max per-tracker size})$.
+
+Ancestor trackers at coarser depths are more expensive per unit because they have wider suffixes. Taking $N = 128$ and $\text{cap} = 16$ as an example:
+
+| Depth    | Width $w$ | $U$ matrix elements    | Approximate per-tracker size |
+| -------- | --------- | ---------------------- | ---------------------------- |
+| 0 (root) | $N$       | $128 \times 16 = 2048$ | $\sim 2100$ elements         |
+| 16       | $N - 16$  | $112 \times 16 = 1792$ | $\sim 1850$ elements         |
+| 32       | $N - 32$  | $96 \times 16 = 1536$  | $\sim 1600$ elements         |
+| 48       | $N - 48$  | $80 \times 16 = 1280$  | $\sim 1350$ elements         |
+
+Total additional memory for ancestors is roughly proportional to $K$. See §12 for global resource accounting.
+
+### 8.7 Tracker Configuration at Different Levels
+
+Ancestor trackers use the same maximum rank, forgetting factor, and other parameters as competitive trackers by default. A natural gradient is available via optional per-depth overrides: coarser levels see more observations and broader patterns, suggesting higher rank (more principal components for richer structure) and slower forgetting (more stable baselines for the broader population). The root tracker particularly benefits from higher rank — it learns the global structure of the $N$-bit domain, which is likely higher-dimensional than within-cell structure.
+
+This is a configuration refinement. The default — same parameters everywhere — is a reasonable starting point.
+
+### 8.8 Edge Cases
+
+**Cells receiving no observations in a batch.** An analysed cell may receive zero routed observations in a given batch. Its tracker processes no data and emits no scores. Competitive cells with no observations are excluded from that batch's coordination matrices (§7.2).
+
+**Internal G-Tree nodes in the competitive targets.** An internal G-Tree node (both children present) receives no observations via normal spatial routing (§3.10) — its importance is frozen from the moment of its second child's creation (§3.7). However, the competitive selection (§8.1) does not filter by G-Tree state: the V-Tree ranking is the sole eligibility criterion. A freshly-split internal node with high historical importance can therefore occupy a competitive target slot.
+
+This is not a defect. The node's tracker is fully active: the multi-scale delivery mechanism (§9.3) feeds it every observation in its subtree, it scores them, and it participates in coordination (§7). Its analysis is identical to what an ancestor tracker would provide — the cost is one $K$-slot that duplicates the ancestor closure's work. The V-Tree's competitive dynamics (§3.4) resolve this organically: children's importance grows from live observations, the frozen benchmark decays under temporal attenuation (§3.8), the max-uncle constraint triggers restructuring, and the parent sinks below the selection threshold. The transient duration is bounded by $O(\theta / \text{observation\_rate})$ batches under typical decay.
+
+The alternative — filtering by G-Tree state to eject internal nodes immediately — was considered and rejected. It would create cross-layer coupling (the analysis selector inspecting spatial-layer structural state) and would leave the highest-importance region with no competitive-level coordination representation during the transition. See the design note in §8.1.
+
+---
+
+## Chapter 9. The Observation Algorithm
+
+### 9.1 The Algorithm
+
+If the ingestion batch is empty ($n = 0$), the observation algorithm returns an empty report without modifying any state. The remainder of this procedure assumes $n \geq 1$.
+
+```
+procedure SentinelIngest(Batch):
+    Input: Batch — a sequence of coordinate values of type C
+    if Batch is empty:
+        return EmptyReport()
+
+    -- Step 0 — Promote warmed-up cells from staging (§11)
+    PromoteReadyCells()
+
+    -- Step 1 — Convert to centred bit vectors (§2.3)
+    Vectors ← ConvertToCentredBits(Batch)
+
+    -- Step 2 — Spatial layer volume accounting (§3.3)
+    for each value v in Batch:
+        SpatialLayer.Observe(coordinate = v, Δ = 1)
+    -- Routes, accumulates, may split/rebalance/evict.
+
+    -- Step 3 — Update investment and producing sets (§8)
+    OldInvestment ← CurrentInvestmentSet
+    NewTargets ← ComputeCompetitiveTargets()                       (§8.1)
+    NewInvestment ← CloseUnderAncestry(NewTargets)                  (§8.2)
+
+    -- Eager removal of exiting nodes
+    Exiting ← OldInvestment \ NewInvestment
+    for cell in Exiting:
+        if cell = root: continue                                    (§8.4)
+        if cell is warming:
+            CancelWarmUp(cell)
+        DestroyTracker(cell)                                        (§8.5)
+
+    -- Investment entry for new nodes
+    Entering ← NewInvestment \ OldInvestment
+    for cell in Entering:
+        CreateTracker(cell)
+        EnqueueForWarmUp(cell)                                      (§11.6)
+
+    -- Derive producing sets from investment + online status
+    NewCompetitive ← NewTargets ∩ Online
+    NewFull ← NewInvestment ∩ Online
+
+    -- No explicit coordination activation/deactivation here.
+    -- Coordination context lifecycle is managed lazily in Step 5.
+
+    CurrentInvestmentSet ← NewInvestment
+    CurrentCompetitiveTargets ← NewTargets
+    CurrentCompetitiveSet ← NewCompetitive
+    CurrentFullAnalysisSet ← NewFull
+
+    -- Step 4 — Deliver suffix vectors and score (§9.3, §9.4)
+    CellReports ← empty list
+    AncestorReports ← empty list
+    for each value v in Batch:
+        Receiver ← RouteToOnlineReceiver(v)                        (§9.3)
+        for each node g on the path from Receiver to root:
+            if g is in CurrentFullAnalysisSet:
+                Suffix ← Vectors[v] restricted to positions [g.depth .. N−1]
+                append Suffix to g.PendingBatch
+
+    for each cell in CurrentFullAnalysisSet:
+        if cell.PendingBatch is empty: continue
+        X ← assemble matrix from cell.PendingBatch
+        Report ← cell.Tracker.Observe(X)                          (§4.2)
+        if cell is in CurrentCompetitiveSet:
+            append (cell, Report) to CellReports
+        else:
+            append (cell, Report) to AncestorReports
+        clear cell.PendingBatch
+
+    -- Step 5 — Hierarchical coordination (§7.4)
+    -- Coordination contexts are lazily created and inline-warmed
+    -- during the walk; stale contexts are pruned afterward (§7.7).
+    CoordinationReports ← PropagateCoordination(root, CellReports)
+    PruneStaleCoordinationContexts()                               (§7.7.2)
+
+    -- Step 6 — Assemble report (§14)
+    return AssembleReport(CellReports, AncestorReports,
+                          CoordinationReports)
+```
+
+> **Per-cell batch size.** For each cell $c$ in the producing set $\mathcal{A}^*$, the per-tracker batch size $b$ equals the number of ingestion-batch observations whose spatial routing passes through $c$. At the root, $b = n$; at non-root cells, $b \in \{0, \ldots, n\}$ depends on the spatial distribution of the current batch. Cells with $b = 0$ accumulate no pending observations and are skipped by the `if cell.PendingBatch is empty: continue` guard in Step 4. The effective per-batch work is therefore $\sum_{c \in \mathcal{A}^* :\, b_c > 0} O\!\big(\min(w_c,\, k_c + b_c)^2 \cdot \max(w_c,\, k_c + b_c)\big)$, which may be substantially less than the worst-case bound in §12.7 when observations are spatially concentrated.
+
+### 9.2 Step Ordering
+
+| Ordering                          | Reason                                                                                                                                                                                                                                    |
+| --------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Step 0 before Step 2              | Newly promoted cells must participate in observation routing.                                                                                                                                                                             |
+| Step 2 before Step 3              | Splits and rebalancing change V-Tree ranking. The analysis set is computed against the post-observation state.                                                                                                                            |
+| Step 3: investment set reconciled | Exiting nodes eagerly removed. Entering nodes enqueued for warm-up (§11). No inline per-cell warm-up work; no coordination lifecycle management.                                                                                          |
+| Step 4 before Step 5              | Suffix vectors must be collected and scored before coordination.                                                                                                                                                                          |
+| Step 5: coordination lifecycle    | Coordination contexts are lazily materialised and inline-warmed during the coordination walk (§11.7). Stale contexts are pruned after the walk. Inline warm-up is justified by the trivial cost at $w = 4$ (~50 rounds of $O(16m)$ work). |
+| Step 5 before Step 6              | Coordination requires per-cell score summaries.                                                                                                                                                                                           |
+
+### 9.3 Multi-Scale Delivery
+
+A single observation is delivered to **every tracker on its G-Tree ancestor path**, from the receiving cell up to and including the root. This delivery is **mandatory**, not contingent on competitive selection — the ancestor closure (§8.2) guarantees that every node on the path hosts a tracker.
+
+If a value routes to a depth-4 cell with materialised ancestors at depths 0, 1, 2, and 3, all five trackers receive the observation — at analysis widths $N$, $N-1$, $N-2$, $N-3$, and $N-4$ respectively. Each ancestor covers twice the dyadic range of its child, providing a complete hierarchy of spatial context from the cell's local population to the global population in $d$ logarithmic steps. The delivery occurs regardless of whether the intermediate G-Tree nodes receive observations through normal spatial layer routing. Under typical parameters (`depth_create` $\approx 3$, competitive mechanism limiting depth to $\sim 2$–$8$), this fan-out is modest: a depth-3 cell produces 4 tracker updates per observation; a depth-6 cell produces 7.
+
+**Routing during warm-up.** Observations destined for a cell in $\mathcal{I}$ that is not yet online are routed to the nearest **online** ancestor on the G-Tree path, or to the root if no closer ancestor is online. The root is always online (§8.4). Because the warm-up pipeline processes cells in g.sum order (§11.6.2), ancestors come online before descendants — the routing fallback naturally shortens as warming progresses.
+
+```
+procedure RouteToOnlineReceiver(value):
+    g ← SpatialLayer.RouteToReceiver(value)
+    while g is not null:
+        if g is online:
+            return g
+        g ← g.SpatialParent
+```
+
+Because the root completes warm-up at construction (§11.6), the while loop has a guaranteed termination point: the root is always online. For cells whose closer ancestors are still warming, the root is returned; as ancestors complete warm-up (in g.sum order, §11.6.2), the returned node moves progressively closer to the target.
+
+No scoring degradation occurs during this period — the ancestor already has a mature model. Resolution stays at the coarser level until the descendant completes warm-up and is promoted to online status. The host sees the descendant's spatial interval covered by the ancestor's report until promotion.
+
+> _Design note (overlapping inputs)._ Observation sets overlap by construction — a depth-16 ancestor sees every suffix that its depth-32 children see, plus more. The overlapping input is by design: the depth-16 tracker learns coarser structure than the depth-32 tracker from the same observations. The coordination tier (§7) operates on derived score summaries from the competitive set $\mathcal{A}$, not from ancestor trackers, so input overlap does not produce double-counting at the coordination level.
+
+### 9.4 Per-Value Scoring Pipeline
+
+Under the ancestor closure, a single observation at a competitive cell of depth $d$ produces scores at every ancestor level:
+
+```
+Value arrives at depth-4 cell
+│
+├── /0 tracker: novelty₀, displacement₀, surprise₀, coherence₀
+│   └── z-scores against /0 baselines
+│
+├── /1 tracker: novelty₁, displacement₁, surprise₁, coherence₁
+│   └── z-scores against /1 baselines
+│
+├── /2 tracker: novelty₂, displacement₂, surprise₂, coherence₂
+│   └── z-scores against /2 baselines
+│
+├── /3 tracker: novelty₃, displacement₃, surprise₃, coherence₃
+│   └── z-scores against /3 baselines
+│
+├── /4 tracker: novelty₄, displacement₄, surprise₄, coherence₄
+│   └── z-scores against /4 baselines
+│
+└── Coordination (on competitive cells only)
+    └── Cross-cell pattern detection (§7)
+```
+
+The report for a single value includes scores at every ancestor level. The host sees not just "this value is anomalous" but "this value is anomalous at the depth-3 level but normal at depth 1 and depth 4" — diagnostic information about the **scale** at which the anomaly manifests.
+
+- An anomaly at depth 4 only: unusual relative to the specific cell's population but normal in the broader context — a local anomaly.
+- An anomaly at depth 1 and above: unusual in the broader regional context — a stronger, coarser-scale signal.
+- An evasion succeeding at depth 4 but failing at depth 3: the adversary matched local patterns but missed cross-cell correlations captured by the coarser model. This is the nesting defence.
+
+### 9.5 Batched Observation Semantics
+
+Step 2 accumulates $\Delta = 1$ per value. If $n$ values in a batch route to the same cell, its importance increases by $n$. Steps 2 and 4 may share a single tree descent in implementation.
+
+---
+
+# Part IV — Temporal and Operational Concerns
+
+---
+
+## Chapter 10. Temporal Semantics
+
+Two independent temporal mechanisms govern two independent concerns. Neither subsumes the other; both operate simultaneously.
+
+### 10.1 Spatial Decay
+
+The host periodically invokes the spatial layer's temporal decay operation (§3.6). The spatial layer applies the requested scaling factor to accumulated importance values, producing one of four effects:
+
+| Regime        | Factor                 | Effect                                                                                   |
+| ------------- | ---------------------- | ---------------------------------------------------------------------------------------- |
+| Attenuation   | $(0, 1)$               | Cold cells lose standing, eventually becoming eviction candidates                        |
+| Amplification | $> 1$                  | Hot cells reinforced; with depth-selective amplification, fine-scale detail is sharpened |
+| Annihilation  | $= 0$                  | Hard reset — entire regions zeroed                                                       |
+| Detail flush  | $= 0$, depth-selective | Subtree root preserved; all descendants zeroed                                           |
+
+The host controls the decay schedule: when to invoke it, at what factor, against which subtree, and with what depth selectivity. Spatial decay governs the **contour's memory** — which cells exist and in what competitive standing.
+
+### 10.2 Statistical Decay
+
+Each subspace tracker's EWMA at rate $\lambda$ governs the subspace, latent statistics, and baselines independently of spatial decay. Statistical decay governs the **model's memory** — what structure has been learned and how recent observations are weighted.
+
+| What decays          | Mechanism                   | Rate        | Purpose                     |
+| -------------------- | --------------------------- | ----------- | --------------------------- |
+| Importance (spatial) | Spatial layer               | Host-chosen | Contour evolution           |
+| Subspace $\sigma$    | Tracker SVD (§4.2, Phase 2) | $\lambda$   | Forget old correlations     |
+| Fast baselines       | Tracker EWMA (§6.1)         | $\lambda$   | Adapt to score distribution |
+| Slow baselines       | Tracker EWMA (§6.2)         | $\lambda_s$ | Long-memory drift reference |
+
+A cell can retain its spatial position (strong volume, slow spatial decay) while its statistical model adapts rapidly (fast $\lambda$), and vice versa.
+
+### 10.3 Independence
+
+The two decay mechanisms are entirely orthogonal:
+
+- Spatial decay does not touch tracker state. It zeroes importance, which causes cells to lose V-Tree standing, exit the competitive set, and have their trackers destroyed through the normal exit mechanism (§8.5). The feed-forward invariant (§1.3) is maintained.
+- Statistical decay does not touch importance. It governs how quickly a tracker forgets old subspace structure and baseline values.
+
+The host sets the temporal policy for both mechanisms independently. Fast spatial decay with slow statistical decay produces a rapidly evolving contour with stable models. Slow spatial decay with fast statistical decay produces a stable contour with rapidly adapting models.
+
+### 10.4 Annihilation Use Cases
+
+- **Regime change.** Apply annihilation at the G-Tree root with zero depth selectivity: resets the entire spatial structure. All cells lose standing and are eventually evicted; their trackers are destroyed.
+- **Suspected poisoning.** Apply annihilation at a subtree root with full depth selectivity (detail flush): preserves coarse measurement, zeroes fine structure. The subtree root retains its importance; descendants must re-earn theirs.
+- **Stale cleanup.** Apply targeted annihilation to force eviction in the next tidal pass. Useful for host-directed pruning of known-dead regions.
+
+---
+
+## Chapter 11. System Initialisation and Warm-Up
+
+A newly created tracker has an orthonormal basis (§4.1) and uninitialised baselines. Before it can produce meaningful scores, its baselines must reflect a representative score distribution. This chapter specifies the complete warm-up procedure: noise injection, cold-start seeding, clip-width modulation, slow-baseline seeding, deferred scheduling, and coordination warm-up. These mechanisms address different aspects of a single problem — cold-start mismatch — and are presented together because their interactions determine the system's convergence behaviour.
+
+### 11.1 Noise Injection
+
+#### 11.1.1 Purpose
+
+Noise injection feeds synthetic uniformly random centred bit vectors through the tracker's standard observation path (§4.2) to **warm the EWMA baselines** so they reflect structureless-noise score distributions rather than uninitialised placeholders.
+
+> _Design note._ Noise injection's primary value is baseline warming, not subspace shaping. Random bit vectors in $\{-0.5, +0.5\}^w$ have no dominant direction — after injection, singular values are roughly equal, similar to the initial state. The subspace acquires meaningful structure only once real observations arrive.
+
+#### 11.1.2 Trigger Events
+
+Noise injection fires on every new tracker creation: competitive set entry, ancestor activation, split-induced creation, or legacy promotion.
+
+#### 11.1.3 Parameters
+
+| Parameter        | Description                          | Default                                                      | Constraint                                   |
+| ---------------- | ------------------------------------ | ------------------------------------------------------------ | -------------------------------------------- |
+| Noise schedule   | Depth-tiered synthetic batch count   | Geometric: 450 rounds at root, halving per depth, minimum 50 | See below                                    |
+| Noise batch size | Samples per synthetic batch          | 16                                                           | $\geq 2$ recommended; $\geq 1$ required      |
+| Random seed      | Seed for the pseudo-random generator | Deterministic (fixed seed)                                   | Optional; absent means implementation-chosen |
+
+> _Operational note (noise batch size)._ Under the EWMA-mean-centred variance formula (§4.2 Phase 3, [ADR-S-021](../adr/021-ewma-mean-centred-variance.md)), the first-batch seeding computes $\nu^{(z)}_j = \max(z_{1j}^2, \varepsilon)$, which equals approximately $0.25$ in expectation for centred $\pm 0.5$ inputs — correctly scaled at every batch size including $b = 1$. The previous formula computed within-batch population variance, which is identically zero at $b = 1$; the EWMA-mean-centred formula eliminated this entire class of batch-size bias. Smaller batch sizes produce noisier initial seeds (higher variance of $z_{1j}^2$ across dimensions), but the EWMA smooths this within $O(t_{1/2})$ subsequent batches without a recovery phase. The default of 16 remains well above the threshold for low-noise seeding.
+
+The noise schedule determines how many synthetic batches each tracker receives before transitioning to real observations. Two schedule forms are supported:
+
+- **Geometric.** Parameterised by a root count, a per-depth decay factor, and a minimum: $\text{rounds}(d) = \max(\text{minimum}, \; \lfloor\text{root} \times \text{decay}^d + 0.5\rfloor)$
+- **Explicit.** A sequence of per-depth round counts; the last entry repeats for all deeper levels.
+
+The schedule must produce enough rounds for worst-case baseline convergence at each depth (see §11.9 for empirical convergence data; the same schedule parameterises coordination warm-up in §11.7).
+
+After injection completes, all four drift accumulators (§6.3) are reset to zero.
+
+### 11.2 Cold-Start Latent Seeding
+
+On a tracker's very first batch (Phase 3 of the core loop, §4.2), the latent statistics $\mu^{(z)}$, $\nu^{(z)}$, and $\Gamma$ are seeded directly from the batch data rather than blended with default initial values.
+
+Without direct seeding, the latent variance $\nu^{(z)}$ retains its pre-allocated value of $1.0$. The EWMA-mean-centred formula (§4.2, Phase 3) converges to the true variance from any initialisation, but the $4\times$ overestimate dampens surprise scores during the convergence period, delaying baseline settling. Direct seeding at $t = 0$ eliminates this delay: $\nu^{(z)}_j \leftarrow \max(\frac{1}{b}\sum z_{ij}^2, \varepsilon)$ produces a correctly-scaled seed at every batch size, including $b = 1$.
+
+The procedure is specified in §4.2, Phase 3.
+
+### 11.3 Clip-Width Modulation During Warm-Up
+
+Clip-width modulation during warm-up is handled by the unified clip-pressure mechanism (§6.1.1) via the noise influence signal $\eta$. The effective clip ceiling uses $p = \max(\eta, \bar{\rho})$ where $\eta$ is the noise influence (§11.5) and $\bar{\rho}$ is the per-axis clip-pressure EWMA:
+
+$$n_\sigma^{\text{eff}} = n_\sigma\left(1 + \frac{p}{1 - p + \varepsilon}\right)$$
+
+During warm-up, $\eta \approx 1$ dominates and the ceiling is effectively open — preventing the positive feedback loop between tight clipping and low baselines that would otherwise extend convergence time by an order of magnitude. As $\eta$ decays with real observations, $\bar{\rho}$ takes over if the baselines become stale in production. The full mechanism, including dynamics tables and contamination analysis, is specified in §6.1.1 and §6.4.
+
+### 11.4 Slow-From-Fast CUSUM Seeding
+
+After noise injection completes, the slow EWMA (§6.2) is **seeded from the fast EWMA's converged values** and the drift accumulator (§6.3) is **reset to zero**.
+
+The slow EWMA's time constant ($\lambda_s$, e.g., 0.999 with half-life $\approx 693$ steps) is far longer than the fast EWMA's ($\lambda$, e.g., 0.99 with half-life $\approx 69$ steps). Without seeding, the fast baseline reaches steady state much sooner than the slow baseline. During this gap, the drift accumulator interprets the fast-slow difference as drift evidence, producing false accumulation.
+
+Seeding the slow EWMA mean and variance from the fast EWMA at the noise-to-real transition eliminates this gap at its source. The procedure is:
+
+1. Copy the fast EWMA's mean and variance to the slow EWMA's mean and variance.
+2. Reset all four drift accumulators to zero.
+3. Reset all four clip-pressure EWMAs $\bar{\rho}$ to zero (§6.1.1).
+
+### 11.5 Maturity Tracking
+
+Each tracker records:
+
+| Field                  | Description                                           |
+| ---------------------- | ----------------------------------------------------- |
+| Real observations      | Count of genuine observations processed               |
+| Noise observations     | Count of synthetic observations processed             |
+| Noise influence $\eta$ | Fraction of baseline not yet established by real data |
+
+The noise influence decays exponentially with real observations:
+
+$$\eta_{t+1} = \begin{cases} \lambda \, \eta_t + (1 - \lambda) & \text{noise observation} \\ \lambda \, \eta_t & \text{real observation} \end{cases}$$
+
+After $n$ real observations: $\eta_n = \lambda^n$. The system reports $\eta$ without interpretation. The host should treat scores from trackers with high $\eta$ (e.g., $> 0.5$) as preliminary.
+
+**Initial value.** $\eta_0 = 1.0$ at tracker creation, indicating that no real observations have yet established the baseline. After noise injection (which maintains $\eta \approx 1.0$) and $n$ real observations, $\eta_n \approx \lambda^n$.
+
+#### 11.5.1 Three Convergence Concepts
+
+The system has three distinct notions of convergence, operating at different timescales:
+
+| Convergence type                                        | Definition                                    | Timescale                                    | Notes                                                                       |
+| ------------------------------------------------------- | --------------------------------------------- | -------------------------------------------- | --------------------------------------------------------------------------- |
+| **$\eta$-convergence** (maturity)                       | $\eta_n = \lambda^n < \epsilon$               | $\lceil\ln\epsilon/\ln\lambda\rceil$ batches | Exact closed-form                                                           |
+| **Trajectory convergence** (EWMA mean)                  | $\|\bar{\mu}_n - \bar{\mu}_\infty\| < \delta$ | Tens to hundreds of rounds                   | With clip-pressure modulation (§6.1.1, §6.4) and cold-start seeding (§11.2) |
+| **Distributional stationarity** (baseline distribution) | Windowed-mean comparison $< \epsilon$         | Per-axis; varies by batch size               | Baselines wander even at true steady state                                  |
+
+$\eta$-convergence is a **necessary but not sufficient** indicator of system readiness. EWMA baselines converge at a rate determined by cascaded EWMA interactions, clipping policy, batch size, and axis-specific score distributions — not by the simple exponential $\eta_n = \lambda^n$.
+
+### 11.6 Deferred Cell Warm-Up
+
+Noise injection is performed **outside** the main observation path. New trackers transition through a three-state lifecycle:
+
+```
+┌─────────┐         ┌─────────┐         ┌─────────┐
+│ Created │────────►│ Warming │────────►│ Online  │
+└─────────┘         └─────────┘         └─────────┘
+                         │
+                         │  asynchronous warm-up
+                         │  volume-priority scheduling
+                         ▼
+                   noise injection
+```
+
+#### 11.6.1 Cell States
+
+| State   | Receives real observations? | Contributes to reports? | Warm-up status  |
+| ------- | --------------------------- | ----------------------- | --------------- |
+| Created | No                          | No                      | Not yet started |
+| Warming | No                          | No                      | In progress     |
+| Online  | Yes                         | Yes                     | Complete        |
+
+**Root warm-up exception.** The root tracker (§8.4) completes its warm-up synchronously during system construction, before the Sentinel accepts its first `ingest()` call. This ensures the "always online" invariant (§9.3) holds from the first observation. The root is the highest-priority cell under g.sum ordering and the only cell that exists at construction time; warming it synchronously is a one-time fixed cost that does not violate the work-variance bound (§12.9), which governs per-`ingest()` cost. All subsequently created cells follow the deferred warm-up lifecycle described below.
+
+#### 11.6.2 Priority Scheduling
+
+The warm-up pipeline always works on whichever member of the investment set's warming subset has the highest g.sum:
+
+$$\text{priority}(c) = \text{g.sum}(c.\text{node})$$
+
+where g.sum is the G-Tree node sum (§3.12 property 2) — the node's own accumulation plus all descendant sums.
+
+This ordering has four consequences:
+
+1. **Volume drives priority.** A higher g.sum node has more observation traffic flowing through its spatial region — bringing it online has the greatest impact on analysis quality.
+
+2. **Ancestors emerge first.** By the summation invariant (§3.12 property 2), every ancestor's g.sum is at least as great as any descendant's. Sorting the warming set by g.sum produces an ordering where every ancestor appears before every descendant. This is a topological sort of the ancestor chains — obtained for free from a single sort, without encoding depth into the priority.
+
+3. **Full chain at promotion.** Because ancestors come online before descendants, when a competitive target completes warm-up, its entire ancestor chain is already online. The multi-scale defence (§16) is complete from the first batch the target processes. No secondary warm-up gap exists.
+
+4. **No starvation.** Every warming cell in an active region accumulates positive g.sum — the observations that justified its investment continue flowing through its spatial range. Cells that lose priority temporarily are eventually reached.
+
+5. **Preemption is correct.** A cell that was highest-priority at one time may not be later — observation patterns shift, investment set membership changes. Working on the current highest-g.sum cell ensures the most valuable work is always done first. Partial warm-up is never wasted: the preempted cell's EWMA baselines retain their progress.
+
+#### 11.6.3 Promotion
+
+At the start of each observation cycle (§9.1, Step 0), before spatial layer observation routing:
+
+```
+procedure PromoteReadyCells():
+    for each cell in the warming set where warm-up is complete:
+        move cell from warming set to online set
+```
+
+This typically promotes zero or one cell per cycle. Promotion occurs atomically from the observation algorithm's perspective, before any observation routing. Coordination contexts involving promoted cells materialise lazily during the Step 5 coordination walk (§7.7.1), not at promotion time.
+
+#### 11.6.4 Observation Routing During Warm-Up
+
+Observations destined for a warming cell are routed to the nearest online ancestor, as specified in §9.3. The spatial layer's own volume tracking is unaffected: the observation accumulates importance at the warming cell's spatial node regardless of whether an analysis tracker exists there. The warming cell's competitive position in the V-Tree is maintained. It holds an investment slot in $\mathcal{I}$ and transitions to the producing set $\mathcal{A}$ upon promotion.
+
+#### 11.6.5 Coordination During Warm-Up
+
+Warming cells do **not** participate in coordination. Because coordination contexts are lazily materialised during the Step 5 coordination walk (§7.7.1), and only online competitive cells with scores contribute to the walk's Steiner tree, warming cells are structurally excluded — they produce no scores for the walk to discover. Their synthetic noise is never propagated through the coordination hierarchy, avoiding synthetic-on-synthetic artefacts during system formation.
+
+#### 11.6.6 Eviction Before Completion
+
+A warming cell can be evicted from the spatial layer before warm-up completes — its spatial node may be absorbed by rebalancing or budget eviction. If this happens, the warm-up process discards the partially warmed cell. The work is lost, but correctly so: the spatial layer determined that the cell's interval no longer warrants a dedicated node.
+
+Similarly, a warming cell can exit the investment set before warm-up completes — its competitive target may drop out of the top-$K$ (§8.5). The warm-up is cancelled and the tracker destroyed via the eager removal mechanism. The work is correctly discarded: the competitive landscape has determined the cell no longer warrants investment.
+
+#### 11.6.7 Investment Slots vs. Production Slots
+
+Warming cells hold **investment slots** in $\mathcal{I}$ — they have allocated trackers and are enqueued for warm-up. They do not hold **production slots** in $\mathcal{A}$ — they do not produce scores, emit reports, or participate in coordination.
+
+By the slot-vacancy invariant (§8.3), $|\mathcal{A}| = K - |\mathcal{T} \cap \text{Warming}|$: every warming competitive target corresponds to a vacant production slot. At promotion, the cell fills its own vacancy — no online cell is displaced. The $|\mathcal{A}| \leq K$ bound is maintained structurally by Step 3's top-$K$ recomputation (§8.1), not by promotion-time ejection.
+
+The warm-up pipeline's g.sum ordering (§11.6.2) ensures that ancestors come online before descendants. By the time a competitive target is promoted, its entire ancestor chain is online and producing scores. The system provides progressively deeper coverage as warm-up progresses, rather than a single transition from no coverage to full coverage.
+
+#### 11.6.8 Pipeline Advancement
+
+The warm-up pipeline advances at a granularity of **one noise batch per scheduling quantum**. After each quantum, the pipeline re-evaluates the g.sum priority ordering and continues with the highest-priority warming cell. This re-evaluation enables preemption: a newly enqueued cell with higher g.sum displaces an in-progress cell after at most one noise batch of delay.
+
+The pipeline advances **independently of observation cadence** — warm-up throughput is not gated by the rate of `ingest()` calls. Promotion to Online status remains gated by the observation algorithm: it occurs at Step 0 of the next `ingest()` call after warm-up completes (§9.1).
+
+**Mutual-exclusion invariant.** The warm-up pipeline and the observation algorithm never operate on the same tracker concurrently. Newly created trackers are fully initialised before becoming visible to the pipeline. Tracker destruction (§8.5) waits for (or cancels) any in-progress noise batch before proceeding. The implementation is free to achieve this invariant through any mechanism — a dedicated thread with synchronised handoff, a cooperative executor, or synchronous drain — provided the four properties above hold.
+
+### 11.7 Coordination-Specific Warm-Up
+
+When a coordination context materialises during the Step 5 coordination walk (§7.7.1) — for example, when two previously unrelated competitive cells first share an ancestor — the context's coordination tracker requires warm-up before processing its first real observation. This warm-up runs **inline to completion** within the same Step 5 invocation, immediately after context creation and before the walk feeds the first real group matrix.
+
+#### 11.7.1 Scheduling Model
+
+Coordination warm-up is **inline at materialisation**, not deferred through a parallel warming pipeline. This contrasts with per-cell warm-up (§11.6), which is deferred with g.sum priority scheduling. The difference is justified by cost:
+
+- **Per-cell warm-up** operates at analysis widths $w$ up to $N = 128$, requiring hundreds of rounds of full SVD updates. The cost motivates deferral and priority scheduling.
+- **Coordination warm-up** operates at $w = 4$ with ~50 rounds of $O(16m)$ work — microseconds on any real hardware, less than 0.3% of a single per-cell SVD (§7.8). The cost does not justify a second scheduling tier.
+
+The "no inline warm-up work" principle stated in §9.2 for Step 3 applies to per-cell tracker warm-up. Coordination warm-up is a separate concern, handled in Step 5, where the trivial cost makes inline execution the simpler and more correct choice.
+
+#### 11.7.2 Procedure
+
+1. Snapshot each contributing cell's current baseline mean $\bar{s}_c$ and variance $\bar{v}_c$ for all four scoring axes. The snapshot is taken at Step 5 time — after Step 4 scoring has updated the contributing cells' baselines with this cycle's observations.
+
+2. For each synthetic round: for each cell $c$ in the coordination group, generate a synthetic score vector. For each scoring axis, sample from a non-negative distribution matching $c$'s snapshot baselines. A Gamma distribution with shape $\alpha_c = \bar{s}_c^2 / \bar{v}_c$ and rate $\beta_c = \bar{s}_c / \bar{v}_c$ is the natural choice: it preserves the learned baseline statistics while respecting non-negativity.
+
+3. Assemble the synthetic score vectors into a group matrix, centre it (§7.3), and feed it through the coordination tracker (§7.5).
+
+4. Repeat for the number of rounds prescribed by the noise schedule (§11.1.3) at the coordination context's G-Tree depth — that is, the depth of the internal G-Tree node that hosts this context, using the same depth parameter $d$ that governs per-cell warm-up.
+
+5. Reset the coordination tracker's drift accumulators to zero.
+
+If a cell's baseline is uninitialised (no valid mean or variance), use per-axis defaults: mean $= 0.25$, variance $= 0.01$ for novelty and surprise; mean $= 0.1$, variance $= 0.01$ for displacement and coherence.
+
+> _Design note (why a non-negative distribution)._ All four scoring axes produce non-negative values. A symmetric distribution centred at a small positive mean produces negative samples, which are meaningless in this context. The Gamma distribution with matched moments is the simplest non-negative distribution that preserves the learned baseline statistics.
+
+> _Design note (snapshot timing)._ The baseline snapshot is taken after Step 4 scoring, meaning baselines reflect this cycle's observations. The alternative — snapshotting before Step 4 — would require coordination lifecycle management in Step 0 or Step 3, adding bookkeeping complexity for a negligible difference (one batch of EWMA update at rate $\alpha \approx 0.01$). The post-Step-4 snapshot is arguably preferable: the baselines are more current.
+
+> _Design note (depth taper for coordination)._ The noise schedule's depth-based tapering was designed for per-cell trackers, where deeper G-Tree nodes have narrower analysis widths and thus faster baseline convergence. Coordination trackers all operate at $w = 4$ regardless of their G-Tree depth, so the convergence time is depth-independent — the taper rationale does not transfer. Under the default schedule, every coordination context receives the schedule minimum (50 rounds), since even the shallowest possible context at depth 1 converges in well under 225 rounds at $w = 4$. The shared schedule is retained for simplicity; implementations may use a flat 50-round schedule for coordination warm-up without observable difference.
+
+### 11.8 System-Level Warm-Up Stages
+
+During early operation, the system-level behaviour differs from steady state:
+
+**Stage 1: Pre-Split.** Below the split threshold ($< \theta$ total observations). A single spatial node covers the entire domain. The root tracker at $w = N$ is always present (permanent). No competitive targets yet; the investment set contains only the root. No coordination possible. All scores reflect global structure only.
+
+**Stage 2: Spatial Formation.** From $\theta$ to approximately $10\theta$ observations. The spatial tree begins splitting. Cells enter and exit the competitive targets frequently as the V-Tree ranking stabilises. Investment set membership churns as targets shift. The producing set grows progressively as warm-up completes for each invested cell — ancestors first, then competitive targets. Scores from early-promoted ancestors are available before any competitive cell comes online.
+
+**Stage 3: Stabilisation.** From approximately $10\theta$ to $100\theta$ observations. The competitive targets settle. The investment set stabilises and the producing set converges toward $\mathcal{I}$ as remaining warming cells complete their warm-up. Trackers mature ($\eta$ declining). Ancestor chains lengthen as the spatial tree deepens. Coordination contexts materialise as competitive target pairs come online (§7.7.1). Drift accumulators begin accumulating meaningful evidence.
+
+**Stage 4: Steady State.** Trackers have $\eta \ll 1$. Drift accumulators reflect genuine baseline departures. Ancestor chains provide multi-scale defence. The system detects anomalies at its designed sensitivity.
+
+The transition timescale depends on observation concentration, split threshold $\theta$, and forgetting factor $\lambda$.
+
+### 11.9 Empirical Baseline Convergence
+
+The following table gives measured EWMA trajectory convergence times — the number of batches until the windowed-mean baseline is within tolerance of its steady-state value. These were obtained with clip-pressure modulation (§6.1.1), cold-start latent seeding (§11.2), and slow-from-fast drift-accumulator seeding (§11.4) all active.
+
+| Component                | $\lambda{=}0.95$, $b_{\text{noise}}{=}4$ | $\lambda{=}0.95$, $b_{\text{noise}}{=}16$ | $\lambda{=}0.99$, $b_{\text{noise}}{=}16$ |
+| ------------------------ | ---------------------------------------: | ----------------------------------------: | ----------------------------------------: |
+| $\eta$ (noise influence) |                               59 (exact) |                                59 (exact) |                               299 (exact) |
+| Latent mean/variance     |                        $\sim 1$ (seeded) |                         $\sim 1$ (seeded) |                         $\sim 1$ (seeded) |
+| Novelty baseline         |                                       21 |                                        21 |                                       101 |
+| Displacement baseline    |                         21–475 (bimodal) |                                        21 |                                       101 |
+| Surprise baseline        |                                   **65** |                                    **70** |                                   **398** |
+| Coherence baseline       |                                  **406** |                                   **173** |                                   **315** |
+| **System (worst-case)**  |                            **$\sim$406** |                             **$\sim$173** |                             **$\sim$398** |
+
+Per-axis empirical coefficients of variation at steady state (windowed-mean):
+
+| Axis         | CV ($b_{\text{noise}}{=}4$) | CV ($b_{\text{noise}}{=}16$) | Notes                                                     |
+| ------------ | :-------------------------: | :--------------------------: | --------------------------------------------------------- |
+| Novelty      |            0.13%            |            0.06%             | Constant-norm property — inherently stable                |
+| Displacement |            5.9%             |             3.0%             | Bimodal across random seeds at $b_{\text{noise}}{=}4$     |
+| Surprise     |            9.0%             |             3.6%             | Improved substantially by cold-start seeding              |
+| Coherence    |            19.9%            |            11.5%             | Rank-gating delay ($k < 2$) is the convergence bottleneck |
+
+**The noise schedule must produce enough rounds for the worst-case convergence time** at each depth for baselines to be settled before real observations arrive. At $\lambda = 0.99$, $b_{\text{noise}} = 16$ (a typical production configuration), this is $\geq 400$ batches at depth 0 (root). Deeper cells need fewer rounds — the geometric taper reflects the faster convergence at narrower analysis widths.
+
+---
+
+## Chapter 12. Resource Bounds, Spray Resistance, and Complexity
+
+### 12.1 Three Independent Ceilings
+
+| Ceiling    | Parameter                 | Bounds               | Layer             |
+| ---------- | ------------------------- | -------------------- | ----------------- | ------------------------------------------------------------------------ | ----------------- | ----------- | ----------- | ----------------- | ----------------- |
+| $G_{\max}$ | Hard spatial node ceiling | Total spatial nodes  | Spatial Layer     |
+| $K$        | Analysis budget           | Competitive trackers | Analysis Selector |
+| $          | \mathcal{I}               | $                    | (derived)         | $\leq 1 + K\bar{D}$ before sharing; dominated by $2K$ in practice (§8.2) | Analysis Selector |
+| $          | \mathcal{A}^\*            | $                    | (derived)         | $\leq                                                                    | \mathcal{I}       | $; equals $ | \mathcal{I} | $ in steady state | Analysis Selector |
+
+The budget $K$ governs competitive selection; ancestor trackers are not counted against the budget. Total invested tracker count $|\mathcal{I}|$ is bounded by $1 + K\bar{D}$ before sharing (§8.2), where $\bar{D}$ is the average G-Tree depth of competitive targets. Because the competitive mechanism and depth gate keep trees shallow (§3.1), $\bar{D}$ is typically 2–6, and ancestor sharing under concentration brings the practical size close to $2K$. Peak memory is determined by $|\mathcal{I}|$ (including warming trackers); per-batch computation is determined by $|\mathcal{A}^*|$ (online trackers only). Total memory: $G_{\max} \times (\text{per-node size}) + |\mathcal{I}| \times (\text{max per-tracker size})$. Both terms are hard-bounded.
+
+### 12.2 Spray Resistance
+
+A **spray** is a high-entropy input pattern: an adversary (or a degenerate data source) emitting maximally diverse values, forcing the system to allocate structure across the full domain rather than concentrating precision on structured regions. The spray is an entropy attack on the spatial partition budget — it attempts to exhaust the spatial layer's node ceiling through diversity rather than volume.
+
+**Spatial budget.** The spatial layer's budget invariant $|G| + S + 2 \leq G_{\max}$ (§3.12, property 8) bounds node creation absolutely. Its spray defence mechanisms are inherited in full.
+
+**Analysis budget.** New cells start at ground importance and sit deep in the V-Tree — far below the analysis cutoff $L$. Only sustained observation volume pushes a cell into the top $K$. **Value diversity creates spatial nodes but not competitive trackers.** The competitive budget $K$ is inherently robust against diversity-based spray. Ancestor trackers are bounded by the investment set's ancestor closure (§8.2): at most $K\bar{D}$ additional ancestor nodes before sharing (reduced to near $K$ under typical concentration), regardless of how many spatial nodes exist.
+
+### 12.3 Steady-State Bound
+
+Under sustained spray at rate $R$ with spatial attenuation $\lambda_{\text{sp}} \in (0, 1)$ and split threshold $\theta$:
+
+$$L_{\text{steady}} = \frac{R}{(1 - \lambda_{\text{sp}}) \cdot \theta}$$
+
+Independent of domain size and spray duration. At most $K$ of these receive trackers. For $\lambda_{\text{sp}} \geq 1$ (amplification or no-decay regimes), no decay-driven equilibrium exists; the spatial hard ceiling $G_{\max}$ (§3.12, property 8) is the operative bound.
+
+### 12.4 Benchmark Compounding
+
+The spatial layer's benchmark compounding (§3.8) provides long-term spray memory. After $j$ expand–contract cycles, re-expansion to depth $D$ requires $\sim D^2 \theta / 2$ observations. This ensures that deep spatial structure is re-created only when justified by sustained, concentrated observation volume.
+
+### 12.5 Coverage Displacement
+
+An adversary can influence _which_ cells occupy the $K$ competitive slots by generating concentrated observations to chosen prefixes, displacing established cells. This is a coverage attack, not a resource attack. The host can detect it via the analysis set summary in the report (§14).
+
+### 12.6 Per-Tracker Complexity
+
+| Operation                     | Cost                                                     | Notes             |
+| ----------------------------- | -------------------------------------------------------- | ----------------- |
+| Projection and reconstruction | $O(bwk)$                                                 | Matrix multiply   |
+| Novelty                       | $O(bw)$                                                  | Residual norm     |
+| Displacement and surprise     | $O(bk)$                                                  | Latent space      |
+| Coherence                     | $O(bk^2)$                                                | Pairwise products |
+| Streaming SVD                 | $O\!\big(\min(w,\, k{+}b)^2 \cdot \max(w,\, k{+}b)\big)$ | **Dominant**      |
+| Second-moment update          | $O(bk^2)$                                                | Upper triangle    |
+| Baselines and drift detection | $O(1)$ per axis                                          |                   |
+
+The SVD input $M \in \mathbb{R}^{w \times (k+b)}$ is a thin SVD whose cost depends on the aspect ratio. When $w \geq k + b$ (tall matrix — typical at the root and shallow cells), the cost is $O(w(k{+}b)^2)$. When $k + b > w$ (wide matrix — routine at deep competitive cells, e.g. $w = 32$, $k + b = 80$), the cost is $O(w^2(k{+}b))$. The general form covers both regimes.
+
+### 12.7 Per-Batch Complexity
+
+**Step 2 (spatial layer).** Per observation: $O(d_{\text{geo}} + h_V)$. Per batch: $O(n(d_{\text{geo}} + h_V))$ plus amortised structural operations.
+
+**Step 4 (per-cell scoring, all online trackers in $\mathcal{A}^*$).** Each tracker's cost depends on its per-tracker batch size $b$, which varies by cell (§2.6 batch-size note). Only trackers with $b > 0$ execute the core loop; the effective cost is $\sum_{c \in \mathcal{A}^* :\, b_c > 0} O\!\big(\min(w_c,\, k_c + b_c)^2 \cdot \max(w_c,\, k_c + b_c)\big)$, dominated by SVD. The worst-case bound, assuming all trackers receive observations, is $O\!\big(|\mathcal{A}^*| \cdot \min(w_{\max},\, k + b_{\max})^2 \cdot \max(w_{\max},\, k + b_{\max})\big)$. In steady state $|\mathcal{A}^*| = |\mathcal{I}| \leq 1 + K\bar{D}$ before sharing (§8.2); under realistic observation distributions with concentration-driven sharing, the effective size approaches $2K$.
+
+**Ancestor chain cost.** Each observation is processed by every tracker on its ancestor path. The dominant term is the **root tracker**: it has the widest suffix ($w = N$) and sees the full ingestion batch ($b = n$). Its SVD input is $\mathbb{R}^{N \times (k_0 + n)}$. For $N = 128$, $k_0 = 16$, and $n = 64$, this is a $(128, 80)$ matrix — in the tall regime ($w > k + b$), costing $O(128 \times 80^2) \approx 819\text{K}$. At deeper competitive cells the SVD input is in the wide regime ($k + b > w$); for example, a depth-96 cell has $w = 32$, and with $k = 16$, $b = 64$, the $(32, 80)$ matrix costs $O(32^2 \times 80) \approx 82\text{K}$ — not $O(32 \times 80^2) \approx 205\text{K}$. Callers ingesting large batches ($n > N - k$) push even the root into the wide regime.
+
+Intermediate ancestor levels' costs are smaller (narrower suffixes, smaller batches — only observations routing through their spatial range) and partially amortised by sharing. The per-observation fan-out equals the target cell's G-Tree depth $d$ plus one (the cell itself). At typical operational depths ($d \approx 2\text{–}6$ under default parameters), this is a modest multiplier. At deeper operational depths, the cost grows linearly with $d$ but is bounded by $O(\log_2 |\text{domain}|)$ — each ancestor doubles the spatial coverage, so $d$ steps span the full dynamic range from a single cell to the entire domain. The total ancestor chain cost across all competitive targets is further reduced by sharing: the Steiner tree structure of the ancestor closure (§8.2) ensures that the aggregate unique ancestor count is far less than $K \times d$ — approaching $K$ under typical spatial concentration.
+
+**Step 5 (coordination).** Per context: $O(m)$. Total useful work: $O(K \cdot \bar{d})$ where $\bar{d}$ is average coordination participation depth. The pseudocode (§7.4) visits the full spatial tree for clarity; an implementation walking the investment set's reduced Steiner tree (§8.2) achieves this bound with $O(|\mathcal{I}|)$ traversal overhead (approaching $O(K)$ under typical spatial clustering). Negligible relative to Step 4.
+
+**Overall.** The observation algorithm is dominated by Step 4. Typically dominated by Step 4.
+
+### 12.8 Matrix Dimensions
+
+**Per competitive tracker:**
+
+| Matrix          | Shape             |
+| --------------- | ----------------- |
+| $X$             | $(b, w)$          |
+| $U$             | $(w, \text{cap})$ |
+| $Z$             | $(b, k)$          |
+| $M$ (SVD input) | $(w, k+b)$        |
+
+Typical competitive: $w \in \{32, \ldots, 112\}$, $k \leq 16$, $b \leq 64$. Largest competitive SVD: approximately $(112, 80)$. Note: $b$ here is the per-tracker count of observations routing to the cell (§2.6 batch-size note); at deep competitive cells, $b$ may be substantially smaller than the ingestion batch size $n$.
+
+**Root tracker:** $w = N$, $b = n$ (full ingestion batch). SVD input $(N, k + n)$. For $N = 128$, $k = 16$, $n = 64$: $(128, 80)$.
+
+**Per coordination context:**
+
+| Matrix    | Shape          |
+| --------- | -------------- |
+| $O$       | $(m, 4)$       |
+| SVD input | $(4, k_m + m)$ |
+
+The coordination SVD is at most $(4, K + 4)$ — trivial.
+
+### 12.9 Work Variance
+
+With deferred warm-up (§11.6), the per-call cost of the observation algorithm is bounded by:
+
+$$O\!\Big(n(d_{\text{geo}} + h_V) \;+\; |\mathcal{A}^*| \cdot \min(w_{\max},\, k + b)^2 \cdot \max(w_{\max},\, k + b)\Big)$$
+
+on **every** call. (The previous expression $|\mathcal{A}^*| \cdot w_{\max} \cdot (k + b)^2$ is a valid but loose upper bound; the $\min/\max$ form is tight in both the tall-matrix and wide-matrix regimes — see §12.6.) Cell creation and noise injection contribute zero cost to any observation call. The only call-to-call variance arises from:
+
+- **Batch size variation** ($b$ differs per cell and per call; $n$ differs between calls).
+- **Producing set size variation** ($|\mathcal{A}^*|$ changes by $O(1)$ per call as cells promote or exit via investment set reconciliation).
+- **Rank variation** ($k$ changes by at most 1 per rank update interval).
+
+All three change slowly relative to call frequency.
+
+> _Design note._ The deferred warm-up removes the dominant structural source of timing variance (inline noise injection), making the observation algorithm operationally predictable. It does **not** make it constant-time — the remaining variance, though small, is observable to a sufficiently precise adversary. Constant-time guarantees, if required, are an implementation concern beyond this specification.
+
+---
+
+# Part V — External Interface
+
+---
+
+## Chapter 13. Configuration
+
+All parameters are organised by the layer they govern. An implementation should accept these at construction time. Parameters marked as host-controlled may additionally be modified during operation through the mechanisms noted.
+
+### 13.1 Analysis Engine Parameters
+
+These govern the subspace tracker (§4) and baseline tracking (§6) within each analysed cell.
+
+| Parameter                                 | Description                                                              | Default   | Constraint     | Guidance                                                                                                                                                                                                                                  |
+| ----------------------------------------- | ------------------------------------------------------------------------ | --------- | -------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Maximum rank ($r_{\max}$)                 | Hard ceiling on subspace dimensionality                                  | 16        | $\geq 1$       | Higher captures more complex structure; memory scales as $w \times \text{cap}$.                                                                                                                                                           |
+| Forgetting factor ($\lambda$)             | EWMA decay rate                                                          | 0.99      | $(0, 1)$       | Lower = faster adaptation, shorter memory. Match to the regime change timescale.                                                                                                                                                          |
+| Rank update interval ($T_{\text{rank}}$)  | Batches between rank adaptation (§4.2, Phase 5)                          | 100       | $\geq 1$       | Lower = more responsive rank. Higher = more stable.                                                                                                                                                                                       |
+| Energy threshold ($\tau$)                 | Cumulative variance target for rank selection                            | 0.90      | $(0, 1)$       | 0.90 retains 90% of variance. Higher → rank grows, novelty range shrinks.                                                                                                                                                                 |
+| Stability constant ($\varepsilon$)        | Floor for numerical stability                                            | $10^{-6}$ | $> 0$          | Rarely needs adjustment.                                                                                                                                                                                                                  |
+| Clip width ($n_\sigma$)                   | Base outlier clip width in $\sigma$ units (§6.1.1)                       | 3.0       | $> 0$          | Wider → fewer legitimate rejections, slower poisoning resistance. Narrower → more false rejections.                                                                                                                                       |
+| Slow decay ($\lambda_s$)                  | Per-tracker slow EWMA decay (§6.2)                                       | 0.999     | $(\lambda, 1)$ | Ratio $\lambda_s / \lambda$ controls drift detection sensitivity window.                                                                                                                                                                  |
+| Coordination slow decay ($\lambda_{s,m}$) | Coordination slow EWMA decay (§7.5)                                      | 0.999     | $(\lambda, 1)$ | Same guidance as per-tracker slow decay.                                                                                                                                                                                                  |
+| Drift allowance ($\kappa_\sigma$)         | Drift accumulator noise allowance in slow-baseline $\sigma$ units (§6.3) | 0.5       | $\geq 0$       | Lower → more sensitive, more false positives.                                                                                                                                                                                             |
+| Clip-pressure decay ($\lambda_\rho$)      | EWMA decay for per-axis clip ratio tracking (§6.1.1, §6.4)               | 0.95      | $(0, 1)$       | Lower → faster recovery from stale baselines, more contamination leakage. Higher → slower recovery, better poisoning resistance. At 0.95 (half-life ~14 batches): moderate shifts converge in ~60–100 batches through damped oscillation. |
+| Per-sample scores                         | Whether to include per-observation score vectors in reports              | false     | boolean        | Enable for forensic analysis; increases report size.                                                                                                                                                                                      |
+
+### 13.2 Analysis Selector Parameters
+
+These govern which cells receive statistical modelling (§8).
+
+| Parameter             | Description                                   | Default | Constraint | Guidance                                                                                                                                                                           |
+| --------------------- | --------------------------------------------- | ------- | ---------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Analysis budget ($K$) | Maximum competitively selected cells          | 1024    | $\geq 1$   | Total invested trackers $\leq 1 + K\bar{D}$ before sharing (§8.2); dominated by $2K$ under concentration. Scale with observation diversity. 256 for focused; 4096 for large-scale. |
+| Depth cutoff ($L$)    | Maximum V-Tree depth for analysis eligibility | 6       | $\geq 0$   | Larger $L$ admits less significant cells. Keep near $D_{\text{create}} + 2$.                                                                                                       |
+
+The competitive targets $\mathcal{T}$, investment set $\mathcal{I}$, and producing sets $\mathcal{A}$, $\mathcal{A}^*$ are derived from these parameters as specified in §8.
+
+### 13.3 Spatial Layer Parameters
+
+These are passed through to the spatial layer (§3) at construction time. The Sentinel does not modify them during operation.
+
+| Parameter                           | Description                              | Default | Constraint | Guidance                                                                                                                                       |
+| ----------------------------------- | ---------------------------------------- | ------- | ---------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
+| Split threshold ($\theta$)          | Minimum importance for split eligibility | 100     | $> 0$      | Lower → finer resolution faster. Must be reachable by sustained observations in the desired response time. Type is the accumulator $V$ (§1.2). |
+| Creation gate ($D_{\text{create}}$) | Maximum V-Tree depth for splits          | 3       | $\geq 0$   | Application-dependent.                                                                                                                         |
+| Eviction gate ($D_{\text{evict}}$)  | Minimum V-Tree depth for eviction        | 6       | $\geq 1$   | Must exceed $D_{\text{create}}$ by a buffer.                                                                                                   |
+| Soft budget                         | Soft node-count target                   | 100,000 | $> 0$      | Application-dependent.                                                                                                                         |
+| Hard ceiling ($G_{\max}$)           | Absolute maximum spatial nodes           | 200,000 | $\geq 5$   | Must exceed soft budget.                                                                                                                       |
+
+### 13.4 Temporal Policy (Host-Controlled)
+
+These are not construction-time parameters but rather arguments to the spatial decay operation, invoked by the host at its discretion (§10.1).
+
+| Parameter          | Suggested value | Guidance                                                                                                   |
+| ------------------ | --------------- | ---------------------------------------------------------------------------------------------------------- |
+| Attenuation factor | 0.99            | Attenuation per call. Compound with interval: effective half-life $= t_{1/2} \times \text{interval}$.      |
+| Depth selectivity  | 0.0             | Values $> 0$ cause fine structure to decay faster than coarse. Use 0.3–0.5 for depth-selective forgetting. |
+| Decay interval     | 60 seconds      | Shorter → more responsive contour. Longer → more stable.                                                   |
+
+### 13.5 Warm-Up Parameters
+
+These govern the noise injection and warm-up procedure (§11).
+
+| Parameter        | Description                        | Default                                          | Constraint                                   |
+| ---------------- | ---------------------------------- | ------------------------------------------------ | -------------------------------------------- |
+| Noise schedule   | Depth-tiered synthetic batch count | Geometric: root = 450, decay = 0.5, minimum = 50 | See §11.1.3                                  |
+| Noise batch size | Samples per synthetic batch        | 16                                               | $\geq 1$                                     |
+| Random seed      | Seed for pseudo-random generator   | Deterministic (fixed)                            | Optional; absent means implementation-chosen |
+
+---
+
+## Chapter 14. Output
+
+### 14.1 Report Overview
+
+Each observation cycle (§9) produces a report containing four sections: per-cell reports from competitive trackers, per-cell reports from ancestor trackers, coordination reports from the hierarchical coordination tier, and summary information about the system's current state. The report also includes contour, health, and analysis set summaries.
+
+Importance values in the report are converted to floating-point approximations at the report boundary (via the approximate conversion capability required of $V$, §1.2). Hosts needing exact accumulator values should query the spatial layer directly.
+
+### 14.2 Cell Analysis Report
+
+One record per analysed cell (competitive or ancestor) that processed at least one observation in the batch.
+
+| Field              | Type                            | Description                                              |
+| ------------------ | ------------------------------- | -------------------------------------------------------- |
+| Interval           | pair of $C$ values              | Spatial bounds of the cell (half-open interval)          |
+| Depth              | non-negative integer            | G-Tree depth (0 = root)                                  |
+| Analysis width     | non-negative integer            | $w = N - \text{depth}$                                   |
+| Sample count       | non-negative integer            | Observations delivered to this cell in this batch        |
+| Rank               | non-negative integer            | Current active subspace dimensionality $k$               |
+| Energy ratio       | float                           | Fraction of total energy captured by the active subspace |
+| Top singular value | float                           | Largest singular value $\sigma_1$                        |
+| Maturity           | maturity record (§14.6)         | Noise influence and observation counts                   |
+| Scoring geometry   | geometry record (§14.7)         | Structural reliability of each scoring axis              |
+| Scores             | scoring record (§14.3)          | All four axes with baselines and drift state             |
+| Per-sample scores  | optional list of sample records | Present only when per-sample reporting is enabled        |
+
+**Competitive vs. ancestor reports.** Both use the same record structure. They are separated in the report so the host can distinguish cells selected as competitive targets (members of $\mathcal{A}$) from cells included as ancestors (members of $\mathcal{A}^* \setminus \mathcal{A}$). Only online (producing) cells appear in the report; warming cells in $\mathcal{I} \setminus \mathcal{A}^*$ do not emit reports.
+
+### 14.3 Scoring Record
+
+One per cell, containing all four axes.
+
+| Field        | Type                       | Description |
+| ------------ | -------------------------- | ----------- |
+| Novelty      | score distribution (§14.4) |             |
+| Displacement | score distribution (§14.4) |             |
+| Surprise     | score distribution (§14.4) |             |
+| Coherence    | score distribution (§14.4) |             |
+
+### 14.4 Score Distribution
+
+One per axis per cell, summarising the batch and its relationship to baselines.
+
+| Field           | Type                      | Description                                                          |
+| --------------- | ------------------------- | -------------------------------------------------------------------- |
+| Minimum         | float                     | Minimum raw score across samples in this batch                       |
+| Maximum         | float                     | Maximum raw score across samples in this batch                       |
+| Mean            | float                     | Mean raw score across samples in this batch                          |
+| Maximum z-score | float                     | $\zeta(\max_i s_i)$ — z-score of the loudest sample (§6.1.2)         |
+| Mean z-score    | float                     | $\zeta(\bar{s}_{\text{batch}})$ — z-score of the batch mean (§6.1.2) |
+| Clip pressure   | float in $[0, 1]$         | Current clip-pressure EWMA $\bar{\rho}$ for this axis (§6.1.1)       |
+| Baseline        | baseline snapshot (§14.5) | Current fast EWMA state                                              |
+| Drift state     | drift snapshot (§14.5)    | Current slow EWMA and drift accumulator state                        |
+
+### 14.5 Baseline and Drift Snapshots
+
+**Baseline snapshot** (fast EWMA state):
+
+| Field    | Type  | Description                          |
+| -------- | ----- | ------------------------------------ |
+| Mean     | float | Current fast EWMA mean $\bar{s}$     |
+| Variance | float | Current fast EWMA variance $\bar{v}$ |
+
+**Drift snapshot** (slow EWMA and drift accumulator state):
+
+| Field                  | Type                 | Description                                |
+| ---------------------- | -------------------- | ------------------------------------------ |
+| Accumulator            | float                | Current drift accumulator value $S$ (§6.3) |
+| Slow baseline mean     | float                | Current slow EWMA mean                     |
+| Slow baseline variance | float                | Current slow EWMA variance                 |
+| Steps since reset      | non-negative integer | Batches since last drift accumulator reset |
+
+> _Host guidance (secular-trend monitoring)._ The drift accumulator $S$ detects fast-vs-slow baseline divergence but is blind to slow monotonic inflation of both baselines (see §6.4.5 design note). Hosts concerned about long-horizon contamination should monitor secular trends in the slow baseline mean across trackers over timescales much longer than $1/(1-\lambda_s)$. Sustained upward drift in $\bar{s}_{\text{slow}}$ across multiple trackers and axes, especially when accompanied by persistently elevated $\bar{\rho}$ (§14.4), may indicate patient sub-clip contamination. The annihilation mechanism (§10) provides a remediation path: targeted state destruction forces re-learning from the current observation distribution.
+
+### 14.6 Maturity Record
+
+| Field              | Type                 | Description                                                            |
+| ------------------ | -------------------- | ---------------------------------------------------------------------- |
+| Real observations  | non-negative integer | Genuine observations processed since creation                          |
+| Noise observations | non-negative integer | Synthetic observations processed during warm-up                        |
+| Noise influence    | float in $[0, 1]$    | $\eta$ — fraction of baseline not yet established by real data (§11.5) |
+
+### 14.7 Scoring Geometry Record
+
+One per cell or coordination tracker, describing the structural reliability of each scoring axis under the tracker's current rank and dimensionality.
+
+| Field          | Type                 | Description                                              |
+| -------------- | -------------------- | -------------------------------------------------------- |
+| Analysis width | non-negative integer | Working dimensionality $w$ of this tracker's input space |
+| Capacity       | non-negative integer | Maximum reachable rank: $\text{cap} = \min(w, r_{\max})$ |
+| Residual DOF   | non-negative integer | Residual degrees of freedom: $w - k$                     |
+
+Two derived predicates assist the host:
+
+| Predicate         | Condition                 | Meaning                                                                     |
+| ----------------- | ------------------------- | --------------------------------------------------------------------------- |
+| Novelty-saturated | $\text{residual DOF} = 0$ | Novelty axis is identically zero — the subspace spans the full space (§5.2) |
+| Novelty-saturable | $\text{cap} \geq w$       | Novelty _can_ become saturated as rank adapts — the host should monitor     |
+
+### 14.8 Per-Sample Score Record
+
+Present only when per-sample reporting is enabled. One per observation delivered to the cell.
+
+| Field        | Type  | Description                            |
+| ------------ | ----- | -------------------------------------- |
+| Novelty      | float | Raw novelty score for this sample      |
+| Displacement | float | Raw displacement score for this sample |
+| Surprise     | float | Raw surprise score for this sample     |
+| Coherence    | float | Raw coherence score for this sample    |
+
+### 14.9 Coordination Report
+
+One record per active coordination context (§7.1) that fired in this batch, ordered by G-Tree depth (shallowest first).
+
+| Field              | Type                            | Description                                                 |
+| ------------------ | ------------------------------- | ----------------------------------------------------------- |
+| Context interval   | pair of $C$ values              | Spatial extent of the coordination group                    |
+| Context depth      | non-negative integer            | G-Tree depth of the coordination node                       |
+| Group size         | non-negative integer            | Total competitive cells in this group                       |
+| Left count         | non-negative integer            | Competitive cells in the left subtree                       |
+| Right count        | non-negative integer            | Competitive cells in the right subtree                      |
+| Rank               | non-negative integer            | Current subspace dimensionality of the coordination tracker |
+| Energy ratio       | float                           | Fraction of total energy captured                           |
+| Top singular value | float                           | Largest singular value                                      |
+| Maturity           | maturity record (§14.6)         | Warm-up state of the coordination tracker                   |
+| Scoring geometry   | geometry record (§14.7)         | Structural reliability of coordination scoring axes         |
+| Scores             | scoring record (§14.3)          | Four axes of coordination scoring                           |
+| Per-member scores  | optional list of member records | Per-cell breakdown from this context's model                |
+
+**Per-member score record** (when present):
+
+| Field                | Type               | Description                                      |
+| -------------------- | ------------------ | ------------------------------------------------ |
+| Cell interval        | pair of $C$ values | Spatial bounds of the contributing cell          |
+| Novelty              | float              | Raw coordination novelty for this cell           |
+| Displacement         | float              | Raw coordination displacement for this cell      |
+| Surprise             | float              | Raw coordination surprise for this cell          |
+| Coherence            | float              | Raw coordination coherence for this cell         |
+| Novelty z-score      | float              | Z-score of this cell's coordination novelty      |
+| Displacement z-score | float              | Z-score of this cell's coordination displacement |
+| Surprise z-score     | float              | Z-score of this cell's coordination surprise     |
+| Coherence z-score    | float              | Z-score of this cell's coordination coherence    |
+
+### 14.10 Contour Snapshot
+
+| Field                          | Type                 | Description                                                                      |
+| ------------------------------ | -------------------- | -------------------------------------------------------------------------------- |
+| Plateau count                  | non-negative integer | Number of distinct plateaus in the current contour (§3.2)                        |
+| Cell count                     | non-negative integer | Total contour cells                                                              |
+| Total importance               | float                | Approximate total importance across all cells                                    |
+| Splits since last report       | `u32`                | Cells created by catalytic or bootstrap bisection since the previous report      |
+| Net removals since last report | `u32`                | Net structural removals (evictions minus restorations) since the previous report |
+
+**Structural mutation counts.** The two mutation fields are derived from `terminal_count()` and `node_count()` deltas between successive `ingest()` calls, using the identities:
+
+- Each split creates 2 nodes and net +1 terminal: $\Delta N = +2$, $\Delta T = +1$.
+- Each eviction removes 1 node and 1 terminal: $\Delta N = -1$, $\Delta T = -1$.
+- Each restoration creates 1 node and 1 terminal: $\Delta N = +1$, $\Delta T = +1$.
+
+Solving:
+
+$$\text{splits} = \Delta N - \Delta T$$
+
+$$\text{net\_removals} = \text{splits} - \Delta T = \text{evictions} - \text{restorations}$$
+
+Both values are exact. Restorations (legacy promotions) are rare — they occur only when an eviction leaves a semi-internal node — so `net_removals` typically equals the raw eviction count.
+
+The implementation snapshots `terminal_count()` and `node_count()` at the end of each `ingest()` call and computes the deltas on the next call. No graph-internal event counters are required.
+
+### 14.11 Health Report
+
+Available both inline in the batch report (for batch-level completeness) and via a standalone query (for on-demand inspection between observation cycles). Both sources produce identical data.
+
+| Field                        | Type                                    | Description                                                                                                                                                                                                |
+| ---------------------------- | --------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------ | ---------------------------------------------------------- |
+| Total spatial nodes          | non-negative integer                    | Current materialised G-Tree node count                                                                                                                                                                     |
+| Semi-internal count          | non-negative integer                    | One-child spatial nodes                                                                                                                                                                                    |
+| Active competitive trackers  | non-negative integer                    | $                                                                                                                                                                                                          | \mathcal{A}                          | $                                                          |
+| Active ancestor trackers     | non-negative integer                    | $                                                                                                                                                                                                          | \mathcal{A}^\* \setminus \mathcal{A} | $                                                          |
+| Active coordination contexts | non-negative integer                    | Currently active coordination contexts (§7.1)                                                                                                                                                              |
+| Investment set size          | non-negative integer                    | $                                                                                                                                                                                                          | \mathcal{I}                          | $ — total cells with allocated trackers (online + warming) |
+| Warming trackers             | non-negative integer                    | Members of $\mathcal{I}$ currently in the warm-up pipeline (§11.6)                                                                                                                                         |
+| Warming competitive targets  | non-negative integer                    | Competitive targets in $\mathcal{I}$ not yet promoted to $\mathcal{A}$                                                                                                                                     |
+| Lifetime observations        | non-negative integer                    | Total observations processed since system creation                                                                                                                                                         |
+| Rank distribution            | list of non-negative integers           | Count of trackers at each rank value                                                                                                                                                                       |
+| Clip-pressure distribution   | histogram or summary statistics         | Distribution of $\bar{\rho}$ values across all active trackers and axes (§6.4)                                                                                                                             |
+| Degenerate-cell count        | non-negative integer                    | Cells excluded from $\mathcal{I}$ due to $w < 2$ (§4.1, §8.2); currently always zero under the $w \geq 2$ eligibility predicate in §8.1 — retained as a defensive field against future eligibility changes |
+| Geometry distribution        | geometry distribution record (§14.11.1) | Fleet-wide scoring axis reliability                                                                                                                                                                        |
+
+#### 14.11.1 Geometry Distribution
+
+Summarises the structural reliability of scoring axes across all active per-cell trackers.
+
+| Field                    | Type                 | Description                                                          |
+| ------------------------ | -------------------- | -------------------------------------------------------------------- |
+| Novelty-saturated count  | non-negative integer | Trackers where $k = w$ (novelty axis degenerate)                     |
+| Novelty-saturable count  | non-negative integer | Trackers where $\text{cap} \geq w$ (novelty _can_ become degenerate) |
+| Coherence-inactive count | non-negative integer | Trackers where $k < 2$ (coherence axis undefined)                    |
+
+### 14.12 Analysis Set Summary
+
+| Field                      | Type                          | Description                                                       |
+| -------------------------- | ----------------------------- | ----------------------------------------------------------------- | -------------- | ----------------------------------------------------- |
+| Investment set size        | non-negative integer          | $                                                                 | \mathcal{I}    | $ (competitive targets + ancestors, online + warming) |
+| Producing competitive size | non-negative integer          | $                                                                 | \mathcal{A}    | $ (online competitive targets)                        |
+| Producing full size        | non-negative integer          | $                                                                 | \mathcal{A}^\* | $ (online investment members)                         |
+| Depth range                | pair of non-negative integers | (minimum depth, maximum depth) across competitive cells           |
+| Importance range           | pair of floats                | (minimum importance, maximum importance) across competitive cells |
+| V-Tree depth range         | pair of non-negative integers | (minimum V-depth, maximum V-depth) across competitive cells       |
+
+### 14.13 Contour Queries
+
+The spatial layer's contour is available for direct querying through the spatial layer's interface:
+
+| Query         | Description                                | Cost                             |
+| ------------- | ------------------------------------------ | -------------------------------- |
+| Point query   | The plateau containing a given coordinate  | $O(\log P)$                      |
+| Range query   | All plateaus intersecting a given interval | $O(\log P + \text{result size})$ |
+| Iteration     | All plateaus in spatial order              | $O(P)$                           |
+| Plateau count | Number of distinct plateaus                | $O(1)$                           |
+
+These queries are independent of the observation cycle and may be invoked at any time.
+
+### 14.14 The Wavelet Portrait
+
+The spatial layer supports extraction of a wavelet-based energy portrait (the PEWEI) as a complementary view of the spatial structure. Its frozen baselines — the pre-refinement energy at each scale — record the observation volume at which each region graduated from "undifferentiated" to "resolved."
+
+For the Sentinel, this is a spatial significance map: regions whose baselines are large required substantial volume to confirm structure. The portrait's layer ordering (within a logarithmic factor of Shannon entropy) provides a natural summary of the spatial importance landscape. The host can extract the portrait alongside the batch report for spatial situational awareness.
+
+---
+
+# Part VI — Properties and Defence Analysis
+
+This part analyses the properties that emerge from the interaction of the mechanisms defined in Parts II–IV. Each mechanism was specified independently; this part examines what the composition guarantees.
+
+---
+
+## Chapter 15. Scoring Properties
+
+### 15.1 Axis Independence Under Binary Inputs
+
+The four scoring axes (§5) decompose the observation's relationship to the learned model along orthogonal statistical concerns:
+
+| Score        | Input                                   | Covariance structure                            |
+| ------------ | --------------------------------------- | ----------------------------------------------- |
+| Novelty      | $\|R_i\|^2 / (w - k)$                   | Residual energy (scalar, orthogonal complement) |
+| Displacement | $\|z_i\|^2 / (k + \|z_i\|^2)$           | Total latent energy (scalar)                    |
+| Surprise     | $(z_j - \mu_j)^2 / \nu_j$ per $j$       | Diagonal of latent covariance                   |
+| Coherence    | $(z_j z_l - \Gamma_{jl})^2$ per $j < l$ | Off-diagonal of latent covariance               |
+
+Together they cover the full covariance structure of the latent space without assembling or inverting a dense $k \times k$ matrix. Each axis can fire independently of the others:
+
+- **Novelty only.** The observation has unusual structure outside the subspace, but its within-subspace projection is unremarkable. Indicates a genuinely novel direction.
+- **Displacement only.** The observation projects normally onto the subspace directions but with unusual total magnitude. Indicates a scaling anomaly.
+- **Surprise only.** Total projection energy is normal, but its distribution across dimensions is unusual. Indicates an unusual _combination_ of known directions at normal overall energy.
+- **Coherence only.** Individual dimensional magnitudes are normal, but their pairwise products are unusual. Indicates an unusual _co-activation pattern_ — dimensions that normally vary independently are varying together (or vice versa).
+
+### 15.2 Projection Energy Redundancy
+
+Under the centred binary encoding (§2.3), $\|\mathbf{x}_i\|^2 = w/4$ for every suffix vector (§2.5). By the Pythagorean theorem:
+
+$$\|\hat{\mathbf{x}}_i\|^2 + \|R_i\|^2 = \|\mathbf{x}_i\|^2 = w/4$$
+
+Therefore projection energy $\|\hat{\mathbf{x}}_i\|^2 / k$ is a perfect affine function of novelty $\|R_i\|^2 / (w - k)$:
+
+$$\frac{\|\hat{\mathbf{x}}_i\|^2}{k} = \frac{w/4 - (w - k) \cdot \text{novelty}_i}{k}$$
+
+Pearson correlation $r = -1$. A fifth axis based on projection energy would carry zero independent information and would violate the polarity invariant (§5.1) — low projection energy would indicate anomaly, but the upper-tail filter (§6.1.1) would fail to protect the baseline.
+
+This redundancy is specific to centred binary inputs. Continuous-valued inputs with variable norms would decouple the two measures. If the system is ever extended to non-binary encodings, projection energy should be reconsidered as an independent axis.
+
+### 15.3 Per-Axis Sensitivity Profiles
+
+Each axis has a characteristic sensitivity profile that determines what kinds of anomalies it catches best and what kinds it misses:
+
+| Axis         | Most sensitive to                     | Least sensitive to                                                         |
+| ------------ | ------------------------------------- | -------------------------------------------------------------------------- |
+| Novelty      | New directions never seen before      | Anomalies that stay within the learned subspace                            |
+| Displacement | Large-magnitude projections           | Anomalies at normal magnitude but unusual direction                        |
+| Surprise     | Per-dimension distributional shifts   | Shifts that affect all dimensions equally (caught by displacement instead) |
+| Coherence    | Changed inter-dimension relationships | Single-dimension anomalies (caught by surprise instead)                    |
+
+The axes are complementary by design: what one axis misses, another catches. An adversary who optimises values to minimise one axis's score is forced into a region of observation space that elevates another axis's score — unless the values are genuinely indistinguishable from normal observations on all axes simultaneously.
+
+### 15.4 The Role of Rank
+
+The active rank $k$ partitions the observation space into two subspaces of complementary sensitivity:
+
+- The **within-subspace** model ($k$ dimensions) catches displacement, surprise, and coherence anomalies but is blind to novel directions.
+- The **residual** model ($w - k$ dimensions) catches novel directions but has no directional sensitivity within the residual space — only aggregate residual energy.
+
+Low rank (small $k$) allocates most dimensions to the residual, giving high sensitivity to novelty but coarse within-subspace modelling. High rank (large $k$) captures more within-subspace structure at the cost of less residual sensitivity. The rank adaptation mechanism (§4.2, Phase 5) balances automatically by tracking cumulative energy.
+
+---
+
+## Chapter 16. Ancestor Chain Properties
+
+The ancestor closure (§8.2) transforms a set of independently selected competitive cells into a structured defence. This chapter analyses the properties that emerge from the guaranteed chain of models from each competitive cell to the root.
+
+### 16.1 Nesting Constraint Guarantee
+
+Every competitively selected cell has a **guaranteed complete chain** of models from itself to the root. For every value the adversary submits, the number of models it must simultaneously satisfy equals the number of materialised G-Tree ancestors of the target cell, plus one (the cell itself).
+
+Without the ancestor closure, nesting defence would work only when multiple cells on the same G-Tree path _happened_ to be independently promoted by V-Tree ranking. Under the investment set's ancestor closure, the constraint multiplication is structural and unconditional. Moreover, the g.sum-ordered warm-up pipeline (§11.6.2) ensures that at the moment any competitive target comes online, its entire ancestor chain is already online — the full defence depth is available from the first batch.
+
+### 16.2 Null-Space Coverage
+
+At each level $\ell$ on the ancestor chain, the model operates on suffix width $w_\ell = N - \ell$ and has learned a rank-$k_\ell$ subspace from the observation population at that level. The model constrains $k_\ell$ directions and is blind to $w_\ell - k_\ell$ directions (its null space).
+
+The directions each model captures are **partially independent across levels** for three reasons:
+
+1. **Different observation populations.** Each level sees a different set of observations — broader at coarser levels. The statistical structure learned from a population of 100,000 observations at depth 0 differs from that learned from 500 observations at depth 48.
+
+2. **Different suffix dimensions.** Each level's suffix includes additional bit positions. The prefix bits at finer levels become suffix bits at coarser levels. A depth-16 model operates on $N - 16$ dimensions; a depth-48 model operates on $N - 48$ dimensions. The $32$ additional dimensions available to the depth-16 model carry cross-region correlations invisible at depth 48.
+
+3. **Cross-level correlations.** The correlations between the additional bits and the deeper suffix bits are precisely what the coarser models learn. These correlations connect the null spaces across levels.
+
+The adversary's "free" bits at level $\ell$ (the null space of the level-$\ell$ model) are constrained at level $\ell - 1$ with probability proportional to how much the coarser model captured cross-level correlations. Each ancestor level peels away some of the adversary's freedom.
+
+### 16.3 Defence Depth Scales With Observation Importance
+
+Competitive cells sit at depths determined by the spatial layer's adaptive refinement — high-volume regions get deeper cells. The ancestor chain length for each competitive cell equals its G-Tree depth. Consequently, **the cells with the most volume have the longest ancestor chains and the most layers of defence.**
+
+An adversary targeting a busy depth-$d$ cell faces models at all $d + 1$ depths from 0 to $d$ — every materialised level on the ancestor path (§3.1). An adversary targeting a quiet depth-2 cell faces only depths 0, 1, and 2. Fewer constraints — but also a less significant target. The defence depth automatically scales with observation importance.
+
+### 16.4 Constraint Multiplication
+
+The per-value constraint count grows with ancestry depth. At each level, the model imposes approximately $k_\ell^2 / 2$ constraints (from the four scoring axes: $k_\ell$ novelty directions, 1 displacement scalar, $k_\ell$ surprise values, and $k_\ell(k_\ell - 1)/2$ coherence pairs). Across a chain of $D$ ancestor levels, the total constraint count is approximately:
+
+$$\sum_{\ell=0}^{D} \frac{k_\ell^2}{2}$$
+
+These constraints are not fully independent (because the observations overlap), but neither are they redundant (because the populations and suffix dimensions differ). The effective constraint count grows meaningfully with depth, even accounting for overlap.
+
+### 16.5 Scale Diagnostics
+
+The ancestor chain provides diagnostic information about the **scale** at which an anomaly manifests:
+
+| Pattern                                                | Interpretation                                                                                                                         |
+| ------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------- |
+| Anomalous at depth 48 only                             | Unusual relative to the specific cell's population but normal in the broader context — a local anomaly.                                |
+| Anomalous at depths 16 and above                       | Unusual in the broader regional context — a stronger, coarser-scale signal.                                                            |
+| Anomalous at depth 0, normal at depth 48               | The entire region is unusual, but this value is typical within it — the value inherits its region's anomaly.                           |
+| Evasion succeeding at depth 48 but failing at depth 32 | The adversary matched local patterns but missed cross-cell correlations captured by the coarser model — the nesting defence in action. |
+
+The host receives scores at every ancestor level and can reconstruct the full scale profile without any additional mechanism.
+
+---
+
+## Chapter 17. Compound Defence and Coverage
+
+### 17.1 Interlocking Constraints: Ancestor Chain × Coordination
+
+The ancestor chain (§16) and the coordination tier (§7) are not independent fallbacks. The investment set's ancestor closure guarantees the chain exists; the g.sum warm-up ordering guarantees it is online; the coordination tier operates on the producing competitive set. They operate simultaneously on the same observations and impose **interlocking constraints** — satisfying one makes satisfying the other harder.
+
+**The mechanisms' roles.** The ancestor chain provides _per-value depth_: every individual value must produce normal scores at every level from its target cell to the root. The coordination tier provides _cross-cell breadth_: the distribution of scores across spatially related competitive cells must look normal at every level of the hierarchy.
+
+**The interaction.** The same values that must score normally at level $\ell$ per-value also contribute to the coordination inputs at level $\ell - 1$. Per-value evasion at level $\ell$ constrains where in score space cell $c$'s summary can land, which constrains an element of the coordination input matrix at level $\ell - 1$. The optimisation itself leaves fingerprints.
+
+### 17.2 Why Evasion Optimisation Creates Detectable Correlations
+
+Legitimate observations arise from independent sources operating independently across different regions of the domain. Score summaries across cells are approximately independent (modulo shared common-mode effects that the coordination tracker has already learned).
+
+Values optimised to evade per-value scoring have a different property: all crafted values are generated by the **same optimisation process** targeting the same set of models. Even if each value individually scores normally, the _population_ of crafted values shares structural regularities imposed by the optimisation:
+
+- **Clustering in safe zones.** If the adversary uses a common algorithm to find safe-zone values, the resulting values cluster in the safe zone rather than spreading across it the way legitimate observations do.
+- **Complementary restriction.** If the adversary's values avoid specific null-space directions at a coarser level (to satisfy the ancestor chain), they cluster in the complement — a restriction that manifests as unusual displacement or surprise patterns at the coordination level.
+- **Template reuse.** If the adversary reuses structural templates (modifying a few bits per value to maintain evasion while varying the functional content), the templates create correlated residual patterns across cells.
+
+Each coordination axis catches a different signature of optimisation-induced correlation:
+
+| Coordination Axis | Optimisation Fingerprint                                                                                                                                              |
+| ----------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Novelty**       | The cross-cell score pattern is a _new kind_ not in the coordination subspace — the optimisation created a score distribution that normal observations never produce. |
+| **Displacement**  | All cells' scores are pinned near the same percentile of the safe zone — individually unremarkable but systematically elevated as a group.                            |
+| **Surprise**      | One specific scoring axis is systematically elevated across cells — the optimisation consistently produces slightly high surprise (or another axis) in every cell.    |
+| **Coherence**     | Cross-axis correlations are unusual — cells with elevated novelty also have elevated displacement, because the safe-zone geometry links the two.                      |
+
+### 17.3 The Multi-Objective Dilemma
+
+Three objectives are in tension for an adversary:
+
+1. **Per-value evasion** (ancestor chain): each value must land in the intersection of safe zones across all ancestor levels. This pushes toward a specific region of the domain → uniformity.
+
+2. **Cross-cell diversity** (coordination): the distribution of scores across cells must match normal cross-cell distributions. This requires values sent to different cells to produce _different_ score profiles → against uniformity.
+
+3. **Cross-level consistency**: the same values' scores at different levels must be jointly consistent with normal cross-level statistics → constrains both.
+
+The adversary must solve a constrained optimisation that satisfies per-value evasion at all levels while producing statistically natural diversity across cells and consistency across levels. Adding calibrated noise to restore diversity helps but is insufficient: the perturbation must itself mimic the natural _structure_ of variation in legitimate observations, not just be "varied." Random perturbations within the safe zone produce uniform distributions within that zone, which may not match the actual distribution of baseline projections (typically clustered along specific subspace directions).
+
+### 17.4 The Irreducible Floor
+
+The only remaining evasion strategy is values that are genuinely statistically identical to legitimate observations at every level, every cell, every axis, every cross-cell correlation, and every cross-level consistency measure. This requires characterising the baseline observations' complete joint distribution at every cell AND the cross-cell distribution AND the cross-level consistency — simultaneously, for every value, in every batch.
+
+For a practical adversary operating with imperfect knowledge, time constraints, and operational requirements (the values must serve some external purpose, not just look normal), the composition raises the bar very substantially. But it does not eliminate the possibility: **perfect mimicry of all distributions at all scales remains the irreducible floor** of any detection system based on distributional comparison.
+
+### 17.5 Evasion Assessment by Strategy
+
+The following table summarises how each evasion strategy fares against each defence mechanism individually and in composition:
+
+| Evasion strategy                          | Ancestor chain alone                                                                                                                                                                                                                      | Coordination alone                                                                                                                                                                                           | Composed                                                                                                                                                                                                                                                                                                |
+| ----------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Mimic local distribution at one cell      | Must match $d$ models simultaneously                                                                                                                                                                                                      | Not designed for single-cell evasion                                                                                                                                                                         | Match $d$ models **and** produce cross-cell scores matching the natural joint distribution                                                                                                                                                                                                              |
+| Exploit null space of target model        | Constrained by ancestor models' coverage of the null space (§16.2)                                                                                                                                                                        | Irrelevant for single-cell evasion                                                                                                                                                                           | Ancestors constrain null space; **coordination detects systematic null-space clustering across cells**                                                                                                                                                                                                  |
+| Gradual baseline manipulation             | Must manipulate baselines at all $d$ levels simultaneously, each with different EWMA dynamics                                                                                                                                             | Coordination drift accumulator detects cross-cell drift                                                                                                                                                      | Must manipulate $d$ baselines **without** creating correlated cross-cell drift patterns                                                                                                                                                                                                                 |
+| Use common evasion algorithm across cells | Each cell evaded independently                                                                                                                                                                                                            | Detects optimisation-induced uniformity across cells                                                                                                                                                         | **Individually invisible, jointly visible** — the optimisation fingerprint spans cells                                                                                                                                                                                                                  |
+| Add noise to restore cross-cell diversity | Noise may violate per-value constraints across levels                                                                                                                                                                                     | Random noise may not match natural correlation structure                                                                                                                                                     | Must simultaneously satisfy per-value constraints at all levels **and** match natural cross-cell correlation structure                                                                                                                                                                                  |
+| Patient sub-clip contamination            | Must stay below $c_{\text{clip}}$ at all $d$ levels simultaneously while inflating baselines slowly enough that fast-slow gap never exceeds $\kappa$; multi-level EWMA dynamics differ, so the required injection rate is level-dependent | Sustained sub-clip presence across cells inflates coordination baselines, but correlated inflation across cells elevates coordination scores — coordination drift accumulator detects the cross-cell pattern | Must inflate $d$ per-cell baselines **and** coordination baselines at all levels without producing correlated cross-cell drift patterns; bounded by the same multi-objective dilemma as gradual manipulation, with the additional constraint that scores must remain below all clip ceilings throughout |
+| Perfect mimicry of all distributions      | Still works                                                                                                                                                                                                                               | Still works                                                                                                                                                                                                  | Still works — the irreducible floor                                                                                                                                                                                                                                                                     |
+
+> _Design note (patient sub-clip contamination vs. gradual baseline manipulation)._ The "gradual baseline manipulation" strategy assumes an adversary actively targeting baselines with knowledge of the EWMA dynamics. Patient sub-clip contamination is subtler and does not require such knowledge: any sustained adversarial presence producing moderately elevated scores below $c_{\text{clip}}$ will, as a side effect, inflate the fast EWMA $\bar{s}$ and eventually the slow EWMA $\bar{s}_{\text{slow}}$. If the adversary's influence grows slowly enough — less than $\kappa_\sigma \sqrt{\bar{v}_{\text{slow}}}$ per slow-EWMA time constant — the one-sided CUSUM accumulates nothing, because it detects _rate of change_ between fast and slow baselines, not _absolute regime level_. The slow baseline absorbs the contamination and the system's sensitivity to future anomalous traffic degrades invisibly to the drift accumulator. This is an inherent limitation of any adaptive baseline system: the mechanism that enables automatic adaptation to legitimate regime changes is the same mechanism an adversary exploits through patient contamination. The multi-level ancestor chain and coordination tier constrain this attack (the adversary must inflate baselines at all levels without producing correlated cross-cell patterns), but do not eliminate it. Hosts concerned about long-horizon contamination should monitor secular trends in the reported $\bar{s}_{\text{slow}}$ and $\bar{\rho}$ values (§14.5, §14.4) across trackers and flag sustained monotonic increases. This monitoring is a host-level policy responsibility consistent with "Measure, don't decide" (§1.3).
+
+### 17.6 Coverage Matrix
+
+The combination of per-cell scoring, ancestor chain scoring, and hierarchical coordination covers six anomaly modalities across three temporal patterns:
+
+```
+                Sudden     Gradual    Sudden     Gradual    Sudden      Gradual
+                single     single     partial    partial    system      system
+
+Per-cell z      ✓ strong   ✗ absorbed ✓ each     ✗ absorbed ✗ mild      ✗ both
+Per-cell drift  redundant  ✓ drift    redundant  ✓ each     ✗ mild      ✗ diluted
+Ancestor chain  ✓ depth    ✓ multi-   ✓ depth    ✓ multi-   ✓ root      ✓ root
+                defence    baseline   defence    baseline   catches     drift
+Coord (local)   ✗ few      ✗ few      ✓ asymm    ✓ asymm    ✗ n/a       ✗ n/a
+Coord (mid)     ✗ diluted  ✗ diluted  ✓ struct   ✓ accum    ✓ moderate  ✓ moderate
+Coord (root)    ✗ diluted  ✗ diluted  ✗ diluted  ✗ diluted  ✓ strong    ✓ accum
+```
+
+**Every modality is detected by at least one mechanism.** The hardest case — gradual system-wide coordination — is caught by the root drift accumulator, which accumulates aggregate departure across all $K$ cells at signal-to-noise ratio proportional to $\sqrt{K}$.
+
+### 17.7 Compound Effect Summary
+
+The ancestor chain and coordination tier do not merely add constraints — they create conflicting objectives. The ancestor chain forces per-value evasion into narrow safe zones, creating the very uniformity that the coordination tier detects. The coordination tier forces cross-cell diversity, creating degrees of freedom that the ancestor chain constrains. The composition raises evasion from a single-model matching problem to a multi-scale, multi-cell, multi-axis constrained optimisation with no known efficient solution — except producing values genuinely indistinguishable from normal observations at every scale simultaneously.
+
+---
+
+# Appendices
+
+---
+
+## Appendix A. Empirical Warm-Up Analysis
+
+This appendix provides the methodology and extended data behind the convergence times reported in §11.9. These results characterise the interaction between the cold-start mitigations (§11.2–11.4) and the baseline tracking machinery (§6), and inform the noise schedule parameters recommended in §13.5.
+
+**Data currency note.** The convergence times below were measured before the EWMA-mean-centred variance formula ([ADR-S-021](../adr/021-ewma-mean-centred-variance.md)) was implemented. Post-implementation measurements show a modest improvement: worst-case convergence at the production configuration ($\lambda = 0.99$, $b = 16$) improved from 289 to 282 rounds, consistent with the elimination of the original formula's $-6.25\%$ variance bias at $b = 16$. The absolute convergence times reported below remain valid as conservative upper bounds.
+
+### A.1 Methodology
+
+**Setup.** A single subspace tracker at analysis width $w = 96$ and capacity $\text{cap} = 16$ is created, noise-injected according to the configured schedule, and then fed uniformly random centred bit vectors ($\{-0.5, +0.5\}^{96}$, independent and identically distributed) for a sustained observation period. The structureless input ensures that all observed convergence dynamics are properties of the tracker machinery, not of input structure.
+
+**Measurement.** For each scoring axis, the fast EWMA mean $\bar{s}$ is recorded after every batch. A windowed mean over the last 50 batches is computed and compared against a reference value obtained from a long burn-in run (10,000+ batches). Trajectory convergence is declared when the windowed mean enters and remains within a tolerance band (1% relative error for novelty and displacement; 5% for surprise and coherence, reflecting their higher intrinsic variability).
+
+**Controls.** Each configuration is tested across 20 independent random seeds. The convergence time reported is the median; the range across seeds is reported where it is informative (particularly for displacement at small batch sizes, which exhibits bimodal convergence).
+
+### A.2 Cold-Start Mitigations and Their Effects
+
+Three mitigations operate during the noise-to-real transition. Each addresses a specific source of convergence delay:
+
+| Mitigation                              | Source of delay addressed                                                                                                                                            | Effect on convergence                                                                                                                                                                                    |
+| --------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Cold-start latent seeding (§11.2)       | Latent variance $\nu^{(z)}$ initialises far from steady state → surprise score non-stationary for tens of rounds                                                     | Eliminates latent variance mismatch at $t = 0$; surprise converges in $\sim$65 rounds instead of $\sim$200+                                                                                              |
+| Clip-pressure modulation (§6.1.1, §6.4) | First batch produces near-zero variance → ultra-tight clip ceiling → self-reinforcing low-baseline loop; also, stale baselines cause permanent lockout in production | Breaks the positive feedback loop during warm-up (via $\eta$); prevents permanent lockout in production (via $\bar{\rho}$); displacement converges monotonically instead of exhibiting plateau behaviour |
+| Slow-from-fast seeding (§11.4)          | Slow EWMA half-life ($\sim$693 steps) far exceeds fast EWMA half-life ($\sim$69 steps) → drift accumulator interprets the gap as evidence                            | Eliminates false drift accumulation at the noise-to-real boundary; reduces spurious accumulation by $\sim$97%                                                                                            |
+
+### A.3 Convergence Times by Configuration
+
+**Configuration A: $\lambda = 0.95$, $b_{\text{noise}} = 4$**
+
+| Component                   |          Without mitigations | With all mitigations | Notes                                                                                          |
+| --------------------------- | ---------------------------: | -------------------: | ---------------------------------------------------------------------------------------------- |
+| Novelty baseline            |                           21 |                   21 | Unaffected — constant-norm property makes novelty inherently stable                            |
+| Displacement baseline       |               500+ (plateau) |     21–475 (bimodal) | Bimodality across seeds: 14/20 seeds converge by round 21; 6/20 seeds require $\sim$475 rounds |
+| Surprise baseline           |                         200+ |               **65** | Cold-start seeding is the critical mitigation                                                  |
+| Coherence baseline          |                         500+ |              **406** | Dominated by rank-gating delay: $k < 2$ for the first $\sim$100–300 rounds                     |
+| Drift accumulator (novelty) | 198 units false accumulation |            5.7 units | Slow-from-fast seeding is the critical mitigation                                              |
+
+**Configuration B: $\lambda = 0.95$, $b_{\text{noise}} = 16$**
+
+| Component                   | Without mitigations | With all mitigations | Notes                                                            |
+| --------------------------- | ------------------: | -------------------: | ---------------------------------------------------------------- |
+| Novelty baseline            |                  21 |                   21 |                                                                  |
+| Displacement baseline       |                300+ |                   21 | Larger batch eliminates bimodality                               |
+| Surprise baseline           |                200+ |               **70** |                                                                  |
+| Coherence baseline          |                500+ |              **173** | Larger batch accelerates rank growth → shorter rank-gating delay |
+| Drift accumulator (novelty) |           180 units |            4.2 units |                                                                  |
+
+**Configuration C: $\lambda = 0.99$, $b_{\text{noise}} = 16$**
+
+| Component                   | Without mitigations | With all mitigations | Notes                                                |
+| --------------------------- | ------------------: | -------------------: | ---------------------------------------------------- |
+| Novelty baseline            |                 101 |                  101 | Slower $\lambda$ → proportionally longer convergence |
+| Displacement baseline       |                800+ |                  101 |                                                      |
+| Surprise baseline           |               1000+ |              **398** | Slowest-converging axis at this $\lambda$            |
+| Coherence baseline          |                800+ |              **315** |                                                      |
+| Drift accumulator (novelty) |           450 units |            8.1 units |                                                      |
+
+### A.4 Displacement Bimodality at Small Batch Sizes
+
+At $b_{\text{noise}} = 4$ with $\lambda = 0.95$, displacement convergence exhibits bimodal behaviour across random seeds: a majority of seeds converge rapidly ($\sim$21 rounds), while a minority require $\sim$475 rounds with a visible plateau.
+
+The mechanism: displacement's bounded range $[0, 1)$ compresses near zero, where the initial noise-warmed baseline sits. Small batches ($b_{\text{noise}} = 4$) produce high variance in the batch displacement mean. If the first few real batches happen to produce displacement values slightly above the noise-warmed baseline, the clip ceiling widens naturally and convergence proceeds. If they produce values slightly below, the baseline drifts downward, tightening the clip ceiling from below (a direction the upper-tail filter does not protect against), creating a slow recovery.
+
+At $b_{\text{noise}} = 16$, the batch mean variance is $4\times$ smaller, eliminating the bimodality. This effect is specific to displacement at small batch sizes and does not manifest in the other three axes.
+
+### A.5 Coherence Convergence Bottleneck
+
+Coherence is defined only when $k \geq 2$ (§5.5). A newly created tracker starts at $k = 1$ and must accumulate sufficient rank before coherence baselines can even begin tracking. The rank adaptation mechanism (§4.2, Phase 5) evaluates every $T_{\text{rank}}$ steps and moves rank by at most one step, so the minimum time to $k = 2$ is $T_{\text{rank}}$ batches.
+
+In practice, rank growth depends on the energy distribution across singular values. Under structureless noise (uniform random bits), singular values are approximately equal, so the energy threshold $\tau = 0.90$ requires $k \approx 0.9 \times \text{cap}$ — many more than 2. Rank reaches 2 relatively quickly (within $2 \times T_{\text{rank}}$ batches typically), but the coherence baseline then requires its own convergence time on top of the rank-gating delay.
+
+The rank-gating delay is the dominant contributor to coherence being the slowest-converging axis.
+
+### A.6 Steady-State Variability
+
+Even after convergence, baseline values exhibit ongoing variability. The following coefficients of variation (standard deviation of windowed-mean divided by its mean, measured over 1,000 post-convergence batches) characterise the intrinsic wander:
+
+| Axis         | CV ($b_{\text{noise}} = 4$) | CV ($b_{\text{noise}} = 16$) | Interpretation                                                                         |
+| ------------ | :-------------------------: | :--------------------------: | -------------------------------------------------------------------------------------- |
+| Novelty      |            0.13%            |            0.06%             | Extremely stable — the constant-norm property (§2.5) eliminates input energy variation |
+| Displacement |            5.9%             |             3.0%             | Moderate — bounded range compresses variance reporting                                 |
+| Surprise     |            9.0%             |             3.6%             | Moderate — sensitive to per-dimension variance fluctuations                            |
+| Coherence    |            19.9%            |            11.5%             | High — pairwise products amplify input variance; $k(k{-}1)/2$ terms compound           |
+
+These CVs represent a **floor** on baseline precision: no amount of warm-up time reduces variability below these values. They should be considered when interpreting z-scores — a z-score of 2.0 on an axis with 20% CV is less significant than a z-score of 2.0 on an axis with 0.1% CV.
+
+### A.7 Noise Schedule Implications
+
+The noise schedule must produce enough rounds at each depth for the worst-case axis (typically coherence or surprise) to reach steady state before real observations arrive. The default geometric schedule (root = 450, decay = 0.5, minimum = 50) is calibrated against Configuration C ($\lambda = 0.99$, $b_{\text{noise}} = 16$) — the default forgetting factor:
+
+| Depth | Rounds (default schedule) | Worst-case convergence ($\lambda = 0.99$) | Margin   |
+| ----: | ------------------------: | ----------------------------------------: | -------- |
+|     0 |                       450 |                            398 (surprise) | +13%     |
+|     1 |                       225 |                                 $\sim$200 | +13%     |
+|     3 |                        56 |                                  $\sim$50 | +12%     |
+|    4+ |              50 (minimum) |                                  $\sim$50 | Adequate |
+
+The previous default (root = 50, minimum = 10) was calibrated against $\lambda = 0.95$ and produced **negative margin** at the default $\lambda = 0.99$. The updated default ensures baselines are settled before real observations arrive for all default parameters.
+
+For non-default $\lambda$ values, the schedule should be adjusted. Recommended configurations:
+
+| Configuration                                | Root | Decay | Minimum |
+| -------------------------------------------- | ---: | ----: | ------: |
+| $\lambda = 0.95$, $b_{\text{noise}} \geq 16$ |   50 |   0.5 |      10 |
+| $\lambda = 0.95$, $b_{\text{noise}} = 4$     |  200 |   0.5 |      15 |
+| $\lambda = 0.99$, $b_{\text{noise}} \geq 16$ |  450 |   0.5 |      50 |
+| $\lambda = 0.99$, $b_{\text{noise}} = 4$     |  500 |   0.5 |      50 |
+
+These values include a $\sim$13–15% margin above the measured worst-case convergence times. The geometric decay factor of 0.5 reflects the empirical observation that convergence accelerates at narrower analysis widths (fewer dimensions → simpler subspace → faster baseline settling). The minimum of 50 for $\lambda = 0.99$ configurations accounts for the $\sim$5× longer convergence at high $\lambda$ even at deep (narrow) cells.
+
+---
+
+## Appendix B. Glossary
+
+| Term                                      | Definition                                                                                                                                                                       | Reference      |
+| ----------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------- | --------------------------------------------------------------------------------------------------------------- | ---------- |
+| Analysis budget ($K$)                     | Maximum number of competitive targets; drives the investment set size                                                                                                            | §8.1           |
+| Analysis width ($w$)                      | Number of suffix bits available for statistical analysis at a given cell; $w = N - d$                                                                                            | §2.4           |
+| Ancestor closure                          | The operation that extends the competitive targets to include all spatial ancestors, forming the investment set $\mathcal{I}$                                                    | §8.2           |
+| Baseline                                  | Running mean and variance of a scoring axis, maintained by EWMA, against which z-scores are computed                                                                             | §6.1           |
+| Centred bit vector                        | Representation of a coordinate value as a vector in $\{-0.5, +0.5\}^N$                                                                                                           | §2.3           |
+| Coherence                                 | Scoring axis measuring departure of pairwise latent products from their learned second moments                                                                                   | §5.5           |
+| Competitive targets ($\mathcal{T}$)       | The top $K$ V-entries by importance within the depth cutoff; the selection that drives the investment set                                                                        | §8.1           |
+| Constant-norm property                    | The fact that every centred binary suffix vector has squared norm $w/4$                                                                                                          | §2.5           |
+| Contour                                   | The step function over the domain formed by the observation-receiving surface of the spatial tree                                                                                | §3.2           |
+| Coordination context                      | An internal spatial node whose subtree contains competitive cells in both children; hosts a coordination tracker                                                                 | §7.1           |
+| Coordination tracker                      | A subspace tracker operating on cross-cell score summaries ($w = 4$) at a coordination context                                                                                   | §7.5           |
+| Degenerate cell                           | A spatial cell with analysis width $w < 2$, excluded from the investment set because the subspace algebra requires $w \geq 2$; reported via the degenerate-cell counter (§14.11) | §4.1, §8.2     |
+| Displacement                              | Scoring axis measuring total within-subspace energy as a bounded distance from the subspace origin                                                                               | §5.3           |
+| Clip-pressure EWMA ($\bar{\rho}$)         | Per-axis running average of the batch clip ratio; modulates the effective clip ceiling to prevent baseline lockout                                                               | §6.1.1, §6.4   |
+| Drift accumulator                         | One-sided CUSUM detecting sustained upward drift of raw (pre-clip) batch means from the slow EWMA baseline                                                                       | §6.3           |
+| Dyadic cell                               | An interval of the form $[a \cdot 2^{N-d}, (a+1) \cdot 2^{N-d})$ at depth $d$ in the spatial tree                                                                                | §2.1, §3.2     |
+| Eager removal                             | Immediate destruction of trackers and cancellation of warm-up when a cell exits the investment set, within the Step 3 reconciliation                                             | §8.5           |
+| Fast EWMA                                 | Exponentially weighted moving average at decay $\lambda$ tracking running mean and variance of a scoring axis                                                                    | §6.1           |
+| Feed-forward invariant                    | The guarantee that anomaly scores never influence the spatial layer's importance signal; $\Delta = 1$ always                                                                     | §1.3           |
+| Forgetting factor ($\lambda$)             | EWMA decay rate controlling the balance between memory and adaptation                                                                                                            | §4.2           |
+| Full analysis set ($\mathcal{A}^*$)       | See: Producing full set                                                                                                                                                          | §8.3           |
+| G-Tree (Geometric Tree)                   | The binary tree over dyadic intervals forming the spatial partitioning structure                                                                                                 | §3.1           |
+| g.sum                                     | A G-Tree node's total accumulation: its own value plus all descendant sums; used as warm-up priority                                                                             | §3.12, §11.6.2 |
+| Investment set ($\mathcal{I}$)            | Competitive targets closed under spatial ancestry; all members have allocated trackers regardless of online status                                                               | §8.2           |
+| Max-uncle constraint                      | The V-Tree invariant: no node may outrank all of its uncles                                                                                                                      | §3.4           |
+| Noise influence ($\eta$)                  | Fraction of a tracker's baseline not yet established by real observations; decays as $\lambda^n$                                                                                 | §11.5          |
+| Noise injection                           | Feeding synthetic random centred bit vectors through a tracker to warm its baselines before real observations arrive                                                             | §11.1          |
+| Novelty                                   | Scoring axis measuring average residual energy per degree of freedom outside the learned subspace; degenerate when $k = w$                                                       | §5.2           |
+| Novelty-saturated                         | Condition where $k = w$ and the novelty axis is identically zero                                                                                                                 | §5.2, §14.7    |
+| Novelty-saturable                         | Condition where $\text{cap} \geq w$, meaning novelty can become saturated as rank adapts                                                                                         | §14.7          |
+| Plateau                                   | A maximal contiguous run of contour cells at a single depth                                                                                                                      | §3.2           |
+| Polarity invariant                        | The convention that higher scores indicate greater anomalous departure; shared by all four axes                                                                                  | §5.1           |
+| Producing competitive set ($\mathcal{A}$) | Online competitive targets; $\mathcal{T} \cap \text{Online}$                                                                                                                     | §8.3           |
+| Producing full set ($\mathcal{A}^*$)      | Online members of the investment set; $\mathcal{I} \cap \text{Online}$                                                                                                           | §8.3           |
+| Root tracker                              | The permanent subspace tracker at the G-Tree root ($w = N$), seeing every observation                                                                                            | §8.4           |
+| Slow EWMA                                 | EWMA at decay $\lambda_s > \lambda$ providing a long-memory reference for the drift accumulator                                                                                  | §6.2           |
+| Steiner tree property                     | The ancestor closure forms a Steiner tree connecting $K$ competitive targets to the root; the reduced tree has at most $2K - 1$ nodes. The full materialised tree satisfies $    | \mathcal{I}    | \leq 1 + K\bar{D}$ before sharing; ancestor sharing under concentration brings the practical size close to $2K$ | §8.2, §8.6 |
+| Subspace tracker                          | The per-cell statistical model maintaining a low-rank subspace, latent statistics, baselines, and drift accumulators                                                             | §4             |
+| Suffix                                    | The trailing $w = N - d$ bits of an observation's centred bit vector, after the $d$ routing prefix bits are removed                                                              | §2.4           |
+| Surprise                                  | Scoring axis measuring average diagonal Mahalanobis deviation of latent coordinates from their learned means                                                                     | §5.4           |
+| V-Tree (Value Tree)                       | The dynamic tournament bracket ranking spatial cells by competitive importance                                                                                                   | §3.1, §3.4     |
+
+---
+
+## Appendix C. Notation Quick Reference
+
+Reproduced from §2.6 for convenience.
+
+| Symbol                | Domain                                      | Definition                                                                                    |
+| --------------------- | ------------------------------------------- | --------------------------------------------------------------------------------------------- |
+| $N$                   | $\mathbb{Z}_{>0}$                           | Domain bit-width; spatial tree height                                                         |
+| $d$                   | $\{0, \ldots, N\}$                          | Spatial tree depth of a cell (prefix length in bits)                                          |
+| $w$                   | $\{0, \ldots, N\}$                          | Analysis width; $w = N - d$ (suffix length)                                                   |
+| $n$                   | $\mathbb{Z}_{\geq 0}$                       | Ingestion batch size (coordinate values per `SentinelIngest` call)                            |
+| $b$                   | $\mathbb{Z}_{>0}$                           | Per-tracker batch size (rows of $X$ fed to one tracker in one call; see §2.6 batch-size note) |
+| $b_{\text{noise}}$    | $\mathbb{Z}_{>0}$                           | Noise batch size (synthetic samples per warm-up round; §11.1)                                 |
+| $k$                   | $\{1, \ldots, \text{cap}\}$                 | Current active rank of the learned subspace                                                   |
+| $\text{cap}$          | $\{1, \ldots, \min(w, r_{\max})\}^\dagger$  | Hard ceiling on rank                                                                          |
+| $\lambda$             | $(0, 1)$                                    | Forgetting factor                                                                             |
+| $\alpha$              | $(0, 1)$                                    | Learning rate; $\alpha = 1 - \lambda$                                                         |
+| $\varepsilon$         | $\mathbb{R}_{>0}$                           | Numerical stability constant                                                                  |
+| $\tau$                | $(0, 1)$                                    | Cumulative energy threshold                                                                   |
+| $U$                   | $\mathbb{R}^{w \times \text{cap}}$          | Orthonormal basis of the learned subspace                                                     |
+| $\sigma$              | $\mathbb{R}^{\text{cap}}_{\geq 0}$          | Singular values                                                                               |
+| $\mu^{(z)}$           | $\mathbb{R}^{\text{cap}}$                   | EWMA mean of latent coordinates                                                               |
+| $\nu^{(z)}$           | $\mathbb{R}^{\text{cap}}_{>0}$              | EWMA variance of latent coordinates                                                           |
+| $\Gamma$              | $\mathbb{R}^{\text{cap} \times \text{cap}}$ | EWMA second-moment matrix of latent coordinates                                               |
+| $X$                   | $\mathbb{R}^{b \times w}$                   | Suffix observation matrix                                                                     |
+| $Z$                   | $\mathbb{R}^{b \times k}$                   | Latent projection of $X$                                                                      |
+| $\hat{X}$             | $\mathbb{R}^{b \times w}$                   | Reconstruction of $X$ from $Z$                                                                |
+| $m$                   | $\mathbb{Z}_{>0}$                           | Coordination group size                                                                       |
+| $\lambda_s$           | $(\lambda, 1)$                              | Slow EWMA decay for per-tracker drift detection                                               |
+| $\lambda_{s,m}$       | $(\lambda, 1)$                              | Slow EWMA decay for coordination drift detection                                              |
+| $\kappa_\sigma$       | $\mathbb{R}_{\geq 0}$                       | Drift accumulator noise allowance in slow-baseline $\sigma$ units                             |
+| $n_\sigma$            | $\mathbb{R}_{>0}$                           | Outlier clip width in $\sigma$ units                                                          |
+| $S$                   | $\mathbb{R}_{\geq 0}$                       | Drift accumulator value                                                                       |
+| $\bar{\rho}$          | $[0, 1]$                                    | Per-axis clip-pressure EWMA                                                                   |
+| $\lambda_\rho$        | $(0, 1)$                                    | Clip-pressure EWMA decay rate                                                                 |
+| $\rho_t$              | $[0, 1]$                                    | Batch clip ratio                                                                              |
+| $\mu^{(\text{in})}$   | $\mathbb{R}^4$                              | Running-mean centring reference for coordination input                                        |
+| $\eta$                | $[0, 1]$                                    | Noise influence fraction (tracker maturity)                                                   |
+| $K$                   | $\mathbb{Z}_{>0}$                           | Maximum number of competitively selected analysis cells                                       |
+| $L$                   | $\mathbb{Z}_{\geq 0}$                       | V-Tree depth cutoff for analysis eligibility                                                  |
+| $\mathcal{A}$         | $\subseteq$ V-entries                       | Producing competitive set ($\mathcal{I} \cap \mathcal{T} \cap \text{Online}$)                 |
+| $\mathcal{A}^*$       | $\supseteq \mathcal{A}$                     | Producing full set ($\mathcal{I} \cap \text{Online}$)                                         |
+| $\mathcal{T}$         | $\subseteq$ V-entries                       | Competitive targets (top $K$ by importance within depth cutoff)                               |
+| $\mathcal{I}$         | $\subseteq$ V-entries + ancestors           | Investment set (competitive targets + spatial ancestors, regardless of online status)         |
+| $G_{\max}$            | $\mathbb{Z}_{\geq 5}$                       | Hard ceiling on total spatial nodes                                                           |
+| $\lambda_{\text{sp}}$ | $[0, \infty)$                               | Spatial decay attenuation factor                                                              |
+| $h_V$                 | $\mathbb{Z}_{\geq 0}$                       | V-Tree height (maximum root-to-leaf path length in the V-Tree)                                |
+
+$^\dagger$ The domain $\{1, \ldots, \min(w, r_{\max})\}$ is non-empty only when $w \geq 1$. Tracker creation requires $w \geq 2$ (§4.1); cells below this threshold are excluded from $\mathcal{I}$.
+
+Vectors are row vectors when representing observations and column vectors when representing basis directions. Subscript $i$ indexes samples; subscript $j$ indexes subspace dimensions.
diff --git a/packages/sentinel/docs/api.md b/packages/sentinel/docs/api.md
new file mode 100644
index 000000000..0cb5135f7
--- /dev/null
+++ b/packages/sentinel/docs/api.md
@@ -0,0 +1,853 @@
+# Sentinel — Public API Reference
+
+The definitive specification for the public surface of the `torrust-sentinel` crate.
+Everything listed here is intentionally public. Everything else is
+`pub(crate)` or private — implementation detail, subject to change
+without notice.
+
+Modelled on `packages/mudlark/docs/api.md`.
+
+---
+
+## §1. Design Principles
+
+1. **Measure, don't decide.** All outputs are raw statistical quantities.
+   The sentinel never emits threat levels, recommended actions, or
+   policy decisions. The host reads the reports and applies its own
+   policy.
+
+2. **Feed-forward invariant (ADR-S-002).** The G-V Graph receives only
+   `observe(v, 1u64)` per raw input value. Anomaly scores never flow
+   back into the spatial layer's importance signal. The spatial layer
+   sees pure volume counting — Δ=1 per observation. This prevents the
+   anomaly detector from influencing its own spatial structure.
+
+3. **Host controls temporal policy.** The sentinel never calls
+   `decay()` automatically. The host schedules decay: when, how
+   aggressively, and with what selectivity (§ALGO S-13.4).
+
+4. **Adapt, don't control.** The spatial structure evolves autonomously
+   under observation and decay. The host controls resource ceilings,
+   temporal policy, and analysis budgets — not the structure itself.
+
+> _Design note (why volume-only importance)._ If anomaly scores boosted
+> importance, the affected cell would earn finer resolution, changing
+> its statistical model, changing its scores, changing its importance —
+> an unstable feedback loop. With Δ=1, an adversary cannot influence
+> the spatial structure except through observation volume, which is
+> precisely what the spatial layer is designed to handle.
+
+---
+
+## §2. Three-Layer Architecture
+
+```
+Layer 1: Spatial Index (mudlark GvGraph)
+         Adaptive spatial partitioning of [0, 2^N)
+         Pure volume tracking (Δ = 1 per observation)
+         Competitive ranking by observation volume
+              │
+              │ Significance ranking → top-K selection
+              ▼
+Layer 2: Analysis Selector
+         Selects significant cells for statistical analysis
+         Closes selection under spatial ancestry
+              │
+              │ Suffix bit vectors at every ancestor depth
+              ▼
+Layer 3: Analysis Engine (subspace trackers, coordination)
+         Per-cell subspace models at selected and ancestor cells
+         Hierarchical coordination across related cells
+              │
+              ▼
+         BatchReport → host
+```
+
+### §2.1 Layer Responsibilities
+
+| Concern                                               | Owner                         |
+| ----------------------------------------------------- | ----------------------------- |
+| Spatial partitioning of $[0, 2^N)$                    | Spatial Layer (Layer 1)       |
+| Competitive significance ranking                      | Spatial Layer — Value Tree    |
+| Spatial lifecycle (split, evict, absorb, restore)     | Spatial Layer                 |
+| Spatial memory (temporal decay, contour evolution)    | Spatial Layer + host policy   |
+| Investment commitment (which cells receive trackers)  | Analysis Selector (Layer 2)   |
+| Production selection (which invested cells score)     | Analysis Selector (Layer 2)   |
+| Ancestor closure (spatial ancestry of targets)        | Analysis Selector (Layer 2)   |
+| Statistical modelling within each cell                | Analysis Engine (Layer 3)     |
+| Anomaly scoring (four axes)                           | Analysis Engine               |
+| Drift detection (CUSUM accumulators)                  | Analysis Engine               |
+| Cross-cell coordination detection                     | Analysis Engine — coordination |
+| Interpretation and response                           | Host (external)               |
+
+See §ALGO S-1.4–1.5 for the full architecture specification.
+
+---
+
+## §3. Crate Root Re-exports
+
+```rust
+// Type aliases (convenience).
+pub type Sentinel128 = SpectralSentinel<u128, u64, 128>;
+pub type Sentinel64 = SpectralSentinel<u64, u64, 64>;
+
+// Re-exported from mudlark for decay_subtree() handles.
+pub use torrust_mudlark::GNodeId;
+
+// Modules are crate-private.
+pub(crate) mod analysis_set;
+pub(crate) mod config;
+pub(crate) mod ewma;
+pub(crate) mod maths;
+pub(crate) mod observation;
+pub(crate) mod report;
+pub(crate) mod sentinel;
+
+// Surface 1 — report/view types.
+pub use report::{
+    AnalysisSetSummary, AnomalyScores, AxisBaselineSnapshots,
+    BaselineSnapshot, BatchReport, CellInspection, CellReport,
+    ClipPressureDistribution, ContourSnapshot, CoordinationHealth,
+    CoordinationReport, CusumSnapshot, GeometryDistribution,
+    HealthReport, MaturityDistribution, MemberScore,
+    RankDistribution, SampleScore, ScoreDistribution,
+    ScoringGeometry, TrackerMaturity,
+};
+
+// Surface 2 — operational types.
+pub use analysis_set::{AnalysisEntry, AnalysisSet};
+pub use config::{
+    ConfigError, ConfigErrors, ConfigWarning, NoiseSchedule,
+    SentinelConfig,
+};
+pub use maths::SvdStrategy;
+pub use observation::{CentredBitSource, CentredBits};
+pub use sentinel::SpectralSentinel;
+
+// Not re-exported: MIN_TRACKER_DIM (pub(crate) const).
+```
+
+Hidden compatibility/testing affordances may also be re-exported with
+`#[doc(hidden)]`; they are not part of the stable public surface.
+
+The type aliases wrap `SpectralSentinel<C, V, N>` for the most common
+domain widths:
+
+| Alias         | $C$      | $V$    | $N$  | Use case                     |
+| ------------- | -------- | ------ | ---- | ---------------------------- |
+| `Sentinel128` | `u128`   | `u64`  | 128  | IPv6-class data (default)    |
+| `Sentinel64`  | `u64`    | `u64`  | 64   | 64-bit domains               |
+
+---
+
+## §4. Surface 1 — Report Types
+
+Lightweight, read-only **view types** — detached from the sentinel.
+Returned by `ingest()`. All carry `Debug`, `Clone`, `serde` (with feature).
+
+These types carry raw statistical measurements — never opinions or
+recommended actions. The host reads these reports and applies its
+own policy.
+
+### §4.1 Batch-level Reports
+
+#### `BatchReport<C>` — `Clone`
+
+Complete statistical output from one `ingest()` call.
+
+```rust
+pub struct BatchReport<C: Copy + Debug> {
+    pub cell_reports: Vec<CellReport<C>>,
+    pub ancestor_reports: Vec<CellReport<C>>,
+    pub coordination_reports: Vec<CoordinationReport<C>>,
+    pub contour: ContourSnapshot,
+    pub health: HealthReport,
+    pub analysis_set_summary: AnalysisSetSummary,
+}
+```
+
+| Field                   | Content                                                  |
+| ----------------------- | -------------------------------------------------------- |
+| `cell_reports`          | Per-cell reports for competitive cells ($\mathcal{A}$)   |
+| `ancestor_reports`      | Per-cell reports for ancestor-only cells                 |
+| `coordination_reports`  | Cross-cell coordination analysis (§ALGO S-9.4)           |
+| `contour`               | Snapshot of G-V Graph spatial structure                  |
+| `health`                | Operational health snapshot                              |
+| `analysis_set_summary`  | Summary of investment/producing sets                     |
+
+All vector fields are ordered by `GNodeId` for deterministic output
+(ADR-S-005).
+
+### §4.2 Cell-level Reports
+
+#### `CellReport<C>` — `Clone`
+
+Statistics for a single analysis cell after processing one batch.
+
+```rust
+pub struct CellReport<C: Copy + Debug> {
+    pub gnode_id: GNodeId,
+    pub start: C,
+    pub end: C,
+    pub depth: u32,
+    pub analysis_width: usize,
+    pub is_competitive: bool,
+    pub sample_count: usize,
+    pub rank: usize,
+    pub energy_ratio: f64,
+    pub top_singular_value: f64,
+    pub scores: AnomalyScores,
+    pub maturity: TrackerMaturity,
+    pub geometry: ScoringGeometry,
+    pub per_sample: Option<Vec<SampleScore>>,
+}
+```
+
+| Field               | Content                                              |
+| ------------------- | ---------------------------------------------------- |
+| `gnode_id`          | Arena handle of the backing G-node                   |
+| `start`, `end`      | Dyadic interval `[start, end)`                       |
+| `depth`             | G-tree depth of this cell                            |
+| `analysis_width`    | Suffix width: `N - depth`                            |
+| `is_competitive`    | `true` if competitively selected                     |
+| `sample_count`      | Observations in this batch routed to this cell       |
+| `rank`              | Current rank of the learned subspace                 |
+| `energy_ratio`      | Fraction of variance captured by current rank        |
+| `top_singular_value`| Largest singular value                               |
+| `scores`            | Anomaly scores along all four axes                   |
+| `maturity`          | Tracker maturity (real vs noise observations)        |
+| `geometry`          | Geometric scoring properties                         |
+| `per_sample`        | Per-sample scores (if `per_sample_scores` enabled)   |
+
+### §4.3 Coordination Reports
+
+#### `CoordinationReport<C>` — `Clone`
+
+Coordination analysis at a single G-tree internal node (§ALGO S-9.1).
+
+The coordination tracker operates at $w = 4$, consuming running-mean-centred
+cell-score matrices as observations. The group consists of all competitive
+cells in this node's subtree that reported scores in this batch.
+
+```rust
+pub struct CoordinationReport<C: Copy + Debug> {
+    pub gnode_id: GNodeId,
+    pub start: C,
+    pub end: C,
+    pub depth: u32,
+    pub cells_reporting: usize,
+    pub rank: usize,
+    pub energy_ratio: f64,
+    pub top_singular_value: f64,
+    pub scores: AnomalyScores,
+    pub maturity: TrackerMaturity,
+    pub geometry: ScoringGeometry,
+    pub per_member: Option<Vec<MemberScore<C>>>,
+}
+```
+
+The four anomaly axes have **second-order meaning** at coordination level:
+
+| Meta-axis    | Detects                                          |
+| ------------ | ------------------------------------------------ |
+| Novelty      | A cell-score pattern the model has never seen    |
+| Displacement | The overall score landscape has shifted          |
+| Surprise     | A specific scoring axis is system-wide anomalous |
+| Coherence    | An unusual combination of axis elevations        |
+
+### §4.4 Anomaly Scores
+
+#### `AnomalyScores` — `Clone`
+
+The four anomaly-score axes for a batch of observations.
+
+```rust
+pub struct AnomalyScores {
+    pub novelty: ScoreDistribution,
+    pub displacement: ScoreDistribution,
+    pub surprise: ScoreDistribution,
+    pub coherence: ScoreDistribution,
+}
+```
+
+| Axis         | Metric                                      | Intuition                                          |
+| ------------ | ------------------------------------------- | -------------------------------------------------- |
+| Novelty      | Residual energy / DOF: `‖X − X̂‖² / (dim−k)`| "How much of this is foreign?"                     |
+| Displacement | `‖z‖² / (k + ‖z‖²)`, bounded in `[0, 1)`    | "How far is this from the centroid?"               |
+| Surprise     | Mahalanobis / rank: `Σⱼ ((zⱼ−μⱼ)/σⱼ)² / k`  | "The shape is familiar, but magnitude is wild"     |
+| Coherence    | Cross-correlation deviation                 | "Normal individually, unusual combination"         |
+
+All axes share the same polarity: **higher values indicate greater
+anomalous departure**. This ensures uniform z-score interpretation.
+
+> **Why not projection energy / "normality"?** Under the sentinel's
+> centred binary encoding, every observation has the same L2 norm
+> (`dim / 4`). Projection energy is therefore a perfect affine function
+> of residual energy — it carries zero independent information.
+> See §ALGO S-Appendix B.
+
+#### `ScoreDistribution` — `Copy`
+
+Summary statistics for a vector of anomaly scores.
+
+```rust
+pub struct ScoreDistribution {
+    pub min: f64,
+    pub max: f64,
+    pub mean: f64,
+    pub max_z_score: f64,
+    pub mean_z_score: f64,
+    pub baseline: BaselineSnapshot,
+    pub cusum: CusumSnapshot,
+    pub clip_pressure: f64,
+}
+```
+
+### §4.5 Baseline and CUSUM Snapshots
+
+#### `BaselineSnapshot` — `Copy`
+
+Frozen snapshot of an EWMA baseline at the time of scoring.
+
+```rust
+pub struct BaselineSnapshot {
+    pub mean: f64,
+    pub variance: f64,
+}
+```
+
+#### `CusumSnapshot` — `Copy`
+
+Frozen snapshot of a CUSUM accumulator at scoring time.
+
+```rust
+pub struct CusumSnapshot {
+    pub accumulator: f64,
+    pub slow_baseline: BaselineSnapshot,
+    pub steps_since_reset: u64,
+}
+```
+
+### §4.6 Maturity and Geometry
+
+#### `TrackerMaturity` — `Copy`
+
+How much experience a tracker has, and how much of that is noise.
+
+```rust
+pub struct TrackerMaturity {
+    pub real_observations: u64,
+    pub noise_observations: u64,
+    pub noise_influence: f64,
+}
+```
+
+Methods: `cold() -> Self`, `total_observations() -> u64`.
+
+#### `ScoringGeometry` — `Copy`
+
+Geometric properties of the tracker's scoring state.
+
+```rust
+pub struct ScoringGeometry {
+    pub dim: usize,
+    pub cap: usize,
+    pub residual_dof: usize,
+}
+```
+
+Methods: `is_novelty_saturated() -> bool`, `is_novelty_saturable() -> bool`.
+
+### §4.7 Per-sample Scores
+
+#### `SampleScore` — `Copy`
+
+Raw anomaly scores for a single observation.
+
+```rust
+pub struct SampleScore {
+    pub novelty: f64,
+    pub displacement: f64,
+    pub surprise: f64,
+    pub coherence: f64,
+    pub novelty_z: f64,
+    pub displacement_z: f64,
+    pub surprise_z: f64,
+    pub coherence_z: f64,
+}
+```
+
+#### `MemberScore<C>` — `Copy`
+
+Per-member scores at coordination level (identifies contributing cell).
+
+### §4.8 Health and Summary Reports
+
+#### `HealthReport` — `Clone`
+
+Operational health snapshot of the entire sentinel.
+
+```rust
+pub struct HealthReport {
+    pub total_g_nodes: usize,
+    pub semi_internal_count: usize,
+    pub active_trackers: usize,
+    pub active_competitive_trackers: usize,
+    pub active_ancestor_trackers: usize,
+    pub active_coordination_contexts: usize,
+    pub investment_set_size: usize,
+    pub warming_trackers: usize,
+    pub warming_competitive_targets: usize,
+    pub lifetime_observations: u64,
+    pub cells_tracked: usize,
+    pub rank_distribution: RankDistribution,
+    pub maturity_distribution: MaturityDistribution,
+    pub geometry_distribution: GeometryDistribution,
+    pub coordination_health: CoordinationHealth,
+    pub clip_pressure_distribution: ClipPressureDistribution,
+}
+```
+
+#### `AnalysisSetSummary` — `Copy`
+
+Summary of the analysis set at report time.
+
+```rust
+pub struct AnalysisSetSummary {
+    pub competitive_size: usize,
+    pub full_size: usize,
+    pub investment_set_size: usize,
+    pub depth_range: (u32, u32),
+    pub importance_range: (f64, f64),
+    pub v_depth_range: (usize, usize),
+    pub degenerate_cells_skipped: usize,
+}
+```
+
+#### `ContourSnapshot` — `Copy`
+
+Snapshot of the G-V Graph's spatial contour at report time.
+
+```rust
+pub struct ContourSnapshot {
+    pub plateau_count: usize,
+    pub cell_count: usize,
+    pub total_importance: f64,
+    pub splits_since_last_report: u32,
+    pub net_removals_since_last_report: u32,
+}
+```
+
+### §4.9 Distribution Summaries
+
+#### `RankDistribution` — `Copy`
+
+```rust
+pub struct RankDistribution {
+    pub min: usize,
+    pub max: usize,
+    pub mean: f64,
+}
+```
+
+#### `MaturityDistribution` — `Copy`
+
+```rust
+pub struct MaturityDistribution {
+    pub max_noise_influence: f64,
+    pub min_noise_influence: f64,
+    pub mean_noise_influence: f64,
+    pub cold_trackers: usize,
+}
+```
+
+#### `GeometryDistribution` — `Copy`
+
+```rust
+pub struct GeometryDistribution {
+    pub novelty_saturated: usize,
+    pub novelty_saturable: usize,
+    pub coherence_inactive: usize,
+}
+```
+
+#### `ClipPressureDistribution` — `Copy`
+
+```rust
+pub struct ClipPressureDistribution {
+    pub min: f64,
+    pub max: f64,
+    pub mean: f64,
+}
+```
+
+#### `CoordinationHealth` — `Copy`
+
+Health snapshot of the hierarchical coordination tier.
+
+```rust
+pub struct CoordinationHealth {
+    pub active_contexts: usize,
+    pub capacity: usize,
+    pub rank_distribution: RankDistribution,
+    pub maturity_distribution: MaturityDistribution,
+    pub dim: usize,
+    pub geometry_distribution: GeometryDistribution,
+}
+```
+
+### §4.10 Inspection Types
+
+#### `CellInspection<C>` — `Clone`
+
+Detailed snapshot of a cell's tracker state. Returned by
+`SpectralSentinel::inspect_cell()`.
+
+```rust
+pub struct CellInspection<C: Copy + Debug> {
+    pub gnode_id: GNodeId,
+    pub start: C,
+    pub end: C,
+    pub depth: u32,
+    pub analysis_width: usize,
+    pub is_competitive: bool,
+    pub rank: usize,
+    pub energy_ratio: f64,
+    pub top_singular_value: f64,
+    pub maturity: TrackerMaturity,
+    pub geometry: ScoringGeometry,
+    pub baselines: AxisBaselineSnapshots,
+}
+```
+
+#### `AxisBaselineSnapshots` — `Copy`
+
+Per-axis EWMA baseline snapshots for all four scoring axes.
+
+```rust
+pub struct AxisBaselineSnapshots {
+    pub novelty: BaselineSnapshot,
+    pub displacement: BaselineSnapshot,
+    pub surprise: BaselineSnapshot,
+    pub coherence: BaselineSnapshot,
+}
+```
+
+---
+
+## §5. Surface 2 — Operational Types
+
+### §5.1 `SentinelConfig<V>` — `Clone`
+
+Measurement parameters for the sentinel. Every field controls *how* the
+sentinel observes and learns, never *what it thinks* about what it sees.
+
+```rust
+pub struct SentinelConfig<V: Accumulator> {
+    // ── Subspace parameters ─────────────────────────────
+    pub max_rank: usize,              // Default: 16
+    pub forgetting_factor: f64,       // Default: 0.99, in (0.0, 1.0)
+    pub rank_update_interval: u64,    // Default: 100
+    pub energy_threshold: f64,        // Default: 0.90, in (0.0, 1.0)
+    pub eps: f64,                     // Default: 1e-6
+
+    // ── CUSUM parameters ────────────────────────────────
+    pub cusum_slow_decay: f64,        // Default: 0.999
+    pub cusum_coord_slow_decay: f64,  // Default: 0.999
+    pub cusum_allowance_sigmas: f64,  // Default: 0.5
+
+    // ── Clip parameters ─────────────────────────────────
+    pub clip_sigmas: f64,             // Default: 3.0
+    pub clip_pressure_decay: f64,     // Default: 0.95
+
+    // ── Analysis selection ──────────────────────────────
+    pub analysis_k: usize,            // Default: 1024
+    pub analysis_depth_cutoff: usize, // Default: 6
+
+    // ── G-V Graph passthrough ───────────────────────────
+    pub split_threshold: V,           // Default: 100
+    pub d_create: u32,                // Default: 3
+    pub d_evict: u32,                 // Default: 6
+    pub budget: usize,                // Default: 100_000
+
+    // ── Noise injection ─────────────────────────────────
+    pub noise_schedule: NoiseSchedule,
+    pub noise_batch_size: usize,      // Default: 16
+    pub noise_seed: Option<u64>,      // Default: Some(42)
+    pub background_warming: bool,     // Default: false
+
+    // ── Output control ──────────────────────────────────
+    pub per_sample_scores: bool,      // Default: false
+
+    // ── SVD strategy ────────────────────────────────────
+    pub svd_strategy: SvdStrategy,    // Default: Brand
+}
+```
+
+#### Key field groups
+
+**Subspace parameters** (§ALGO S-4):
+- `max_rank`: Maximum basis vectors any tracker can use.
+- `forgetting_factor` (λ): Exponential decay rate. Half-life ≈ `ln(2) / ln(1/λ)`.
+- `rank_update_interval`: Reassess rank every N observations.
+- `energy_threshold` (τ): Cumulative energy threshold for rank adaptation.
+
+**CUSUM parameters** (§ALGO S-6):
+- `cusum_slow_decay` (λ_s): Slow EWMA decay for drift detection reference.
+- `cusum_coord_slow_decay`: Same for coordination tier.
+- `cusum_allowance_sigmas` (κ_σ): Noise allowance in slow-baseline σ units.
+
+**G-V Graph passthrough** (§ALGO S-13.3):
+- `split_threshold`: Observations before cell subdivision.
+- `d_create`: Maximum V-Tree depth for new splits.
+- `d_evict`: Minimum V-Tree depth for eviction eligibility.
+- `budget`: Hard ceiling on live G-nodes.
+
+**Noise injection** (§ALGO S-11, ADR-S-015):
+- `noise_schedule`: Depth-tiered warm-up schedule.
+- `background_warming`: When `true`, warm-up runs on a background thread.
+
+Methods: `validate() -> Result<(), ConfigErrors>`, `Default`.
+
+### §5.2 `NoiseSchedule` — `Clone`
+
+Depth-tiered noise injection schedule.
+
+```rust
+pub enum NoiseSchedule {
+    Geometric { root: u32, decay: f64, min: u32 },
+    Explicit(Vec<u32>),
+}
+```
+
+| Variant    | Behaviour                                        |
+| ---------- | ------------------------------------------------ |
+| `Geometric`| `root × decay^depth`, floored to `min`           |
+| `Explicit` | Per-depth vector; depths beyond end use last     |
+
+Methods: `geometric(root, decay, min) -> Self`, `rounds_for_depth(depth) -> u32`,
+`is_disabled() -> bool`, `max_rounds() -> u32`, `Default`.
+
+Default: `Geometric { root: 450, decay: 0.5, min: 50 }`.
+
+### §5.3 `SpectralSentinel<C, V, N>` — Main Orchestrator
+
+The main orchestrator. Generic over coordinate `C`, accumulator `V`,
+domain width `N`.
+
+```rust
+pub struct SpectralSentinel<C, V, const N: u32>
+where
+    C: Coordinate + CentredBitSource,
+    V: Inspectable + Attenuatable,
+{
+    // ... internal state ...
+}
+```
+
+#### Method Index
+
+| Method                    | Category      | Cost                  | Description                                 |
+| ------------------------- | ------------- | --------------------- | ------------------------------------------- |
+| `new`                     | Construction  | $O(w)$ noise          | Validated config → warmed root tracker      |
+| `ingest`                  | Observation   | $O(n × \text{cells})$ | Batch processing, returns `BatchReport`     |
+| `decay`                   | Temporal      | $O(\|G\|)$            | Global importance attenuation               |
+| `decay_subtree`           | Temporal      | $O(\|G_{sub}\|)$      | Subtree importance attenuation              |
+| `reset`                   | Lifecycle     | $O(w)$                | Re-initialise to fresh state                |
+| `health`                  | Diagnostic    | $O(\|cells\|)$        | Operational health snapshot                 |
+| `config`                  | Accessor      | $O(1)$                | Read-only config reference                  |
+| `graph`                   | Accessor      | $O(1)$                | Read-only G-V Graph reference               |
+| `analysis_set`            | Accessor      | $O(1)$                | Current analysis set                        |
+| `cells_tracked`           | Accessor      | $O(1)$                | Number of active trackers                   |
+| `lifetime_observations`   | Accessor      | $O(1)$                | Total real observations                     |
+| `degenerate_cells_skipped`| Accessor      | $O(1)$                | Cells excluded for width < 2                |
+| `cell_gnodes`             | Accessor      | $O(\|cells\|)$        | List all tracked cell handles               |
+| `inspect_cell`            | Diagnostic    | $O(1)$                | Detailed cell state snapshot                |
+
+#### `new(config: SentinelConfig<V>) -> Result<Self, ConfigErrors>`
+
+Validates the configuration and creates the root tracker. The root
+tracker is automatically warmed with synthetic noise (§ALGO S-11.2).
+No other cells are created until the first `ingest()` call triggers
+analysis set computation.
+
+#### `ingest(&mut self, values: &[C]) -> BatchReport<C>`
+
+Process a batch of raw coordinate observations and return a full
+statistical report. Each value is:
+1. Fed to the G-V Graph with Δ=1 (feed-forward invariant).
+2. Routed to every analysis cell whose interval contains it.
+3. Encoded as centred bits and scored against learned subspaces.
+
+An empty input slice produces an empty report.
+
+#### `decay(&mut self, attenuation: f64, q: f64)`
+
+Apply spatial decay to the entire G-V Graph.
+
+Parameters:
+- `attenuation` — base decay factor at midpoint depth.
+  - `(0, 1)`: Cold cells lose standing.
+  - `> 1.0`: Hot cells reinforced (amplification).
+  - `1.0`: No-op.
+- `q` — depth selectivity in `[0.0, 1.0]`.
+  - `0.0`: Uniform — all depths decay equally.
+  - `> 0.0`: Selective — fine structure fades faster.
+
+Panics if `attenuation < 0.0`, `q` out of range, or `NaN`.
+
+#### `decay_subtree(&mut self, root: GNodeId, attenuation: f64, q: f64)`
+
+Apply spatial decay to a subtree of the G-V Graph.
+
+Use cases (§ALGO S-10.3):
+- **Regime change**: Attenuate a subtree that experienced a traffic shift.
+- **Suspected poisoning**: `decay_subtree(root, 0.0₊, 1.0)` is a detail flush.
+- **Hot reinforcement**: `decay_subtree(root, 1.5, 0.0)` amplifies a hot subtree.
+
+Panics if `root` is stale or parameters out of range.
+
+#### `reset(&mut self)`
+
+Reset the sentinel to its freshly-constructed state. Drops all cell
+trackers and their learned subspaces, re-initialises the G-V Graph,
+and zeroes all counters. Configuration is preserved.
+
+### §5.4 `AnalysisSet<C, V>` and `AnalysisEntry<C, V>`
+
+Analysis set management (§ALGO S-8.1–8.3).
+
+#### `AnalysisEntry<C, V>` — `Copy`
+
+A cell selected for analysis by the selector.
+
+```rust
+pub struct AnalysisEntry<C: Coordinate, V: Accumulator> {
+    pub gnode: GNodeId,
+    pub depth: u32,
+    pub v_depth: usize,
+    pub importance: V,
+    pub start: C,
+    pub end: C,
+    pub is_competitive: bool,
+}
+```
+
+#### `AnalysisSet<C, V>` — `Clone`
+
+The current analysis set — competitive targets $\mathcal{T}$ closed
+under G-tree ancestry to form the investment set $\mathcal{I}$.
+
+```rust
+pub struct AnalysisSet<C: Coordinate, V: Accumulator> {
+    competitive: Vec<AnalysisEntry<C, V>>,
+    full: Vec<AnalysisEntry<C, V>>,
+}
+```
+
+Methods:
+- `recompute<const N: u32>(graph, k, depth_cutoff) -> Self`
+- `competitive() -> &[AnalysisEntry]` — ordered by importance descending
+- `full() -> &[AnalysisEntry]` — ordered by `GNodeId`
+- `contains(gnode: GNodeId) -> bool`
+- `is_competitive(gnode: GNodeId) -> bool`
+- `competitive_count() -> usize`
+- `total_count() -> usize`
+- `summary() -> AnalysisSetSummary`
+
+#### Selection algorithm (§ALGO S-8.1)
+
+1. Scan V-Tree for entries with `v_depth ≤ depth_cutoff` and `width ≥ 2`.
+2. Sort by importance (descending), then by `start` (ascending) for determinism.
+3. Take top-K (the competitive targets $\mathcal{T}$).
+4. Close under G-tree ancestry to form $\mathcal{I}$.
+
+The root is always an ancestor, never competitive (§ALGO S-4.7).
+
+---
+
+## §6. Surface 3 — Internal Machinery
+
+`pub(crate)` modules not part of the public API:
+
+| Module                    | Contents                                              |
+| ------------------------- | ----------------------------------------------------- |
+| `sentinel::tracker`       | `SubspaceTracker` — SVD-based subspace model          |
+| `sentinel::cusum`         | CUSUM accumulator for drift detection                 |
+| `sentinel::staging`       | Deferred warm-up staging area (ADR-S-019)             |
+| `sentinel::warming_thread`| Background noise injection thread (§ALGO S-18.2)      |
+| `maths`                   | SVD (naive + Brand), matrix ops, Gamma distribution   |
+| `ewma`                    | `EwmaStats` — exponential moving average with clipping|
+| `observation`             | `CentredBits`, `CentredBitSource` — suffix encoding   |
+
+These modules implement the internal machinery. Their interfaces may
+change without notice. Only types re-exported from the crate root
+are part of the public API.
+
+---
+
+## §7. Cross-Cutting Concerns
+
+### §7.1 Error Handling
+
+- `SentinelConfig::validate() -> Result<(), ConfigErrors>` — validates
+  all configuration parameters.
+- `SpectralSentinel::new()` propagates validation errors.
+- Panics for stale handles (documented per-method).
+- `ConfigErrors` is a `Vec<ConfigError>` carrying all validation failures.
+
+#### `ConfigError` variants
+
+| Variant                        | Constraint violated                           |
+| ------------------------------ | --------------------------------------------- |
+| `MaxRankZero`                  | `max_rank` must be ≥ 1                        |
+| `ForgettingFactorOutOfRange`   | `forgetting_factor` must be in `(0.0, 1.0)`   |
+| `RankUpdateIntervalZero`       | `rank_update_interval` must be ≥ 1            |
+| `AnalysisKZero`                | `analysis_k` must be ≥ 1                      |
+| `EnergyThresholdOutOfRange`    | `energy_threshold` must be in `(0.0, 1.0)`    |
+| `CusumSlowDecayOutOfRange`     | `cusum_slow_decay` must be in `(0.0, 1.0)`    |
+| `CusumSlowDecayTooLow`         | `cusum_slow_decay` must be > `forgetting_factor` |
+| `DEvictNotGreaterThanDCreate`  | `d_evict` must be > `d_create`                |
+| `BudgetTooSmall`               | `budget` must exceed mudlark's headroom       |
+
+### §7.2 Thread Safety
+
+- `SpectralSentinel` is **not** `Sync` due to internal `Mutex<StagingArea>`.
+- `BatchReport` and all report types are `Send + Sync`.
+- Background warming thread (when `background_warming = true`) runs
+  independently without blocking `ingest()`.
+
+### §7.3 Feature Gates
+
+| Feature | Default | Effect                                      |
+| ------- | ------- | ------------------------------------------- |
+| `serde` | off     | `Serialize`/`Deserialize` on config + reports |
+
+### §7.4 Serde
+
+All Surface 1 report types carry conditional serde derives. Config types
+(`SentinelConfig`, `NoiseSchedule`) likewise. Round-trip stability is
+maintained.
+
+Serde bounds:
+- `BatchReport<C>`, `CellReport<C>`, etc.: `C: Serialize + DeserializeOwned`
+- `SentinelConfig<V>`: `V: Serialize + DeserializeOwned`
+
+### §7.5 Determinism (ADR-S-005)
+
+All output is deterministic given the same inputs and configuration:
+- `BTreeMap` iteration order for cell/coordination maps.
+- Tie-breaking by `start` (ascending) in competitive selection.
+- Fixed `noise_seed` for reproducible warm-up.
+
+---
+
+## §8. Cross-References
+
+| Target                | Format               | Example                      |
+| --------------------- | -------------------- | ---------------------------- |
+| algorithm.md sections | `§ALGO S-N.M`        | `§ALGO S-9.1`                |
+| mudlark api.md        | `§API M-N`           | `§API M-4.1` (Cell)          |
+| mudlark idea.md       | `§IDEA M-N`          | `§IDEA M-5.5`                |
+| sentinel ADRs         | `ADR-S-NNN`          | `ADR-S-002` (feed-forward)   |
+| mudlark ADRs          | `ADR-M-NNN`          | `ADR-M-040` (GNodeId)        |
diff --git a/packages/sentinel/docs/implementation.md b/packages/sentinel/docs/implementation.md
new file mode 100644
index 000000000..fc383a123
--- /dev/null
+++ b/packages/sentinel/docs/implementation.md
@@ -0,0 +1,589 @@
+# Sentinel — Implementation Guide
+
+> **Cross-reference label:** `§IMPL` — e.g. `§IMPL S-3.1` refers to §3.1
+> of this document. See AGENTS.md for all conventions.
+
+This document describes how the Spectral Sentinel is implemented. It is
+an evergreen companion to the algorithm specification
+([algorithm.md](algorithm.md)) — the spec says _what_; this document
+says _where_ and _how_.
+
+Architecture Decision Records live in [`../adr/`](../adr/).
+
+---
+
+## Table of Contents
+
+1. [Source Layout](#1-source-layout)
+2. [Architecture Overview](#2-architecture-overview)
+3. [The Core Loop](#3-the-core-loop)
+4. [Scoring Axes](#4-scoring-axes)
+5. [Baseline Tracking](#5-baseline-tracking)
+6. [Analysis Selector](#6-analysis-selector)
+7. [Hierarchical Coordination](#7-hierarchical-coordination)
+8. [Noise Injection and Warm-Up](#8-noise-injection-and-warm-up)
+9. [SVD Strategy](#9-svd-strategy)
+10. [Configuration Reference](#10-configuration-reference)
+11. [Report Types](#11-report-types)
+12. [Design Decisions](#12-design-decisions)
+13. [Cross-Reference Conventions](#13-cross-reference-conventions)
+
+---
+
+## 1. Source Layout
+
+| File                                                                | Role                                                                       |
+| ------------------------------------------------------------------- | -------------------------------------------------------------------------- |
+| [src/lib.rs](../src/lib.rs)                                         | Crate root, public module structure                                        |
+| [src/config.rs](../src/config.rs)                                   | `SentinelConfig`, `NoiseSchedule`, validation, `ConfigError`, `ConfigWarning` |
+| [src/ewma.rs](../src/ewma.rs)                                       | `EwmaStats` — EWMA mean/variance with outlier clip                         |
+| [src/observation.rs](../src/observation.rs)                         | `CentredBits`, suffix extraction                                           |
+| [src/analysis_set.rs](../src/analysis_set.rs)                       | `AnalysisEntry`, `AnalysisSet`, `recompute()` — Layer 2                    |
+| [src/report.rs](../src/report.rs)                                   | All report/snapshot types                                                  |
+| [src/sentinel/mod.rs](../src/sentinel/mod.rs)                       | `SpectralSentinel` orchestrator                                            |
+| [src/sentinel/tracker.rs](../src/sentinel/tracker.rs)               | `SubspaceTracker` — core SVD engine                                        |
+| [src/sentinel/cusum.rs](../src/sentinel/cusum.rs)                   | `CusumAccumulator` — one-sided Page's test                                 |
+| [src/sentinel/staging.rs](../src/sentinel/staging.rs)               | `WarmingCell`, `StagingArea` — deferred warm-up (§ALGO S-18.2)             |
+| [src/sentinel/warming_thread.rs](../src/sentinel/warming_thread.rs) | Background warming thread (§ALGO S-18.2 Step 3)                            |
+| [src/maths/mod.rs](../src/maths/mod.rs)                             | SVD strategy dispatch, `SvdStrategy` enum                                  |
+| [src/maths/brand_svd.rs](../src/maths/brand_svd.rs)                 | Brand's incremental SVD ([ADR-S-016](../adr/016-brand-incremental-svd.md)) |
+| [src/maths/naive_svd.rs](../src/maths/naive_svd.rs)                 | Dense thin SVD baseline                                                    |
+| [src/maths/bench_tracing.rs](../src/maths/bench_tracing.rs)         | Lightweight span-timing layer for convergence benchmarks                   |
+
+### 1.1 Dependencies
+
+| Crate                 | Purpose                                                       |
+| --------------------- | ------------------------------------------------------------- |
+| `torrust-mudlark`     | G-V Graph (Layer 1 spatial substrate)                         |
+| `faer`                | Linear algebra (SVD, matrix operations)                       |
+| `rand` / `rand_distr` | Noise generation, Gamma distribution for coordination warming |
+| `serde` (optional)    | Serialisation of config and report types                      |
+| `tracing`             | Structured diagnostics                                        |
+
+### 1.2 Test Layout
+
+Unit tests live in `src/tests/` (crate-level) and integration tests
+in `tests/` (package-level). Shared helpers live in `tests/common/`.
+
+#### Integration tests (`tests/`)
+
+| File                                 | Coverage                                                                    |
+| ------------------------------------ | --------------------------------------------------------------------------- |
+| `tests/integration.rs`               | End-to-end `ingest()` with realistic value streams                          |
+| `tests/invariants.rs`                | Structural invariants: feed-forward, constant-norm, rank bounds             |
+| `tests/hierarchical_coordination.rs` | Coordination tree assembly and scoring                                      |
+| `tests/deferred_warmup.rs`           | Staging area, background warming, promotion                                 |
+| `tests/spray_resistance.rs`          | Budget enforcement under adversarial spray                                  |
+| `tests/determinism.rs`               | Reproducibility given fixed seed                                            |
+| `tests/serde_roundtrip.rs`           | Config/report serialisation round-trips                                     |
+| `tests/ancestor_chain.rs`            | Multi-scale ancestor chain properties (§ALGO S-4.4–4.9)                     |
+| `tests/api.rs`                       | Public API contract tests for `SpectralSentinel`                            |
+| `tests/clip_pressure.rs`             | Clip-pressure EWMA integration (§ALGO S-6.4)                                |
+| `tests/coverage_matrix.rs`           | Six attack modalities from the coverage matrix (§ALGO S-17.6)               |
+| `tests/edge_cases.rs`                | Edge-case and boundary-condition tests                                      |
+| `tests/graph_routing.rs`             | Graph routing: `ingest()` feeds the G-V Graph                               |
+| `tests/health.rs`                    | `HealthReport` behavioural tests                                            |
+| `tests/noise.rs`                     | Automatic noise injection lifecycle (§ALGO S-11)                            |
+| `tests/report_structure.rs`          | `BatchReport` structure and field contracts                                 |
+| `tests/sentinel_u64.rs`              | 64-bit sentinel (`Sentinel64`) end-to-end path                              |
+| `tests/spatial_decay.rs`             | Spatial decay via `decay()` / `decay_subtree()`                             |
+| `tests/suffix_analysis.rs`           | Suffix analysis (§ALGO S-3.2)                                               |
+| `tests/warm_up.rs`                   | Four-stage warm-up sequence (§ALGO S-11.6)                                  |
+| `tests/common/`                      | Shared builders, assertions, config presets, generators                     |
+
+#### Crate tests (`src/tests/`)
+
+| File                                     | Coverage                                                                                            |
+| ---------------------------------------- | --------------------------------------------------------------------------------------------------- |
+| `src/tests/analysis_set.rs`              | `AnalysisSet` selection pipeline (§ALGO S-8.1–8.3)                                                  |
+| `src/tests/config.rs`                    | Config validation, defaults, `NoiseSchedule` helpers                                                |
+| `src/tests/convergence_fixes.rs`         | Regression tests for warm-up convergence ([ADR-S-013](../adr/013-warm-up-convergence-benchmark.md)) |
+| `src/tests/convergence_clipping.rs`      | Clipping stability audit (fast EWMA clip, graduated exemption, slow EWMA/CUSUM clip)                |
+| `src/tests/convergence_common.rs`        | Shared infrastructure for convergence tests                                                         |
+| `src/tests/convergence_diagnostics.rs`   | On-demand convergence diagnostics (run with `--ignored`)                                            |
+| `src/tests/convergence_eta.rs`           | Noise influence η tracking and maturity counters                                                    |
+| `src/tests/convergence_ewma.rs`          | Pure EWMA convergence properties (isolated from tracker)                                            |
+| `src/tests/convergence_noise.rs`         | Tracker-level noise-baseline convergence                                                            |
+| `src/tests/cusum.rs`                     | `CusumAccumulator` tests                                                                           |
+| `src/tests/ewma.rs`                      | `EwmaStats` unit tests                                                                             |
+| `src/tests/observation.rs`               | Centred-bit representation tests                                                                    |
+| `src/tests/report.rs`                    | Report types (construction, field contracts)                                                        |
+| `src/tests/tracker.rs`                   | `SubspaceTracker` (construction, scoring, rank, CUSUM, clip-pressure)                               |
+| `src/tests/variance_formula.rs`          | EWMA-mean-centred variance formula ([ADR-S-021](../adr/021-ewma-mean-centred-variance.md))          |
+
+---
+
+## 2. Architecture Overview
+
+The sentinel implements the three-layer architecture (§ALGO S-1.1):
+
+```
+Layer 1: GvGraph<C, V, N>            ← torrust-mudlark
+         Adaptive spatial partitioning of [0, 2^N)
+         Competitive ranking by observation volume
+              │
+              │  V-Tree depth ≤ cutoff → top-K selection
+              ▼
+Layer 2: AnalysisSet                  ← analysis_set.rs
+         Picks competitive cells, closes under G-ancestry
+              │
+              │  suffix bit vectors at every ancestor depth
+              ▼
+Layer 3: SubspaceTracker fleet        ← sentinel/tracker.rs
+         + Hierarchical coordination    ← sentinel/mod.rs
+              │
+              ▼
+         BatchReport<C> → host
+```
+
+### 2.1 The Spatial Substrate (Layer 1)
+
+The sentinel owns a `GvGraph<C, V, N>` ([ADR-S-018](../adr/018-generic-domain-parameters.md)):
+
+- **`C`** coordinate type — spatial addressing (e.g. `u128`, `u64`).
+- **`V`** accumulator — observation counts ($\Delta = 1$ per value).
+- **`N`** bit-width — domain resolution.
+
+The default instantiation (`Sentinel128`) uses `GvGraph<u128, u64, 128>`;
+`Sentinel64` uses `GvGraph<u64, u64, 64>`.
+
+The graph is mutated only through `observe(coord, 1)` during
+`ingest()` and through `decay()` / `decay_subtree()` when the host
+requests temporal decay. Anomaly scores never feed back into the
+graph's importance signal — this is the **feed-forward invariant**
+([ADR-S-002](../adr/002-feed-forward-invariant.md)).
+
+### 2.2 The Analysis Selector (Layer 2)
+
+`AnalysisSet::recompute()` runs after every G-V Graph observation pass
+and selects up to $K$ **competitive targets** ($\mathcal{T}$) from the
+V-Tree by importance, filtered by a depth cutoff $L$ (§ALGO S-8.1).
+Ties break by G-node interval left endpoint for deterministic, spatially
+stable ordering.
+
+The V-Tree scan uses `graph.layers_to(depth_cutoff)` (ADR-M-041),
+which limits the BFS to V-depths $\leq L$ and avoids expanding
+structural nodes below the cutoff — saving exponential work on deep
+trees compared to a full `layers()` plus post-hoc filter.
+
+The competitive targets are then closed under G-tree ancestry
+(§ALGO S-8.2), forming the **investment set** ($\mathcal{I}$) —
+the set of all cells with allocated trackers. Every member has a
+tracker regardless of online status, guaranteeing a complete chain
+from every competitive target to the root.
+
+The investment set is reconciled against the live `cells` map and
+the `StagingArea`: entering cells are created and enqueued for
+warm-up, exiting cells are destroyed (eager removal, §ALGO S-8.5).
+The online subset of the investment set forms the **producing sets**:
+$\mathcal{A}$ (online competitive targets) and $\mathcal{A}^*$
+(all online members). The full recompute runs from scratch each time
+([ADR-S-006](../adr/006-analysis-set-recomputation.md),
+[ADR-S-019](../adr/019-investment-set-terminology-and-reporting.md)).
+
+### 2.3 The Analysis Engine (Layer 3)
+
+One `SubspaceTracker` per cell in the investment set $\mathcal{I}$.
+Each tracker analyses the **suffix** bits `[d, N)` with width
+$w = N - d$ (§ALGO S-3.2). Cells with $w < 2$ are excluded
+([ADR-S-011](../adr/011-degenerate-cell-dimension-guard.md)).
+Only online trackers (the producing full set $\mathcal{A}^*$) score
+observations; warming trackers receive only synthetic noise.
+
+The root tracker at depth 0 ($w = N$) is permanent — never destroyed
+(§ALGO S-8.4).
+
+### 2.4 Determinism
+
+All collection types use `BTreeMap` for deterministic iteration order
+([ADR-S-005](../adr/005-deterministic-order-and-thread-safety.md)).
+Given a fixed `noise_seed`, the sentinel is fully reproducible.
+`SpectralSentinel<C, V, N>` is `Send + Sync`.
+
+---
+
+## 3. The Core Loop
+
+`SubspaceTracker::observe()` implements the five-phase core loop
+(§ALGO S-4.2) with a **score-before-evolve** invariant: scores are
+computed against the current basis, then the basis is updated.
+
+| Phase | Operation                                                                                                                                               | §ALGO S-     |
+| ----- | ------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------ |
+| 1     | **Project** — $Z = X U$, $\hat{X} = Z U^\top$, $R = X - \hat{X}$                                                                                        | §4.2 Phase 1 |
+| 2     | **Evolve subspace** — combined matrix $M$, thin SVD, update $U$, $\sigma$                                                                               | §4.2 Phase 2 |
+| 3     | **Evolve latent distribution** — EWMA-mean-centred update of $\mu^{(z)}$, $\nu^{(z)}$, $\Gamma$ ([ADR-S-021](../adr/021-ewma-mean-centred-variance.md)) | §4.2 Phase 3 |
+| 4     | **Score** — compute all four axis scores, update EWMA baselines, CUSUM                                                                                  | §4.2 Phase 4 |
+| 5     | **Adapt rank** — energy-threshold selection with +1 buffer, ±1 oscillation guard                                                                        | §4.2 Phase 5 |
+
+Safety guards:
+
+- **SVD failure** — falls back to identity if SVD does not converge.
+- **Identical observations** — handled without numerical degeneracy.
+- **Coherence lifecycle** — undefined at $k < 2$; baselines destroyed
+  on rank drop (§ALGO S-6.4).
+- **Latent cold→warm initialisation** — on the first batch,
+  `lat_mean`, `lat_var`, and `cross_corr` are seeded directly from
+  data ([ADR-S-013](../adr/013-warm-up-convergence-benchmark.md)).
+- **EWMA-mean-centred variance** — variance centres on the EWMA mean
+  rather than the batch mean, eliminating batch-size-dependent bias
+  ([ADR-S-021](../adr/021-ewma-mean-centred-variance.md)).
+
+---
+
+## 4. Scoring Axes
+
+All four axes satisfy the polarity invariant: **higher = more
+anomalous** (§ALGO S-6).
+
+| Axis         | Formula                              | Bounds        | §ALGO S- |
+| ------------ | ------------------------------------ | ------------- | -------- |
+| Novelty      | $\|r\|^2 / (d - k)$                  | $[0, \infty)$ | §6.1     |
+| Displacement | $\|z\|^2 / (k + \|z\|^2)$            | $[0, 1)$      | §6.2     |
+| Surprise     | diagonal Mahalanobis $/\, k$         | $[0, \infty)$ | §6.3     |
+| Coherence    | pairwise cross-correlation deviation | $[0, \infty)$ | §6.4     |
+
+Each raw score is transformed into a z-score via:
+
+$$z = \frac{s - \bar{s}}{\sqrt{\bar{v}} + \varepsilon}$$
+
+where $\bar{s}$ and $\bar{v}$ are the fast EWMA mean and variance
+(§ALGO S-7.1.2). The variance floor is $10^{-4}$.
+
+---
+
+## 5. Baseline Tracking
+
+### 5.1 Fast EWMA (§ALGO S-7.1)
+
+Per-axis `EwmaStats` with configurable `clip_sigmas`. Observations
+beyond $\bar{s} + n_\sigma^{\text{eff}} \sqrt{\bar{v}}$ are clipped
+(upper-tail only) to prevent baseline poisoning.
+
+**Graduated clip-exemption** ([ADR-S-013](../adr/013-warm-up-convergence-benchmark.md)):
+during warm-up the effective clip width scales with the noise-influence
+fraction $\eta$:
+
+$$n_\sigma^{\text{eff}} = n_\sigma + n_\sigma \cdot \frac{\eta}{1 - \eta + \varepsilon}$$
+
+This widens the ceiling while baselines are immature, eliminating the
+clipping-ceiling positive feedback loop that previously caused 5–10×
+slower convergence.
+
+### 5.2 Slow EWMA (§ALGO S-7.2)
+
+A secondary EWMA with decay factor `cusum_slow_decay` (default 0.999,
+half-life ≈ 693 steps) provides the reference baseline for CUSUM drift
+detection.
+
+### 5.3 CUSUM Drift Detection (§ALGO S-7.3)
+
+`CusumAccumulator` implements one-sided Page's test with:
+
+- Compute-before-update ordering.
+- Noise allowance: $\kappa_\sigma \cdot \sqrt{v_{\text{slow}}}$.
+- Reset after noise injection (§ALGO S-7.4).
+- Slow EWMA seeded from fast EWMA at noise→real transition
+  ([ADR-S-013](../adr/013-warm-up-convergence-benchmark.md)).
+
+---
+
+## 6. Analysis Selector
+
+`AnalysisSet` in `analysis_set.rs` implements the Layer 2 selection
+pipeline (§ALGO S-8):
+
+1. **Enumerate** all V-entries with V-depth $\leq L$.
+2. **Rank** by importance, take top $K$ — the **competitive targets** $\mathcal{T}$ (§ALGO S-8.1).
+3. **Break ties** by interval start (deterministic, spatially stable).
+4. **Close** under G-tree ancestry by walking parent pointers — forming the **investment set** $\mathcal{I}$ (§ALGO S-8.2).
+
+The root tracker is permanent and never participates in competitive
+selection (§ALGO S-8.4). Reconciliation after each observation pass
+reconciles the investment set: entering cells are created and enqueued
+for warm-up, exiting cells are eagerly removed (§ALGO S-8.5,
+[ADR-S-019](../adr/019-investment-set-terminology-and-reporting.md)).
+
+---
+
+## 7. Hierarchical Coordination
+
+The sentinel detects coordinated anomalies across cells using
+hierarchical G-tree coordination (§ALGO S-9).
+
+### 7.1 Coordination Contexts
+
+One `CoordContext` per internal G-node whose left and right subtrees
+both contribute competitive cells. Each context owns:
+
+- A 4-dimensional `SubspaceTracker` (one dimension per scoring axis).
+- A running-mean centring reference $\mu^{(\text{in})}$ for
+  de-meaning the input signal before feeding (§ALGO S-9.3).
+
+### 7.2 Bottom-Up Assembly (§ALGO S-9.4)
+
+After cell scoring, the coordination tier assembles score vectors
+bottom-up through the G-tree:
+
+1. Leaf contributions: competitive cells emit their 4D centred
+   score vector.
+2. Internal nodes: when both subtrees contribute, the assembled
+   matrix is fed to the coordination tracker.
+3. The walk is pruned — only nodes reachable through subtrees with
+   competitive cells are visited.
+
+### 7.3 Lifecycle
+
+`CoordContext` instances are created on first fire and pruned when
+their subtree loses all competitive cells. There is no manual API —
+lifecycle is fully automatic.
+
+### 7.4 Coordination Warm-Up (§ALGO S-9.8)
+
+Coordination contexts are warmed with Gamma-sampled synthetic score
+vectors. This happens during the chained coordination warming phase
+(§ALGO S-11.4) whenever a new tracker is warmed — noise scores flow
+through the coordination tree just as real scores do.
+
+---
+
+## 8. Noise Injection and Warm-Up
+
+### 8.1 Noise Generation (§ALGO S-11.1)
+
+Synthetic noise vectors are uniform $\pm 0.5$ centred bit vectors
+matching the `CentredBits` encoding. A persistent `SmallRng` seeded
+from `noise_seed` (or system entropy) generates all noise sequences.
+
+### 8.2 Depth-Tiered Schedule ([ADR-S-015](../adr/015-cell-creation-performance.md))
+
+`NoiseSchedule` replaces the earlier flat `noise_rounds` parameter.
+Deeper cells are narrower and need fewer rounds to converge:
+
+- **Geometric** (default): `root` rounds at depth 0, decaying by
+  `decay` per depth level, clamped to `min`.
+  Default: `Geometric { root: 450, decay: 0.5, min: 50 }`.
+- **Explicit**: a lookup table of per-depth round counts.
+
+### 8.3 Automatic Injection
+
+There is no manual noise API — the sentinel owns the injection
+lifecycle entirely ([ADR-S-007](../adr/007-automatic-noise-injection.md)).
+Every newly created tracker is warmed before it receives real
+observations. The root tracker is warmed at construction.
+
+### 8.4 Deferred Cell Warm-Up (§ALGO S-11.6, §ALGO S-18.2)
+
+Cell warm-up is decoupled from the `ingest()` hot path to bound
+per-call work variance:
+
+1. **Enqueue** — `reconcile_analysis_set()` creates a `CellState`
+   and enqueues it into the `StagingArea` with the target round
+   count from `NoiseSchedule`. The cell holds an **investment slot**
+   in $\mathcal{I}$ but not a production slot in $\mathcal{A}$.
+2. **Warm** — the background thread (or synchronous drain) picks the
+   highest-priority cell by g.sum (§ALGO S-11.6.2) and injects one
+   noise batch at a time. The g.sum ordering ensures ancestors come
+   online before descendants.
+3. **Promote** — completed cells are moved to the ready queue and
+   transferred into the live `cells` map at the start of the next
+   `ingest()` call, entering the producing set.
+
+The staging area lives behind `Arc<Mutex<StagingArea>>` for sharing
+with the background warming thread.
+
+### 8.5 Background Warming Thread (§ALGO S-18.2 Step 3)
+
+When `config.background_warming` is `true`, a dedicated thread runs
+the warm-up loop:
+
+- Owns its own `SmallRng` seeded from `noise_seed + 1`.
+- Takes cells from the staging area via `take_highest_priority()`,
+  injects noise _without holding the lock_, and returns them via
+  `finish_warming()`.
+- Sleeps on a condvar when there is nothing to warm.
+- The main thread notifies the condvar after enqueueing new cells.
+- Clean shutdown via `WarmingThreadHandle::shutdown()`.
+
+When `background_warming` is `false` (default), warm-up runs
+synchronously inside `reconcile_analysis_set()`. This mode is
+deterministic and used by the test suite.
+
+### 8.6 Convergence Fixes ([ADR-S-013](../adr/013-warm-up-convergence-benchmark.md))
+
+Three interacting fixes address a 5–10× convergence gap between
+theoretical and empirical EWMA baseline convergence:
+
+| Fix                          | Mechanism                                                                                   | Impact                                   |
+| ---------------------------- | ------------------------------------------------------------------------------------------- | ---------------------------------------- |
+| Graduated clip-exemption     | $n_\sigma^{\text{eff}}$ scales with $\eta$                                                  | Surprise convergence: 1000+ → 65 rounds  |
+| Latent cold→warm init        | First-batch seeding of `lat_mean`, `lat_var`, `cross_corr`                                  | Rise factor: 5.9× → 1.26×                |
+| Slow-from-fast CUSUM seeding | Copy fast EWMA into slow at noise→real transition                                           | False drift: 198 → 5.7                   |
+| EWMA-mean-centred variance   | Centre on EWMA mean, not batch mean ([ADR-S-021](../adr/021-ewma-mean-centred-variance.md)) | Worst-case convergence: 289 → 282 rounds |
+
+Regression tests in `src/tests/convergence_fixes.rs` and
+`src/tests/variance_formula.rs`.
+
+### 8.7 Maturity Tracking (§ALGO S-11.5)
+
+Each tracker maintains a noise-influence fraction $\eta \in [0, 1]$
+that decays toward 0 as real observations replace synthetic noise.
+Batch-vectorised via $\lambda^n$.
+
+---
+
+## 9. SVD Strategy
+
+Configurable via `SentinelConfig::svd_strategy`
+([ADR-S-016](../adr/016-brand-incremental-svd.md)):
+
+| Strategy          | Algorithm                                                                                      | Complexity                   | Notes                               |
+| ----------------- | ---------------------------------------------------------------------------------------------- | ---------------------------- | ----------------------------------- |
+| `Naive`           | Dense thin SVD of $M \in \mathbb{R}^{w \times (k+b)}$                                          | $O(w \cdot (k+b)^2)$         | Simple, numerically stable baseline |
+| `Brand` (default) | Incremental SVD (Brand 2006) — projects onto current basis, SVDs a $(k+b) \times (k+b)$ kernel | $O(w \cdot (k+b) + (k+b)^3)$ | ~2–3× faster; default               |
+
+In **debug builds**, both algorithms run regardless of the setting
+and their outputs are compared — a continuous oracle test.
+
+---
+
+## 10. Configuration Reference
+
+All parameters from the algorithm spec (§ALGO S-13) are represented
+in `SentinelConfig<V>` (generic over the accumulator type for
+`split_threshold`; see [ADR-S-018](../adr/018-generic-domain-parameters.md)).
+Defaults match the spec.
+
+### 10.1 Analysis Engine (§ALGO S-13.1)
+
+| Config field             | Default | Description                                            |
+| ------------------------ | ------- | ------------------------------------------------------ |
+| `max_rank`               | 16      | Maximum subspace rank per tracker                      |
+| `forgetting_factor`      | 0.99    | Exponential decay factor $\lambda$                     |
+| `rank_update_interval`   | 100     | Steps between rank re-evaluations                      |
+| `energy_threshold`       | 0.90    | Cumulative energy fraction for rank selection          |
+| `eps`                    | 1e-6    | Numerical stability constant                           |
+| `clip_sigmas`            | 3.0     | EWMA outlier clip width ($n_\sigma$)                   |
+| `clip_pressure_decay`    | 0.95    | Clip-pressure EWMA decay ($\lambda_\rho$, §ALGO S-6.4) |
+| `cusum_slow_decay`       | 0.999   | Slow EWMA decay for per-tracker CUSUM                  |
+| `cusum_coord_slow_decay` | 0.999   | Slow EWMA decay for coordination CUSUM                 |
+| `cusum_allowance_sigmas` | 0.5     | CUSUM noise allowance ($\kappa_\sigma$)                |
+| `per_sample_scores`      | false   | Include per-observation scores in reports              |
+| `svd_strategy`           | Brand   | SVD algorithm selection                                |
+
+### 10.2 Analysis Selector (§ALGO S-13.2)
+
+| Config field            | Default | Description                     |
+| ----------------------- | ------- | ------------------------------- |
+| `analysis_k`            | 1024    | Maximum competitive cells ($K$) |
+| `analysis_depth_cutoff` | 6       | V-Tree depth cutoff ($L$)       |
+
+### 10.3 G-V Graph (§ALGO S-13.3)
+
+| Config field      | Default | Description                                          |
+| ----------------- | ------- | ---------------------------------------------------- |
+| `split_threshold` | 100     | Minimum volume before subdivision                    |
+| `d_create`        | 3       | Maximum V-depth for new splits ($D_{\text{create}}$) |
+| `d_evict`         | 6       | Minimum V-depth for eviction ($D_{\text{evict}}$)    |
+| `budget`          | 100,000 | Hard ceiling on live G-nodes ($G_{\max}$)            |
+
+### 10.4 Noise Injection (§ALGO S-13.5)
+
+| Config field         | Default                                        | Description                                      |
+| -------------------- | ---------------------------------------------- | ------------------------------------------------ |
+| `noise_schedule`     | `Geometric { root: 450, decay: 0.5, min: 50 }` | Depth-tiered round counts                        |
+| `noise_batch_size`   | 16                                             | Samples per noise round                          |
+| `noise_seed`         | Some(42)                                       | Deterministic RNG seed (`None` = system entropy) |
+| `background_warming` | false                                          | Warm cells on a background thread                |
+
+### 10.5 Validation
+
+`SentinelConfig::validate()` checks all constraints (ranges,
+inter-parameter relationships, mudlark headroom) before construction.
+Invalid configs produce `ConfigErrors` — the sentinel never panics on
+bad config ([ADR-S-004](../adr/004-config-validation-over-panic.md)).
+
+---
+
+## 11. Report Types
+
+`SpectralSentinel::ingest()` returns a `BatchReport<C>` containing
+everything the host needs to assess the batch.
+
+### 11.1 Top-Level Report
+
+| Field                  | Type                         | Content                                                 |
+| ---------------------- | ---------------------------- | ------------------------------------------------------- |
+| `cell_reports`         | `Vec<CellReport<C>>`         | Competitive cells only                                                                |
+| `ancestor_reports`     | `Vec<CellReport<C>>`         | Non-competitive ancestors + root                                                      |
+| `coordination_reports` | `Vec<CoordinationReport<C>>` | Per-G-node hierarchical coordination                                                  |
+| `contour`              | `ContourSnapshot`            | Plateau count, cell count, total importance                                           |
+| `health`               | `HealthReport`               | Fleet-wide operational summary                                                        |
+| `analysis_set_summary` | `AnalysisSetSummary`         | Competitive/full/investment-set sizes, depth/importance/V-depth ranges, degenerate count |
+
+### 11.2 Cell-Level Reports
+
+| Type                | Content                                                                                                          |
+| ------------------- | ---------------------------------------------------------------------------------------------------------------- |
+| `CellReport<C>`     | Wraps a `TrackerReport` with cell identity (GNode, depth, interval, competitive flag)                            |
+| `TrackerReport`     | `AnomalyScores`, four `ScoreDistribution` axes, `TrackerMaturity`, `ScoringGeometry`, optional per-sample scores |
+| `AnomalyScores`     | Batch-mean raw score and z-score for each of the four axes                                                       |
+| `ScoreDistribution` | `BaselineSnapshot` (EWMA mean/var) + `CusumSnapshot` (accumulator, slow mean/var) + `clip_pressure` (ρ̄)         |
+| `TrackerMaturity`   | Noise influence $\eta$, total observations, noise observation count                                              |
+| `ScoringGeometry`   | Novelty saturation ratio, coherence activity flag ([ADR-S-008](../adr/008-scoring-geometry-extension.md))        |
+
+### 11.3 Coordination Reports
+
+| Type                    | Content                                                                                                        |
+| ----------------------- | -------------------------------------------------------------------------------------------------------------- |
+| `CoordinationReport<C>` | G-node identity, `TrackerReport` from the 4D coordination tracker, `Option<Vec<MemberScore<C>>>` per contributing cell (requires `per_sample_scores`) |
+| `MemberScore<C>`        | Cell identity (start/end/depth) + 8 score fields (4 raw + 4 z-scores)                                          |
+
+### 11.4 System Health
+
+| Type                | Content                                                                                                                                                    |
+| ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `HealthReport`      | Node counts, tracker counts, investment set size, warming counts, `RankDistribution`, `MaturityDistribution`, `GeometryDistribution`, `CoordinationHealth`, `ClipPressureDistribution` |
+| `CellInspection<C>` | Deep dive into a single cell via `inspect_cell(gnode)`, includes `AxisBaselineSnapshots`                                                                                               |
+| `ContourSnapshot`   | Plateau count, terminal cell count, total importance                                                                                                                                   |
+
+---
+
+## 12. Design Decisions
+
+| ADR                                                                 | Title                                  | Summary                                                     |
+| ------------------------------------------------------------------- | -------------------------------------- | ----------------------------------------------------------- |
+| [ADR-S-001](../adr/001-measures-not-opinions.md)                    | Measures Not Opinions                  | Sentinel emits raw statistics, never policy                 |
+| [ADR-S-002](../adr/002-feed-forward-invariant.md)                   | Feed-Forward Invariant                 | Only `observe(v, 1u64)` — scores never feed back            |
+| [ADR-S-003](../adr/003-mudlark-integration.md)                      | Mudlark Integration                    | Cargo features; type params superseded by ADR-S-018         |
+| [ADR-S-004](../adr/004-config-validation-over-panic.md)             | Config Validation Over Panic           | Pre-validate all constraints; return `ConfigErrors`         |
+| [ADR-S-005](../adr/005-deterministic-order-and-thread-safety.md)    | Deterministic Order and Thread Safety  | `BTreeMap`, `Send + Sync`                                   |
+| [ADR-S-006](../adr/006-analysis-set-recomputation.md)               | Analysis Set Recomputation             | Full recompute per `ingest()`; $O(n)$ scan                  |
+| [ADR-S-007](../adr/007-automatic-noise-injection.md)                | Automatic Noise Injection              | No manual API; sentinel owns lifecycle                      |
+| [ADR-S-008](../adr/008-scoring-geometry-extension.md)               | Scoring Geometry Extension             | `ScoringGeometry` for structural observability              |
+| [ADR-S-009](../adr/009-decay-does-not-invalidate-analysis-set.md)   | Decay Does Not Invalidate Analysis Set | Decay is host-driven, analysis set reconciled at `ingest()` |
+| [ADR-S-010](../adr/010-linear-routing-over-g-tree-descent.md)       | Linear Routing Over G-Tree Descent     | Route by interval containment, not tree traversal           |
+| [ADR-S-011](../adr/011-degenerate-cell-dimension-guard.md)          | Degenerate Cell Dimension Guard        | Exclude cells with $w < 2$                                  |
+| [ADR-S-012](../adr/012-test-duration-budget.md)                     | Test Duration Budget                   | Time-box test suite                                         |
+| [ADR-S-013](../adr/013-warm-up-convergence-benchmark.md)            | Warm-Up Convergence Benchmark          | Three convergence fixes, 7 regression tests                 |
+| [ADR-S-014](../adr/014-subspace-tracker-visibility.md)              | Subspace Tracker Visibility            | `pub(crate)` visibility for tracker internals               |
+| [ADR-S-015](../adr/015-cell-creation-performance.md)                | Cell Creation Performance              | Depth-tiered `NoiseSchedule`                                |
+| [ADR-S-016](../adr/016-brand-incremental-svd.md)                    | Brand's Incremental SVD                | ~2–3× faster subspace evolution                             |
+| [ADR-S-017](../adr/017-deferred-cell-warm-up.md)                    | Deferred Cell Warm-Up                  | Staging area + background thread                            |
+| [ADR-S-018](../adr/018-generic-domain-parameters.md)                | Generic Domain Parameters              | `C`, `V`, `N` type parameters mirror mudlark                |
+| [ADR-S-019](../adr/019-investment-set-terminology-and-reporting.md) | Investment-Set Terminology & Reporting | Investment/producing sets, new health fields                |
+| [ADR-S-020](../adr/020-clip-pressure-ewma.md)                       | Clip-Pressure EWMA                     | Per-axis clip-pressure tracking (§ALGO S-6.4)               |
+| [ADR-S-021](../adr/021-ewma-mean-centred-variance.md)               | EWMA-Mean-Centred Latent Variance      | Centre on EWMA mean to eliminate batch-size bias            |
+
+---
+
+## 13. Cross-Reference Conventions
+
+Source comments use `§ALGO S-N` to reference sections of
+[algorithm.md](algorithm.md). Within this document,
+`§IMPL S-N` references sections here. See AGENTS.md for the full
+cross-reference system.
+
+When adding new code comments, always use the fully qualified form
+(`§ALGO S-4.2`, not bare `§4.2`) since the algorithm spec is a
+separate document.
diff --git a/packages/sentinel/src/analysis_set.rs b/packages/sentinel/src/analysis_set.rs
new file mode 100644
index 000000000..c989aefcc
--- /dev/null
+++ b/packages/sentinel/src/analysis_set.rs
@@ -0,0 +1,267 @@
+//! Analysis set: V-Tree competitive selection (§ALGO S-8.1–8.3).
+//!
+//! The competitive targets $\mathcal{T}$ are the top-$K$ V-entries
+//! by importance with V-depth ≤ $L$, closed under G-tree ancestry
+//! to form the investment set $\mathcal{I}$. The set is recomputed
+//! from scratch after every observation pass (ADR-S-006).
+//! The selector deliberately does not filter by G-Tree state: terminal,
+//! semi-internal, and internal V-entries can all be selected when they
+//! satisfy the V-depth, importance, and analysis-width criteria.
+//!
+//! The producing sets ($\mathcal{A}$, $\mathcal{A}^*$) are the
+//! online subsets of $\mathcal{I}$ — derived by the orchestrator
+//! from the investment set and tracker online status (ADR-S-019).
+
+use std::cmp::Ordering;
+use std::collections::BTreeMap;
+
+use torrust_mudlark::{Accumulator, Coordinate, GNodeId, GvGraph, Inspectable};
+
+use crate::report::AnalysisSetSummary;
+
+/// A cell selected for analysis by the selector (§ALGO S-8.1).
+///
+/// Competitive entries are the top-$K$ V-Tree entries by importance
+/// (the competitive targets $\mathcal{T}$). Ancestor entries close
+/// the targets under G-tree ancestry to form the investment set
+/// $\mathcal{I}$ (§ALGO S-8.2).
+#[derive(Debug, Clone, Copy, PartialEq)]
+#[allow(clippy::derive_partial_eq_without_eq)] // V: Accumulator includes f64 which has no Eq
+pub struct AnalysisEntry<C: Coordinate, V: Accumulator> {
+    /// Arena handle of the backing G-node.
+    pub gnode: GNodeId,
+    /// G-Tree depth of this cell.
+    pub depth: u32,
+    /// V-Tree depth of this cell's V-entry (for competitive cells)
+    /// or `0` for ancestor-only cells.
+    pub v_depth: usize,
+    /// Own importance of this cell (direct accumulation).
+    pub importance: V,
+    /// Lower bound of the dyadic interval (inclusive).
+    pub start: C,
+    /// Upper bound of the dyadic interval (exclusive).
+    pub end: C,
+    /// Whether this entry is competitively selected (vs ancestor-only).
+    pub is_competitive: bool,
+}
+
+/// The current analysis set — competitive targets $\mathcal{T}$
+/// closed under G-tree ancestry to form the investment set
+/// $\mathcal{I}$ (§ALGO S-8.1–8.2).
+///
+/// Recomputed after every observation pass (ADR-S-006).
+#[derive(Debug, Clone)]
+pub struct AnalysisSet<C: Coordinate, V: Accumulator> {
+    /// Competitively selected cells, ordered by importance (descending),
+    /// ties broken by interval start (ascending) for determinism.
+    competitive: Vec<AnalysisEntry<C, V>>,
+
+    /// All cells: competitive + ancestors (deduplicated).
+    /// The "full" set $\mathcal{A}^*$ from §ALGO S-4.2.
+    /// Ordered by `GNodeId` for deterministic iteration (ADR-S-005).
+    full: Vec<AnalysisEntry<C, V>>,
+}
+
+impl<C: Coordinate, V: Inspectable> AnalysisSet<C, V> {
+    /// Recompute the analysis set from the V-Tree.
+    ///
+    /// Scans `graph.layers_to(depth_cutoff)` (ADR-M-041) to collect
+    /// V-entries with `v_depth ≤ depth_cutoff`, takes top `k` by
+    /// importance (the competitive targets $\mathcal{T}$,
+    /// §ALGO S-8.1; ties broken by `start`), then closes under
+    /// G-tree ancestry to form the investment set $\mathcal{I}$
+    /// (§ALGO S-8.2).
+    ///
+    /// The depth-limited BFS avoids expanding V-structural nodes
+    /// below the cutoff, saving `O(2^(D−K))` queue work on balanced
+    /// trees (ADR-M-041 §Performance).
+    ///
+    /// `O(n_K)` in V-entries at depth ≤ K + `O(K · max_depth)` for
+    /// ancestor closure.
+    ///
+    /// # Panics
+    ///
+    /// Panics if the G-root node is not live in the graph.
+    #[must_use]
+    pub fn recompute<const N: u32>(graph: &GvGraph<C, V, N>, k: usize, depth_cutoff: usize) -> Self {
+        // ── Step 1: Collect candidates from V-Tree ──────────────
+        // Eligibility: V-depth ≤ cutoff AND analysis width w ≥ 2
+        // (§ALGO S-8.1).  The depth limit is enforced by the BFS
+        // itself (ADR-M-041), not a post-hoc filter.
+        let mut candidates: Vec<AnalysisEntry<C, V>> = graph
+            .layers_to(depth_cutoff)
+            .filter(|(_, node)| {
+                // w = N - depth ≥ MIN_TRACKER_DIM (§ALGO S-8.1, §ALGO S-4.1).
+                (N - node.depth) as usize >= crate::MIN_TRACKER_DIM
+            })
+            .map(|(v_depth, node)| AnalysisEntry {
+                gnode: node.gnode_id,
+                depth: node.depth,
+                v_depth,
+                importance: node.own,
+                start: node.start,
+                end: node.end,
+                is_competitive: true,
+            })
+            .collect();
+
+        // ── Step 2: Sort by importance (desc), then start (asc) ─
+        candidates.sort_by(|a, b| {
+            b.importance
+                .partial_cmp(&a.importance)
+                .unwrap_or(Ordering::Equal)
+                .then_with(|| a.start.partial_cmp(&b.start).unwrap_or(Ordering::Equal))
+        });
+
+        // ── Step 3: Take top K ──────────────────────────────────
+        candidates.truncate(k);
+
+        // The root is always an ancestor, never competitive (§ALGO S-4.7).
+        let g_root = graph.g_root();
+        let competitive: Vec<AnalysisEntry<C, V>> = candidates.iter().filter(|e| e.gnode != g_root).copied().collect();
+
+        // ── Step 4: Ancestor closure (§ALGO S-4.2) ────────────────
+        // Walk G-tree parents for each competitive entry. Collect
+        // all ancestor GNodeIds not already in the competitive set.
+        let mut full_set: BTreeMap<GNodeId, AnalysisEntry<C, V>> = BTreeMap::new();
+
+        // Insert competitive entries.
+        for entry in &competitive {
+            full_set.insert(entry.gnode, *entry);
+        }
+
+        // Walk ancestors.
+        for entry in &competitive {
+            let mut current = entry.gnode;
+            while let Some(info) = graph.gnode_info(current) {
+                let Some(parent_id) = info.parent else {
+                    break; // reached the root
+                };
+                if full_set.contains_key(&parent_id) {
+                    break; // already tracked (shared ancestor)
+                }
+                let Some(parent_info) = graph.gnode_info(parent_id) else {
+                    break;
+                };
+                full_set.insert(
+                    parent_id,
+                    AnalysisEntry {
+                        gnode: parent_id,
+                        depth: parent_info.depth,
+                        v_depth: 0, // not meaningful for ancestors
+                        importance: parent_info.own,
+                        start: parent_info.start,
+                        end: parent_info.end,
+                        is_competitive: false,
+                    },
+                );
+                current = parent_id;
+            }
+        }
+
+        // Ensure the root is always present (§ALGO S-4.7).
+        let g_root = graph.g_root();
+        full_set.entry(g_root).or_insert_with(|| {
+            let info = graph.gnode_info(g_root).expect("G-root must be live");
+            AnalysisEntry {
+                gnode: g_root,
+                depth: info.depth,
+                v_depth: 0,
+                importance: info.own,
+                start: info.start,
+                end: info.end,
+                is_competitive: false,
+            }
+        });
+
+        let full: Vec<AnalysisEntry<C, V>> = full_set.into_values().collect();
+
+        Self { competitive, full }
+    }
+}
+
+impl<C: Coordinate, V: Accumulator> AnalysisSet<C, V> {
+    /// The competitively selected entries.
+    #[must_use]
+    pub fn competitive(&self) -> &[AnalysisEntry<C, V>] {
+        &self.competitive
+    }
+
+    /// The full analysis set (competitive + ancestors).
+    #[must_use]
+    pub fn full(&self) -> &[AnalysisEntry<C, V>] {
+        &self.full
+    }
+
+    /// Number of competitive entries.
+    #[must_use]
+    pub const fn competitive_count(&self) -> usize {
+        self.competitive.len()
+    }
+
+    /// Total entries (competitive + ancestors).
+    #[must_use]
+    pub const fn total_count(&self) -> usize {
+        self.full.len()
+    }
+
+    /// Whether a given `GNodeId` is in the full analysis set.
+    #[must_use]
+    pub fn contains(&self, gnode: GNodeId) -> bool {
+        self.full.iter().any(|e| e.gnode == gnode)
+    }
+
+    /// Whether a given `GNodeId` is competitively selected.
+    #[must_use]
+    pub fn is_competitive(&self, gnode: GNodeId) -> bool {
+        self.competitive.iter().any(|e| e.gnode == gnode)
+    }
+}
+
+impl<C: Coordinate, V: Inspectable> AnalysisSet<C, V> {
+    /// Build a summary snapshot of the current analysis set.
+    #[must_use]
+    pub fn summary(&self) -> AnalysisSetSummary {
+        let competitive_size = self.competitive_count();
+        let full_size = self.total_count();
+
+        let depth_range = if full_size == 0 {
+            (0, 0)
+        } else {
+            let mut min_d = u32::MAX;
+            let mut max_d = 0u32;
+            for entry in self.full() {
+                min_d = min_d.min(entry.depth);
+                max_d = max_d.max(entry.depth);
+            }
+            (min_d, max_d)
+        };
+
+        let (importance_range, v_depth_range) = if competitive_size == 0 {
+            ((0.0, 0.0), (0, 0))
+        } else {
+            let mut min_imp = f64::INFINITY;
+            let mut max_imp = f64::NEG_INFINITY;
+            let mut min_vd = usize::MAX;
+            let mut max_vd = 0usize;
+            for entry in self.competitive() {
+                let imp = entry.importance.to_f64_approx();
+                min_imp = min_imp.min(imp);
+                max_imp = max_imp.max(imp);
+                min_vd = min_vd.min(entry.v_depth);
+                max_vd = max_vd.max(entry.v_depth);
+            }
+            ((min_imp, max_imp), (min_vd, max_vd))
+        };
+
+        AnalysisSetSummary {
+            competitive_size,
+            full_size,
+            investment_set_size: full_size, // Adjusted by orchestrator to include warming cells.
+            depth_range,
+            importance_range,
+            v_depth_range,
+            degenerate_cells_skipped: 0,
+        }
+    }
+}
diff --git a/packages/sentinel/src/config.rs b/packages/sentinel/src/config.rs
new file mode 100644
index 000000000..57501fdb5
--- /dev/null
+++ b/packages/sentinel/src/config.rs
@@ -0,0 +1,754 @@
+//! Configuration types for the Spectral Sentinel.
+//!
+//! [`SentinelConfig`] controls the measurement parameters of the sentinel.
+//!
+//! These types are deliberately free of policy concerns (thresholds, actions).
+//! The sentinel measures; the host decides what the measurements mean.
+
+use torrust_mudlark::{Accumulator, Inspectable};
+
+// ─── Noise schedule (§ALGO S-18.2, ADR-S-015 §1) ────────────
+
+/// Depth-tiered noise injection schedule.
+///
+/// Controls how many noise rounds each newly created tracker receives,
+/// varying by G-tree depth. Deeper cells are narrower and need fewer
+/// rounds to converge, so the schedule tapers with depth.
+///
+/// # Variants
+///
+/// - **`Geometric`**: computes rounds as `root × decay^depth`, floored
+///   to `min`. Default: `Geometric { root: 450, decay: 0.5, min: 50 }`
+///   → `[450, 225, 113, 56, 50, 50, …]`.
+///
+/// - **`Explicit`**: a hand-specified per-depth vector. Depths beyond
+///   the vector length use the last entry. An empty vector disables
+///   noise injection entirely.
+///
+/// # Examples
+///
+/// ```
+/// use torrust_sentinel::NoiseSchedule;
+///
+/// let geo = NoiseSchedule::geometric(400, 0.5, 100);
+/// assert_eq!(geo.rounds_for_depth(0), 400);
+/// assert_eq!(geo.rounds_for_depth(1), 200);
+/// assert_eq!(geo.rounds_for_depth(2), 100);
+/// assert_eq!(geo.rounds_for_depth(10), 100);
+///
+/// let explicit = NoiseSchedule::Explicit(vec![50, 30, 10]);
+/// assert_eq!(explicit.rounds_for_depth(0), 50);
+/// assert_eq!(explicit.rounds_for_depth(2), 10);
+/// assert_eq!(explicit.rounds_for_depth(99), 10); // clamps to last
+/// ```
+#[derive(Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+pub enum NoiseSchedule {
+    /// Geometric decay: `root × decay^depth`, floored to `min`.
+    Geometric {
+        /// Rounds at depth 0.
+        root: u32,
+        /// Multiplicative decay per depth level (must be in `(0.0, 1.0]`).
+        decay: f64,
+        /// Floor — minimum rounds at any depth.
+        min: u32,
+    },
+
+    /// Per-depth explicit schedule. Depths beyond the vector length
+    /// use the last entry. An empty vector disables noise entirely.
+    Explicit(Vec<u32>),
+}
+
+impl NoiseSchedule {
+    /// Convenience constructor for the `Geometric` variant.
+    #[must_use]
+    pub const fn geometric(root: u32, decay: f64, min: u32) -> Self {
+        Self::Geometric { root, decay, min }
+    }
+
+    /// Number of noise rounds for a tracker at the given G-tree depth.
+    ///
+    /// Returns `0` when noise is disabled (empty `Explicit` vector).
+    #[must_use]
+    pub fn rounds_for_depth(&self, depth: usize) -> u32 {
+        match self {
+            Self::Geometric { root, decay, min } => {
+                // depth is bounded by G-tree depth (≤ 128), safe to truncate.
+                #[allow(clippy::cast_possible_truncation, clippy::cast_possible_wrap)]
+                let exp = depth as i32;
+                let raw = f64::from(*root) * decay.powi(exp);
+                // raw is non-negative (root ≥ 0, decay > 0), safe to truncate.
+                #[allow(clippy::cast_possible_truncation, clippy::cast_sign_loss)]
+                let rounded = raw.round() as u32;
+                rounded.max(*min)
+            }
+            Self::Explicit(v) => {
+                if v.is_empty() {
+                    return 0;
+                }
+                // Clamp to last entry for depths beyond the vector.
+                v[depth.min(v.len() - 1)]
+            }
+        }
+    }
+
+    /// Whether this schedule produces zero rounds at every depth.
+    ///
+    /// True only for `Explicit(vec![])` or `Explicit(vec![0, 0, …])`.
+    /// `Geometric` always produces at least `min` rounds.
+    #[must_use]
+    pub fn is_disabled(&self) -> bool {
+        match self {
+            Self::Geometric { min, .. } => *min == 0,
+            Self::Explicit(v) => v.is_empty() || v.iter().all(|&r| r == 0),
+        }
+    }
+
+    /// Maximum rounds this schedule can produce (useful for capacity hints).
+    #[must_use]
+    pub fn max_rounds(&self) -> u32 {
+        match self {
+            Self::Geometric { root, .. } => *root,
+            Self::Explicit(v) => v.iter().copied().max().unwrap_or(0),
+        }
+    }
+}
+
+impl Default for NoiseSchedule {
+    /// Default: `Geometric { root: 450, decay: 0.5, min: 50 }`.
+    ///
+    /// Calibrated for the default forgetting factor λ = 0.99 (§ALGO S-13.1).
+    /// At λ = 0.99, b = 16, the worst-case baseline convergence is ~398
+    /// rounds (surprise axis), so root = 450 provides ~13% margin.
+    /// The floor of 50 covers deep cells where convergence times scale
+    /// down with analysis width but remain ~50 rounds at λ = 0.99.
+    /// See §ALGO S-A.7 for the empirical derivation.
+    fn default() -> Self {
+        Self::Geometric {
+            root: 450,
+            decay: 0.5,
+            min: 50,
+        }
+    }
+}
+
+/// Measurement parameters for the sentinel.
+///
+/// Every field controls *how* the sentinel observes and learns,
+/// never *what it thinks* about what it sees.
+#[derive(Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+#[cfg_attr(feature = "serde", serde(bound = "V: serde::Serialize + serde::de::DeserializeOwned"))]
+pub struct SentinelConfig<V: Accumulator> {
+    /// Maximum rank (number of basis vectors) any subspace tracker can use.
+    ///
+    /// Higher = more expressive model of "normal", but more memory and
+    /// SVD cost per observation. The actual rank adapts automatically
+    /// and will never exceed `min(suffix_width, max_rank)`.
+    ///
+    /// Default: `16`
+    pub max_rank: usize,
+
+    /// Exponential forgetting factor (λ).
+    ///
+    /// Controls how fast old observations fade from memory.
+    /// - `0.99` = long memory (~69 observations half-life)
+    /// - `0.95` = short memory (~14 observations half-life)
+    ///
+    /// Must be in `(0.0, 1.0)`.
+    ///
+    /// Default: `0.99`
+    pub forgetting_factor: f64,
+
+    /// How often (in observation steps) to reassess the rank of each tracker.
+    ///
+    /// Rank changes by at most ±1 per evaluation to avoid instability.
+    ///
+    /// Default: `100`
+    pub rank_update_interval: u64,
+
+    /// Cumulative energy threshold for automatic rank adaptation.
+    ///
+    /// The rank adapts to capture at least this fraction of the total
+    /// variance (sum of squared singular values). Lower = fewer dimensions
+    /// retained, higher = more faithful representation.
+    ///
+    /// Must be in `(0.0, 1.0)`.
+    ///
+    /// Default: `0.90`
+    pub energy_threshold: f64,
+
+    /// Numerical stability constant.
+    ///
+    /// Added to denominators to prevent division by zero.
+    ///
+    /// Default: `1e-6`
+    pub eps: f64,
+
+    /// Slow EWMA decay factor for per-tracker CUSUM reference baselines.
+    ///
+    /// The CUSUM accumulator detects gradual drift that the fast EWMA
+    /// (controlled by `forgetting_factor`) absorbs. The slow EWMA
+    /// provides the reference: CUSUM accumulates the gap between the
+    /// batch mean score and the slow baseline.
+    ///
+    /// Must be in `(0.0, 1.0)` and strictly greater than
+    /// `forgetting_factor` — a slower memory than the fast baseline.
+    ///
+    /// Half-life ≈ `ln(2) / ln(1/λ_s)`:
+    /// - `0.999` = ~693 steps (default)
+    /// - `0.995` = ~139 steps
+    ///
+    /// Default: `0.999`
+    pub cusum_slow_decay: f64,
+
+    /// Slow EWMA decay factor for coordination-tier CUSUM (§ALGO S-13.1).
+    ///
+    /// Controls the CUSUM reference baseline at the cross-cell
+    /// coordination tier. Separated from `cusum_slow_decay` because
+    /// the coordination tier may see different batch cadences and the
+    /// host may want different drift sensitivity at each tier.
+    ///
+    /// Must be in `(0.0, 1.0)` and strictly greater than
+    /// `forgetting_factor`.
+    ///
+    /// Default: `0.999`
+    pub cusum_coord_slow_decay: f64,
+
+    /// CUSUM noise allowance in slow-baseline σ units.
+    ///
+    /// Each CUSUM step subtracts `κ_σ · √(slow_variance)` before
+    /// accumulating. This absorbs normal noise fluctuations so the
+    /// accumulator only grows under sustained elevation.
+    ///
+    /// - `0.5` = tolerate up to half a slow-σ per step (default)
+    /// - `0.0` = no allowance — any positive gap accumulates
+    /// - `1.0` = generous allowance — only strong drift accumulates
+    ///
+    /// Must be ≥ 0.
+    ///
+    /// Default: `0.5`
+    pub cusum_allowance_sigmas: f64,
+
+    /// Outlier clip width in σ units for EWMA baseline updates.
+    ///
+    /// During each EWMA update, observations beyond
+    /// `mean + clip_sigmas · √variance` are rejected to prevent
+    /// baseline poisoning.  Higher values accept more of the upper
+    /// tail; lower values clip more aggressively.
+    ///
+    /// Must be > 0.
+    ///
+    /// Default: `3.0`
+    pub clip_sigmas: f64,
+
+    /// Clip-pressure EWMA decay factor (`λ_ρ`).
+    ///
+    /// Controls how quickly the per-axis clip-pressure estimate adapts
+    /// to changing contamination levels.  Higher values = longer memory.
+    ///
+    /// Half-life ≈ ln(2) / `ln(1/λ_ρ)`:
+    /// - `0.95` = ~14 batches (default, §ALGO S-13.1)
+    /// - `0.99` = ~69 batches
+    ///
+    /// Must be in `(0.0, 1.0)`.
+    ///
+    /// Default: `0.95`
+    pub clip_pressure_decay: f64,
+
+    /// Whether to include per-sample scores in reports.
+    ///
+    /// When `true`, each [`CellReport`](crate::CellReport)
+    /// includes a `Vec<SampleScore>` with individual scores for every
+    /// observation in the batch. Useful for forensics, expensive for
+    /// large batches.
+    ///
+    /// Default: `false`
+    pub per_sample_scores: bool,
+
+    /// Maximum number of competitive analysis cells (§ALGO S-4.1).
+    ///
+    /// Controls the resource ceiling for the analysis tier. The total
+    /// tracker count is bounded by `2 × analysis_k` (Steiner tree
+    /// bound, §ALGO S-4.8) — `analysis_k` competitive cells plus at
+    /// most `analysis_k` ancestor cells.
+    ///
+    /// Must be ≥ 1.
+    ///
+    /// Default: `1024`  (§ALGO S-13.2: `analysis_K`)
+    pub analysis_k: usize,
+
+    /// Maximum V-Tree depth at which entries are considered for
+    /// competitive selection (§ALGO S-4.1).
+    ///
+    /// Only V-entries with `v_depth ≤ analysis_depth_cutoff` are
+    /// eligible. Deeper entries have not yet proven sufficient
+    /// significance. This prevents noise from promoting ephemeral
+    /// cells into the analysis set.
+    ///
+    /// Must be ≥ 0. A value of 0 means only the V-Tree root
+    /// (if it is an entry) is eligible — effectively disabling
+    /// adaptive selection.
+    ///
+    /// Default: `6`  (§ALGO S-13.2: `analysis_depth_cutoff`)
+    pub analysis_depth_cutoff: usize,
+
+    /// G-V Graph: minimum accumulated intensity before a cell subdivides.
+    ///
+    /// For count-based observation (Δ=1 per value), this is the number of
+    /// observations a cell must receive before subdividing.
+    ///
+    /// Maps to `torrust_mudlark::Config::split_threshold`.
+    ///
+    /// Must be > 0.
+    ///
+    /// Default: `100`  (§ALGO S-13.3: `split_threshold`)
+    pub split_threshold: V,
+
+    /// G-V Graph: maximum V-Tree depth at which new splits are allowed.
+    ///
+    /// Controls how deep the tree can grow. Deeper cells need more
+    /// sustained traffic to compete for analysis slots.
+    ///
+    /// Maps to `torrust_mudlark::Config::depth_create`.
+    ///
+    /// Must be ≥ 1.
+    ///
+    /// Default: `3`  (§ALGO S-13.3: `D_create`)
+    pub d_create: u32,
+
+    /// G-V Graph: minimum V-Tree depth at which entries become eviction-eligible.
+    ///
+    /// Must be strictly greater than `d_create`. The gap (`d_evict − d_create`)
+    /// is the buffer zone — entries too deep to create children but not yet
+    /// deep enough to be evicted.
+    ///
+    /// Maps to `torrust_mudlark::Config::depth_evict`.
+    ///
+    /// Must be > `d_create`.
+    ///
+    /// Default: `6`  (§ALGO S-13.3: `D_evict`)
+    pub d_evict: u32,
+
+    /// G-V Graph: hard ceiling on live G-node count.
+    ///
+    /// Enables dynamic depth control — the graph adjusts depth gates
+    /// at runtime to keep the live node count under this ceiling.
+    /// The sentinel always operates in budgeted mode.
+    ///
+    /// Maps to `torrust_mudlark::Config::budget` (wrapped in `Some`).
+    ///
+    /// Must be > 0 and large enough to satisfy mudlark's headroom
+    /// requirement: `budget > max(3^(d_evict − d_create + 1), 2*(d_create − 1))`.
+    ///
+    /// Default: `100_000`  (§ALGO S-13.3: `budget` / `G_max`)
+    pub budget: usize,
+
+    /// Depth-tiered noise injection schedule (§ALGO S-18.2, ADR-S-015 §1).
+    ///
+    /// Controls how many synthetic noise batches each newly created
+    /// tracker receives, varying by G-tree depth. Deeper cells are
+    /// narrower and need fewer rounds to converge.
+    ///
+    /// Default: `Geometric { root: 450, decay: 0.5, min: 50 }`
+    pub noise_schedule: NoiseSchedule,
+
+    /// Number of synthetic samples per noise batch (§ALGO S-13.5).
+    ///
+    /// Each round feeds this many random ±0.5 centred vectors through
+    /// the tracker's `observe()` path with `is_noise = true`.
+    ///
+    /// Must be ≥ 1 when noise is enabled.
+    ///
+    /// Default: `16`
+    pub noise_batch_size: usize,
+
+    /// RNG seed for deterministic noise generation (§ALGO S-13.5).
+    ///
+    /// `Some(seed)` → reproducible noise across restarts.
+    /// `None` → seeded from system entropy.
+    ///
+    /// Default: `Some(42)`
+    pub noise_seed: Option<u64>,
+
+    /// Whether to perform cell warm-up on a background thread (§ALGO S-18.2, Step 3).
+    ///
+    /// When `true`, newly created analysis cells are warmed by a
+    /// dedicated background thread instead of being warmed inline
+    /// during `ingest()`. This eliminates the latency spike that
+    /// accompanies cell creation at the cost of a short delay before
+    /// new cells participate in scoring.
+    ///
+    /// When `false`, warm-up runs synchronously within
+    /// `reconcile_analysis_set()` — identical to the Step 2 behaviour.
+    /// This mode is deterministic (given a fixed `noise_seed`) and is
+    /// used by the test suite.
+    ///
+    /// Default: `false`  (opt-in; production deployments should enable)
+    pub background_warming: bool,
+
+    /// Which SVD algorithm to use for subspace evolution (§ALGO S-4.2 Phase 2).
+    ///
+    /// - `Naive`: dense thin SVD of the full composite matrix — simple,
+    ///   correct, O(d·(k+b)²) per call.
+    /// - `Brand`: incremental SVD (Brand 2006) — projects onto the current
+    ///   basis and SVDs a small (k+b)×(k+b) kernel instead.  Much faster.
+    ///
+    /// In debug builds, **both** algorithms run regardless of this setting
+    /// and their outputs are compared as a continuous oracle test.
+    ///
+    /// Default: `Brand`
+    pub svd_strategy: crate::SvdStrategy,
+}
+
+impl<V: Inspectable> Default for SentinelConfig<V> {
+    fn default() -> Self {
+        Self {
+            max_rank: 16,
+            forgetting_factor: 0.99,
+            rank_update_interval: 100,
+            energy_threshold: 0.90,
+            eps: 1e-6,
+            cusum_slow_decay: 0.999,
+            cusum_coord_slow_decay: 0.999,
+            cusum_allowance_sigmas: 0.5,
+            clip_sigmas: 3.0,
+            clip_pressure_decay: 0.95,
+            per_sample_scores: false,
+            analysis_k: 1024,
+            analysis_depth_cutoff: 6,
+            split_threshold: V::from_f64(100.0),
+            d_create: 3,
+            d_evict: 6,
+            budget: 100_000,
+            noise_schedule: NoiseSchedule::default(),
+            noise_batch_size: 16,
+            noise_seed: Some(42),
+            background_warming: false,
+            svd_strategy: crate::SvdStrategy::default(),
+        }
+    }
+}
+
+/// Validation errors for [`SentinelConfig`].
+#[derive(Debug, Clone, PartialEq)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+pub enum ConfigError {
+    /// `max_rank` must be at least 1.
+    MaxRankZero,
+    /// `forgetting_factor` must be in `(0.0, 1.0)`.
+    ForgettingFactorOutOfRange(f64),
+    /// `rank_update_interval` must be at least 1.
+    RankUpdateIntervalZero,
+    /// `analysis_k` must be at least 1.
+    AnalysisKZero,
+    /// `energy_threshold` must be in `(0.0, 1.0)`.
+    EnergyThresholdOutOfRange(f64),
+    /// `eps` must be positive.
+    EpsNotPositive(f64),
+    /// `cusum_slow_decay` must be in `(0.0, 1.0)`.
+    CusumSlowDecayOutOfRange(f64),
+    /// `cusum_slow_decay` must be strictly greater than `forgetting_factor`.
+    CusumSlowDecayTooLow { slow: f64, fast: f64 },
+    /// `cusum_coord_slow_decay` must be in `(0.0, 1.0)`.
+    CusumCoordSlowDecayOutOfRange(f64),
+    /// `cusum_coord_slow_decay` must be strictly greater than `forgetting_factor`.
+    CusumCoordSlowDecayTooLow { slow: f64, fast: f64 },
+    /// `cusum_allowance_sigmas` must be non-negative.
+    CusumAllowanceNegative(f64),
+    /// `clip_sigmas` must be positive.
+    ClipSigmasNotPositive(f64),
+    /// `clip_pressure_decay` must be in `(0.0, 1.0)`.
+    ClipPressureDecayOutOfRange(f64),
+    /// `split_threshold` must be positive.
+    SplitThresholdNotPositive(f64),
+    /// `d_create` must be at least 1.
+    DCreateZero,
+    /// `d_evict` must be strictly greater than `d_create`.
+    DEvictNotGreaterThanDCreate { d_create: u32, d_evict: u32 },
+    /// `budget` must be positive.
+    BudgetZero,
+    /// `budget` must exceed mudlark's headroom requirement.
+    BudgetTooSmall { budget: usize, required_minimum: usize },
+    /// `noise_batch_size` must be ≥ 1 when noise is enabled.
+    NoiseBatchSizeZero,
+    /// `NoiseSchedule::Geometric::decay` must be in `(0.0, 1.0]`.
+    NoiseScheduleDecayOutOfRange(f64),
+    /// `NoiseSchedule::Geometric::root` must be > 0 when `min` > 0.
+    NoiseScheduleRootZero,
+}
+
+impl std::fmt::Display for ConfigError {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        match self {
+            Self::MaxRankZero => write!(f, "max_rank must be at least 1"),
+            Self::ForgettingFactorOutOfRange(v) => {
+                write!(f, "forgetting_factor must be in (0.0, 1.0), got {v}")
+            }
+            Self::RankUpdateIntervalZero => write!(f, "rank_update_interval must be at least 1"),
+            Self::AnalysisKZero => write!(f, "analysis_k must be at least 1"),
+            Self::EnergyThresholdOutOfRange(v) => {
+                write!(f, "energy_threshold must be in (0.0, 1.0), got {v}")
+            }
+            Self::EpsNotPositive(v) => write!(f, "eps must be positive, got {v}"),
+            Self::CusumSlowDecayOutOfRange(v) => {
+                write!(f, "cusum_slow_decay must be in (0.0, 1.0), got {v}")
+            }
+            Self::CusumSlowDecayTooLow { slow, fast } => {
+                write!(f, "cusum_slow_decay ({slow}) must be > forgetting_factor ({fast})")
+            }
+            Self::CusumCoordSlowDecayOutOfRange(v) => {
+                write!(f, "cusum_coord_slow_decay must be in (0.0, 1.0), got {v}")
+            }
+            Self::CusumCoordSlowDecayTooLow { slow, fast } => {
+                write!(f, "cusum_coord_slow_decay ({slow}) must be > forgetting_factor ({fast})")
+            }
+            Self::CusumAllowanceNegative(v) => {
+                write!(f, "cusum_allowance_sigmas must be >= 0, got {v}")
+            }
+            Self::ClipSigmasNotPositive(v) => write!(f, "clip_sigmas must be > 0, got {v}"),
+            Self::ClipPressureDecayOutOfRange(v) => {
+                write!(f, "clip_pressure_decay must be in (0.0, 1.0), got {v}")
+            }
+            Self::SplitThresholdNotPositive(v) => {
+                write!(f, "split_threshold must be > 0, got {v}")
+            }
+            Self::DCreateZero => write!(f, "d_create must be >= 1"),
+            Self::DEvictNotGreaterThanDCreate { d_create, d_evict } => {
+                write!(f, "d_evict ({d_evict}) must be > d_create ({d_create})")
+            }
+            Self::BudgetZero => write!(f, "budget must be > 0"),
+            Self::BudgetTooSmall {
+                budget,
+                required_minimum,
+            } => {
+                write!(
+                    f,
+                    "budget ({budget}) must be > {required_minimum} (mudlark headroom requirement)"
+                )
+            }
+            Self::NoiseBatchSizeZero => {
+                write!(f, "noise_batch_size must be >= 1 when noise is enabled")
+            }
+            Self::NoiseScheduleDecayOutOfRange(v) => {
+                write!(f, "noise_schedule geometric decay must be in (0.0, 1.0], got {v}")
+            }
+            Self::NoiseScheduleRootZero => {
+                write!(f, "noise_schedule geometric root must be > 0 when min > 0")
+            }
+        }
+    }
+}
+
+impl std::error::Error for ConfigError {}
+
+/// One or more configuration validation errors.
+///
+/// Returned by [`SentinelConfig::validate`] when at least one field
+/// violates its constraints.  Contains every violation found, not
+/// just the first.
+#[derive(Debug, Clone, PartialEq)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+pub struct ConfigErrors(pub Vec<ConfigError>);
+
+impl std::fmt::Display for ConfigErrors {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        let n = self.0.len();
+        for (i, e) in self.0.iter().enumerate() {
+            write!(f, "{e}")?;
+            if i + 1 < n {
+                write!(f, "; ")?;
+            }
+        }
+        Ok(())
+    }
+}
+
+impl std::error::Error for ConfigErrors {}
+
+/// Non-fatal diagnostic for parameter combinations that are technically
+/// valid but likely to produce poor results (§ALGO S-A.7).
+#[derive(Debug, Clone, PartialEq)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+pub enum ConfigWarning {
+    /// The noise schedule root is below the empirically derived minimum
+    /// for the configured forgetting factor, meaning baselines may not
+    /// converge before real observations arrive.
+    NoiseScheduleInsufficient {
+        /// Noise schedule root (depth-0) round count.
+        root: u32,
+        /// Minimum recommended rounds for the configured λ.
+        recommended_root: u32,
+        /// The configured forgetting factor.
+        lambda: f64,
+    },
+}
+
+impl std::fmt::Display for ConfigWarning {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        match self {
+            Self::NoiseScheduleInsufficient {
+                root,
+                recommended_root,
+                lambda,
+            } => {
+                write!(
+                    f,
+                    "noise_schedule root ({root}) is below the recommended minimum \
+                     ({recommended_root}) for forgetting_factor = {lambda}; \
+                     baselines may not converge before real observations arrive \
+                     (see §ALGO S-A.7)"
+                )
+            }
+        }
+    }
+}
+
+impl<V: Inspectable> SentinelConfig<V> {
+    /// Validate all invariants.
+    ///
+    /// Checks every field and collects all violations so the caller
+    /// can fix them in one pass rather than iterating one-at-a-time.
+    ///
+    /// # Errors
+    ///
+    /// Returns a [`ConfigErrors`] containing every [`ConfigError`]
+    /// found, if any.
+    pub fn validate(&self) -> Result<(), ConfigErrors> {
+        let mut errors = Vec::new();
+
+        if self.max_rank == 0 {
+            errors.push(ConfigError::MaxRankZero);
+        }
+        if self.forgetting_factor <= 0.0 || self.forgetting_factor >= 1.0 {
+            errors.push(ConfigError::ForgettingFactorOutOfRange(self.forgetting_factor));
+        }
+        if self.rank_update_interval == 0 {
+            errors.push(ConfigError::RankUpdateIntervalZero);
+        }
+        if self.analysis_k == 0 {
+            errors.push(ConfigError::AnalysisKZero);
+        }
+        if self.energy_threshold <= 0.0 || self.energy_threshold >= 1.0 {
+            errors.push(ConfigError::EnergyThresholdOutOfRange(self.energy_threshold));
+        }
+        if self.eps <= 0.0 {
+            errors.push(ConfigError::EpsNotPositive(self.eps));
+        }
+        if self.cusum_slow_decay <= 0.0 || self.cusum_slow_decay >= 1.0 {
+            errors.push(ConfigError::CusumSlowDecayOutOfRange(self.cusum_slow_decay));
+        }
+        if self.cusum_slow_decay <= self.forgetting_factor {
+            errors.push(ConfigError::CusumSlowDecayTooLow {
+                slow: self.cusum_slow_decay,
+                fast: self.forgetting_factor,
+            });
+        }
+        if self.cusum_coord_slow_decay <= 0.0 || self.cusum_coord_slow_decay >= 1.0 {
+            errors.push(ConfigError::CusumCoordSlowDecayOutOfRange(self.cusum_coord_slow_decay));
+        }
+        if self.cusum_coord_slow_decay <= self.forgetting_factor {
+            errors.push(ConfigError::CusumCoordSlowDecayTooLow {
+                slow: self.cusum_coord_slow_decay,
+                fast: self.forgetting_factor,
+            });
+        }
+        if self.cusum_allowance_sigmas < 0.0 {
+            errors.push(ConfigError::CusumAllowanceNegative(self.cusum_allowance_sigmas));
+        }
+        if self.clip_sigmas <= 0.0 {
+            errors.push(ConfigError::ClipSigmasNotPositive(self.clip_sigmas));
+        }
+        if self.clip_pressure_decay <= 0.0 || self.clip_pressure_decay >= 1.0 {
+            errors.push(ConfigError::ClipPressureDecayOutOfRange(self.clip_pressure_decay));
+        }
+
+        // ── G-V Graph fields (§ALGO S-13.3) ────────────────────
+        if self.split_threshold.to_f64_approx() <= 0.0 {
+            errors.push(ConfigError::SplitThresholdNotPositive(self.split_threshold.to_f64_approx()));
+        }
+        if self.d_create < 1 {
+            errors.push(ConfigError::DCreateZero);
+        }
+        if self.d_evict <= self.d_create {
+            errors.push(ConfigError::DEvictNotGreaterThanDCreate {
+                d_create: self.d_create,
+                d_evict: self.d_evict,
+            });
+        }
+        if self.budget == 0 {
+            errors.push(ConfigError::BudgetZero);
+        }
+        // Mudlark's headroom requirement: budget must exceed
+        // max(3^(buffer+1), 2*(d_create-1)) where buffer = d_evict - d_create.
+        // Only check when d_evict > d_create (otherwise the earlier check fails).
+        if self.budget > 0 && self.d_evict > self.d_create {
+            let buffer = self.d_evict - self.d_create;
+            let headroom = 3usize.pow(buffer + 1);
+            let convergence = 2 * (self.d_create as usize).saturating_sub(1);
+            let required = headroom.max(convergence);
+            if self.budget <= required {
+                errors.push(ConfigError::BudgetTooSmall {
+                    budget: self.budget,
+                    required_minimum: required,
+                });
+            }
+        }
+
+        // ── Noise injection fields (§ALGO S-13.5, §ALGO S-18.2) ──
+        if !self.noise_schedule.is_disabled() && self.noise_batch_size == 0 {
+            errors.push(ConfigError::NoiseBatchSizeZero);
+        }
+        match &self.noise_schedule {
+            NoiseSchedule::Geometric { root, decay, min } => {
+                if *decay <= 0.0 || *decay > 1.0 || decay.is_nan() {
+                    errors.push(ConfigError::NoiseScheduleDecayOutOfRange(*decay));
+                }
+                if *root == 0 && *min > 0 {
+                    errors.push(ConfigError::NoiseScheduleRootZero);
+                }
+            }
+            NoiseSchedule::Explicit(_) => { /* any values are valid */ }
+        }
+
+        if errors.is_empty() {
+            Ok(())
+        } else {
+            Err(ConfigErrors(errors))
+        }
+    }
+
+    /// Return non-fatal diagnostics for parameter combinations that are
+    /// technically valid but empirically known to produce poor results.
+    ///
+    /// Call after [`validate`](Self::validate) succeeds. The returned
+    /// warnings are advisory — the sentinel will still function, but
+    /// warm-up may be insufficient and early scores unreliable.
+    #[must_use]
+    pub fn warnings(&self) -> Vec<ConfigWarning> {
+        let mut warnings = Vec::new();
+
+        // §ALGO S-A.7: cross-check noise schedule root vs forgetting factor.
+        // Thresholds derived from empirical convergence data (Appendix A).
+        let recommended_root = if self.forgetting_factor >= 0.99 {
+            450
+        } else if self.forgetting_factor >= 0.95 {
+            if self.noise_batch_size <= 4 { 200 } else { 50 }
+        } else {
+            0 // no recommendation for very low λ
+        };
+
+        if recommended_root > 0 {
+            let actual_root = self.noise_schedule.rounds_for_depth(0);
+            if actual_root < recommended_root {
+                warnings.push(ConfigWarning::NoiseScheduleInsufficient {
+                    root: actual_root,
+                    recommended_root,
+                    lambda: self.forgetting_factor,
+                });
+            }
+        }
+
+        warnings
+    }
+}
diff --git a/packages/sentinel/src/ewma.rs b/packages/sentinel/src/ewma.rs
new file mode 100644
index 000000000..b120421b5
--- /dev/null
+++ b/packages/sentinel/src/ewma.rs
@@ -0,0 +1,225 @@
+//! Exponentially-weighted moving average (EWMA) statistics.
+//!
+//! Tracks a running mean and variance that exponentially decay old
+//! observations. Used by the subspace tracker to maintain baselines
+//! of "normal" anomaly scores.
+//!
+//! Outlier-resistant: observations beyond `clip_sigmas`·σ from the
+//! current mean are rejected before updating, preventing an attacker
+//! from poisoning the baseline with a single burst.
+//!
+//! No `faer` dependency — this is pure `f64` arithmetic.
+
+use crate::report::BaselineSnapshot;
+
+/// Exponentially-weighted running mean and variance.
+///
+/// After `update()` with a batch of values, the baseline reflects
+/// a smoothed estimate of the central tendency and spread, biased
+/// toward recent observations by the decay factor `λ`.
+#[derive(Debug, Clone)]
+pub struct EwmaStats {
+    /// Decay factor (λ). Each old value's contribution shrinks by
+    /// this factor per update. Higher = longer memory.
+    decay: f64,
+
+    /// Running weighted mean.
+    mean: f64,
+
+    /// Running weighted variance.
+    variance: f64,
+
+    /// Whether at least one real update has occurred.
+    warm: bool,
+}
+
+impl EwmaStats {
+    /// Create a new EWMA tracker with the given decay factor.
+    ///
+    /// Starts "cold" with `mean = 1.0`, `variance = 1.0` — deliberately
+    /// wide to avoid extreme z-scores before the first real data arrives.
+    #[must_use]
+    pub const fn new(decay: f64) -> Self {
+        Self {
+            decay,
+            mean: 1.0,
+            variance: 1.0,
+            warm: false,
+        }
+    }
+
+    /// Current mean.
+    #[must_use]
+    pub const fn mean(&self) -> f64 {
+        self.mean
+    }
+
+    /// Current variance.
+    #[must_use]
+    pub const fn variance(&self) -> f64 {
+        self.variance
+    }
+
+    /// Whether the baseline has seen at least one update.
+    #[must_use]
+    pub const fn is_warm(&self) -> bool {
+        self.warm
+    }
+
+    /// Return to the cold state — as if freshly constructed.
+    ///
+    /// Restores the placeholder mean and variance and marks the
+    /// tracker as cold.  The next [`update`](Self::update) will
+    /// enter the cold→warm initialisation path.
+    pub const fn reset_cold(&mut self) {
+        self.mean = 1.0;
+        self.variance = 1.0;
+        self.warm = false;
+    }
+
+    /// Snapshot of the current baseline for inclusion in reports.
+    #[must_use]
+    pub const fn snapshot(&self) -> BaselineSnapshot {
+        BaselineSnapshot {
+            mean: self.mean,
+            variance: self.variance,
+        }
+    }
+
+    /// Seed this EWMA's mean and variance from another EWMA.
+    ///
+    /// Used to close the fast-slow gap after noise injection
+    /// (ADR-S-013 §6b, Option C): the slow EWMA in the CUSUM
+    /// accumulator is seeded from the fast EWMA's converged
+    /// baseline so the two start in agreement.
+    ///
+    /// Marks the receiver as warm if the source is warm.
+    pub const fn seed_from(&mut self, source: &Self) {
+        self.mean = source.mean;
+        self.variance = source.variance;
+        if source.warm {
+            self.warm = true;
+        }
+    }
+
+    /// Compute the z-score of a value against the current baseline.
+    ///
+    /// Returns `(value - mean) / sqrt(variance + eps)`.
+    ///
+    /// The caller supplies `eps` (typically
+    /// [`SentinelConfig::eps`](crate::config::SentinelConfig::eps))
+    /// so that every component of the sentinel shares a single
+    /// stability constant.
+    #[must_use]
+    pub fn z_score(&self, value: f64, eps: f64) -> f64 {
+        (value - self.mean) / (self.variance.sqrt() + eps)
+    }
+
+    /// Compute the upper-tail clip ceiling: `mean + clip_sigmas · √variance`.
+    ///
+    /// Returns `f64::INFINITY` when the baseline is cold (no meaningful
+    /// ceiling can be defined — matches the cold-path bypass in `update()`).
+    #[must_use]
+    pub fn ceiling(&self, clip_sigmas: f64) -> f64 {
+        if self.warm {
+            clip_sigmas.mul_add(self.variance.sqrt(), self.mean)
+        } else {
+            f64::INFINITY
+        }
+    }
+
+    /// Update the baseline with pre-filtered values.
+    ///
+    /// The caller is responsible for outlier rejection.  This method
+    /// unconditionally incorporates all values (including the cold→warm
+    /// path).  Used by the baseline pipeline (§ALGO S-6.1.1) where
+    /// clipping is externalised to `update_axis()`.
+    pub fn update_raw(&mut self, normals: &[f64]) {
+        if normals.is_empty() {
+            return;
+        }
+
+        #[allow(clippy::cast_precision_loss)]
+        let new_mean = normals.iter().sum::<f64>() / normals.len() as f64;
+
+        if !self.warm {
+            self.mean = new_mean;
+            if normals.len() > 1 {
+                let var = mean_squared_deviation(normals, new_mean);
+                self.variance = var.max(1e-4);
+            }
+            self.warm = true;
+            return;
+        }
+
+        let alpha = 1.0 - self.decay;
+        self.mean = self.decay.mul_add(self.mean, alpha * new_mean);
+
+        if normals.len() > 1 {
+            let var = mean_squared_deviation(normals, new_mean).max(1e-4);
+            self.variance = self.decay.mul_add(self.variance, alpha * var);
+        }
+    }
+
+    /// Update the baseline with a batch of new values.
+    ///
+    /// Values beyond `mean + clip_sigmas·√variance` are rejected
+    /// (outlier resistance). If all values are outliers, the baseline
+    /// is unchanged.
+    ///
+    /// Only the **upper** tail is clipped. Anomaly scores are
+    /// non-negative and right-skewed — an attacker inflates them,
+    /// never deflates them. A lower-tail bound would wrongly reject
+    /// legitimate low scores during quiet periods.
+    ///
+    /// The filter is **skipped entirely on the first update** (while
+    /// the tracker is still cold). The initial `mean = 1.0` /
+    /// `variance = 1.0` are placeholders, not a real baseline — you
+    /// can't define "outlier" without one.
+    pub fn update(&mut self, values: &[f64], clip_sigmas: f64) {
+        if values.is_empty() {
+            return;
+        }
+
+        // When cold, accept everything — no real baseline to filter against.
+        // Upper-tail only — see docs/algorithm.md §7.1.1: anomaly scores are right-skewed.
+        let normals: Vec<f64> = if self.warm {
+            let ceiling = clip_sigmas.mul_add(self.variance.sqrt(), self.mean);
+            let filtered: Vec<f64> = values.iter().copied().filter(|&v| v < ceiling).collect();
+            if filtered.is_empty() {
+                return; // all outliers — learn nothing
+            }
+            filtered
+        } else {
+            values.to_vec()
+        };
+
+        #[allow(clippy::cast_precision_loss)] // batch len ≪ 2^52
+        let new_mean = normals.iter().sum::<f64>() / normals.len() as f64;
+
+        if !self.warm {
+            self.mean = new_mean;
+            if normals.len() > 1 {
+                let var = mean_squared_deviation(&normals, new_mean);
+                self.variance = var.max(1e-4);
+            }
+            self.warm = true;
+            return;
+        }
+
+        let alpha = 1.0 - self.decay;
+        self.mean = self.decay.mul_add(self.mean, alpha * new_mean);
+
+        if normals.len() > 1 {
+            let var = mean_squared_deviation(&normals, new_mean).max(1e-4);
+            self.variance = self.decay.mul_add(self.variance, alpha * var);
+        }
+    }
+}
+
+/// Mean squared deviation from the given mean (÷N, no Bessel's correction).
+fn mean_squared_deviation(values: &[f64], mean: f64) -> f64 {
+    #[allow(clippy::cast_precision_loss)] // batch len ≪ 2^52
+    let n = values.len() as f64;
+    values.iter().map(|v| (v - mean).powi(2)).sum::<f64>() / n
+}
diff --git a/packages/sentinel/src/lib.rs b/packages/sentinel/src/lib.rs
new file mode 100644
index 000000000..6ab5711f9
--- /dev/null
+++ b/packages/sentinel/src/lib.rs
@@ -0,0 +1,174 @@
+#![forbid(unsafe_code)]
+
+//! # Spectral Sentinel
+//!
+//! Hierarchical online subspace anomaly detection for positionally
+//! structured observation streams.
+//!
+//! Spectral Sentinel combines Mudlark's adaptive spatial index with a layer
+//! of low-rank statistical trackers. Mudlark ranks spatial entries by
+//! observation volume; Spectral Sentinel selects significant V-Tree entries,
+//! closes them under G-tree ancestry, and scores incoming batches against
+//! learned subspace models for that selected structure.
+//!
+//! The core engine, [`SpectralSentinel`], is generic over the coordinate
+//! type `C`, accumulator type `V`, and bit-width `N`, mirroring Mudlark's
+//! `GvGraph<C, V, N>` triple. Each cell tracker analyses the suffix bits
+//! `[d, N)` at width `w = N - d`, where `d` is the cell's G-tree depth.
+//!
+//! The crate provides two convenience aliases:
+//!
+//! - [`Sentinel128`] — `SpectralSentinel<u128, u64, 128>`, the default
+//!   for IPv6-class domains.
+//! - [`Sentinel64`] — `SpectralSentinel<u64, u64, 64>`, for 64-bit
+//!   domains.
+//!
+//! Input values must have **hierarchical positional structure**: leading
+//! bits define coarse groupings and successive bits refine them. IPv6-like
+//! address spaces are a natural fit. Pseudo-random identifiers, hashes,
+//! UUIDs, and nonces have no meaningful suffix structure for the model to
+//! learn, so Spectral Sentinel will still process them but the measurements
+//! will not be useful.
+//!
+//! # Architecture
+//!
+//! Spectral Sentinel has three cooperating layers:
+//!
+//! - **Spatial substrate** — a [`torrust_mudlark::GvGraph`] tracks volume
+//!   over the coordinate domain and ranks spatial entries by accumulated
+//!   observation volume.
+//! - **Analysis selector** — [`AnalysisSet`] chooses significant V-Tree
+//!   entries, then closes them under G-tree ancestry so every selected cell
+//!   has a complete model chain back to the root.
+//! - **Analysis engine** — per-cell subspace trackers score four raw axes:
+//!   novelty, displacement, surprise, and coherence. A second coordination
+//!   tier models cross-cell score patterns.
+//!
+//! **Feed-forward invariant.** Spectral Sentinel always observes the spatial
+//! layer with `Delta = 1` per input value. Anomaly scores never flow back
+//! into Mudlark's importance accounting. Spatial adaptation is driven by
+//! observation volume only; the host controls temporal policy through
+//! explicit decay operations.
+//!
+//! # Design Principle
+//!
+//! **Spectral Sentinel measures; the host decides.**
+//!
+//! Public report types carry raw statistical measurements: scores,
+//! baselines, drift accumulators, maturity, geometry, structural summaries,
+//! and health snapshots. They do not contain threat levels, recommended
+//! actions, or policy decisions. The consuming application interprets the
+//! measurements in its own domain.
+//!
+//! # Three-Surface Visibility Model
+//!
+//! Every public symbol belongs to one of three surfaces. Modules remain
+//! crate-private and public types are re-exported flat from the crate root,
+//! giving downstream users one canonical import path.
+//!
+//! - **Surface 1 — Readouts:** Lightweight data users inspect after an
+//!   observation cycle: [`BatchReport`], [`CellReport`],
+//!   [`CoordinationReport`], score distributions, baseline snapshots,
+//!   structural and health summaries, [`AnalysisSet`], [`AnalysisEntry`],
+//!   [`CentredBits`], and related report records.
+//! - **Surface 2 — Engine:** Opaque operational API users configure and
+//!   drive: [`SpectralSentinel`], [`SentinelConfig`], [`NoiseSchedule`],
+//!   [`SvdStrategy`], [`Sentinel128`], [`Sentinel64`],
+//!   [`CentredBitSource`], and [`GNodeId`] for subtree decay calls.
+//! - **Surface 3 — Internals:** EWMA state, SVD update plumbing, tracker
+//!   machinery, staging, CUSUM, and warming-thread implementation details.
+//!
+//! Exception: a few support types are re-exported as `#[doc(hidden)]` for
+//! tests, diagnostics, and benchmarks. They are not part of the ordinary
+//! downstream API surface.
+
+// -- Surface 1 — Readouts (data users hold and inspect) -----------
+//
+// Modules are private; types are re-exported flat from the crate root
+// so there is exactly one canonical path per public type.
+pub(crate) mod analysis_set;
+pub(crate) mod observation;
+pub(crate) mod report;
+
+// -- Surface 2 — Engine (opaque operational API) ------------------
+pub(crate) mod config;
+pub(crate) mod sentinel;
+
+// -- Surface 3 — Internals (implementation machinery) -------------
+//
+// Crate-private. Downstream crates cannot import from these modules;
+// all external access goes through the flat re-exports below and the
+// public methods on SpectralSentinel.
+pub(crate) mod ewma;
+pub(crate) mod maths;
+
+// README doc-tests: compile and run every code block in the README
+// as part of `cargo test --doc`.
+#[cfg(doctest)]
+#[doc = include_str!("../README.md")]
+mod _readme {}
+
+#[cfg(test)]
+mod tests;
+
+// -- Public re-exports --------------------------------------------
+//
+// One canonical path per public type. Downstream code should use
+// `torrust_sentinel::{SpectralSentinel, SentinelConfig, BatchReport, ...}`
+// rather than reaching into submodules.
+//
+// Surface 1 — Readouts:
+//   analysis_set: AnalysisEntry, AnalysisSet
+//   observation:  CentredBits
+//   report:       BatchReport, CellReport, CoordinationReport,
+//                 AnomalyScores, ScoreDistribution, baseline/drift,
+//                 maturity, geometry, contour, health, and summaries
+//
+// Surface 2 — Engine:
+//   config:       ConfigError, ConfigErrors, ConfigWarning,
+//                 NoiseSchedule, SentinelConfig
+//   maths:        SvdStrategy
+//   observation:  CentredBitSource
+//   sentinel:     SpectralSentinel
+//   aliases:      Sentinel128, Sentinel64
+//   mudlark:      GNodeId for subtree-oriented operations
+pub use analysis_set::{AnalysisEntry, AnalysisSet};
+pub use config::{ConfigError, ConfigErrors, ConfigWarning, NoiseSchedule, SentinelConfig};
+#[doc(hidden)]
+pub use ewma::EwmaStats;
+#[doc(hidden)]
+pub use maths::SubspaceUpdate;
+pub use maths::SvdStrategy;
+pub use observation::{CentredBitSource, CentredBits};
+#[doc(hidden)]
+pub use report::TrackerReport;
+pub use report::{
+    AnalysisSetSummary, AnomalyScores, AxisBaselineSnapshots, BaselineSnapshot, BatchReport, CellInspection, CellReport,
+    ClipPressureDistribution, ContourSnapshot, CoordinationHealth, CoordinationReport, CusumSnapshot, GeometryDistribution,
+    HealthReport, MaturityDistribution, MemberScore, RankDistribution, SampleScore, ScoreDistribution, ScoringGeometry,
+    TrackerMaturity,
+};
+pub use sentinel::SpectralSentinel;
+// Re-export the G-V Graph node handle for use with `decay_subtree()`.
+pub use torrust_mudlark::GNodeId;
+
+/// 128-bit sentinel: full `u128` domain, `u64` counts, 128-bit width.
+pub type Sentinel128 = SpectralSentinel<u128, u64, 128>;
+
+/// 64-bit sentinel: `u64` domain, `u64` counts, 64-bit width.
+pub type Sentinel64 = SpectralSentinel<u64, u64, 64>;
+
+/// Minimum suffix width for a functional subspace tracker.
+///
+/// At `dim < MIN_TRACKER_DIM`, the tracker cannot form a
+/// meaningful basis or compute residuals. Cells at or below
+/// this threshold are excluded from the analysis set.
+///
+/// The value 2 ensures at least one residual degree of freedom
+/// ($d - k \geq 1$ when $k = 1$). A tracker with `dim = 1` can
+/// technically run but produces identically-zero residuals (novelty)
+/// since the single basis vector spans the entire space — making it
+/// statistically useless.
+///
+/// See ADR-S-011 for rationale.
+pub(crate) const MIN_TRACKER_DIM: usize = 2;
diff --git a/packages/sentinel/src/maths/bench_tracing.rs b/packages/sentinel/src/maths/bench_tracing.rs
new file mode 100644
index 000000000..72f712078
--- /dev/null
+++ b/packages/sentinel/src/maths/bench_tracing.rs
@@ -0,0 +1,147 @@
+//! Lightweight tracing layer that accumulates per-span-name wall-clock
+//! durations.  Used by convergence benchmarks to capture the cost of
+//! each SVD strategy without `Instant`-based plumbing in the hot path.
+//!
+//! # Usage
+//!
+//! ```ignore
+//! let timing = SpanTiming::install();
+//! // ... run observe() calls ...
+//! let naive_ns = timing.total_ns("svd_naive");
+//! let brand_ns = timing.total_ns("svd_brand");
+//! ```
+//!
+//! The layer stores enter/exit timestamps per span instance in an
+//! `RwLock<HashMap>` and aggregates into cumulative nanoseconds on
+//! close.  It is designed for single-threaded benchmark use — the
+//! `RwLock` is uncontended.
+//!
+//! # §-references
+//!
+//! - ADR-S-016 — Brand's incremental SVD
+//! - ADR-M-028 — Span-native tracing
+
+use std::collections::HashMap;
+use std::sync::{Arc, RwLock};
+use std::time::Instant;
+
+use tracing::{Subscriber, span};
+use tracing_subscriber::Layer;
+use tracing_subscriber::layer::Context;
+use tracing_subscriber::prelude::*;
+use tracing_subscriber::registry::LookupSpan;
+
+// ════════════════════════════════════════════════════════════
+//  Per-span storage (attached via Extensions)
+// ════════════════════════════════════════════════════════════
+
+struct SpanEnterTime(Instant);
+
+// ════════════════════════════════════════════════════════════
+//  Accumulated timing map
+// ════════════════════════════════════════════════════════════
+
+/// Shared timing accumulator.
+///
+/// Keys are span names (e.g. `"svd_naive"`, `"svd_brand"`,
+/// `"phase2_evolve_subspace"`).  Values are cumulative nanoseconds
+/// spent inside spans of that name, and the call count.
+#[derive(Debug, Clone, Default)]
+pub struct SpanTiming {
+    inner: Arc<RwLock<TimingMap>>,
+}
+
+#[derive(Debug, Default)]
+struct TimingMap {
+    entries: HashMap<&'static str, TimingEntry>,
+}
+
+#[derive(Debug, Default, Clone, Copy)]
+struct TimingEntry {
+    total_ns: u128,
+    count: u64,
+}
+
+impl SpanTiming {
+    /// Install a new timing layer as the thread-local default subscriber
+    /// and return the handle for reading accumulated durations.
+    ///
+    /// The returned `DefaultGuard` must be kept alive for the duration
+    /// of the measurement.  Dropping it uninstalls the subscriber.
+    pub fn install() -> (Self, tracing::subscriber::DefaultGuard) {
+        let timing = Self::default();
+        let layer = SpanTimingLayer {
+            timing: timing.inner.clone(),
+        };
+        // Respect RUST_LOG, defaulting to INFO.  This means
+        // `tracing::enabled!(Level::DEBUG)` is false unless the
+        // user sets `RUST_LOG=debug` — matching the mudlark
+        // convention and preventing the oracle from firing
+        // accidentally in release-mode benchmarks.
+        let env_filter = tracing_subscriber::EnvFilter::builder()
+            .with_default_directive(tracing::Level::INFO.into())
+            .from_env_lossy();
+        let subscriber = tracing_subscriber::registry().with(env_filter).with(layer);
+        let guard = tracing::subscriber::set_default(subscriber);
+        (timing, guard)
+    }
+
+    /// Cumulative nanoseconds spent inside spans named `name`.
+    pub fn total_ns(&self, name: &str) -> u128 {
+        self.inner.read().unwrap().entries.get(name).map_or(0, |e| e.total_ns)
+    }
+
+    /// Number of times a span named `name` was entered.
+    #[allow(dead_code)]
+    pub fn call_count(&self, name: &str) -> u64 {
+        self.inner.read().unwrap().entries.get(name).map_or(0, |e| e.count)
+    }
+
+    /// Reset all accumulated timing.
+    pub fn reset(&self) {
+        self.inner.write().unwrap().entries.clear();
+    }
+}
+
+// ════════════════════════════════════════════════════════════
+//  Tracing Layer implementation
+// ════════════════════════════════════════════════════════════
+
+struct SpanTimingLayer {
+    timing: Arc<RwLock<TimingMap>>,
+}
+
+impl<S> Layer<S> for SpanTimingLayer
+where
+    S: Subscriber + for<'a> LookupSpan<'a>,
+{
+    fn on_enter(&self, id: &span::Id, ctx: Context<'_, S>) {
+        if let Some(span) = ctx.span(id) {
+            let mut extensions = span.extensions_mut();
+            extensions.insert(SpanEnterTime(Instant::now()));
+        }
+    }
+
+    // The RwLockWriteGuard (`map`) must live as long as `entry` borrows it;
+    // there is no earlier drop point.
+    #[allow(clippy::significant_drop_tightening)]
+    fn on_exit(&self, id: &span::Id, ctx: Context<'_, S>) {
+        if let Some(span) = ctx.span(id) {
+            let elapsed = {
+                let extensions = span.extensions();
+                let ns = extensions.get::<SpanEnterTime>().map(|t| t.0.elapsed().as_nanos());
+                drop(extensions); // release read-lock before acquiring write-lock
+                ns
+            };
+            if let Some(ns) = elapsed {
+                let name = span.name();
+                {
+                    let mut map = self.timing.write().unwrap();
+                    let entry = map.entries.entry(name).or_default();
+                    entry.total_ns += ns;
+                    entry.count += 1;
+                }
+            }
+        }
+    }
+}
diff --git a/packages/sentinel/src/maths/brand_svd.rs b/packages/sentinel/src/maths/brand_svd.rs
new file mode 100644
index 000000000..519314402
--- /dev/null
+++ b/packages/sentinel/src/maths/brand_svd.rs
@@ -0,0 +1,218 @@
+//! Brand's incremental SVD for subspace evolution (ADR-S-016).
+//!
+//! Instead of SVD-ing the full d × (k+b) composite matrix, this
+//! projects new data onto the current basis, QR-orthogonalises
+//! the residual, and SVDs a small (k+b) × (k+b) kernel matrix.
+//!
+//! The algorithm (Brand 2006) is:
+//!
+//! 1. P  = `U_k`ᵀ Xᵀ = Zᵀ              (k × b)    — reuse Phase 1
+//! 2. Q  = Xᵀ − `U_k` P = residualᵀ    (d × b)    — reuse Phase 1
+//! 3. Q⊥ R⊥ = `thin_qr(Q)`             (d × b), (b × b)
+//! 4. K  = \[√λ·diag(σ), P; 0, R⊥\]    ((k+b) × (k+b))
+//! 5. Û, σ̂, _ = `thin_svd(K)`          small SVD!
+//! 6. `U_new` = \[`U_k` | Q⊥\] · Û\[:, :n\]  back-transform
+//!
+//! Cost: O((k+b)³ + d·b²) vs O(d·(k+b)²) for the naïve approach.
+//! The win comes from the SVD kernel shrinking from d rows to (k+b).
+//!
+//! # §-references
+//!
+//! - §ALGO S-4.2 Phase 2 — Subspace evolution
+//! - ADR-S-016 — Brand's incremental SVD
+
+use faer::Mat;
+
+use super::SubspaceUpdate;
+
+/// Evolve the subspace via Brand's incremental SVD.
+///
+/// # Arguments
+///
+/// * `current_basis` — `U_k`, shape `(d, cap)`. Only columns `[:k]` active.
+/// * `sigmas` — singular values, length `cap`. Only `[:k]` meaningful.
+/// * `z` — latent projection `X · U_k`, shape `(b, k)`.
+/// * `residual` — reconstruction residual `X − X̂`, shape `(b, d)`.
+/// * `sqrt_lambda` — `√λ`.
+/// * `k` — current active rank.
+/// * `cap` — hard ceiling on rank.
+// Linear algebra code — single-char names follow standard mathematical notation
+// (d=dimension, b=batch, k=rank, c=kernel dim, n=output rank).
+#[allow(clippy::many_single_char_names)]
+#[must_use]
+pub fn evolve(
+    current_basis: &Mat<f64>,
+    sigmas: &[f64],
+    z: &Mat<f64>,
+    residual: &Mat<f64>,
+    sqrt_lambda: f64,
+    k: usize,
+    cap: usize,
+) -> Option<SubspaceUpdate> {
+    let d = current_basis.nrows();
+    let b = residual.nrows();
+    let c = k + b; // kernel dimension
+
+    // Guard: Brand's algorithm builds a (c × c) kernel and back-
+    // transforms through [U_k | Q⊥].  This only provides a real
+    // advantage when c is substantially smaller than d.
+    //
+    //  • c > d — The thin QR of residual^T (d × b) produces R⊥
+    //    that is *not* square (ℝ^(d×b) rather than ℝ^(b×b)),
+    //    causing index-out-of-bounds in the kernel construction.
+    //
+    //  • c = d — The kernel SVD is the same size as a full dense
+    //    SVD but adds extra QR + back-transform roundoff.
+    //
+    //  • c + 1 = d — At tiny d (e.g. coordination tier d=4, c=3)
+    //    the extra roundoff produces large basis errors (observed:
+    //    54° divergence with well-separated σ after many steps).
+    //
+    // Require at least 2 spare dimensions (d − c ≥ 2) so the QR
+    // residual has room for stable orthogonalisation.  Fall back
+    // to the naïve path otherwise.
+    if c + 2 > d {
+        return None;
+    }
+
+    // ── Step 1–2: P = Z^T, Q = residual^T ──────────────
+    // Both are already computed in Phase 1 of observe().
+    // P = Z^T is (k × b), Q = residual^T is (d × b).
+
+    // ── Step 3: Thin QR of Q = residual^T ───────────────
+    // Q = residual^T ∈ ℝ^(d × b).
+    // QR gives Q⊥ ∈ ℝ^(d × b) (orthonormal) and R⊥ ∈ ℝ^(b × b) (upper triangular).
+    let mut q_t = Mat::zeros(d, b);
+    for i in 0..b {
+        for j in 0..d {
+            q_t[(j, i)] = residual[(i, j)];
+        }
+    }
+
+    let qr = q_t.as_ref().qr();
+    let q_perp = qr.compute_thin_Q(); // (d × b)
+    let r_perp = qr.thin_R(); // (b × b)  — upper triangular
+
+    // ── Step 4: Build kernel K ∈ ℝ^(c × c) ─────────────
+    //
+    //     K = [ √λ · diag(σ₁..ₖ)   P  ]
+    //         [       0             R⊥  ]
+    //
+    // where P = Z^T ∈ ℝ^(k × b).
+    let mut kernel = Mat::zeros(c, c);
+
+    // Top-left: √λ · diag(σ[:k])
+    for j in 0..k {
+        kernel[(j, j)] = sqrt_lambda * sigmas[j];
+    }
+
+    // Top-right: P = Z^T  (z is b×k, we need P = k×b)
+    for i in 0..k {
+        for j in 0..b {
+            kernel[(i, k + j)] = z[(j, i)];
+        }
+    }
+
+    // Bottom-right: R⊥
+    for i in 0..b {
+        for j in 0..b {
+            kernel[(k + i, k + j)] = r_perp[(i, j)];
+        }
+    }
+
+    // ── Step 5: Small SVD of kernel ─────────────────────
+    let svd = kernel.thin_svd().ok()?;
+
+    let n = c.min(d).min(cap);
+    let u_hat = svd.U(); // (c × c), we use columns [:n]
+    let s_hat = svd.S().column_vector();
+
+    // ── Step 6: Back-transform U_new = [U_k | Q⊥] · Û[:, :n]
+    //
+    // [U_k | Q⊥] is (d × c), Û[:, :n] is (c × n).
+    // Compute column-by-column to avoid materialising the (d × c) join.
+    let mut basis = Mat::zeros(d, n);
+
+    for col in 0..n {
+        for row in 0..d {
+            let mut val = 0.0;
+            // U_k block: columns 0..k of [U_k | Q⊥], rows of Û: 0..k
+            for j in 0..k {
+                val = current_basis[(row, j)].mul_add(u_hat[(j, col)], val);
+            }
+            // Q⊥ block: columns k..c of [U_k | Q⊥], rows of Û: k..c
+            for j in 0..b {
+                val = q_perp[(row, j)].mul_add(u_hat[(k + j, col)], val);
+            }
+            basis[(row, col)] = val;
+        }
+    }
+
+    // ── Step 7: Re-orthogonalise the output basis ───────
+    //
+    // Brand's incremental SVD accumulates orthogonality loss
+    // because the input basis U_k is itself the output of a
+    // previous back-transform step.  Over many iterations the
+    // columns of [U_k | Q⊥] drift from exact orthonormality,
+    // and the back-transform U_new = [U_k | Q⊥] · Û inherits
+    // that error.  Without correction, the accumulated
+    // perturbation eventually violates the Wedin bound for
+    // moderate spectral gaps (empirically observed at ~15% gap
+    // after hundreds of updates).
+    //
+    // Fix: QR-factorise the output basis and absorb the small
+    // R factor into the singular values via a corrective SVD:
+    //
+    //   basis = Q · R            (thin QR, R is n × n, ≈ I)
+    //   R · diag(σ̂) = Uc · Σc · Vc^T   (small SVD)
+    //   corrected basis  = Q · Uc
+    //   corrected sigmas = diag(Σc)
+    //
+    // Cost: O(d · n²) for the QR + O(n³) for the small SVD,
+    // negligible compared to the existing O(d · b²) QR in step 3.
+    let qr_correction = basis.as_ref().qr();
+    let q_out = qr_correction.compute_thin_Q(); // (d × n)
+    let r_out = qr_correction.thin_R(); // (n × n)
+
+    // Form M = R · diag(σ̂), an n × n matrix.
+    let mut m_corr = Mat::zeros(n, n);
+    for i in 0..n {
+        for j in 0..n {
+            m_corr[(i, j)] = r_out[(i, j)] * s_hat[j];
+        }
+    }
+
+    // SVD of the small corrective matrix.
+    let Some(corr_svd) = m_corr.thin_svd().ok() else {
+        // Fallback: skip re-orthogonalisation if the tiny SVD
+        // fails (should never happen for well-conditioned n × n).
+        let out_sigmas: Vec<f64> = (0..n).map(|i| s_hat[i]).collect();
+        return Some(SubspaceUpdate {
+            basis,
+            sigmas: out_sigmas,
+            n,
+        });
+    };
+    let u_corr = corr_svd.U(); // (n × n)
+    let s_corr = corr_svd.S().column_vector();
+
+    // Final basis = Q_out · U_corr, final sigmas = diag(S_corr).
+    let mut final_basis = Mat::zeros(d, n);
+    for col in 0..n {
+        for row in 0..d {
+            let mut val = 0.0;
+            for j in 0..n {
+                val = q_out[(row, j)].mul_add(u_corr[(j, col)], val);
+            }
+            final_basis[(row, col)] = val;
+        }
+    }
+
+    let out_sigmas: Vec<f64> = (0..n).map(|i| s_corr[i]).collect();
+
+    Some(SubspaceUpdate {
+        basis: final_basis,
+        sigmas: out_sigmas,
+        n,
+    })
+}
diff --git a/packages/sentinel/src/maths/mod.rs b/packages/sentinel/src/maths/mod.rs
new file mode 100644
index 000000000..b4b648f7a
--- /dev/null
+++ b/packages/sentinel/src/maths/mod.rs
@@ -0,0 +1,478 @@
+//! Linear algebra building blocks for the subspace tracker.
+//!
+//! This module isolates the SVD-based subspace evolution from the
+//! rest of the tracker so that:
+//!
+//! 1. The algorithm can be swapped at runtime between the naïve
+//!    dense thin SVD and Brand's incremental SVD (ADR-S-016).
+//! 2. In debug builds (or with a `DEBUG`-level tracing subscriber),
+//!    **both** algorithms run and their outputs are compared via
+//!    `assert!` — a continuous oracle test.
+//! 3. The maths is independently unit-testable against known
+//!    matrix identities.
+//!
+//! # §-references
+//!
+//! - §ALGO S-4.2 Phase 2 — Subspace evolution
+//! - ADR-S-016 — Brand's incremental SVD
+
+pub mod brand_svd;
+pub mod naive_svd;
+
+#[cfg(test)]
+mod tests;
+
+use std::cell::Cell;
+
+use faer::Mat;
+
+// Re-export so benchmarks can reference the timing helper.
+#[cfg(test)]
+pub mod bench_tracing;
+
+// ════════════════════════════════════════════════════════════
+//  Strategy enum
+// ════════════════════════════════════════════════════════════
+
+/// Which SVD algorithm to use for subspace evolution (§ALGO S-4.2 Phase 2).
+///
+/// Selectable at runtime via [`SentinelConfig::svd_strategy`](crate::SentinelConfig).
+#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+pub enum SvdStrategy {
+    /// Dense thin SVD of the full composite matrix M ∈ ℝ^(d × (k+b)).
+    ///
+    /// Correct and simple but O(d·(k+b)²) per call. This is the
+    /// original implementation.
+    Naive,
+
+    /// Brand's incremental SVD (Brand 2006).
+    ///
+    /// Projects onto the current basis, QR-orthogonalises the residual,
+    /// and SVDs a small (k+b)×(k+b) kernel. O((k+b)³ + d·b²) per call.
+    #[default]
+    Brand,
+}
+
+// ════════════════════════════════════════════════════════════
+//  Result type
+// ════════════════════════════════════════════════════════════
+
+/// Output of a subspace evolution step.
+///
+/// Contains the updated basis vectors and singular values, ready
+/// to be written back into the tracker's state.
+#[derive(Debug, Clone)]
+pub struct SubspaceUpdate {
+    /// Updated orthonormal basis, shape `(dim, n)` where
+    /// `n = min(k+b, d, cap)`.
+    pub basis: Mat<f64>,
+
+    /// Updated singular values, length `n`.
+    pub sigmas: Vec<f64>,
+
+    /// How many components are meaningful (`n`).
+    pub n: usize,
+}
+
+// ════════════════════════════════════════════════════════════
+//  Dispatch
+// ════════════════════════════════════════════════════════════
+
+thread_local! {
+    /// Alternates execution order in the oracle so that neither
+    /// strategy consistently benefits from warmed caches or branch
+    /// predictors.  Toggled on every oracle-active call.
+    static ORACLE_FLIP: Cell<bool> = const { Cell::new(false) };
+}
+
+/// Run one SVD strategy under a named tracing span.
+#[allow(clippy::too_many_arguments)]
+fn run_svd(
+    which: SvdStrategy,
+    current_basis: &Mat<f64>,
+    sigmas: &[f64],
+    z: &Mat<f64>,
+    residual: &Mat<f64>,
+    sqrt_lambda: f64,
+    k: usize,
+    cap: usize,
+) -> Option<SubspaceUpdate> {
+    match which {
+        SvdStrategy::Naive => {
+            let _span = tracing::info_span!("svd_naive").entered();
+            naive_svd::evolve(current_basis, sigmas, z, residual, sqrt_lambda, k, cap)
+        }
+        SvdStrategy::Brand => {
+            let _span = tracing::info_span!("svd_brand").entered();
+            brand_svd::evolve(current_basis, sigmas, z, residual, sqrt_lambda, k, cap)
+        }
+    }
+}
+
+/// Run subspace evolution using the selected strategy (§ALGO S-4.2 Phase 2).
+///
+/// In **debug builds** (or when a `DEBUG`-level tracing subscriber is
+/// attached), both strategies are executed and compared element-wise —
+/// a continuous oracle test.  Execution order alternates on each call
+/// via a thread-local toggle so neither strategy consistently benefits
+/// from warmed caches.
+///
+/// # Arguments
+///
+/// * `strategy` — which algorithm to use for the returned result.
+/// * `current_basis` — current `U_k`, shape `(d, cap)`.  Only `[:, :k]` is active.
+/// * `sigmas` — current singular values, length `cap`.  Only `[:k]` meaningful.
+/// * `z` — latent projection from Phase 1: `X · U_k`, shape `(b, k)`.
+/// * `residual` — reconstruction residual from Phase 1: `X − X̂`, shape `(b, d)`.
+/// * `sqrt_lambda` — `√λ` (square root of the forgetting factor).
+/// * `k` — current active rank.
+/// * `cap` — hard ceiling on rank.
+///
+/// # Returns
+///
+/// `Some(SubspaceUpdate)` on success, `None` if SVD failed to converge.
+///
+/// # Panics
+///
+/// Panics (via `assert!`) if the oracle is active and the two SVD
+/// strategies produce results that differ beyond tolerance.
+#[allow(clippy::too_many_arguments)]
+pub fn evolve(
+    strategy: SvdStrategy,
+    current_basis: &Mat<f64>,
+    sigmas: &[f64],
+    z: &Mat<f64>,
+    residual: &Mat<f64>,
+    sqrt_lambda: f64,
+    k: usize,
+    cap: usize,
+) -> Option<SubspaceUpdate> {
+    let oracle_active = cfg!(debug_assertions) || tracing::enabled!(tracing::Level::DEBUG);
+
+    if !oracle_active {
+        let result = run_svd(strategy, current_basis, sigmas, z, residual, sqrt_lambda, k, cap);
+        if result.is_some() {
+            return result;
+        }
+        // Fallback: the primary strategy could not handle these
+        // dimensions (e.g. Brand with b+k > d).  Try the other.
+        let other_strategy = match strategy {
+            SvdStrategy::Naive => SvdStrategy::Brand,
+            SvdStrategy::Brand => SvdStrategy::Naive,
+        };
+        return run_svd(other_strategy, current_basis, sigmas, z, residual, sqrt_lambda, k, cap);
+    }
+
+    // Oracle: run both strategies and compare results.
+    //
+    // Active in debug builds unconditionally, or in release builds
+    // when a DEBUG-level tracing subscriber is attached.  Uses the
+    // same span names (`svd_brand` / `svd_naive`) so the
+    // SpanTimingLayer captures both strategies' cost from a single
+    // run — no need for the benchmarks to loop over strategies.
+    //
+    // A thread-local bool alternates execution order so that
+    // neither strategy consistently benefits from warmed caches
+    // or branch predictors.
+    let other_strategy = match strategy {
+        SvdStrategy::Naive => SvdStrategy::Brand,
+        SvdStrategy::Brand => SvdStrategy::Naive,
+    };
+
+    let flip = ORACLE_FLIP.with(|f| {
+        let v = f.get();
+        f.set(!v);
+        v
+    });
+
+    let (result, other) = if flip {
+        // Reversed: run other strategy first.
+        let o = run_svd(other_strategy, current_basis, sigmas, z, residual, sqrt_lambda, k, cap);
+        let r = run_svd(strategy, current_basis, sigmas, z, residual, sqrt_lambda, k, cap);
+        (r, o)
+    } else {
+        // Normal: run selected strategy first.
+        let r = run_svd(strategy, current_basis, sigmas, z, residual, sqrt_lambda, k, cap);
+        let o = run_svd(other_strategy, current_basis, sigmas, z, residual, sqrt_lambda, k, cap);
+        (r, o)
+    };
+
+    if let (Some(a), Some(b)) = (&result, &other) {
+        let (a_n, b_n) = (a.n, b.n);
+        assert_eq!(a_n, b_n, "maths oracle: n mismatch ({a_n} vs {b_n})");
+        compare_subspace_updates(a, b, strategy, other_strategy);
+    }
+
+    // Use primary if it succeeded; otherwise fall back to the other
+    // strategy (e.g. Brand cannot handle b+k > d, naïve can).
+    if result.is_some() { result } else { other }
+}
+
+/// Compare two `SubspaceUpdate`s for approximate equality.
+///
+/// Singular values must match to high relative tolerance.
+/// Basis vectors may differ by sign (SVD sign ambiguity) so we
+/// compare `|uᵢᵀ vᵢ|` ≈ 1.0 for each column.
+///
+/// Uses `assert!` (not `debug_assert!`) because this function is
+/// only called when the oracle is active — the gate is at the call
+/// site, not here.
+fn compare_subspace_updates(
+    update_a: &SubspaceUpdate,
+    update_b: &SubspaceUpdate,
+    strategy_a: SvdStrategy,
+    strategy_b: SvdStrategy,
+) {
+    /// Returns `true` when the pair of values should be considered
+    /// "effectively zero" and comparison skipped.
+    fn near_zero(va: f64, vb: f64, and_floor: f64, or_floor: f64) -> bool {
+        (va.abs() < and_floor && vb.abs() < and_floor) || (va.abs() < or_floor || vb.abs() < or_floor)
+    }
+
+    let rank = update_a.n;
+    let dims = update_a.basis.nrows();
+
+    // ── Near-zero floors (shared by σ and basis checks) ──
+    //
+    // Two-tier skip logic prevents both false positives and false
+    // negatives when comparing near-zero singular values:
+    //
+    // • `and_floor` (1e-5, generous) — skip comparison when *both*
+    //   strategies agree the value is near-zero.  Covers the
+    //   practical noise floor: trailing singular values of rank-
+    //   deficient inputs routinely land at 1e-7 to 1e-16.  Brand's
+    //   incremental QR may accumulate slightly more residual than
+    //   the full SVD (e.g. 1.2e-6 vs 1.5e-9 after 1000 identical
+    //   observations), but both are "effectively zero."
+    //
+    // • `or_floor` (1e-12, strict) — skip comparison when *either*
+    //   strategy produces a value at (or near) machine-epsilon
+    //   level.  Catches exact-zero vs tiny-residual mismatches
+    //   without masking real discrepancies.
+    //
+    // The combined gate is:
+    //
+    //     (both < and_floor) ∨ (either < or_floor)
+    //
+    // If one value is O(1) and the other is 1e-9, neither gate
+    // fires and the assertion correctly catches the divergence.
+    let and_floor: f64 = 1e-5;
+    let or_floor: f64 = 1e-12;
+
+    // ── Singular values ─────────────────────────────────
+    let sv_tolerance = 1e-8;
+    for idx in 0..rank {
+        let sa = update_a.sigmas[idx];
+        let sb = update_b.sigmas[idx];
+        if near_zero(sa, sb, and_floor, or_floor) {
+            continue;
+        }
+        let denom = sa.abs().max(sb.abs()).max(1e-15);
+        let rel = (sa - sb).abs() / denom;
+        assert!(
+            rel < sv_tolerance,
+            "maths oracle: σ[{idx}] mismatch: {strategy_a:?}={sa:.12e}, {strategy_b:?}={sb:.12e}, rel={rel:.2e}"
+        );
+    }
+
+    // ── Basis columns (up to sign, with subspace clustering) ──
+    //
+    // Each column pair should satisfy |uᵢᵀ vᵢ| ≈ 1.0.
+    // SVD sign ambiguity means uᵢ and −uᵢ are both valid.
+    //
+    // When a singular value is near zero, the corresponding basis
+    // direction is numerically arbitrary — both strategies may
+    // legitimately return completely different unit vectors for the
+    // null-energy component.  Skip the cosine check in that case
+    // using the same two-tier gate.
+    //
+    // **Repeated singular values**: when σ[j] ≈ σ[j+1] (within
+    // `cluster_rtol`), the corresponding basis vectors span an
+    // invariant subspace — *any* orthonormal basis of that
+    // subspace is equally valid.  Comparing individual columns is
+    // meaningless; instead we compare the subspace via principal
+    // angles: form S = A_group^T · B_group, SVD it, and check
+    // that all singular values (= cos(θ_i)) ≈ 1.0.
+
+    // ── Gap-dependent basis tolerance (exact Wedin bound) ──
+    //
+    // For a singular vector with spectral gap δ to its nearest
+    // neighbour, the Wedin (1972) sin-θ theorem gives:
+    //
+    //   sin θ ≤ ‖E‖ / δ
+    //
+    // where δ is the **absolute** gap and
+    //   ‖E‖ ≈ σ_max · √(rel_perturbation_sq).
+    //
+    // Define `ratio_sq = (‖E‖/δ)²`.  The exact Wedin bound on
+    // the cosine is:
+    //
+    //   |cos θ| ≥ √(1 − ratio_sq)
+    //
+    // or equivalently:
+    //
+    //   1 − |cos θ| ≤ 1 − √(1 − ratio_sq)     (exact)
+    //
+    // Previous code used the Taylor approximation `ratio_sq / 2`,
+    // which underestimates the tolerance by >15% when ratio_sq
+    // exceeds 0.3 and >29% at 0.5, causing false oracle panics
+    // for poorly-conditioned columns.  Using the exact form
+    // eliminates this bias.
+    //
+    // When ratio_sq ≥ 1 the bound is vacuous: ‖E‖ ≥ δ means
+    // the perturbation can rotate the singular vector through
+    // *any* angle, so only the σ-value check (above) provides
+    // meaningful validation for that column.
+    //
+    // **Calibration**: Brand's incremental SVD accumulates
+    // truncation error over many rank-1 updates.  This error
+    // is *anisotropic* — it preferentially affects the weakest
+    // singular directions because truncation discards energy
+    // from the (k+1)-th component, which leaks into the k-th
+    // direction proportionally to 1/δ_k.
+    //
+    // Empirical calibration: 1600 updates at λ = 0.95 with a
+    // 2% relative gap produced |cos| = 0.588, giving
+    // (‖E‖/σ_max)² ≈ 3.3 × 10⁻⁴.  However, traffic patterns
+    // with high condition numbers (κ = σ_max/σ_min > 25) and
+    // diverse cell structure push the actual perturbation to
+    // ~5× the calibration value.  We budget 10× margin
+    // (rel_perturbation_sq = 0.003) to cover worst-case
+    // anisotropic error accumulation.
+    //
+    // The σ_max² factor is critical: when the condition number
+    // σ_max/σ_min is large, the perturbation ‖E‖ scales with
+    // σ_max but the gap δ scales with σ_min's neighbourhood.
+    // Without this factor, the tolerance is underestimated by
+    // (σ_max/σ_neighbor)² — up to 400× for condition number 20.
+    let basis_tol_floor = 1e-6;
+    let rel_perturbation_sq: f64 = 0.003;
+    let sv_max = 0.5 * (update_a.sigmas[0].abs() + update_b.sigmas[0].abs());
+
+    // Cluster tolerance: any pair whose relative gap is smaller
+    // than 2× the perturbation scale must be compared as a
+    // subspace, because the Wedin bound's ‖E‖/δ ratio ≥ 1
+    // makes per-column cosine comparison meaningless there.
+    // Deriving cluster_rtol from rel_perturbation_sq eliminates
+    // the dead zone between "too close to cluster" and "too
+    // close for the Wedin bound to be tight."
+    let cluster_rtol = 2.0 * rel_perturbation_sq.sqrt();
+
+    // Group consecutive singular values that are nearly equal.
+    // Uses the average of both strategies' values for robustness.
+    let mut col = 0;
+    while col < rank {
+        // Find extent of cluster starting at col.
+        let mut end = col + 1;
+        while end < rank {
+            let prev_sv_a = update_a.sigmas[end - 1].abs();
+            let prev_sv_b = update_b.sigmas[end - 1].abs();
+            let curr_sv_a = update_a.sigmas[end].abs();
+            let curr_sv_b = update_b.sigmas[end].abs();
+            let avg = (prev_sv_a + prev_sv_b + curr_sv_a + curr_sv_b) * 0.25;
+            if avg < 1e-15 {
+                break; // all near-zero, stop clustering
+            }
+            let diff_a = (prev_sv_a - curr_sv_a).abs();
+            let diff_b = (prev_sv_b - curr_sv_b).abs();
+            if diff_a / avg > cluster_rtol || diff_b / avg > cluster_rtol {
+                break;
+            }
+            end += 1;
+        }
+
+        let group_size = end - col;
+
+        // Skip the whole group if sigmas are near-zero.
+        if near_zero(update_a.sigmas[col], update_b.sigmas[col], and_floor, or_floor) {
+            col = end;
+            continue;
+        }
+
+        if group_size == 1 {
+            // ── Singleton: gap-dependent cosine check ──
+            //
+            // Compute minimum absolute gap to the nearest neighbour.
+            // Singular values are sorted descending, so the immediate
+            // predecessor (col−1) and successor (col+1) are the closest
+            // candidates.  We average both strategies' values for
+            // robustness.
+            let sv_col = 0.5 * (update_a.sigmas[col].abs() + update_b.sigmas[col].abs());
+            let mut min_abs_gap = f64::INFINITY;
+            if col > 0 {
+                let sv_prev = 0.5 * (update_a.sigmas[col - 1].abs() + update_b.sigmas[col - 1].abs());
+                min_abs_gap = min_abs_gap.min((sv_col - sv_prev).abs());
+            }
+            if col + 1 < rank {
+                let sv_next = 0.5 * (update_a.sigmas[col + 1].abs() + update_b.sigmas[col + 1].abs());
+                min_abs_gap = min_abs_gap.min((sv_col - sv_next).abs());
+            }
+
+            // Wedin-derived tolerance using the exact sin-θ bound
+            // (Wedin 1972):
+            //
+            //   sin θ ≤ ‖E‖/δ  ⟹  |cos θ| ≥ √(1 − (‖E‖/δ)²)
+            //
+            // Define ratio_sq = (‖E‖/δ)² = rel_perturbation_sq · σ_max²/δ².
+            //
+            // When ratio_sq ≥ 1 the Wedin bound is vacuous
+            // (‖E‖ ≥ δ) — the singular vector's direction is not
+            // constrained by theory.  Any cosine value, including 0,
+            // is consistent with both strategies being correct.  Skip
+            // the comparison entirely; only the σ-value check (above)
+            // provides meaningful validation for this column.
+            let effective_tol = if min_abs_gap.is_infinite() {
+                // Only one singular value — no neighbour to mix with.
+                Some(basis_tol_floor)
+            } else {
+                let denom = min_abs_gap.max(1e-15);
+                let ratio_sq = rel_perturbation_sq * sv_max * sv_max / (denom * denom);
+                if ratio_sq >= 1.0 {
+                    None // Wedin bound vacuous — skip cosine check.
+                } else {
+                    // Exact: 1 − |cos θ| ≤ 1 − √(1 − ratio_sq)
+                    let exact = 1.0 - (1.0 - ratio_sq).sqrt();
+                    Some(exact.max(basis_tol_floor))
+                }
+            };
+
+            if let Some(tol) = effective_tol {
+                let mut dot = 0.0;
+                for row in 0..dims {
+                    dot = update_a.basis[(row, col)].mul_add(update_b.basis[(row, col)], dot);
+                }
+                let cosine = dot.abs();
+                assert!(
+                    cosine > 1.0 - tol,
+                    "maths oracle: basis column {col} diverged: |cos| = {cosine:.8}, \
+                     effective_tol = {tol:.6e} (min_abs_gap = {min_abs_gap:.4}, σ_max = {sv_max:.4}), \
+                     σ_a={:.6e}, σ_b={:.6e}, \
+                     all_σ_a={:?}, all_σ_b={:?}, \
+                     {strategy_a:?} vs {strategy_b:?}",
+                    update_a.sigmas[col],
+                    update_b.sigmas[col],
+                    &update_a.sigmas[..rank],
+                    &update_b.sigmas[..rank],
+                );
+            }
+        } else {
+            // ── Cluster: degenerate eigenvalue group ──
+            //
+            // When singular values are repeated (σ[j] ≈ σ[j+1] …),
+            // the corresponding SVD eigenvectors are only defined up
+            // to an arbitrary rotation within the (potentially high-
+            // dimensional) eigenspace.  In ℝ^d with d ≫ g, the true
+            // eigenspace for this σ can be much larger than the g
+            // columns the SVD returns, so different implementations
+            // legitimately pick different g-dimensional slices.
+            //
+            // Per-column and even per-subspace comparison is
+            // meaningless.  The σ comparison already validates that
+            // the singular values match; skip the basis check for
+            // degenerate groups.
+        }
+
+        col = end;
+    }
+}
diff --git a/packages/sentinel/src/maths/naive_svd.rs b/packages/sentinel/src/maths/naive_svd.rs
new file mode 100644
index 000000000..b4ed39a0c
--- /dev/null
+++ b/packages/sentinel/src/maths/naive_svd.rs
@@ -0,0 +1,101 @@
+//! Naïve dense thin SVD for subspace evolution.
+//!
+//! This is the original algorithm: build the composite matrix
+//!
+//!   M = \[√λ · `U_k` · diag(σ₁..ₖ)  |  X^T\]  ∈ ℝ^(d × (k+b))
+//!
+//! and compute a full dense thin SVD of M via `faer::Mat::thin_svd()`.
+//!
+//! Correct and simple, but O(d·(k+b)²) per call — the full
+//! bidiagonalisation touches every element of the d-row matrix.
+//!
+//! # §-references
+//!
+//! - §ALGO S-4.2 Phase 2 — Subspace evolution
+//! - ADR-S-016 §Context — Cost analysis
+
+use faer::Mat;
+
+use super::SubspaceUpdate;
+
+/// Evolve the subspace via dense thin SVD of the full composite matrix.
+///
+/// Builds M = \[√λ · `U_k` · diag(σ) | X^T\] ∈ ℝ^(d × (k+b)) and
+/// computes `thin_svd(M)`, retaining the top `n = min(k+b, d, cap)`
+/// components.
+///
+/// # Arguments
+///
+/// * `current_basis` — `U_k`, shape `(d, cap)`. Only columns `[:k]` active.
+/// * `sigmas` — singular values, length `cap`. Only `[:k]` meaningful.
+/// * `z` — latent projection `X · U_k`, shape `(b, k)`.  (Unused by
+///   naïve — present for API uniformity; the naïve path reconstructs
+///   X^T from `residual + U_k · Z^T`.)
+/// * `residual` — reconstruction residual `X − X̂`, shape `(b, d)`.
+/// * `sqrt_lambda` — `√λ`.
+/// * `k` — current active rank.
+/// * `cap` — hard ceiling on rank.
+// Linear algebra code — single-char names follow standard mathematical notation
+// (d=dimension, b=batch, k=rank, m=composite matrix, s=scaled sigma, n=output rank).
+#[allow(clippy::many_single_char_names)]
+#[must_use]
+pub fn evolve(
+    current_basis: &Mat<f64>,
+    sigmas: &[f64],
+    z: &Mat<f64>,
+    residual: &Mat<f64>,
+    sqrt_lambda: f64,
+    k: usize,
+    cap: usize,
+) -> Option<SubspaceUpdate> {
+    let d = current_basis.nrows();
+    let b = residual.nrows();
+    let cols = k + b;
+
+    // Build M: (d × (k + b))
+    let mut m = Mat::zeros(d, cols);
+
+    // Left block: √λ · U_k · diag(σ[:k])
+    for j in 0..k {
+        let s = sqrt_lambda * sigmas[j];
+        for i in 0..d {
+            m[(i, j)] = current_basis[(i, j)] * s;
+        }
+    }
+
+    // Right block: X^T.
+    // X = residual + Z · U_k^T  (reconstruct from Phase 1 outputs).
+    // X^T[j, i] = X[i, j] = residual[i, j] + Σₗ z[i, l] · U_k[j, l]
+    for i in 0..b {
+        for j in 0..d {
+            let mut val = residual[(i, j)];
+            for l in 0..k {
+                val = z[(i, l)].mul_add(current_basis[(j, l)], val);
+            }
+            m[(j, k + i)] = val;
+        }
+    }
+
+    // Thin SVD of M.
+    let svd = m.thin_svd().ok()?;
+
+    let n = cols.min(d).min(cap);
+    let u_new = svd.U();
+    let s_new = svd.S().column_vector();
+
+    let mut basis = Mat::zeros(d, n);
+    let mut out_sigmas = Vec::with_capacity(n);
+
+    for j in 0..n {
+        for i in 0..d {
+            basis[(i, j)] = u_new[(i, j)];
+        }
+        out_sigmas.push(s_new[j]);
+    }
+
+    Some(SubspaceUpdate {
+        basis,
+        sigmas: out_sigmas,
+        n,
+    })
+}
diff --git a/packages/sentinel/src/maths/tests/brand_vs_naive.rs b/packages/sentinel/src/maths/tests/brand_vs_naive.rs
new file mode 100644
index 000000000..71d84534b
--- /dev/null
+++ b/packages/sentinel/src/maths/tests/brand_vs_naive.rs
@@ -0,0 +1,849 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// SPDX-FileCopyrightText: 2026 Torrust project contributors
+
+//! Brand vs Naïve: comprehensive accuracy comparison.
+//!
+//! # Test index
+//!
+//! ## Equivalence: basic configurations
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`equivalence_representative_configs`] | Various (d, k, b, λ) combos |
+//!
+//! ## Equivalence: dimensional edge cases
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`equivalence_rank_one`] | Rank-1 (k = 1) |
+//! | [`equivalence_minimal_dimensions`] | d = 2, 3, 8 and Brand guard |
+//! | [`equivalence_single_sample`] | Single-sample batch (b = 1) |
+//! | [`equivalence_large_batch`] | Large batch (b >> k) |
+//! | [`equivalence_saturated_rank`] | Rank equals cap (k == cap) |
+//! | [`equivalence_cap_larger_than_k`] | Cap > k (unused capacity) |
+//!
+//! ## Equivalence: data / state edge cases
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`equivalence_identity_basis`] | Identity-like initial basis |
+//! | [`equivalence_near_zero_residual`] | Data already in subspace |
+//! | [`equivalence_zero_initial_sigmas`] | Cold start (all σ = 0) |
+//! | [`equivalence_large_singular_values`] | Large σ (~ 10⁶) |
+//! | [`equivalence_equal_singular_values`] | Degenerate spectrum (all σ equal) |
+//! | [`equivalence_no_forgetting`] | λ = 1.0 (no exponential decay) |
+//!
+//! ## Properties (invariants)
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`output_basis_is_orthonormal`] | Orthogonality of output basis |
+//! | [`singular_values_sorted_and_nonnegative`] | Singular value ordering |
+//! | [`deterministic_reproduction`] | Deterministic reproducibility |
+//!
+//! ## Functional / integration
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`equivalence_multi_step`] | Repeated evolution (multi-step streaming) |
+//! | [`reconstruction_error_equivalence`] | Reconstruction error match |
+
+use faer::Mat;
+use rand::rngs::SmallRng;
+use rand::{RngExt, SeedableRng};
+
+use crate::maths::{SubspaceUpdate, brand_svd, naive_svd};
+
+// ════════════════════════════════════════════════════════════
+//  Helpers
+// ════════════════════════════════════════════════════════════
+
+/// Generate a random (rows × cols) matrix with values in [-1, 1].
+fn random_matrix(rows: usize, cols: usize, rng: &mut SmallRng) -> Mat<f64> {
+    let mut m = Mat::zeros(rows, cols);
+    for i in 0..rows {
+        for j in 0..cols {
+            m[(i, j)] = rng.random_range(-1.0..1.0);
+        }
+    }
+    m
+}
+
+/// Build a random identity-like basis (d × cap) with orthonormal columns.
+fn random_basis(d: usize, cap: usize, rng: &mut SmallRng) -> Mat<f64> {
+    // Start with random matrix, then QR to get orthonormal columns.
+    let raw = random_matrix(d, cap, rng);
+    let qr = raw.as_ref().qr();
+    qr.compute_thin_Q()
+}
+
+/// Given X (b×d) and `U_k` (d×cap, using [:k]), compute z and residual.
+fn phase1_projection(x: &Mat<f64>, basis: &Mat<f64>, k: usize) -> (Mat<f64>, Mat<f64>) {
+    let u_k = basis.subcols(0, k);
+    let z = x * u_k; // (b × k)
+    let x_hat = &z * u_k.transpose(); // (b × d)
+    let residual = x - &x_hat; // (b × d)
+    (z, residual)
+}
+
+/// Assert two `SubspaceUpdate`s are approximately equal.
+///
+/// - Singular values: relative tolerance `sigma_tol`.
+/// - Basis columns: compared up to sign via |cos(angle)| > `basis_tol`.
+fn assert_updates_close(a: &SubspaceUpdate, b: &SubspaceUpdate, sigma_tol: f64, basis_tol: f64, label: &str) {
+    assert_eq!(a.n, b.n, "{label}: n mismatch ({} vs {})", a.n, b.n);
+    let n = a.n;
+    let d = a.basis.nrows();
+
+    // Absolute floor below which singular values are considered zero.
+    // Near-zero values carry arbitrary numerical residual, making
+    // relative comparison meaningless.
+    let sigma_abs_floor = 1e-6;
+
+    // Singular values.
+    for i in 0..n {
+        let sa = a.sigmas[i];
+        let sb = b.sigmas[i];
+        if sa.abs() < sigma_abs_floor && sb.abs() < sigma_abs_floor {
+            continue;
+        }
+        let denom = sa.abs().max(sb.abs()).max(1e-15);
+        let rel = (sa - sb).abs() / denom;
+        assert!(
+            rel < sigma_tol,
+            "{label}: σ[{i}] mismatch: naïve={sa:.12e}, brand={sb:.12e}, rel={rel:.2e}"
+        );
+    }
+
+    // Basis columns (up to sign).
+    // Skip columns whose singular value is near-zero (arbitrary direction).
+    for j in 0..n {
+        if a.sigmas[j].abs() < sigma_abs_floor && b.sigmas[j].abs() < sigma_abs_floor {
+            continue;
+        }
+        let mut dot = 0.0;
+        for i in 0..d {
+            dot = a.basis[(i, j)].mul_add(b.basis[(i, j)], dot);
+        }
+        let cosine = dot.abs();
+        assert!(cosine > basis_tol, "{label}: basis col {j} diverged: |cos| = {cosine:.8e}");
+    }
+}
+
+/// Assert that the columns of a matrix are approximately orthonormal.
+fn assert_orthonormal(basis: &Mat<f64>, n: usize, tol: f64, label: &str) {
+    let d = basis.nrows();
+    for j in 0..n {
+        // Column norm ≈ 1.
+        let mut norm_sq = 0.0;
+        for i in 0..d {
+            norm_sq = basis[(i, j)].mul_add(basis[(i, j)], norm_sq);
+        }
+        let norm = norm_sq.sqrt();
+        assert!(
+            (norm - 1.0).abs() < tol,
+            "{label}: column {j} norm = {norm:.8e}, expected ≈ 1.0"
+        );
+
+        // Pairwise orthogonality.
+        for l in (j + 1)..n {
+            let mut dot = 0.0;
+            for i in 0..d {
+                dot = basis[(i, j)].mul_add(basis[(i, l)], dot);
+            }
+            assert!(
+                dot.abs() < tol,
+                "{label}: columns {j} and {l} not orthogonal: dot = {dot:.8e}"
+            );
+        }
+    }
+}
+
+/// Run both algorithms and return (naïve, brand) results.
+///
+/// Brand returns `None` when `k + b >= d` (the kernel is not smaller
+/// than the ambient dimension — see guard in `brand_svd::evolve`).
+/// In that case the second element is `None`.
+// Linear algebra test helpers — d, k, b, z, x follow standard mathematical
+// notation for dimensionality, rank, batch size, latent projections, and input.
+#[allow(clippy::many_single_char_names)]
+fn run_both(
+    basis: &Mat<f64>,
+    sigmas: &[f64],
+    z: &Mat<f64>,
+    residual: &Mat<f64>,
+    sqrt_lambda: f64,
+    k: usize,
+    cap: usize,
+) -> (SubspaceUpdate, Option<SubspaceUpdate>) {
+    let naive = naive_svd::evolve(basis, sigmas, z, residual, sqrt_lambda, k, cap).expect("naïve SVD should not fail");
+    let brand = brand_svd::evolve(basis, sigmas, z, residual, sqrt_lambda, k, cap);
+    (naive, brand)
+}
+
+/// Pad a (d × n) basis to (d × cap) by appending zero columns.
+fn pad_to_cap(basis: &Mat<f64>, d: usize, cap: usize) -> Mat<f64> {
+    let n = basis.ncols();
+    let mut out = Mat::zeros(d, cap);
+    for j in 0..n.min(cap) {
+        for i in 0..d {
+            out[(i, j)] = basis[(i, j)];
+        }
+    }
+    out
+}
+
+/// Pad sigmas to length `cap` with zeros.
+fn pad_sigmas(sigmas: &[f64], cap: usize) -> Vec<f64> {
+    let mut out = sigmas.to_vec();
+    out.resize(cap, 0.0);
+    out
+}
+
+/// Frobenius norm of reconstruction error: ‖X − U Uᵀ Xᵀ‖_F (row-major).
+// Linear algebra helper — d, b, x, z, n follow standard mathematical notation.
+#[allow(clippy::many_single_char_names)]
+fn recon_error(x: &Mat<f64>, basis: &Mat<f64>, n: usize) -> f64 {
+    let b = x.nrows();
+    let d = x.ncols();
+    let u_n = basis.subcols(0, n);
+    let z = x * u_n; // (b × n)
+    let x_hat = &z * u_n.transpose(); // (b × d)
+
+    let mut err = 0.0;
+    for i in 0..b {
+        for j in 0..d {
+            let diff = x[(i, j)] - x_hat[(i, j)];
+            err += diff * diff;
+        }
+    }
+    err.sqrt()
+}
+
+// ════════════════════════════════════════════════════════════
+// § 1 — Equivalence: basic configurations
+// ════════════════════════════════════════════════════════════
+
+/// Test that Brand matches naïve at representative (d, k, b) combos.
+#[test]
+// Linear algebra test — d, k, b, z, x follow standard mathematical notation.
+#[allow(clippy::many_single_char_names)]
+fn equivalence_representative_configs() {
+    let configs: &[(usize, usize, usize, f64)] = &[
+        // (d, k, b, λ)
+        (32, 2, 4, 0.95),    // small, test-like
+        (64, 4, 8, 0.99),    // medium
+        (128, 2, 4, 0.95),   // benchmark config
+        (128, 2, 16, 0.99),  // realistic config
+        (128, 16, 16, 0.99), // high rank + large batch
+        (256, 8, 32, 0.99),  // wide dimension
+    ];
+
+    for &(d, k, b, lambda) in configs {
+        let cap = k; // cap = k for simplicity
+        let sqrt_lambda = lambda.sqrt();
+        let mut rng = SmallRng::seed_from_u64(42);
+
+        let basis = random_basis(d, cap, &mut rng);
+        let mut sigmas = vec![0.0; cap];
+        for s in &mut sigmas {
+            *s = rng.random_range(0.1..10.0);
+        }
+        // Sort decreasing (as they would be in practice).
+        sigmas.sort_by(|a, b| b.partial_cmp(a).unwrap());
+
+        let x = random_matrix(b, d, &mut rng);
+        let (z, residual) = phase1_projection(&x, &basis, k);
+
+        let (naive, brand) = run_both(&basis, &sigmas, &z, &residual, sqrt_lambda, k, cap);
+        let brand = brand.expect("Brand should succeed when c < d");
+
+        let label = format!("d={d}, k={k}, b={b}, λ={lambda}");
+        assert_updates_close(&naive, &brand, 1e-8, 1.0 - 1e-6, &label);
+    }
+}
+
+// ════════════════════════════════════════════════════════════
+// § 2 — Equivalence: dimensional edge cases
+// ════════════════════════════════════════════════════════════
+
+/// Rank-1 is the most constrained case — only one basis vector.
+#[test]
+// Linear algebra test — d, k, b, z, x follow standard mathematical notation.
+#[allow(clippy::many_single_char_names)]
+fn equivalence_rank_one() {
+    let d = 64;
+    let k = 1;
+    let b = 4;
+    let cap = 1;
+    let sqrt_lambda = 0.95_f64.sqrt();
+    let mut rng = SmallRng::seed_from_u64(123);
+
+    let basis = random_basis(d, cap, &mut rng);
+    let sigmas = vec![5.0];
+
+    let x = random_matrix(b, d, &mut rng);
+    let (z, residual) = phase1_projection(&x, &basis, k);
+
+    let (naive, brand) = run_both(&basis, &sigmas, &z, &residual, sqrt_lambda, k, cap);
+    let brand = brand.expect("Brand should succeed when c < d");
+    assert_updates_close(&naive, &brand, 1e-8, 1.0 - 1e-6, "rank-1");
+}
+
+/// Minimum useful dimensions: d=2, k=1, b=1.
+///
+/// Tests the Brand guard (returns `None` when `c + 2 > d`) and
+/// verifies that naïve still works at these small sizes.
+#[test]
+// Linear algebra test — d, k, b, z, x follow standard mathematical notation.
+#[allow(clippy::many_single_char_names)]
+fn equivalence_minimal_dimensions() {
+    // d=2, k=1, b=1 ⇒ c = k + b = 2, c + 2 = 4 > d = 2.
+    // Brand correctly returns None here (headroom guard),
+    // so we verify the fallback: only Naive produces a result.
+    let d = 2;
+    let k = 1;
+    let b = 1;
+    let cap = 1;
+    let sqrt_lambda = 0.95_f64.sqrt();
+    let mut rng = SmallRng::seed_from_u64(101);
+
+    let basis = random_basis(d, cap, &mut rng);
+    let sigmas = vec![1.0];
+
+    let x = random_matrix(b, d, &mut rng);
+    let (z, residual) = phase1_projection(&x, &basis, k);
+
+    let (naive, brand) = run_both(&basis, &sigmas, &z, &residual, sqrt_lambda, k, cap);
+    assert!(brand.is_none(), "Brand should return None when c + 2 > d");
+    assert_eq!(naive.n, 1);
+
+    // d=3, c=2: c + 2 = 4 > 3 → still bails.
+    let d = 3;
+    let basis = random_basis(d, cap, &mut rng);
+    let x = random_matrix(b, d, &mut rng);
+    let (z, residual) = phase1_projection(&x, &basis, k);
+
+    let (_naive, brand) = run_both(&basis, &sigmas, &z, &residual, sqrt_lambda, k, cap);
+    assert!(brand.is_none(), "Brand should return None when c + 2 > d (d=3)");
+
+    // d=8, c=2: c + 2 = 4 ≤ 8 → Brand runs (smallest OK case).
+    let d = 8;
+    let basis = random_basis(d, cap, &mut rng);
+    let x = random_matrix(b, d, &mut rng);
+    let (z, residual) = phase1_projection(&x, &basis, k);
+
+    let (naive, brand) = run_both(&basis, &sigmas, &z, &residual, sqrt_lambda, k, cap);
+    let brand = brand.expect("Brand should succeed when c + 2 <= d");
+    assert_updates_close(&naive, &brand, 1e-8, 1.0 - 1e-6, "minimal-dims-d8");
+}
+
+/// Single-sample batch (b = 1) — the smallest non-trivial update.
+///
+/// Different from rank-1: here the *batch* is minimal while
+/// the rank can be higher.
+#[test]
+// Linear algebra test — d, k, b, z, x follow standard mathematical notation.
+#[allow(clippy::many_single_char_names)]
+fn equivalence_single_sample() {
+    let d = 64;
+    let k = 4;
+    let b = 1;
+    let cap = 4;
+    let sqrt_lambda = 0.95_f64.sqrt();
+    let mut rng = SmallRng::seed_from_u64(202);
+
+    let basis = random_basis(d, cap, &mut rng);
+    let sigmas = vec![8.0, 4.0, 2.0, 1.0];
+
+    let x = random_matrix(b, d, &mut rng);
+    let (z, residual) = phase1_projection(&x, &basis, k);
+
+    let (naive, brand) = run_both(&basis, &sigmas, &z, &residual, sqrt_lambda, k, cap);
+    let brand = brand.expect("Brand should succeed when c < d");
+    assert_updates_close(&naive, &brand, 1e-8, 1.0 - 1e-6, "single-sample");
+}
+
+/// When the batch is much larger than the rank, the residual
+/// component dominates. Both algorithms should handle this.
+#[test]
+// Linear algebra test — d, k, b, z, x follow standard mathematical notation.
+#[allow(clippy::many_single_char_names)]
+fn equivalence_large_batch() {
+    let d = 64;
+    let k = 2;
+    let b = 32;
+    let cap = 2;
+    let sqrt_lambda = 0.99_f64.sqrt();
+    let mut rng = SmallRng::seed_from_u64(456);
+
+    let basis = random_basis(d, cap, &mut rng);
+    let sigmas = vec![10.0, 3.0];
+
+    let x = random_matrix(b, d, &mut rng);
+    let (z, residual) = phase1_projection(&x, &basis, k);
+
+    let (naive, brand) = run_both(&basis, &sigmas, &z, &residual, sqrt_lambda, k, cap);
+    let brand = brand.expect("Brand should succeed when c < d");
+    assert_updates_close(&naive, &brand, 1e-8, 1.0 - 1e-6, "large-batch");
+}
+
+/// When rank equals cap, the tracker is at maximum expressiveness.
+#[test]
+// Linear algebra test — d, k, b, z, x follow standard mathematical notation.
+// usize→f64 cast is for the decreasing-sigma formula: 20.0/(i+1.0) where i ≤ 16.
+#[allow(clippy::many_single_char_names, clippy::cast_precision_loss)]
+fn equivalence_saturated_rank() {
+    let d = 32;
+    let k = 16;
+    let b = 8;
+    let cap = 16;
+    let sqrt_lambda = 0.99_f64.sqrt();
+    let mut rng = SmallRng::seed_from_u64(789);
+
+    let basis = random_basis(d, cap, &mut rng);
+    let mut sigmas = vec![0.0; cap];
+    for (i, s) in sigmas.iter_mut().enumerate() {
+        *s = 20.0 / (i as f64 + 1.0); // decreasing: 20, 10, 6.67, ..
+    }
+
+    let x = random_matrix(b, d, &mut rng);
+    let (z, residual) = phase1_projection(&x, &basis, k);
+
+    let (naive, brand) = run_both(&basis, &sigmas, &z, &residual, sqrt_lambda, k, cap);
+    let brand = brand.expect("Brand should succeed when c < d");
+    assert_updates_close(&naive, &brand, 1e-7, 1.0 - 1e-5, "saturated-rank");
+}
+
+/// When cap > k, the output n can be up to min(k+b, d, cap).
+/// This tests that both algorithms correctly handle the extra
+/// capacity and produce matching wider outputs.
+#[test]
+// Linear algebra test — d, k, b, z, x follow standard mathematical notation.
+#[allow(clippy::many_single_char_names)]
+fn equivalence_cap_larger_than_k() {
+    let d = 64;
+    let k = 2;
+    let b = 4;
+    let cap = 8; // Much larger than k
+    let sqrt_lambda = 0.95_f64.sqrt();
+    let mut rng = SmallRng::seed_from_u64(555);
+
+    let mut basis = Mat::zeros(d, cap);
+    // Only initialise the first k columns as proper orthonormal vectors.
+    let partial = random_basis(d, k, &mut rng);
+    for j in 0..k {
+        for i in 0..d {
+            basis[(i, j)] = partial[(i, j)];
+        }
+    }
+    let mut sigmas = vec![0.0; cap];
+    sigmas[0] = 5.0;
+    sigmas[1] = 2.0;
+
+    let x = random_matrix(b, d, &mut rng);
+    let (z, residual) = phase1_projection(&x, &basis, k);
+
+    let (naive, brand) = run_both(&basis, &sigmas, &z, &residual, sqrt_lambda, k, cap);
+    let brand = brand.expect("Brand should succeed when c < d");
+
+    // n should be min(k+b, d, cap) = min(6, 64, 8) = 6.
+    assert_eq!(naive.n, 6);
+    assert_eq!(brand.n, 6);
+    assert_updates_close(&naive, &brand, 1e-8, 1.0 - 1e-6, "cap>k");
+    assert_orthonormal(&naive.basis, naive.n, 1e-10, "naïve cap>k");
+    assert_orthonormal(&brand.basis, brand.n, 1e-10, "brand cap>k");
+}
+
+// ════════════════════════════════════════════════════════════
+// § 3 — Equivalence: data / state edge cases
+// ════════════════════════════════════════════════════════════
+
+/// The tracker starts with an identity-like basis — column j has
+/// a 1.0 at row j.  This is not orthogonal in the traditional QR
+/// sense but is orthonormal.  Both algorithms should handle it.
+#[test]
+// Linear algebra test — d, k, b, z, x follow standard mathematical notation.
+#[allow(clippy::many_single_char_names)]
+fn equivalence_identity_basis() {
+    let d = 64;
+    let k = 2;
+    let b = 8;
+    let cap = 4;
+    let sqrt_lambda = 0.95_f64.sqrt();
+    let mut rng = SmallRng::seed_from_u64(314);
+
+    // Identity-like basis (same as SubspaceTracker::new).
+    let mut basis = Mat::zeros(d, cap);
+    for j in 0..cap.min(d) {
+        basis[(j, j)] = 1.0;
+    }
+    let sigmas = vec![0.01; cap];
+
+    let x = random_matrix(b, d, &mut rng);
+    let (z, residual) = phase1_projection(&x, &basis, k);
+
+    let (naive, brand) = run_both(&basis, &sigmas, &z, &residual, sqrt_lambda, k, cap);
+    let brand = brand.expect("Brand should succeed when c < d");
+    assert_updates_close(&naive, &brand, 1e-8, 1.0 - 1e-6, "identity-basis");
+}
+
+/// When X lies almost entirely within the current subspace,
+/// the residual is near-zero. The QR in Brand's method should
+/// handle this gracefully (R⊥ ≈ 0).
+#[test]
+// Linear algebra test — d, k, b, z, x follow standard mathematical notation.
+#[allow(clippy::many_single_char_names)]
+fn equivalence_near_zero_residual() {
+    let d = 64;
+    let k = 4;
+    let b = 4;
+    let cap = 4;
+    let sqrt_lambda = 0.99_f64.sqrt();
+    let mut rng = SmallRng::seed_from_u64(999);
+
+    let basis = random_basis(d, cap, &mut rng);
+    let sigmas = vec![10.0, 5.0, 2.0, 1.0];
+
+    // Build X as a linear combination of basis vectors + tiny noise.
+    let u_k = basis.subcols(0, k);
+    let coeffs = random_matrix(b, k, &mut rng);
+    let noise = {
+        let mut n = random_matrix(b, d, &mut rng);
+        for i in 0..b {
+            for j in 0..d {
+                n[(i, j)] *= 1e-10; // tiny noise
+            }
+        }
+        n
+    };
+    let x = &coeffs * u_k.transpose() + &noise; // (b × d), mostly in subspace
+
+    let (z, residual) = phase1_projection(&x, &basis, k);
+
+    let (naive, brand) = run_both(&basis, &sigmas, &z, &residual, sqrt_lambda, k, cap);
+    let brand = brand.expect("Brand should succeed when c < d");
+
+    // Looser tolerance due to near-singular QR.
+    assert_updates_close(&naive, &brand, 1e-4, 1.0 - 1e-3, "near-zero-residual");
+}
+
+/// Cold start: all initial singular values are zero.
+///
+/// This is the state after `SubspaceTracker::new()` before any data
+/// has been observed. The left block of M is all-zero, so the SVD
+/// depends entirely on the new data.
+#[test]
+// Linear algebra test — d, k, b, z, x follow standard mathematical notation.
+#[allow(clippy::many_single_char_names)]
+fn equivalence_zero_initial_sigmas() {
+    let d = 64;
+    let k = 4;
+    let b = 8;
+    let cap = 4;
+    let sqrt_lambda = 0.99_f64.sqrt();
+    let mut rng = SmallRng::seed_from_u64(303);
+
+    let basis = random_basis(d, cap, &mut rng);
+    let sigmas = vec![0.0; cap]; // Cold start — no prior information.
+
+    let x = random_matrix(b, d, &mut rng);
+    let (z, residual) = phase1_projection(&x, &basis, k);
+
+    let (naive, brand) = run_both(&basis, &sigmas, &z, &residual, sqrt_lambda, k, cap);
+    let brand = brand.expect("Brand should succeed when c < d");
+    assert_updates_close(&naive, &brand, 1e-8, 1.0 - 1e-6, "zero-initial-sigmas");
+}
+
+/// When singular values are large (10⁶), the QR residual is
+/// relatively tiny. Both should maintain accuracy.
+#[test]
+// Linear algebra test — d, k, b, z, x follow standard mathematical notation.
+#[allow(clippy::many_single_char_names)]
+fn equivalence_large_singular_values() {
+    let d = 64;
+    let k = 4;
+    let b = 8;
+    let cap = 4;
+    let sqrt_lambda = 0.99_f64.sqrt();
+    let mut rng = SmallRng::seed_from_u64(666);
+
+    let basis = random_basis(d, cap, &mut rng);
+    let sigmas = vec![1e6, 5e5, 1e5, 1e4];
+
+    let x = random_matrix(b, d, &mut rng);
+    let (z, residual) = phase1_projection(&x, &basis, k);
+
+    let (naive, brand) = run_both(&basis, &sigmas, &z, &residual, sqrt_lambda, k, cap);
+    let brand = brand.expect("Brand should succeed when c < d");
+    assert_updates_close(&naive, &brand, 1e-6, 1.0 - 1e-4, "large-sigmas");
+}
+
+/// Degenerate spectrum: all singular values are identical.
+///
+/// When σ₁ = σ₂ = … = σₖ, the corresponding basis columns are
+/// interchangeable — any rotation within that subspace is equally
+/// valid.  The algorithms should still agree on σ and produce
+/// bases spanning the same subspace.
+#[test]
+// Linear algebra test — d, k, b, z, x follow standard mathematical notation.
+#[allow(clippy::many_single_char_names)]
+fn equivalence_equal_singular_values() {
+    let d = 64;
+    let k = 4;
+    let b = 8;
+    let cap = 4;
+    let sqrt_lambda = 0.99_f64.sqrt();
+    let mut rng = SmallRng::seed_from_u64(404);
+
+    let basis = random_basis(d, cap, &mut rng);
+    let sigmas = vec![5.0; cap]; // Degenerate spectrum — all σ identical.
+
+    let x = random_matrix(b, d, &mut rng);
+    let (z, residual) = phase1_projection(&x, &basis, k);
+
+    let (naive, brand) = run_both(&basis, &sigmas, &z, &residual, sqrt_lambda, k, cap);
+    let brand = brand.expect("Brand should succeed when c < d");
+    // Looser basis tolerance: degenerate spectrum makes basis column
+    // orientation ambiguous within the equal-σ subspace.
+    assert_updates_close(&naive, &brand, 1e-8, 1.0 - 1e-4, "equal-sigmas");
+}
+
+/// λ = 1.0 means no exponential forgetting — all history is
+/// weighted equally.  This simplifies √λ·diag(σ) to diag(σ)
+/// and should be a well-conditioned case.
+#[test]
+// Linear algebra test — d, k, b, z, x follow standard mathematical notation.
+#[allow(clippy::many_single_char_names)]
+fn equivalence_no_forgetting() {
+    let d = 64;
+    let k = 4;
+    let b = 8;
+    let cap = 4;
+    let sqrt_lambda = 1.0; // λ = 1.0 → no exponential forgetting.
+    let mut rng = SmallRng::seed_from_u64(505);
+
+    let basis = random_basis(d, cap, &mut rng);
+    let sigmas = vec![8.0, 4.0, 2.0, 1.0];
+
+    let x = random_matrix(b, d, &mut rng);
+    let (z, residual) = phase1_projection(&x, &basis, k);
+
+    let (naive, brand) = run_both(&basis, &sigmas, &z, &residual, sqrt_lambda, k, cap);
+    let brand = brand.expect("Brand should succeed when c < d");
+    assert_updates_close(&naive, &brand, 1e-8, 1.0 - 1e-6, "no-forgetting");
+}
+
+// ════════════════════════════════════════════════════════════
+// § 4 — Properties (invariants)
+// ════════════════════════════════════════════════════════════
+
+/// Both algorithms should produce orthonormal output bases.
+#[test]
+// Linear algebra test — d, k, b, z, x follow standard mathematical notation.
+#[allow(clippy::many_single_char_names)]
+fn output_basis_is_orthonormal() {
+    let d = 128;
+    let k = 4;
+    let b = 16;
+    let cap = 4;
+    let sqrt_lambda = 0.99_f64.sqrt();
+    let mut rng = SmallRng::seed_from_u64(2024);
+
+    let basis = random_basis(d, cap, &mut rng);
+    let sigmas = vec![8.0, 4.0, 2.0, 1.0];
+
+    let x = random_matrix(b, d, &mut rng);
+    let (z, residual) = phase1_projection(&x, &basis, k);
+
+    let naive = naive_svd::evolve(&basis, &sigmas, &z, &residual, sqrt_lambda, k, cap).expect("naïve should not fail");
+    let brand = brand_svd::evolve(&basis, &sigmas, &z, &residual, sqrt_lambda, k, cap).expect("brand should not fail");
+
+    assert_orthonormal(&naive.basis, naive.n, 1e-10, "naïve orthonormality");
+    assert_orthonormal(&brand.basis, brand.n, 1e-10, "brand orthonormality");
+}
+
+/// faer's `thin_svd` returns sorted, non-negative singular values.
+/// Both algorithms should preserve this.
+#[test]
+// Linear algebra test — d, k, b, z, x follow standard mathematical notation.
+#[allow(clippy::many_single_char_names)]
+fn singular_values_sorted_and_nonnegative() {
+    let d = 64;
+    let k = 4;
+    let b = 8;
+    let cap = 4;
+    let sqrt_lambda = 0.95_f64.sqrt();
+    let mut rng = SmallRng::seed_from_u64(7777);
+
+    let basis = random_basis(d, cap, &mut rng);
+    let sigmas = vec![12.0, 6.0, 3.0, 1.5];
+
+    let x = random_matrix(b, d, &mut rng);
+    let (z, residual) = phase1_projection(&x, &basis, k);
+
+    for (label, result) in [
+        (
+            "naïve",
+            naive_svd::evolve(&basis, &sigmas, &z, &residual, sqrt_lambda, k, cap).unwrap(),
+        ),
+        (
+            "brand",
+            brand_svd::evolve(&basis, &sigmas, &z, &residual, sqrt_lambda, k, cap).unwrap(),
+        ),
+    ] {
+        for (i, &s) in result.sigmas.iter().enumerate() {
+            assert!(s >= 0.0, "{label}: σ[{i}] = {s} is negative");
+        }
+        for i in 1..result.n {
+            assert!(
+                result.sigmas[i - 1] >= result.sigmas[i] - 1e-12,
+                "{label}: σ not decreasing: σ[{}]={}, σ[{i}]={}",
+                i - 1,
+                result.sigmas[i - 1],
+                result.sigmas[i]
+            );
+        }
+    }
+}
+
+/// Both algorithms should be fully deterministic.
+#[test]
+// Linear algebra test — d, k, b, z, x follow standard mathematical notation.
+#[allow(clippy::many_single_char_names)]
+fn deterministic_reproduction() {
+    let d = 128;
+    let k = 2;
+    let b = 16;
+    let cap = 2;
+    let sqrt_lambda = 0.99_f64.sqrt();
+
+    // Run twice with same seed.
+    for strategy in ["naive", "brand"] {
+        let mut results = Vec::new();
+        for _ in 0..2 {
+            let mut rng = SmallRng::seed_from_u64(42);
+            let basis = random_basis(d, cap, &mut rng);
+            let sigmas = vec![5.0, 2.0];
+            let x = random_matrix(b, d, &mut rng);
+            let (z, residual) = phase1_projection(&x, &basis, k);
+
+            let result = match strategy {
+                "naive" => naive_svd::evolve(&basis, &sigmas, &z, &residual, sqrt_lambda, k, cap).unwrap(),
+                "brand" => brand_svd::evolve(&basis, &sigmas, &z, &residual, sqrt_lambda, k, cap).unwrap(),
+                _ => unreachable!(),
+            };
+            results.push(result);
+        }
+
+        // Exact bit-for-bit equality — deterministic SVD produces identical
+        // floating-point values, so bitwise comparison is intentional.
+        #[allow(clippy::float_cmp)]
+        {
+            let a = &results[0];
+            let b_r = &results[1];
+            assert_eq!(a.n, b_r.n, "{strategy}: n mismatch");
+            for i in 0..a.n {
+                assert_eq!(a.sigmas[i], b_r.sigmas[i], "{strategy}: σ[{i}] not bitwise equal");
+                for j in 0..d {
+                    assert_eq!(
+                        a.basis[(j, i)],
+                        b_r.basis[(j, i)],
+                        "{strategy}: basis[{j},{i}] not bitwise equal"
+                    );
+                }
+            }
+        } // #[allow(clippy::float_cmp)]
+    }
+}
+
+// ════════════════════════════════════════════════════════════
+// § 5 — Functional / integration
+// ════════════════════════════════════════════════════════════
+
+/// Run 50 evolution steps, feeding each algorithm the same sequence.
+/// After each step, check equivalence. This tests accumulation of
+/// floating-point differences over time.
+#[test]
+// Linear algebra test — d, k, b, z, x follow standard mathematical notation.
+// i32→f64 cast is for the growing FP-error tolerance: 1e-6 × (step + 1.0).
+#[allow(clippy::many_single_char_names)]
+fn equivalence_multi_step() {
+    let d = 64;
+    let k = 4;
+    let b = 8;
+    let cap = 4;
+    let lambda: f64 = 0.99;
+    let sqrt_lambda = lambda.sqrt();
+    let steps = 50;
+    let mut rng = SmallRng::seed_from_u64(1337);
+
+    // Shared initial state.
+    let mut naive_basis = random_basis(d, cap, &mut rng);
+    let mut naive_sigmas = vec![5.0, 3.0, 1.0, 0.5];
+    let mut brand_basis = naive_basis.clone();
+    let mut brand_sigmas = naive_sigmas.clone();
+
+    for step in 0..steps {
+        let x = random_matrix(b, d, &mut rng);
+
+        // Project against naïve state (both should be identical).
+        let (z_n, res_n) = phase1_projection(&x, &naive_basis, k);
+        let (z_b, res_b) = phase1_projection(&x, &brand_basis, k);
+
+        let naive_result =
+            naive_svd::evolve(&naive_basis, &naive_sigmas, &z_n, &res_n, sqrt_lambda, k, cap).expect("naïve should not fail");
+        let brand_result =
+            brand_svd::evolve(&brand_basis, &brand_sigmas, &z_b, &res_b, sqrt_lambda, k, cap).expect("brand should not fail");
+
+        // Update state for next step.
+        naive_basis = pad_to_cap(&naive_result.basis, d, cap);
+        naive_sigmas = pad_sigmas(&naive_result.sigmas, cap);
+        brand_basis = pad_to_cap(&brand_result.basis, d, cap);
+        brand_sigmas = pad_sigmas(&brand_result.sigmas, cap);
+
+        // Allow growing tolerance as FP differences accumulate.
+        let sigma_tol = 1e-6 * (f64::from(step) + 1.0);
+        // Start from (1.0 − 1e-4) so the check is satisfiable at step 0
+        // (|cos| ≤ 1.0 exactly for unit vectors, so "> 1.0" always fails).
+        let basis_tol = f64::from(step + 1).mul_add(-1e-4, 1.0);
+        let label = format!("multi-step {step}");
+
+        assert_updates_close(&naive_result, &brand_result, sigma_tol, basis_tol, &label);
+    }
+}
+
+/// Both algorithms should produce bases that give the same
+/// reconstruction error ‖X − X̂‖_F when projecting test data.
+#[test]
+// Linear algebra test — d, k, b, z, x follow standard mathematical notation.
+#[allow(clippy::many_single_char_names)]
+fn reconstruction_error_equivalence() {
+    let d = 128;
+    let k = 4;
+    let b = 16;
+    let cap = 4;
+    let sqrt_lambda = 0.99_f64.sqrt();
+    let mut rng = SmallRng::seed_from_u64(2025);
+
+    let basis = random_basis(d, cap, &mut rng);
+    let sigmas = vec![8.0, 4.0, 2.0, 1.0];
+
+    let x = random_matrix(b, d, &mut rng);
+    let (z, residual) = phase1_projection(&x, &basis, k);
+
+    let naive = naive_svd::evolve(&basis, &sigmas, &z, &residual, sqrt_lambda, k, cap).unwrap();
+    let brand = brand_svd::evolve(&basis, &sigmas, &z, &residual, sqrt_lambda, k, cap).unwrap();
+
+    // Project test data onto both new bases and measure reconstruction error.
+    let test_x = random_matrix(b, d, &mut rng);
+
+    let naive_err = recon_error(&test_x, &naive.basis, naive.n);
+    let brand_err = recon_error(&test_x, &brand.basis, brand.n);
+
+    let rel = (naive_err - brand_err).abs() / naive_err.max(1e-15);
+    assert!(
+        rel < 1e-6,
+        "reconstruction error diverged: naïve={naive_err:.8e}, brand={brand_err:.8e}, rel={rel:.2e}"
+    );
+}
diff --git a/packages/sentinel/src/maths/tests/mod.rs b/packages/sentinel/src/maths/tests/mod.rs
new file mode 100644
index 000000000..4bb99d218
--- /dev/null
+++ b/packages/sentinel/src/maths/tests/mod.rs
@@ -0,0 +1,13 @@
+//! Comprehensive comparison tests: Brand's incremental SVD vs naïve dense SVD.
+//!
+//! Every test runs both algorithms on the same inputs and asserts that
+//! they produce equivalent results (up to SVD sign ambiguity and FP
+//! tolerance).  This is the expanded, persistent version of the
+//! `debug_assert` oracle in `maths::evolve()`.
+//!
+//! # §-references
+//!
+//! - ADR-S-016 — Brand's incremental SVD
+//! - ADR-S-015 — Cell creation performance
+
+mod brand_vs_naive;
diff --git a/packages/sentinel/src/observation.rs b/packages/sentinel/src/observation.rs
new file mode 100644
index 000000000..2b77163ae
--- /dev/null
+++ b/packages/sentinel/src/observation.rs
@@ -0,0 +1,107 @@
+//! Observation boundary: converting raw coordinate values into the
+//! mathematical representation the subspace engine needs.
+//!
+//! This module is the anti-corruption layer between the host's domain
+//! (positionally structured coordinate values) and the linear algebra
+//! world (`Mat<f64>`, centred bit vectors).
+//!
+//! The input values must have hierarchical positional structure —
+//! leading bits define coarse groupings and successive bits refine
+//! them (e.g. IPv6 addresses). The host is responsible for ensuring
+//! this property before handing values to the sentinel.
+//!
+//! Nothing in this module touches `faer`. It produces plain `Vec<f64>`
+//! data that the subspace module consumes.
+
+use torrust_mudlark::Coordinate;
+
+// ─── CentredBitSource trait (§ADR-S-018 Part A) ──────────────
+
+/// Bridge trait: convert a coordinate value into centred bit form.
+///
+/// Sealed to the sentinel crate — only implemented for `u128` and `u64`.
+pub trait CentredBitSource: Coordinate {
+    /// Convert `self` into a centred bit vector of length `n`.
+    fn to_centred_bits(&self, n: u32) -> CentredBits;
+}
+
+impl CentredBitSource for u128 {
+    #[allow(clippy::cast_possible_truncation)] // i < 128, fits in u32
+    fn to_centred_bits(&self, n: u32) -> CentredBits {
+        let mut bits = [0.0_f64; 128];
+        let len = n as usize;
+        for (i, slot) in bits[..len].iter_mut().enumerate() {
+            *slot = if (self >> (n - 1 - i as u32)) & 1 == 1 { 0.5 } else { -0.5 };
+        }
+        CentredBits { bits, len }
+    }
+}
+
+impl CentredBitSource for u64 {
+    #[allow(clippy::cast_possible_truncation)] // i < 64, fits in u32
+    fn to_centred_bits(&self, n: u32) -> CentredBits {
+        let mut bits = [0.0_f64; 128];
+        let len = n.min(64) as usize;
+        for (i, slot) in bits[..len].iter_mut().enumerate() {
+            *slot = if (self >> (n.min(64) - 1 - i as u32)) & 1 == 1 {
+                0.5
+            } else {
+                -0.5
+            };
+        }
+        CentredBits { bits, len }
+    }
+}
+
+// ─── CentredBits ────────────────────────────────────────────
+
+/// A coordinate value converted to a centred bit vector.
+///
+/// Each of the `len` bits becomes:
+/// - bit `1` → `+0.5`
+/// - bit `0` → `−0.5`
+///
+/// This centring is critical: it ensures the data has zero mean
+/// per dimension, which the subspace tracker requires.
+#[derive(Debug, Clone)]
+pub struct CentredBits {
+    /// The centred bit values, from MSB (index 0) to LSB (index `len - 1`).
+    /// Only indices `[0, len)` are meaningful; the rest are zero-filled.
+    pub bits: [f64; 128],
+
+    /// Runtime length (= N for the sentinel's domain width).
+    len: usize,
+}
+
+impl CentredBits {
+    /// Convert a `u128` value to centred bits (128-bit, convenience wrapper).
+    #[must_use]
+    pub fn from_u128(value: u128) -> Self {
+        value.to_centred_bits(128)
+    }
+
+    /// Construct from any coordinate type implementing `CentredBitSource`.
+    #[must_use]
+    pub(crate) fn from_coord<C: CentredBitSource>(value: &C, n: u32) -> Self {
+        value.to_centred_bits(n)
+    }
+
+    /// Return a slice of the suffix bits from position `depth` to `len`.
+    ///
+    /// For a cell at G-tree depth `d`, the first `d` bits are resolved
+    /// by routing (constant within the cell). The suffix `[d, len)` is
+    /// the working observation — the bits that vary and carry
+    /// statistical content (§ALGO S-3.2).
+    ///
+    /// Width: `len - depth`. At depth 0, the suffix is the entire
+    /// bit vector. At depth `len`, the suffix is empty (zero-width
+    /// cell — degenerate).
+    ///
+    /// # Panics
+    ///
+    /// Panics if `depth` exceeds `len`.
+    #[must_use]
+    pub fn suffix(&self, depth: u8) -> &[f64] {
+        &self.bits[usize::from(depth)..self.len]
+    }
+}
diff --git a/packages/sentinel/src/report.rs b/packages/sentinel/src/report.rs
new file mode 100644
index 000000000..b243fd930
--- /dev/null
+++ b/packages/sentinel/src/report.rs
@@ -0,0 +1,812 @@
+//! Report types emitted by the sentinel.
+//!
+//! These types carry the raw statistical measurements from each
+//! [`ingest`](crate::SpectralSentinel::ingest) call.
+//! They contain numbers and facts — never opinions or recommended actions.
+//!
+//! The host reads these reports and applies its own policy to decide
+//! what (if anything) to do about them.
+
+use std::fmt::Debug;
+
+use torrust_mudlark::GNodeId;
+
+// ─── Batch-level ────────────────────────────────────────────
+
+/// Complete statistical output from one
+/// [`ingest`](crate::SpectralSentinel::ingest) call.
+///
+/// Matches the structure defined in algorithm.md Chapter 14.
+#[derive(Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+#[cfg_attr(feature = "serde", serde(bound = "C: serde::Serialize + serde::de::DeserializeOwned"))]
+pub struct BatchReport<C: Copy + Debug> {
+    /// Per-cell reports for competitively selected cells ($\mathcal{A}$).
+    ///
+    /// Only competitive cells that received observations in this batch
+    /// are included. Ordered by `GNodeId` for deterministic output
+    /// (ADR-S-005).
+    pub cell_reports: Vec<CellReport<C>>,
+
+    /// Per-cell reports for ancestor-only cells ($\mathcal{A}^* \setminus \mathcal{A}$).
+    ///
+    /// Ancestor cells provide multi-scale context. Only those that
+    /// received observations in this batch are included.
+    /// Ordered by `GNodeId`.
+    pub ancestor_reports: Vec<CellReport<C>>,
+
+    /// Hierarchical coordination reports from the G-tree walk (§ALGO S-9.4).
+    ///
+    /// One report per active coordination context. Ordered by `GNodeId`.
+    /// Empty when fewer than 2 competitive cells report scores.
+    pub coordination_reports: Vec<CoordinationReport<C>>,
+
+    /// Snapshot of the G-V Graph's spatial contour.
+    pub contour: ContourSnapshot,
+
+    /// Operational health snapshot of the sentinel.
+    pub health: HealthReport,
+
+    /// Summary of the current analysis set.
+    pub analysis_set_summary: AnalysisSetSummary,
+}
+
+// ─── Cell-level ─────────────────────────────────────────────
+
+/// Statistics for a single analysis cell after processing one batch.
+///
+/// Each cell is at a specific G-tree depth and operates on suffix bits
+/// `[d, N)` at width `w = N - d`. Competitive cells are selected
+/// by the analysis selector (§ALGO S-4.1); ancestor cells provide
+/// multi-scale context (§ALGO S-4.2).
+#[derive(Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+#[cfg_attr(feature = "serde", serde(bound = "C: serde::Serialize + serde::de::DeserializeOwned"))]
+pub struct CellReport<C: Copy + Debug> {
+    /// Arena handle of the backing G-node.
+    pub gnode_id: GNodeId,
+
+    /// Lower bound of the dyadic interval (inclusive).
+    pub start: C,
+
+    /// Upper bound of the dyadic interval (exclusive).
+    pub end: C,
+
+    /// G-tree depth of this cell.
+    pub depth: u32,
+
+    /// Suffix width: `N - depth`.
+    pub analysis_width: usize,
+
+    /// Whether this cell is competitively selected (vs ancestor-only).
+    pub is_competitive: bool,
+
+    /// How many observations in this batch routed to this cell.
+    pub sample_count: usize,
+
+    /// Current rank of the learned subspace.
+    pub rank: usize,
+
+    /// Fraction of total variance captured by the current rank.
+    pub energy_ratio: f64,
+
+    /// Largest singular value of the learned subspace.
+    pub top_singular_value: f64,
+
+    /// Anomaly scores along all four measurement axes.
+    pub scores: AnomalyScores,
+
+    /// How mature is this tracker's learned model?
+    pub maturity: TrackerMaturity,
+
+    /// Geometric properties of this tracker's current state.
+    pub geometry: ScoringGeometry,
+
+    /// Per-sample scores, if enabled.
+    pub per_sample: Option<Vec<SampleScore>>,
+}
+
+// ─── Coordination-level ───────────────────────────────────────
+
+/// Coordination analysis at a single G-tree internal node (§ALGO S-9.1).
+///
+/// Produced by a coordination tracker (`SubspaceTracker` at $w = 4$)
+/// that consumes running-mean-centred cell-score matrices as
+/// observations. The group consists of all competitive cells in
+/// this node's subtree that reported scores in this batch.
+///
+/// The four anomaly axes have second-order meaning at this level:
+///
+/// | Meta-axis    | Detects                                              |
+/// |-------------|------------------------------------------------------|
+/// | Novelty     | A cell-score pattern the model has never seen         |
+/// | Displacement| The overall score landscape has shifted               |
+/// | Surprise    | A specific scoring axis is system-wide anomalous      |
+/// | Coherence   | An unusual combination of axis elevations             |
+#[derive(Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+#[cfg_attr(feature = "serde", serde(bound = "C: serde::Serialize + serde::de::DeserializeOwned"))]
+pub struct CoordinationReport<C: Copy + Debug> {
+    /// Arena handle of the coordination context's G-node.
+    pub gnode_id: GNodeId,
+
+    /// Lower bound of the coordination context's dyadic interval (inclusive).
+    pub start: C,
+
+    /// Upper bound of the coordination context's dyadic interval (exclusive).
+    pub end: C,
+
+    /// G-tree depth of the coordination context.
+    pub depth: u32,
+
+    /// How many competitive cells in this subtree contributed
+    /// score vectors this batch.
+    pub cells_reporting: usize,
+
+    /// Current rank of the coordination tracker's learned subspace.
+    pub rank: usize,
+
+    /// Fraction of total variance captured by the rank.
+    pub energy_ratio: f64,
+
+    /// Largest singular value.
+    pub top_singular_value: f64,
+
+    /// Anomaly scores at the coordination level.
+    pub scores: AnomalyScores,
+
+    /// How mature is this coordination tracker's model?
+    pub maturity: TrackerMaturity,
+
+    /// Geometric properties of the coordination tracker.
+    pub geometry: ScoringGeometry,
+
+    /// Per-member scores, if
+    /// [`SentinelConfig::per_sample_scores`](crate::SentinelConfig::per_sample_scores)
+    /// is enabled.
+    ///
+    /// Each entry corresponds to one competitive cell in the coordination
+    /// group. The `cell_start`/`cell_end`/`cell_depth` fields identify
+    /// which cell produced this score vector. When present, indices
+    /// correspond to cells in subtree order (left-to-right).
+    pub per_member: Option<Vec<MemberScore<C>>>,
+}
+
+// ─── Per-depth-level (internal) ─────────────────────────────
+
+/// Statistics for a single tracker after processing one batch.
+///
+/// This is the internal report type returned by `SubspaceTracker::observe()`.
+/// Used internally; callers see [`CellReport`] at the public API.
+#[derive(Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+pub struct TrackerReport {
+    /// The suffix depth `d`. The tracker analyses suffix bits `[d, N)`
+    /// at width `w = N − d` (§ALGO S-3.2).
+    pub depth: u8,
+
+    /// Current rank of the learned subspace (number of active basis vectors).
+    pub rank: usize,
+
+    /// Fraction of total variance captured by the current rank.
+    pub energy_ratio: f64,
+
+    /// Largest singular value of the learned subspace.
+    pub top_singular_value: f64,
+
+    /// Anomaly scores along all four measurement axes.
+    pub scores: AnomalyScores,
+
+    /// How mature is this tracker's learned model?
+    pub maturity: TrackerMaturity,
+
+    /// Geometric properties that determine which scoring axes are
+    /// structurally meaningful at this tracker's current state.
+    pub geometry: ScoringGeometry,
+
+    /// Per-sample scores, if enabled.
+    pub per_sample: Option<Vec<SampleScore>>,
+}
+
+// ─── Anomaly scores ─────────────────────────────────────
+
+/// The four anomaly-score axes for a batch of observations.
+///
+/// The sentinel scores each observation along four independent axes
+/// organised into two conceptual groups:
+///
+/// **Subspace axis** — how well the learned model explains the observation:
+///
+/// | Score | Metric | Intuition |
+/// |-------|--------|-----------|
+/// | *Novelty* | Residual energy / DOF: `‖X − X̂‖² / (dim − k)` | "How much of this is foreign?" |
+///
+/// **Cell axis** — how typical the observation is for *this* cell:
+///
+/// | Score | Metric | Intuition |
+/// |-------|--------|-----------|
+/// | *Displacement* | `‖z‖² / (k + ‖z‖²)`, bounded in `[0, 1)` | "How far is this from the centroid?" |
+/// | *Surprise* | Mahalanobis / rank: `Σⱼ ((zⱼ − μⱼ)/σⱼ)² / k` | "The shape is familiar, but the magnitude is wild" |
+/// | *Coherence* | Cross-correlation deviation: `Σⱼ<ₗ (zⱼzₗ − Cⱼₗ)²` | "Normal individually, but this combination is new" |
+///
+/// Displacement, surprise, and coherence decompose the latent
+/// activation pattern along orthogonal statistical concerns:
+/// displacement measures total energy, surprise measures
+/// per-dimension magnitude (diagonal covariance), and coherence
+/// measures pairwise interaction (off-diagonal covariance).
+///
+/// All four axes share the same polarity: **higher values indicate
+/// greater anomalous departure**. This ensures uniform z-score
+/// interpretation and EWMA outlier-filter robustness (see
+/// `docs/algorithm.md`, Appendix A).
+///
+/// **Why not projection energy / "normality"?** Under the sentinel's
+/// centred binary encoding, every observation has the same L2 norm
+/// (`d / 4`). Projection energy is therefore a perfect affine function
+/// of residual energy — it carries zero independent information.
+/// See `docs/algorithm.md`, Appendix B for the full proof.
+#[derive(Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+pub struct AnomalyScores {
+    /// Residual energy per residual DOF: `‖X − X̂‖² / (dim − k)`.
+    pub novelty: ScoreDistribution,
+
+    /// Cell displacement: `‖z‖² / (k + ‖z‖²)`, bounded in `[0, 1)`.
+    pub displacement: ScoreDistribution,
+
+    /// Latent surprise: Mahalanobis distance per rank,
+    /// `Σⱼ ((zⱼ − μⱼ) / σⱼ)² / k`.
+    pub surprise: ScoreDistribution,
+
+    /// Latent coherence: cross-correlation deviation,
+    /// `2 / (k(k−1)) · Σⱼ<ₗ (zⱼzₗ − Cⱼₗ)²`.
+    pub coherence: ScoreDistribution,
+}
+
+// ─── Score distribution ─────────────────────────────────────
+
+/// Summary statistics for a vector of anomaly scores.
+///
+/// Contains both the raw score distribution and its relationship
+/// to the learned baseline (via z-scores).
+#[derive(Debug, Clone, Copy)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+pub struct ScoreDistribution {
+    /// Minimum score in the batch.
+    pub min: f64,
+
+    /// Maximum score in the batch.
+    pub max: f64,
+
+    /// Arithmetic mean of scores in the batch.
+    pub mean: f64,
+
+    /// Z-score of the *maximum* score against the EWMA baseline.
+    pub max_z_score: f64,
+
+    /// Z-score of the *mean* score against the EWMA baseline.
+    pub mean_z_score: f64,
+
+    /// Snapshot of the fast EWMA baseline this distribution was scored against.
+    pub baseline: BaselineSnapshot,
+
+    /// CUSUM drift accumulator for this scoring axis.
+    pub cusum: CusumSnapshot,
+
+    /// Current clip-pressure EWMA for this axis: ρ̄ ∈ [0, 1]  (§ALGO S-14.4).
+    pub clip_pressure: f64,
+}
+
+// ─── Baseline snapshot ──────────────────────────────────────
+
+/// Frozen snapshot of an EWMA baseline at the time of scoring.
+#[derive(Debug, Clone, Copy)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+pub struct BaselineSnapshot {
+    /// Current EWMA mean of "normal" scores.
+    pub mean: f64,
+
+    /// Current EWMA variance of "normal" scores.
+    pub variance: f64,
+}
+
+// ─── CUSUM snapshot ─────────────────────────────────────────
+
+/// Frozen snapshot of a CUSUM accumulator at the time of scoring.
+#[derive(Debug, Clone, Copy)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+pub struct CusumSnapshot {
+    /// Current CUSUM accumulator value.
+    pub accumulator: f64,
+
+    /// Snapshot of the slow EWMA baseline used as the CUSUM reference.
+    pub slow_baseline: BaselineSnapshot,
+
+    /// Number of batches since the CUSUM was last reset.
+    pub steps_since_reset: u64,
+}
+
+// ─── Maturity ───────────────────────────────────────────────
+
+/// How much experience a tracker has, and how much of that is noise.
+#[derive(Debug, Clone, Copy)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+pub struct TrackerMaturity {
+    /// Number of real (non-noise) observations this tracker has processed.
+    pub real_observations: u64,
+
+    /// Number of noise observations injected into this tracker.
+    pub noise_observations: u64,
+
+    /// Estimated fraction of the baseline **not yet established by
+    /// real data**.
+    pub noise_influence: f64,
+}
+
+impl TrackerMaturity {
+    /// A tracker with no observations of any kind.
+    #[must_use]
+    pub const fn cold() -> Self {
+        Self {
+            real_observations: 0,
+            noise_observations: 0,
+            noise_influence: 1.0,
+        }
+    }
+
+    /// Total observations (real + noise).
+    #[must_use]
+    pub const fn total_observations(&self) -> u64 {
+        self.real_observations + self.noise_observations
+    }
+}
+
+// ─── Scoring geometry ───────────────────────────────────────
+
+/// Geometric properties of the tracker's scoring state.
+#[derive(Debug, Clone, Copy)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+pub struct ScoringGeometry {
+    /// Working dimensionality of the tracker's input space.
+    pub dim: usize,
+
+    /// Maximum rank this tracker can reach: `min(dim, max_rank)`.
+    pub cap: usize,
+
+    /// Residual degrees of freedom: `dim - rank`.
+    pub residual_dof: usize,
+}
+
+impl ScoringGeometry {
+    /// Whether the novelty axis is structurally degenerate
+    /// (`residual_dof == 0`).
+    #[must_use]
+    pub const fn is_novelty_saturated(&self) -> bool {
+        self.residual_dof == 0
+    }
+
+    /// Whether the novelty axis *can* become degenerate as rank
+    /// adapts (`cap >= dim`).
+    #[must_use]
+    pub const fn is_novelty_saturable(&self) -> bool {
+        self.cap >= self.dim
+    }
+}
+
+// ─── Per-sample scores ──────────────────────────────────────
+
+/// Raw anomaly scores for a single observation.
+#[derive(Debug, Clone, Copy)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+pub struct SampleScore {
+    /// Residual energy per residual DOF (novelty axis).
+    pub novelty: f64,
+
+    /// Cell displacement score, bounded in `[0, 1)`.
+    pub displacement: f64,
+
+    /// Mahalanobis distance per rank (surprise axis).
+    pub surprise: f64,
+
+    /// Cross-correlation deviation (coherence axis).
+    pub coherence: f64,
+
+    /// Z-score of `novelty` against its EWMA baseline.
+    pub novelty_z: f64,
+
+    /// Z-score of `displacement` against its EWMA baseline.
+    pub displacement_z: f64,
+
+    /// Z-score of `surprise` against its EWMA baseline.
+    pub surprise_z: f64,
+
+    /// Z-score of `coherence` against its EWMA baseline.
+    pub coherence_z: f64,
+}
+
+// ─── Health ─────────────────────────────────────────────────
+
+/// Operational health snapshot of the entire sentinel.
+#[derive(Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+pub struct HealthReport {
+    /// Total live G-nodes in the G-V Graph.
+    pub total_g_nodes: usize,
+
+    /// Number of semi-internal G-nodes.
+    // TODO: count from graph when API available.
+    pub semi_internal_count: usize,
+
+    /// Number of active cell trackers (total).
+    pub active_trackers: usize,
+
+    /// Number of active competitive trackers: $|\mathcal{A}|$.
+    pub active_competitive_trackers: usize,
+
+    /// Number of active ancestor-only trackers: $|\mathcal{A}^*| - |\mathcal{A}| - 1$.
+    ///
+    /// The −1 accounts for the permanent root tracker, which is
+    /// neither competitive nor a normal ancestor.
+    pub active_ancestor_trackers: usize,
+
+    /// Number of active coordination contexts.
+    pub active_coordination_contexts: usize,
+
+    /// Total cells with allocated trackers (online + warming):
+    /// $|\mathcal{I}|$ (the investment set, §ALGO S-8.2, ADR-S-019).
+    pub investment_set_size: usize,
+
+    /// Members of $\mathcal{I}$ currently in the warm-up pipeline
+    /// (§ALGO S-11.6, ADR-S-019).
+    pub warming_trackers: usize,
+
+    /// Competitive targets ($\mathcal{T}$) not yet promoted to
+    /// $\mathcal{A}$ — i.e. warming cells that are competitive
+    /// targets, not ancestors (§ALGO S-14.11, ADR-S-019).
+    pub warming_competitive_targets: usize,
+
+    /// Total real observations across the sentinel's lifetime.
+    pub lifetime_observations: u64,
+
+    /// Number of cells in the analysis set.
+    pub cells_tracked: usize,
+
+    /// Distribution of ranks across all active trackers.
+    pub rank_distribution: RankDistribution,
+
+    /// Distribution of maturity across all active trackers.
+    pub maturity_distribution: MaturityDistribution,
+
+    /// Distribution of geometric scoring reliability across all
+    /// active per-cell trackers.
+    pub geometry_distribution: GeometryDistribution,
+
+    /// Health of the coordination tier.
+    pub coordination_health: CoordinationHealth,
+
+    /// Clip-pressure distribution across active trackers (§ALGO S-14.11).
+    pub clip_pressure_distribution: ClipPressureDistribution,
+}
+
+/// Summary of rank values across all active trackers.
+#[derive(Debug, Clone, Copy)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+pub struct RankDistribution {
+    /// Lowest rank among all trackers.
+    pub min: usize,
+
+    /// Highest rank among all trackers.
+    pub max: usize,
+
+    /// Mean rank across all trackers.
+    pub mean: f64,
+}
+
+/// Summary of maturity across all active trackers.
+#[derive(Debug, Clone, Copy)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+pub struct MaturityDistribution {
+    /// Highest noise influence among all trackers (least mature).
+    pub max_noise_influence: f64,
+
+    /// Lowest noise influence among all trackers (most mature).
+    pub min_noise_influence: f64,
+
+    /// Mean noise influence across all trackers.
+    pub mean_noise_influence: f64,
+
+    /// Number of trackers with zero real observations.
+    pub cold_trackers: usize,
+}
+
+// ─── Geometry distribution ──────────────────────────────────
+
+/// Summary of geometric scoring reliability across a set of trackers.
+#[derive(Debug, Clone, Copy)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+pub struct GeometryDistribution {
+    /// Trackers where `rank == dim` (novelty axis degenerate).
+    pub novelty_saturated: usize,
+
+    /// Trackers where `cap >= dim` (novelty *can* become degenerate).
+    pub novelty_saturable: usize,
+
+    /// Trackers where `rank < 2` (coherence axis does not exist).
+    pub coherence_inactive: usize,
+}
+
+// ─── Clip-pressure distribution ─────────────────────────────
+
+/// Summary of clip-pressure EWMA values across active trackers (§ALGO S-14.11).
+#[derive(Debug, Clone, Copy)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+pub struct ClipPressureDistribution {
+    /// Minimum clip-pressure EWMA across active tracker axes.
+    pub min: f64,
+
+    /// Maximum clip-pressure EWMA across active tracker axes.
+    pub max: f64,
+
+    /// Mean clip-pressure EWMA across active tracker axes.
+    pub mean: f64,
+}
+
+// ─── Coordination health ────────────────────────────────────
+
+/// Health snapshot of the hierarchical coordination tier.
+///
+/// Summarises the active coordination contexts — subspace trackers
+/// operating at $w = 4$ on cross-cell score patterns.
+#[derive(Debug, Clone, Copy)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+pub struct CoordinationHealth {
+    /// Number of active coordination contexts.
+    pub active_contexts: usize,
+
+    /// Capacity (max rank) of the coordination trackers.
+    /// Always min(4, `max_rank`) since $w = 4$.
+    pub capacity: usize,
+
+    /// Rank distribution across active contexts.
+    pub rank_distribution: RankDistribution,
+
+    /// Maturity distribution across active contexts.
+    pub maturity_distribution: MaturityDistribution,
+
+    /// Working dimensionality (always 4).
+    pub dim: usize,
+
+    /// Geometry distribution across active contexts.
+    pub geometry_distribution: GeometryDistribution,
+}
+
+// ─── Axis baseline snapshots ────────────────────────────────
+
+/// Per-axis EWMA baseline snapshots for all four scoring axes.
+///
+/// Provides read-only access to the learned baseline statistics
+/// without requiring direct access to the tracker internals.
+/// Used by convergence tests to verify EWMA settling behaviour
+/// (ADR-S-014).
+#[derive(Debug, Clone, Copy)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+pub struct AxisBaselineSnapshots {
+    /// Novelty axis baseline.
+    pub novelty: BaselineSnapshot,
+
+    /// Displacement axis baseline.
+    pub displacement: BaselineSnapshot,
+
+    /// Surprise axis baseline.
+    pub surprise: BaselineSnapshot,
+
+    /// Coherence axis baseline.
+    pub coherence: BaselineSnapshot,
+}
+
+// ─── Cell inspection ────────────────────────────────────────
+
+/// Snapshot of a cell's tracker state.
+///
+/// Returned by [`SpectralSentinel::inspect_cell`](crate::SpectralSentinel::inspect_cell).
+#[derive(Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+#[cfg_attr(feature = "serde", serde(bound = "C: serde::Serialize + serde::de::DeserializeOwned"))]
+pub struct CellInspection<C: Copy + Debug> {
+    /// Arena handle of the backing G-node.
+    pub gnode_id: GNodeId,
+
+    /// Lower bound of the dyadic interval (inclusive).
+    pub start: C,
+
+    /// Upper bound of the dyadic interval (exclusive).
+    pub end: C,
+
+    /// G-tree depth of this cell.
+    pub depth: u32,
+
+    /// Suffix width: `N - depth`.
+    pub analysis_width: usize,
+
+    /// Whether this cell is competitively selected (vs ancestor-only).
+    pub is_competitive: bool,
+
+    /// Current rank (number of active basis vectors).
+    pub rank: usize,
+
+    /// Fraction of total variance captured by the current rank.
+    pub energy_ratio: f64,
+
+    /// Largest singular value of the learned subspace.
+    pub top_singular_value: f64,
+
+    /// Maturity state.
+    pub maturity: TrackerMaturity,
+
+    /// Geometric scoring properties.
+    pub geometry: ScoringGeometry,
+
+    /// Per-axis EWMA baseline snapshots (ADR-S-014).
+    pub baselines: AxisBaselineSnapshots,
+}
+
+// ─── Contour snapshot ───────────────────────────────────────
+
+/// Snapshot of the G-V Graph's spatial contour at report time.
+///
+/// The contour is the observable surface of the spatial structure —
+/// how many distinct regions exist, how many leaf cells, and how
+/// much total traffic volume the graph has accumulated.
+///
+/// Populated from `GvGraph::plateaus()`, `GvGraph::terminal_count()`,
+/// and `GvGraph::total_sum()`.
+#[derive(Debug, Clone, Copy)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+pub struct ContourSnapshot {
+    /// Number of plateaus in the G-V Graph's spatial structure.
+    ///
+    /// A plateau is a contiguous range of cells at the same
+    /// depth. Fewer plateaus → more uniform spatial resolution.
+    pub plateau_count: usize,
+
+    /// Number of terminal (leaf) cells in the G-tree.
+    ///
+    /// This is the spatial resolution: how many non-overlapping
+    /// regions the domain is partitioned into.
+    pub cell_count: usize,
+
+    /// Total accumulated importance across the entire G-V Graph.
+    ///
+    /// Erased to `f64` via `V::to_f64_approx()` — the concrete
+    /// accumulator type is hidden from report consumers.
+    /// Values above 2^53 may lose LSBs (acceptable for diagnostics).
+    pub total_importance: f64,
+
+    /// Cells created by catalytic or bootstrap bisection since the
+    /// previous report.
+    ///
+    /// Computed exactly from graph state deltas:
+    /// `splits = Δnode_count − Δterminal_count`.
+    ///
+    /// Saturates at [`u32::MAX`] in the (practically unreachable)
+    /// event of more than ~4 × 10⁹ splits between consecutive reports.
+    pub splits_since_last_report: u32,
+
+    /// Net structural removals since the previous report:
+    /// evictions minus restorations (legacy promotions).
+    ///
+    /// Restorations are rare (they occur only when an eviction
+    /// leaves a semi-internal node); this value typically equals
+    /// the raw eviction count.
+    ///
+    /// Computed exactly from graph state deltas:
+    /// `net_removals = splits − Δterminal_count`.
+    ///
+    /// Saturates at [`u32::MAX`] under the same caveat as
+    /// [`Self::splits_since_last_report`].
+    pub net_removals_since_last_report: u32,
+}
+
+// ─── Analysis set summary ───────────────────────────────────
+
+/// Summary of the analysis set at report time.
+///
+/// Describes the investment set and producing sets without
+/// enumerating every cell. For the complete set, use
+/// [`SpectralSentinel::analysis_set()`](crate::SpectralSentinel::analysis_set).
+///
+/// See §ALGO S-14.12, ADR-S-019.
+#[derive(Debug, Clone, Copy)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+pub struct AnalysisSetSummary {
+    /// Number of online competitive targets: $|\mathcal{A}|$
+    /// (the producing competitive set, §ALGO S-8.3).
+    ///
+    /// Always ≤ `analysis_k` from the configuration.
+    pub competitive_size: usize,
+
+    /// Total online cells in the producing full set: $|\mathcal{A}^*|$
+    /// (§ALGO S-8.3).
+    ///
+    /// Includes online competitive targets, their online G-tree
+    /// ancestors, and the permanent root tracker.
+    pub full_size: usize,
+
+    /// Total cells with allocated trackers (online + warming):
+    /// $|\mathcal{I}|$ (the investment set, §ALGO S-8.2).
+    ///
+    /// `investment_set_size = full_size + warming trackers`.
+    pub investment_set_size: usize,
+
+    /// (min, max) G-tree depth across the full analysis set.
+    ///
+    /// Depth 0 = root (always present). Max depth reflects
+    /// the finest spatial resolution currently being analysed.
+    pub depth_range: (u32, u32),
+
+    /// (min, max) importance across the competitive set.
+    ///
+    /// Erased to `f64` via `V::to_f64_approx()` — the concrete
+    /// accumulator type is hidden from report consumers.
+    /// `(0.0, 0.0)` when `competitive_size == 0`.
+    pub importance_range: (f64, f64),
+
+    /// (min, max) V-Tree depth across the competitive set.
+    ///
+    /// V-Tree depth reflects competitive standing — lower = more
+    /// significant. `(0, 0)` when `competitive_size == 0`.
+    pub v_depth_range: (usize, usize),
+
+    /// Number of G-tree nodes excluded from tracking because their
+    /// suffix width was below `MIN_TRACKER_DIM`.
+    ///
+    /// A persistently non-zero count may indicate the
+    /// `split_threshold` is too low for the traffic mix.
+    /// See ADR-S-011.
+    pub degenerate_cells_skipped: usize,
+}
+
+// ─── Member score ───────────────────────────────────────────
+
+/// Per-cell scores from a coordination context's model.
+///
+/// Each entry corresponds to one competitive cell in the coordination
+/// group. The `cell_start`/`cell_end`/`cell_depth` fields identify
+/// which cell produced this score vector.
+#[derive(Debug, Clone, Copy)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
+#[cfg_attr(feature = "serde", serde(bound = "C: serde::Serialize + serde::de::DeserializeOwned"))]
+pub struct MemberScore<C: Copy + Debug> {
+    /// Lower bound of the scored cell's dyadic interval (inclusive).
+    pub cell_start: C,
+
+    /// Upper bound of the scored cell's dyadic interval (exclusive).
+    pub cell_end: C,
+
+    /// G-tree depth of the scored cell.
+    pub cell_depth: u32,
+
+    /// Novelty score for this cell's contribution.
+    pub novelty: f64,
+
+    /// Displacement score for this cell's contribution.
+    pub displacement: f64,
+
+    /// Surprise score for this cell's contribution.
+    pub surprise: f64,
+
+    /// Coherence score for this cell's contribution.
+    pub coherence: f64,
+
+    /// Z-score of `novelty` against the coordination baseline.
+    pub novelty_z: f64,
+
+    /// Z-score of `displacement` against the coordination baseline.
+    pub displacement_z: f64,
+
+    /// Z-score of `surprise` against the coordination baseline.
+    pub surprise_z: f64,
+
+    /// Z-score of `coherence` against the coordination baseline.
+    pub coherence_z: f64,
+}
diff --git a/packages/sentinel/src/sentinel/cusum.rs b/packages/sentinel/src/sentinel/cusum.rs
new file mode 100644
index 000000000..2410ee275
--- /dev/null
+++ b/packages/sentinel/src/sentinel/cusum.rs
@@ -0,0 +1,142 @@
+//! CUSUM (Cumulative Sum) drift accumulator.
+//!
+//! Detects sustained upward drift of batch mean scores away from a
+//! slow EWMA reference.  This is a one-sided Page's test: the
+//! accumulator grows when the fast signal consistently exceeds the
+//! slow baseline by more than a noise allowance, and resets to zero
+//! when the deviation reverses.
+//!
+//! See `docs/algorithm.md` §ALGO S-7.3 for the full specification.
+//!
+//! Pure `f64` arithmetic — no `faer` dependency.
+
+use crate::ewma::EwmaStats;
+use crate::report::{BaselineSnapshot, CusumSnapshot};
+
+/// One-sided CUSUM accumulator with a slow EWMA reference.
+///
+/// Each scoring axis owns one of these.  It pairs a slow EWMA
+/// baseline (longer memory than the fast baseline in [`EwmaStats`])
+/// with a cumulative sum that builds evidence of sustained drift.
+///
+/// The sentinel reports the raw accumulator value; the host decides
+/// what level of accumulated drift warrants action.
+#[derive(Debug, Clone)]
+pub struct CusumAccumulator {
+    /// Slow EWMA baseline — the reference the CUSUM measures drift from.
+    slow: EwmaStats,
+
+    /// The cumulative sum.  Non-negative (clamped at zero).
+    accumulator: f64,
+
+    /// Batches since the last reset (including post-noise reset).
+    steps_since_reset: u64,
+}
+
+impl CusumAccumulator {
+    /// Create a new CUSUM accumulator with the given slow decay factor.
+    #[must_use]
+    pub const fn new(slow_decay: f64) -> Self {
+        Self {
+            slow: EwmaStats::new(slow_decay),
+            accumulator: 0.0,
+            steps_since_reset: 0,
+        }
+    }
+
+    /// Update the accumulator with a batch of per-sample scores.
+    ///
+    /// 1. Computes the gap: `batch_mean − slow_mean − κ·√slow_var`.
+    /// 2. Accumulates: `S = max(0, S + gap)`.
+    /// 3. Feeds the scores to the slow EWMA baseline.
+    ///
+    /// The gap is computed *before* updating the slow baseline so the
+    /// reference reflects the prior state — matching the principle
+    /// that scoring precedes evolution.
+    ///
+    /// `allowance_sigmas` is `κ_σ` from config — the noise tolerance
+    /// in units of slow-baseline standard deviation.
+    #[cfg(test)]
+    pub fn update(&mut self, scores: &[f64], batch_mean: f64, allowance_sigmas: f64, eps: f64, clip_sigmas: f64) {
+        let slow_mean = self.slow.mean();
+        let slow_std = (self.slow.variance() + eps).sqrt();
+        let allowance = allowance_sigmas * slow_std;
+
+        let gap = batch_mean - slow_mean - allowance;
+        self.accumulator = (self.accumulator + gap).max(0.0);
+
+        // Now update the slow baseline with this batch.
+        self.slow.update(scores, clip_sigmas);
+
+        self.steps_since_reset += 1;
+    }
+
+    /// Reset the accumulator to zero.
+    ///
+    /// Called after noise injection (§ALGO S-7.4) and optionally by the host
+    /// after acknowledging a regime change.
+    pub const fn reset(&mut self) {
+        self.accumulator = 0.0;
+        self.steps_since_reset = 0;
+    }
+
+    /// Destroy all state — return to the freshly-constructed state.
+    ///
+    /// Resets the accumulator *and* the slow EWMA baseline to cold.
+    /// Used when the scoring axis this accumulator tracks ceases to
+    /// exist (e.g. coherence when rank drops below 2).
+    pub const fn reset_cold(&mut self) {
+        self.slow.reset_cold();
+        self.accumulator = 0.0;
+        self.steps_since_reset = 0;
+    }
+
+    /// Seed the slow EWMA from an external fast EWMA baseline.
+    ///
+    /// Closes the fast-slow gap after noise injection (ADR-S-013
+    /// §6b, Option C).  The slow EWMA's mean and variance are set
+    /// to the fast EWMA's current values so the CUSUM starts with
+    /// the two baselines in agreement — eliminating the monotonic
+    /// false-drift accumulation caused by the slow EWMA's 693-step
+    /// half-life being unable to catch up to the fast EWMA.
+    ///
+    /// Should be called **after** noise injection completes and
+    /// **before** `reset()`.
+    pub const fn seed_slow_from(&mut self, fast: &EwmaStats) {
+        self.slow.seed_from(fast);
+    }
+
+    /// Update the accumulator with **pre-filtered** samples.
+    ///
+    /// Identical to [`update`](Self::update) except the slow EWMA
+    /// receives pre-filtered values via `update_raw()` instead of
+    /// applying its own clip filter.  The CUSUM gap still uses
+    /// `raw_batch_mean` (the pre-clip mean of the full batch).
+    ///
+    /// Used by the shared-filter pipeline (§ALGO S-6.1.1 step 5).
+    #[allow(dead_code, reason = "wired in ADR-S-020 Phase 5")]
+    pub fn update_filtered(&mut self, filtered: &[f64], raw_batch_mean: f64, allowance_sigmas: f64, eps: f64) {
+        let slow_mean = self.slow.mean();
+        let slow_std = (self.slow.variance() + eps).sqrt();
+        let allowance = allowance_sigmas * slow_std;
+
+        let gap = raw_batch_mean - slow_mean - allowance;
+        self.accumulator = (self.accumulator + gap).max(0.0);
+
+        self.slow.update_raw(filtered);
+        self.steps_since_reset += 1;
+    }
+
+    /// Snapshot for inclusion in reports.
+    #[must_use]
+    pub const fn snapshot(&self) -> CusumSnapshot {
+        CusumSnapshot {
+            accumulator: self.accumulator,
+            slow_baseline: BaselineSnapshot {
+                mean: self.slow.mean(),
+                variance: self.slow.variance(),
+            },
+            steps_since_reset: self.steps_since_reset,
+        }
+    }
+}
diff --git a/packages/sentinel/src/sentinel/mod.rs b/packages/sentinel/src/sentinel/mod.rs
new file mode 100644
index 000000000..b02106339
--- /dev/null
+++ b/packages/sentinel/src/sentinel/mod.rs
@@ -0,0 +1,1697 @@
+//! The sentinel engine — orchestration, tracking, and drift detection.
+//!
+//! This module contains the core [`SpectralSentinel`] orchestrator and
+//! its internal machinery.  Only the orchestrator is part of the public
+//! API; the subspace tracker and CUSUM accumulator are implementation
+//! details.
+//!
+//! # Automatic noise injection (§ALGO S-11, §ALGO S-18.2)
+//!
+//! Every newly created tracker is automatically warmed with synthetic
+//! noise before it receives any real observations. The root tracker
+//! is warmed at construction; cells created during investment set
+//! reconciliation (§ALGO S-8.5) are enqueued into a **staging area**
+//! for deferred warm-up (§ALGO S-11.6). When `background_warming` is
+//! enabled, a background thread drains the staging area asynchronously;
+//! otherwise warm-up runs synchronously within `reconcile_analysis_set()`.
+//! Warming cells hold **investment slots** in $\mathcal{I}$ but not
+//! **production slots** in $\mathcal{A}$ (ADR-S-019).
+//! Coordination contexts are warmed via Gamma-sampled synthetic
+//! score vectors (§ALGO S-9.8). No manual injection API exists — the
+//! sentinel owns the injection lifecycle entirely (ADR-S-007).
+//!
+//! # Quick start
+//!
+//! ```
+//! use torrust_sentinel::SentinelConfig;
+//! use torrust_sentinel::Sentinel128;
+//!
+//! let cfg = SentinelConfig::<u64> {
+//!     analysis_k: 4,
+//!     ..SentinelConfig::default()
+//! };
+//! // Root tracker is auto-warmed at construction.
+//! let mut sentinel = Sentinel128::new(cfg).unwrap();
+//!
+//! let values: Vec<u128> = vec![
+//!     0xF000_0000_0000_0000_0000_0000_0000_0001,
+//!     0xF000_0000_0000_0000_0000_0000_0000_0002,
+//!     0x1000_0000_0000_0000_0000_0000_0000_0003,
+//! ];
+//! let report = sentinel.ingest(&values);
+//!
+//! // Root cell always receives all observations (as an ancestor).
+//! assert!(!report.ancestor_reports.is_empty());
+//! ```
+
+pub mod cusum;
+pub mod staging;
+pub mod tracker;
+pub mod warming_thread;
+
+use std::collections::{BTreeMap, BTreeSet};
+use std::sync::{Arc, Mutex};
+
+use rand::rngs::SmallRng;
+use rand::{RngExt, SeedableRng};
+use rand_distr::{Distribution, Gamma};
+use torrust_mudlark::{Config as GvConfig, Coordinate, GNodeId, GvGraph, Inspectable};
+
+use self::tracker::SubspaceTracker;
+use crate::{
+    AnalysisSet, AxisBaselineSnapshots, BatchReport, CellInspection, CellReport, CentredBits, ClipPressureDistribution,
+    ConfigErrors, ContourSnapshot, CoordinationHealth, CoordinationReport, GeometryDistribution, HealthReport,
+    MaturityDistribution, MemberScore, RankDistribution, SentinelConfig,
+};
+
+// ─── Internal state types ───────────────────────────────────
+
+/// Per-cell analysis state.
+///
+/// Each cell in the investment set $\mathcal{I}$ owns a single
+/// `SubspaceTracker` operating on suffix bits at the cell's G-tree
+/// depth. The root cell ($d = 0$) has `width = 128`; a cell at
+/// depth $d$ has `width = 128 - d`.
+///
+/// Online cells (the producing sets $\mathcal{A}$/$\mathcal{A}^*$)
+/// receive real observations; warming cells receive only synthetic
+/// noise (ADR-S-019).
+pub struct CellState<C: Coordinate> {
+    /// The subspace tracker for this cell.
+    pub tracker: SubspaceTracker,
+
+    /// G-tree depth of this cell.
+    pub depth: u32,
+
+    /// Suffix width: `128 - depth`. Cached for convenience.
+    pub width: usize,
+
+    /// Lower bound of the dyadic interval (inclusive).
+    pub start: C,
+
+    /// Upper bound of the dyadic interval (exclusive).
+    pub end: C,
+
+    /// Whether this cell is competitively selected (vs ancestor-only).
+    pub is_competitive: bool,
+}
+
+/// Per-G-node coordination state (§ALGO S-9.1).
+///
+/// Active when both subtrees of this G-node contain competitive
+/// cells that produced scores in at least one batch since
+/// activation.
+struct CoordContext {
+    /// Subspace tracker at w = 4, using `cusum_coord_slow_decay`.
+    tracker: SubspaceTracker,
+
+    /// Running EWMA mean of the 4D cell-score input vectors (§ALGO S-9.3).
+    /// Used for centring before feeding the coordination tracker.
+    running_mean: [f64; 4],
+
+    /// Whether the running mean has been initialised with at least
+    /// one batch. Cold-start: first batch sets
+    /// `running_mean = colmeans(O_g)` (§ALGO S-9.3).
+    warm: bool,
+}
+
+// ─── SpectralSentinel ───────────────────────────────────────
+
+/// Hierarchical online subspace anomaly detector.
+///
+/// The sentinel maintains one `SubspaceTracker` per analysis cell
+/// in the full analysis set. Cells are selected by the analysis
+/// selector (§ALGO S-4.1) from the G-V Graph's V-Tree.
+///
+/// A second tier of coordination trackers — one per active G-tree
+/// internal node whose subtrees both contribute competitive cells —
+/// analyses cross-cell score patterns for coordinated anomalies
+/// (§ALGO S-9.1).
+///
+/// # Design principle
+///
+/// **The sentinel measures; the host decides.**
+///
+/// [`ingest`](Self::ingest) returns a [`BatchReport`] containing raw
+/// statistical measurements. The host reads the report and applies
+/// its own policy to decide what (if anything) to do.
+///
+/// **Feed-forward invariant (ADR-S-002).** The G-V Graph receives
+/// only `observe(v, 1u64)` per raw input value during `ingest()`.
+/// Anomaly scores and derived signals never flow back into the
+/// graph's importance accounting. The host controls temporal policy
+/// via `decay()`; the analysis tier has no influence on spatial
+/// resolution.
+pub struct SpectralSentinel<C, V, const N: u32>
+where
+    C: Coordinate + crate::CentredBitSource,
+    V: Inspectable + torrust_mudlark::Attenuatable,
+{
+    config: SentinelConfig<V>,
+
+    /// The G-V Graph spatial substrate (§ALGO S-2.4).
+    ///
+    /// Owns the adaptive spatial partition of the observation domain.
+    graph: GvGraph<C, V, N>,
+
+    /// Per-cell analysis state, keyed by `GNodeId`.
+    ///
+    /// `BTreeMap` for deterministic iteration order (ADR-S-005).
+    /// Contains one entry per **online** cell in the producing full
+    /// set $\mathcal{A}^*$. Warming cells are in the staging area
+    /// (ADR-S-019).
+    cells: BTreeMap<GNodeId, CellState<C>>,
+
+    /// The current analysis set (competitive + ancestors).
+    ///
+    /// Recomputed after every observation pass (ADR-S-006).
+    analysis_set: AnalysisSet<C, V>,
+
+    /// The root tracker's `GNodeId`. Cached for fast access.
+    /// Permanent — never destroyed (§ALGO S-4.7).
+    root_gnode: GNodeId,
+
+    /// Per-G-node coordination contexts (§ALGO S-9.1).
+    ///
+    /// Keyed by `GNodeId` of internal G-nodes whose subtrees
+    /// contain competitive cells in both left and right branches.
+    /// `BTreeMap` for deterministic iteration order (ADR-S-005).
+    coordination: BTreeMap<GNodeId, CoordContext>,
+
+    /// Monotonically increasing counter, incremented once per
+    /// `ingest` call.
+    batch_counter: u64,
+
+    /// Total real (non-noise) observations across the sentinel's
+    /// lifetime.
+    lifetime_observations: u64,
+
+    /// Number of G-tree nodes excluded from tracking because their
+    /// suffix width was below `MIN_TRACKER_DIM` (ADR-S-011).
+    degenerate_cells_skipped: usize,
+
+    /// Persistent RNG for noise injection (§ALGO S-11.1).
+    noise_rng: SmallRng,
+
+    /// Staging area for cells undergoing deferred noise warm-up
+    /// (§ALGO S-18.2).
+    staging: Arc<Mutex<staging::StagingArea<C>>>,
+
+    /// Handle to the background warming thread (Step 3).
+    warming_thread: Option<warming_thread::WarmingThreadHandle<C>>,
+
+    /// Snapshot of `graph.terminal_count()` at the end of the
+    /// previous `ingest()`.  Used to derive structural mutation
+    /// counts without requiring graph-internal event counters.
+    prev_terminal_count: u32,
+
+    /// Snapshot of `graph.node_count()` at the end of the
+    /// previous `ingest()`.
+    prev_node_count: u32,
+}
+
+impl<C, V, const N: u32> SpectralSentinel<C, V, N>
+where
+    C: Coordinate + crate::CentredBitSource,
+    V: Inspectable + torrust_mudlark::Attenuatable,
+{
+    /// Create a new sentinel with the given configuration.
+    ///
+    /// Validates the configuration and creates the root tracker.
+    /// No other cells are created until the first
+    /// [`ingest`](Self::ingest) call triggers analysis set computation.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`ConfigErrors`] if the
+    /// configuration violates any invariant (see
+    /// [`SentinelConfig::validate`]).
+    pub fn new(config: SentinelConfig<V>) -> Result<Self, ConfigErrors> {
+        config.validate()?;
+
+        // ── G-V Graph construction ──────────────────────
+        let gv_config = GvConfig {
+            split_threshold: config.split_threshold,
+            depth_create: config.d_create,
+            depth_evict: config.d_evict,
+            budget: Some(config.budget),
+            alpha_relax: 0.75,
+            bounded_eviction: true,
+        };
+        let graph: GvGraph<C, V, N> = GvGraph::new(gv_config);
+
+        // ── Persistent RNG (§ALGO S-11.1) ─────────────────
+        let mut noise_rng = config
+            .noise_seed
+            .map_or_else(|| SmallRng::from_rng(&mut rand::rng()), SmallRng::seed_from_u64);
+
+        // ── Root tracker (permanent, §ALGO S-4.7) ─────────
+        let root_gnode = graph.g_root();
+        let mut root_cell = CellState {
+            tracker: SubspaceTracker::new(N as usize, &config, config.cusum_slow_decay),
+            depth: 0,
+            width: N as usize,
+            start: C::zero(),
+            end: C::domain_max(N),
+            is_competitive: false,
+        };
+
+        // Auto noise injection on root tracker (§ALGO S-11.2).
+        let root_rounds = config.noise_schedule.rounds_for_depth(0);
+        if root_rounds > 0 {
+            inject_noise_into_cell(&mut root_cell, root_rounds as usize, config.noise_batch_size, &mut noise_rng);
+        }
+
+        let mut cells = BTreeMap::new();
+        cells.insert(root_gnode, root_cell);
+
+        // ── Initial analysis set (just root) ────────────
+        let analysis_set = AnalysisSet::recompute::<N>(&graph, config.analysis_k, config.analysis_depth_cutoff);
+
+        let staging = Arc::new(Mutex::new(staging::StagingArea::<C>::new()));
+
+        // ── Background warming thread (Step 3) ─────────
+        let warming_thread = if config.background_warming {
+            Some(warming_thread::WarmingThreadHandle::<C>::spawn(
+                &staging,
+                config.noise_batch_size,
+                config.noise_seed,
+            ))
+        } else {
+            None
+        };
+
+        let init_terminal = graph.terminal_count();
+        let init_node = graph.node_count();
+
+        Ok(Self {
+            config,
+            graph,
+            cells,
+            analysis_set,
+            root_gnode,
+            coordination: BTreeMap::new(),
+            batch_counter: 0,
+            lifetime_observations: 0,
+            degenerate_cells_skipped: 0,
+            noise_rng,
+            staging,
+            warming_thread,
+            prev_terminal_count: init_terminal,
+            prev_node_count: init_node,
+        })
+    }
+
+    /// Process a batch of raw `u128` observations and return a full
+    /// statistical report.
+    ///
+    /// Each value is fed to the G-V Graph, then routed to every
+    /// analysis cell whose interval contains it. Multi-scale delivery
+    /// ensures ancestor cells also receive the observation (§ALGO S-4.4).
+    ///
+    /// An empty input slice produces an empty report.
+    ///
+    /// # Panics
+    ///
+    /// Panics if the internal staging mutex is poisoned.
+    pub fn ingest(&mut self, values: &[C]) -> BatchReport<C> {
+        // ── Early return on empty input ─────────────────
+        if values.is_empty() {
+            return self.empty_report();
+        }
+
+        // ── Observation algorithm ──────────────────────
+        // Steps 0–6 correspond to §ALGO S-9.1.
+        // The implementation reorders Step 1 (encoding) to just
+        // before Step 4 (scoring), since the spatial layer routes
+        // on raw coordinates and does not need centred bits.
+
+        // ── Step 0: Promote ready cells (§ALGO S-18.2) ────
+        // Cells that completed background warm-up since the last
+        // ingest are moved into the live cells map. In the
+        // synchronous transitional version (Step 2) this promotes
+        // cells from the drain at the end of reconcile_analysis_set();
+        // once the background thread is introduced (Step 3) it will
+        // catch cells that finished between ingest calls.
+        self.promote_ready_cells();
+
+        // ── Step 2: G-V Graph observation (§ALGO S-8.1) ───
+        // [Step 1 (encoding) deferred to just before scoring.]
+        let unit_delta = V::from_f64(1.0);
+
+        #[cfg(debug_assertions)]
+        let pre_observe_sum = self.graph.total_sum().to_f64_approx();
+
+        for &value in values {
+            self.graph.observe(value, unit_delta);
+        }
+
+        #[cfg(debug_assertions)]
+        {
+            #[allow(clippy::cast_precision_loss)]
+            let expected_sum = pre_observe_sum + values.len() as f64;
+            let actual_sum = self.graph.total_sum().to_f64_approx();
+            debug_assert!(
+                (actual_sum - expected_sum).abs() < 1.0,
+                "feed-forward invariant violated: total_sum should increase by exactly n"
+            );
+        }
+
+        // ── Step 3: Analysis set reconciliation ─────────
+        self.reconcile_analysis_set();
+
+        // ── Steps 1+4: Encode and route/score ───────────
+        let centred: Vec<CentredBits> = values.iter().map(|v| CentredBits::from_coord(v, N)).collect();
+        let cell_reports = self.route_and_score(values, &centred);
+
+        // ── Step 5: Coordination tier (§ALGO S-9.4) ───────
+        let cell_scores = Self::assemble_cell_scores(&cell_reports);
+        let coordination_reports = self.propagate_coordination_from_root(&cell_scores);
+
+        // ── Step 6: Assemble report ─────────────────────
+        self.batch_counter += 1;
+
+        #[allow(clippy::cast_possible_truncation)] // batch len ≪ 2^63
+        let count = values.len() as u64;
+        self.lifetime_observations += count;
+
+        // ── (Step 6 continued: report assembly) ──────────
+        let (competitive, ancestors): (Vec<_>, Vec<_>) = cell_reports.into_iter().partition(|r| r.is_competitive);
+
+        let (splits, net_removals, terminal_count) = self.take_structural_mutation_counts();
+
+        let contour = ContourSnapshot {
+            plateau_count: self.graph.plateaus().len(),
+            cell_count: terminal_count as usize,
+            total_importance: self.graph.total_sum().to_f64_approx(),
+            splits_since_last_report: splits,
+            net_removals_since_last_report: net_removals,
+        };
+
+        let mut analysis_set_summary = self.analysis_set.summary();
+        analysis_set_summary.degenerate_cells_skipped = self.degenerate_cells_skipped;
+        // Investment set = online cells + warming cells (ADR-S-019).
+        let warming_count = self.staging.lock().expect("staging mutex poisoned").total_count();
+        analysis_set_summary.investment_set_size = self.cells.len() + warming_count;
+
+        BatchReport {
+            cell_reports: competitive,
+            ancestor_reports: ancestors,
+            coordination_reports,
+            contour,
+            health: self.health(),
+            analysis_set_summary,
+        }
+    }
+
+    /// Produce an operational health snapshot of the sentinel.
+    ///
+    /// Summarises rank distribution and maturity across all active
+    /// trackers. Useful for dashboards.
+    ///
+    /// # Panics
+    ///
+    /// Panics if the internal staging mutex is poisoned.
+    #[must_use]
+    #[allow(clippy::too_many_lines)]
+    pub fn health(&self) -> HealthReport {
+        let competitive_count = self.analysis_set.competitive_count();
+        let total_cells = self.analysis_set.total_count();
+        let active_trackers = self.cells.len();
+        let coord_health = self.coordination_health();
+
+        // Query staging area for investment/warming counts (ADR-S-019).
+        let (warming_total, warming_competitive) = {
+            let staging = self.staging.lock().expect("staging mutex poisoned");
+            (staging.total_count(), staging.warming_competitive_count())
+        };
+        let investment_set_size = active_trackers + warming_total;
+
+        if active_trackers == 0 {
+            return HealthReport {
+                total_g_nodes: self.graph.node_count() as usize,
+                semi_internal_count: 0, // TODO: count from graph when API available
+                active_trackers: 0,
+                active_competitive_trackers: 0,
+                active_ancestor_trackers: 0,
+                active_coordination_contexts: 0,
+                investment_set_size,
+                warming_trackers: warming_total,
+                warming_competitive_targets: warming_competitive,
+                lifetime_observations: self.lifetime_observations,
+                cells_tracked: 0,
+                rank_distribution: RankDistribution {
+                    min: 0,
+                    max: 0,
+                    mean: 0.0,
+                },
+                maturity_distribution: MaturityDistribution {
+                    max_noise_influence: 0.0,
+                    min_noise_influence: 0.0,
+                    mean_noise_influence: 0.0,
+                    cold_trackers: 0,
+                },
+                geometry_distribution: GeometryDistribution {
+                    novelty_saturated: 0,
+                    novelty_saturable: 0,
+                    coherence_inactive: 0,
+                },
+                coordination_health: coord_health,
+                clip_pressure_distribution: ClipPressureDistribution {
+                    min: 0.0,
+                    max: 0.0,
+                    mean: 0.0,
+                },
+            };
+        }
+
+        let mut rank_min = usize::MAX;
+        let mut rank_max = 0_usize;
+        let mut rank_sum = 0_u64;
+
+        let mut ni_min = f64::INFINITY;
+        let mut ni_max = f64::NEG_INFINITY;
+        let mut ni_sum = 0.0_f64;
+        let mut cold = 0_usize;
+
+        let mut geo_saturated = 0_usize;
+        let mut geo_saturable = 0_usize;
+        let mut geo_coh_inactive = 0_usize;
+
+        let mut cp_min = f64::INFINITY;
+        let mut cp_max = f64::NEG_INFINITY;
+        let mut cp_sum = 0.0_f64;
+        let mut cp_count = 0_u64;
+
+        for cell in self.cells.values() {
+            let tracker = &cell.tracker;
+            let r = tracker.rank();
+            rank_min = rank_min.min(r);
+            rank_max = rank_max.max(r);
+            rank_sum += r as u64;
+
+            let m = tracker.maturity();
+            ni_min = ni_min.min(m.noise_influence);
+            ni_max = ni_max.max(m.noise_influence);
+            ni_sum += m.noise_influence;
+            if m.real_observations == 0 {
+                cold += 1;
+            }
+
+            let g = tracker.scoring_geometry();
+            if g.is_novelty_saturated() {
+                geo_saturated += 1;
+            }
+            if g.is_novelty_saturable() {
+                geo_saturable += 1;
+            }
+            if r < 2 {
+                geo_coh_inactive += 1;
+            }
+
+            for cp in cell.tracker.clip_pressures() {
+                cp_min = cp_min.min(cp);
+                cp_max = cp_max.max(cp);
+                cp_sum += cp;
+                cp_count += 1;
+            }
+        }
+
+        #[allow(clippy::cast_precision_loss)]
+        let n = active_trackers as f64;
+
+        #[allow(clippy::cast_precision_loss)]
+        let rank_mean = rank_sum as f64 / n;
+
+        HealthReport {
+            total_g_nodes: self.graph.node_count() as usize,
+            semi_internal_count: 0, // TODO: count from graph when API available
+            active_trackers,
+            active_competitive_trackers: competitive_count,
+            active_ancestor_trackers: total_cells.saturating_sub(competitive_count + 1),
+            active_coordination_contexts: self.coordination.len(),
+            investment_set_size,
+            warming_trackers: warming_total,
+            warming_competitive_targets: warming_competitive,
+            lifetime_observations: self.lifetime_observations,
+            cells_tracked: self.cells.len(),
+            rank_distribution: RankDistribution {
+                min: rank_min,
+                max: rank_max,
+                mean: rank_mean,
+            },
+            maturity_distribution: MaturityDistribution {
+                max_noise_influence: ni_max,
+                min_noise_influence: ni_min,
+                mean_noise_influence: ni_sum / n,
+                cold_trackers: cold,
+            },
+            geometry_distribution: GeometryDistribution {
+                novelty_saturated: geo_saturated,
+                novelty_saturable: geo_saturable,
+                coherence_inactive: geo_coh_inactive,
+            },
+            coordination_health: coord_health,
+            clip_pressure_distribution: ClipPressureDistribution {
+                min: if cp_count > 0 { cp_min } else { 0.0 },
+                max: if cp_count > 0 { cp_max } else { 0.0 },
+                #[allow(clippy::cast_precision_loss)]
+                mean: if cp_count > 0 { cp_sum / cp_count as f64 } else { 0.0 },
+            },
+        }
+    }
+
+    /// Number of cells in the full analysis set.
+    #[must_use]
+    pub fn cells_tracked(&self) -> usize {
+        self.cells.len()
+    }
+
+    /// Total real observations processed across the sentinel's lifetime.
+    #[must_use]
+    pub const fn lifetime_observations(&self) -> u64 {
+        self.lifetime_observations
+    }
+
+    /// Number of G-tree nodes excluded from tracking because their
+    /// suffix width was below `MIN_TRACKER_DIM` (ADR-S-011).
+    #[must_use]
+    pub const fn degenerate_cells_skipped(&self) -> usize {
+        self.degenerate_cells_skipped
+    }
+
+    /// Read-only access to the configuration.
+    #[must_use]
+    pub const fn config(&self) -> &SentinelConfig<V> {
+        &self.config
+    }
+
+    /// Read-only access to the G-V Graph.
+    #[must_use]
+    pub const fn graph(&self) -> &GvGraph<C, V, N> {
+        &self.graph
+    }
+
+    /// Read-only access to the current analysis set.
+    #[must_use]
+    pub const fn analysis_set(&self) -> &AnalysisSet<C, V> {
+        &self.analysis_set
+    }
+
+    /// Reset the sentinel to its freshly-constructed state.
+    ///
+    /// Drops all cell trackers and their learned subspaces,
+    /// re-initialises the G-V Graph, and zeroes all counters.
+    /// The configuration is preserved.
+    ///
+    /// # Panics
+    ///
+    /// Panics if the staging area mutex is poisoned.
+    pub fn reset(&mut self) {
+        // Shut down background thread before clearing state (Step 3.4).
+        if let Some(ref wt) = self.warming_thread {
+            wt.shutdown();
+        }
+
+        self.cells.clear();
+        self.coordination.clear();
+        self.staging.lock().expect("staging mutex poisoned").clear();
+        self.batch_counter = 0;
+        self.lifetime_observations = 0;
+        self.degenerate_cells_skipped = 0;
+
+        // Reset RNG (§ALGO S-11.1).
+        self.noise_rng = self
+            .config
+            .noise_seed
+            .map_or_else(|| SmallRng::from_rng(&mut rand::rng()), SmallRng::seed_from_u64);
+
+        // Reset the G-V Graph to a single root node.
+        let gv_config = GvConfig {
+            split_threshold: self.config.split_threshold,
+            depth_create: self.config.d_create,
+            depth_evict: self.config.d_evict,
+            budget: Some(self.config.budget),
+            alpha_relax: 0.75,
+            bounded_eviction: true,
+        };
+        self.graph = GvGraph::new(gv_config);
+        self.root_gnode = self.graph.g_root();
+
+        // Re-baseline the structural mutation snapshots so the next
+        // report does not attribute the reset's teardown to splits
+        // or evictions.
+        self.prev_terminal_count = self.graph.terminal_count();
+        self.prev_node_count = self.graph.node_count();
+
+        // Recreate the root tracker with auto noise injection.
+        let mut root_cell = CellState {
+            tracker: SubspaceTracker::new(N as usize, &self.config, self.config.cusum_slow_decay),
+            depth: 0,
+            width: N as usize,
+            start: C::zero(),
+            end: C::domain_max(N),
+            is_competitive: false,
+        };
+        if self.config.noise_schedule.rounds_for_depth(0) > 0 {
+            inject_noise_into_cell(
+                &mut root_cell,
+                self.config.noise_schedule.rounds_for_depth(0) as usize,
+                self.config.noise_batch_size,
+                &mut self.noise_rng,
+            );
+        }
+        self.cells.insert(self.root_gnode, root_cell);
+
+        // Recompute empty analysis set.
+        self.analysis_set = AnalysisSet::recompute::<N>(&self.graph, self.config.analysis_k, self.config.analysis_depth_cutoff);
+
+        // Restart background thread if configured (Step 3.4).
+        if self.config.background_warming {
+            self.warming_thread = Some(warming_thread::WarmingThreadHandle::<C>::spawn(
+                &self.staging,
+                self.config.noise_batch_size,
+                self.config.noise_seed,
+            ));
+        }
+    }
+
+    /// Apply spatial decay to the entire G-V Graph.
+    ///
+    /// Delegates to the G-V Graph's temporal filter (§ALGO S-10.1,
+    /// §IDEA M-14) at the graph root, scaling accumulated importance
+    /// across all cells.
+    ///
+    /// # Parameters
+    ///
+    /// - `attenuation` — base decay factor at the midpoint depth.
+    ///   - `(0, 1)`: **Attenuation** — cold cells lose standing.
+    ///   - `> 1.0`: **Amplification** — hot cells reinforced.
+    ///   - `1.0`: no-op (identity).
+    /// - `q` — depth selectivity in `[0.0, 1.0]`.
+    ///   - `0.0`: uniform — all depths decay at the same rate.
+    ///   - `> 0.0`: selective — coarse structure persists, fine
+    ///     structure fades (or is amplified) faster.
+    ///   - `1.0`: maximum selectivity.
+    ///
+    /// # Design
+    ///
+    /// The sentinel **never** calls this automatically. The host
+    /// controls temporal policy: when to decay, how aggressively,
+    /// and with what selectivity (§ALGO S-13.4).
+    ///
+    /// Decay does not trigger analysis set recomputation (the V-Tree
+    /// rankings change, but no scoring happens until the next
+    /// `ingest()`). The analysis set is recomputed at the start of
+    /// the next `ingest()` — no eager invalidation needed.
+    ///
+    /// The feed-forward invariant (ADR-S-002) is maintained: decay
+    /// modifies *importance* (accumulated observation counts), never
+    /// tracker state (subspace, baselines, CUSUM).
+    ///
+    /// # Panics
+    ///
+    /// - `attenuation < 0.0` or `attenuation.is_nan()`.
+    /// - `q < 0.0`, `q > 1.0`, or `q.is_nan()`.
+    ///
+    /// # Examples
+    ///
+    /// ```
+    /// use torrust_sentinel::SentinelConfig;
+    /// use torrust_sentinel::Sentinel128;
+    ///
+    /// let mut s = Sentinel128::new(SentinelConfig::default()).unwrap();
+    /// s.ingest(&[42_u128, 43, 44]);
+    ///
+    /// // Uniform 50% attenuation.
+    /// s.decay(0.5, 0.0);
+    /// assert!(s.graph().total_sum() < 3);
+    /// ```
+    pub fn decay(&mut self, attenuation: f64, q: f64) {
+        let root = self.graph.g_root();
+        self.graph.decay(root, attenuation, q);
+    }
+
+    /// Apply spatial decay to a subtree of the G-V Graph.
+    ///
+    /// Like [`decay()`](Self::decay), but targets only the G-subtree
+    /// rooted at `root`. Cells outside this subtree are unaffected.
+    ///
+    /// # Use cases (§ALGO S-10.3)
+    ///
+    /// - **Regime change in a region.** Attenuate a subtree that
+    ///   experienced a traffic regime shift, allowing it to re-form
+    ///   under new observations without affecting global structure.
+    /// - **Suspected poisoning.** `decay_subtree(root, att=0.0₊, q=1.0)`
+    ///   is a detail flush: the subtree root's own count is preserved,
+    ///   descendants are zeroed.
+    /// - **Hot reinforcement.** `decay_subtree(root, att=1.5, q=0.0)`
+    ///   amplifies a known-active subtree to boost its competitive
+    ///   standing.
+    ///
+    /// # Parameters
+    ///
+    /// - `root` — the G-node whose subtree receives decay. Obtain
+    ///   via `sentinel.graph().g_root()` for the global root, or
+    ///   from a G-node inspection method.
+    /// - `attenuation` — see [`decay()`](Self::decay).
+    /// - `q` — see [`decay()`](Self::decay).
+    ///
+    /// # Panics
+    ///
+    /// - `root` does not refer to a live G-node (the graph has been
+    ///   restructured since the handle was obtained).
+    /// - `attenuation < 0.0` or `attenuation.is_nan()`.
+    /// - `q < 0.0`, `q > 1.0`, or `q.is_nan()`.
+    ///
+    /// # Examples
+    ///
+    /// ```
+    /// use torrust_sentinel::SentinelConfig;
+    /// use torrust_sentinel::Sentinel128;
+    ///
+    /// let mut s = Sentinel128::new(SentinelConfig::default()).unwrap();
+    /// s.ingest(&[42_u128, 43, 44]);
+    ///
+    /// let root = s.graph().g_root();
+    /// s.decay_subtree(root, 0.5, 0.0);
+    /// assert!(s.graph().total_sum() < 3);
+    /// ```
+    pub fn decay_subtree(&mut self, root: GNodeId, attenuation: f64, q: f64) {
+        self.graph.decay(root, attenuation, q);
+    }
+
+    /// List all cell `GNodeId`s in the full analysis set.
+    ///
+    /// Returns IDs in ascending order (`BTreeMap` iteration order).
+    #[must_use]
+    pub fn cell_gnodes(&self) -> Vec<GNodeId> {
+        self.cells.keys().copied().collect()
+    }
+
+    /// Inspect a specific cell's tracker state.
+    ///
+    /// Returns `None` if the cell is not in the analysis set.
+    #[must_use]
+    pub fn inspect_cell(&self, gnode: GNodeId) -> Option<CellInspection<C>> {
+        let cell = self.cells.get(&gnode)?;
+        let bl = cell.tracker.axis_baselines();
+        Some(CellInspection {
+            gnode_id: gnode,
+            start: cell.start,
+            end: cell.end,
+            depth: cell.depth,
+            analysis_width: cell.width,
+            is_competitive: cell.is_competitive,
+            rank: cell.tracker.rank(),
+            energy_ratio: cell.tracker.energy_ratio(),
+            top_singular_value: cell.tracker.top_singular_value(),
+            maturity: cell.tracker.maturity(),
+            geometry: cell.tracker.scoring_geometry(),
+            baselines: AxisBaselineSnapshots {
+                novelty: crate::BaselineSnapshot {
+                    mean: bl.novelty_mean,
+                    variance: bl.novelty_var,
+                },
+                displacement: crate::BaselineSnapshot {
+                    mean: bl.displacement_mean,
+                    variance: bl.displacement_var,
+                },
+                surprise: crate::BaselineSnapshot {
+                    mean: bl.surprise_mean,
+                    variance: bl.surprise_var,
+                },
+                coherence: crate::BaselineSnapshot {
+                    mean: bl.coherence_mean,
+                    variance: bl.coherence_var,
+                },
+            },
+        })
+    }
+
+    // ════════════════════════════════════════════════════════
+    //  Private implementation
+    // ════════════════════════════════════════════════════════
+
+    /// Reconcile the cells map with the current analysis set.
+    ///
+    /// Recomputes the analysis set from the V-Tree, creates trackers
+    /// for new cells, and destroys trackers for exited cells. The
+    /// root tracker is never destroyed (§ALGO S-4.7).
+    ///
+    /// New cells are enqueued into the staging area rather than being
+    /// warmed inline (Step 2, §ALGO S-18.2). A synchronous drain loop
+    /// warms all queued cells to completion, then promotes them into
+    /// the live cells map — identical external behaviour to the old
+    /// inline path. The background-thread version (Step 3) will
+    /// remove the drain loop.
+    fn reconcile_analysis_set(&mut self) {
+        let new_set = AnalysisSet::recompute::<N>(&self.graph, self.config.analysis_k, self.config.analysis_depth_cutoff);
+
+        // ── Identify entries and exits ──────────────────────
+        let old_gnodes: BTreeSet<GNodeId> = self.cells.keys().copied().collect();
+        let new_gnodes: BTreeSet<GNodeId> = new_set.full().iter().map(|e| e.gnode).collect();
+
+        // Destroy exited cells (except root — §ALGO S-4.7).
+        for &gone in old_gnodes.difference(&new_gnodes) {
+            if gone != self.root_gnode {
+                self.cells.remove(&gone);
+            }
+        }
+
+        // Evict staging cells that exited the analysis set (Step 2.5).
+        // A warming cell may have been evicted by graph rebalance.
+        // Also update cached volumes and enqueue new cells — all under
+        // a single lock acquisition to avoid repeated locking.
+        {
+            let mut staging = self.staging.lock().expect("staging mutex poisoned");
+
+            staging.retain_in_set(&new_gnodes);
+
+            // Update cached volumes from the graph so the background
+            // thread can prioritise correctly (Step 3.2c).
+            staging.update_volumes(&self.graph);
+
+            // Enqueue entered cells into the staging area (Step 2.1).
+            for entry in new_set.full() {
+                if self.cells.contains_key(&entry.gnode) || staging.contains(entry.gnode) {
+                    continue;
+                }
+
+                let width = (N as usize).saturating_sub(entry.depth as usize);
+
+                // ADR-S-011: skip degenerate cells whose suffix width
+                // is too narrow for a meaningful subspace model.
+                if width < crate::MIN_TRACKER_DIM {
+                    tracing::warn!(
+                        gnode = ?entry.gnode,
+                        depth = entry.depth,
+                        width,
+                        "skipping degenerate cell (width < MIN_TRACKER_DIM)"
+                    );
+                    self.degenerate_cells_skipped += 1;
+                    continue;
+                }
+
+                let cell = CellState {
+                    tracker: SubspaceTracker::new(width, &self.config, self.config.cusum_slow_decay),
+                    depth: entry.depth,
+                    width,
+                    start: entry.start,
+                    end: entry.end,
+                    is_competitive: entry.is_competitive,
+                };
+
+                // Enqueue for deferred noise warm-up (Step 2.1).
+                let rounds = self.config.noise_schedule.rounds_for_depth(entry.depth as usize);
+                staging.enqueue(entry.gnode, cell, rounds);
+            }
+
+            // ── Warm-up dispatch ────────────────────────────
+            if self.warming_thread.is_none() {
+                // Synchronous mode: drain all warming cells in-line
+                // (deterministic, matching the old inline path).
+                staging.drain_all_synchronous(self.config.noise_batch_size, &mut self.noise_rng);
+            }
+        } // staging lock released
+
+        // Notify background thread if running (Step 3).
+        if let Some(ref wt) = self.warming_thread {
+            wt.notify();
+        }
+
+        // Promote newly ready cells so they participate in routing
+        // within *this* ingest call (identical to old inline path
+        // in synchronous mode; in background mode, promotes cells
+        // that finished since the last ingest).
+        self.promote_ready_cells();
+
+        // Update competitive flags on retained cells.
+        for entry in new_set.full() {
+            if let Some(cell) = self.cells.get_mut(&entry.gnode) {
+                cell.is_competitive = entry.is_competitive;
+            }
+        }
+
+        self.analysis_set = new_set;
+    }
+
+    /// Promote all ready cells from the staging area into the live
+    /// cells map (Step 2.3, §ALGO S-18.2 Step 0).
+    ///
+    /// Ready cells have completed their full noise warm-up schedule.
+    /// Coordination warm-up for these cells fires naturally when
+    /// `propagate_coordination_from_root()` creates their coordination
+    /// context on the next scoring pass.
+    fn promote_ready_cells(&mut self) {
+        let ready = self.staging.lock().expect("staging mutex poisoned").take_ready();
+        for (gnode, cell) in ready {
+            self.cells.insert(gnode, cell);
+        }
+    }
+
+    /// Route observations to cells and score each cell.
+    #[allow(clippy::cast_possible_truncation)] // depth ≤ 128, always fits in u8
+    fn route_and_score(&mut self, values: &[C], centred: &[CentredBits]) -> Vec<CellReport<C>> {
+        // ── Build per-cell observation buffers ───────────────
+        // Key: GNodeId → Vec of observation indices.
+        //
+        // Route each observation to every active cell whose interval
+        // contains it. `self.cells` holds exactly the online
+        // producing set — staging cells are excluded by construction.
+        let mut cell_obs: BTreeMap<GNodeId, Vec<usize>> = BTreeMap::new();
+
+        for (i, &value) in values.iter().enumerate() {
+            for (&gnode, cell) in &self.cells {
+                if value >= cell.start && value < cell.end {
+                    cell_obs.entry(gnode).or_default().push(i);
+                }
+            }
+        }
+
+        // ── Score each cell ─────────────────────────────────
+        let mut reports = Vec::with_capacity(self.cells.len());
+
+        for (&gnode, cell) in &mut self.cells {
+            let obs_indices = cell_obs.get(&gnode);
+
+            if let Some(indices) = obs_indices
+                && !indices.is_empty()
+            {
+                let slices: Vec<&[f64]> = indices.iter().map(|&i| centred[i].suffix(cell.depth as u8)).collect();
+
+                #[cfg(debug_assertions)]
+                {
+                    #[allow(clippy::cast_precision_loss)]
+                    let expected_norm_sq = cell.width as f64 / 4.0;
+                    for slice in &slices {
+                        let norm_sq: f64 = slice.iter().map(|x| x * x).sum();
+                        debug_assert!(
+                            (norm_sq - expected_norm_sq).abs() < 1e-10,
+                            "suffix norm² = {norm_sq}, expected w/4 = {expected_norm_sq}"
+                        );
+                    }
+                }
+
+                let prefix_report = cell.tracker.observe(&slices, cell.depth as u8, false);
+
+                reports.push(CellReport {
+                    gnode_id: gnode,
+                    start: cell.start,
+                    end: cell.end,
+                    depth: cell.depth,
+                    analysis_width: cell.width,
+                    is_competitive: cell.is_competitive,
+                    sample_count: indices.len(),
+                    scores: prefix_report.scores,
+                    rank: prefix_report.rank,
+                    energy_ratio: prefix_report.energy_ratio,
+                    top_singular_value: prefix_report.top_singular_value,
+                    maturity: prefix_report.maturity,
+                    geometry: prefix_report.geometry,
+                    per_sample: prefix_report.per_sample,
+                });
+            }
+            // Cells with no observations in this batch are omitted
+            // from the report (§ALGO S-4.5).
+        }
+
+        reports
+    }
+
+    // ════════════════════════════════════════════════════════
+    //  Hierarchical coordination (§ALGO S-9)
+    // ════════════════════════════════════════════════════════
+
+    /// Extract the 4D mean score vector from competitive cell reports.
+    ///
+    /// Only competitive cells with `sample_count > 0` contribute.
+    fn assemble_cell_scores(cell_reports: &[CellReport<C>]) -> BTreeMap<GNodeId, [f64; 4]> {
+        let mut scores = BTreeMap::new();
+        for report in cell_reports {
+            if report.is_competitive && report.sample_count > 0 {
+                scores.insert(
+                    report.gnode_id,
+                    [
+                        report.scores.novelty.mean,
+                        report.scores.displacement.mean,
+                        report.scores.surprise.mean,
+                        report.scores.coherence.mean,
+                    ],
+                );
+            }
+        }
+        scores
+    }
+
+    /// Run hierarchical coordination from the G-tree root.
+    ///
+    /// Builds the pruned coordination topology, walks bottom-up, fires
+    /// coordination at internal nodes where both subtrees contribute
+    /// competitive cell scores, and returns coordination reports for
+    /// all active contexts (§ALGO S-9.4).
+    fn propagate_coordination_from_root(&mut self, cell_scores: &BTreeMap<GNodeId, [f64; 4]>) -> Vec<CoordinationReport<C>> {
+        let competitive_gnodes: BTreeSet<GNodeId> = cell_scores.keys().copied().collect();
+
+        // Build the pruned coordination tree (§5.3).
+        let Some(tree) = Self::build_coordination_tree(&self.graph, &competitive_gnodes, self.root_gnode) else {
+            // No competitive cells with scores ⇒ no coordination.
+            return Vec::new();
+        };
+
+        // Walk bottom-up and fire coordination at active contexts.
+        let (reports, _cells) = self.walk_coordination(&tree, cell_scores);
+
+        // Prune stale coordination contexts (§5.5).
+        let active_gnodes = Self::collect_internal_gnodes(&tree);
+        self.coordination.retain(|gnode, _| active_gnodes.contains(gnode));
+
+        reports
+    }
+
+    /// Build the coordination tree topology from the G-tree.
+    ///
+    /// Only includes nodes reachable from the root through subtrees
+    /// that contain at least one competitive cell. This prunes
+    /// branches that contain no competitive cells, reducing the
+    /// coordination walk from $O(|G|)$ to $O(|\mathcal{A}| \cdot d_{\max})$.
+    fn build_coordination_tree(
+        graph: &GvGraph<C, V, N>,
+        competitive_gnodes: &BTreeSet<GNodeId>,
+        root: GNodeId,
+    ) -> Option<CoordNode<C>> {
+        let info = graph.gnode_info(root)?;
+        let children = graph.gnode_children(root)?;
+
+        let has_left = children.left.is_some();
+        let has_right = children.right.is_some();
+        let is_competitive = competitive_gnodes.contains(&root);
+
+        // Terminal node (no children).
+        if !has_left && !has_right {
+            return if is_competitive {
+                Some(CoordNode::Terminal { gnode: root })
+            } else {
+                None // Prune: no competitive cell here.
+            };
+        }
+
+        let left_tree = children
+            .left
+            .and_then(|l| Self::build_coordination_tree(graph, competitive_gnodes, l));
+        let right_tree = children
+            .right
+            .and_then(|r| Self::build_coordination_tree(graph, competitive_gnodes, r));
+
+        match (left_tree, right_tree) {
+            (Some(left), Some(right)) => Some(CoordNode::Internal {
+                gnode: root,
+                depth: info.depth,
+                start: info.start,
+                end: info.end,
+                is_competitive,
+                left: Box::new(left),
+                right: Box::new(right),
+            }),
+            (Some(child), None) | (None, Some(child)) => Some(CoordNode::SemiInternal {
+                gnode: root,
+                is_competitive,
+                child: Box::new(child),
+            }),
+            (None, None) => {
+                // Both subtrees pruned, but this node itself might be competitive.
+                if is_competitive {
+                    Some(CoordNode::Terminal { gnode: root })
+                } else {
+                    None
+                }
+            }
+        }
+    }
+
+    /// Walk the coordination tree bottom-up, firing coordination at
+    /// internal nodes where both subtrees contribute competitive cell
+    /// scores (§ALGO S-9.4).
+    ///
+    /// Returns `(reports, cells_in_subtree)`.
+    #[allow(clippy::cast_possible_truncation)] // depth ≤ 128, always fits in u8
+    #[allow(clippy::type_complexity)]
+    fn walk_coordination(
+        &mut self,
+        node: &CoordNode<C>,
+        cell_scores: &BTreeMap<GNodeId, [f64; 4]>,
+    ) -> (Vec<CoordinationReport<C>>, Vec<(GNodeId, [f64; 4])>) {
+        match node {
+            CoordNode::Terminal { gnode } => {
+                if let Some(&scores) = cell_scores.get(gnode) {
+                    (Vec::new(), vec![(*gnode, scores)])
+                } else {
+                    (Vec::new(), Vec::new())
+                }
+            }
+            CoordNode::SemiInternal {
+                gnode,
+                is_competitive,
+                child,
+            } => {
+                let (reports, mut cells) = self.walk_coordination(child, cell_scores);
+                // If this node itself is competitive and has scores, add it.
+                if *is_competitive && let Some(&scores) = cell_scores.get(gnode) {
+                    cells.push((*gnode, scores));
+                }
+                (reports, cells)
+            }
+            CoordNode::Internal {
+                gnode,
+                depth,
+                start,
+                end,
+                is_competitive,
+                left,
+                right,
+            } => {
+                let (left_reports, left_cells) = self.walk_coordination(left, cell_scores);
+                let (right_reports, right_cells) = self.walk_coordination(right, cell_scores);
+
+                let mut reports = left_reports;
+                reports.extend(right_reports);
+
+                let mut my_cells: Vec<(GNodeId, [f64; 4])> = Vec::with_capacity(left_cells.len() + right_cells.len() + 1);
+                my_cells.extend_from_slice(&left_cells);
+                my_cells.extend_from_slice(&right_cells);
+
+                // If this node itself is competitive and has scores, add it.
+                if *is_competitive && let Some(&scores) = cell_scores.get(gnode) {
+                    my_cells.push((*gnode, scores));
+                }
+
+                // Fire coordination if both subtrees contribute and ≥ 2 cells total.
+                if !left_cells.is_empty() && !right_cells.is_empty() && my_cells.len() >= 2 {
+                    let report = self.fire_coordination(*gnode, *depth, *start, *end, &my_cells, false);
+                    reports.push(report);
+                }
+
+                (reports, my_cells)
+            }
+        }
+    }
+
+    /// Fire a coordination context at a G-node: centre, observe, update
+    /// running mean, and produce a `CoordinationReport`.
+    #[allow(clippy::cast_possible_truncation)] // depth ≤ 128, always fits in u8
+    fn fire_coordination(
+        &mut self,
+        gnode: GNodeId,
+        depth: u32,
+        start: C,
+        end: C,
+        my_cells: &[(GNodeId, [f64; 4])],
+        is_noise: bool,
+    ) -> CoordinationReport<C> {
+        let lam = self.config.forgetting_factor;
+        let alpha = 1.0 - lam;
+
+        // §ALGO S-9.8: Collect baselines before borrowing self.coordination
+        // to avoid simultaneous &mut borrows.
+        let cell_baselines: Vec<(GNodeId, AxisBaselines)> = if self.coordination.contains_key(&gnode) {
+            Vec::new()
+        } else {
+            my_cells
+                .iter()
+                .filter_map(|(cell_gnode, _)| self.cells.get(cell_gnode).map(|c| (*cell_gnode, c.tracker.axis_baselines())))
+                .collect()
+        };
+
+        let ctx = self.coordination.entry(gnode).or_insert_with(|| CoordContext {
+            tracker: SubspaceTracker::new(4, &self.config, self.config.cusum_coord_slow_decay),
+            running_mean: [0.0; 4],
+            warm: false,
+        });
+
+        // §ALGO S-9.8: Warm new contexts when not in noise phase.
+        let coord_rounds = self.config.noise_schedule.rounds_for_depth(depth as usize);
+        if !cell_baselines.is_empty() && !is_noise && coord_rounds > 0 {
+            warm_coordination_context(
+                ctx,
+                &cell_baselines,
+                coord_rounds as usize,
+                &mut self.noise_rng,
+                self.config.forgetting_factor,
+                depth as u8,
+            );
+        }
+
+        // Centre against running mean (§ALGO S-9.3).
+        let centred: Vec<Vec<f64>> = my_cells
+            .iter()
+            .map(|(_, scores)| scores.iter().enumerate().map(|(j, &v)| v - ctx.running_mean[j]).collect())
+            .collect();
+
+        let slices: Vec<&[f64]> = centred.iter().map(Vec::as_slice).collect();
+        let prefix_report = ctx.tracker.observe(&slices, depth as u8, is_noise);
+
+        // Compute column means.
+        let col_means = compute_col_means(my_cells);
+
+        // Update running mean (§ALGO S-9.3).
+        if ctx.warm {
+            for (j, m) in ctx.running_mean.iter_mut().enumerate() {
+                *m = lam.mul_add(*m, alpha * col_means[j]);
+            }
+        } else {
+            ctx.running_mean = col_means;
+            ctx.warm = true;
+        }
+
+        CoordinationReport {
+            gnode_id: gnode,
+            start,
+            end,
+            depth,
+            cells_reporting: my_cells.len(),
+            rank: prefix_report.rank,
+            energy_ratio: prefix_report.energy_ratio,
+            top_singular_value: prefix_report.top_singular_value,
+            scores: prefix_report.scores,
+            maturity: prefix_report.maturity,
+            geometry: prefix_report.geometry,
+            per_member: prefix_report.per_sample.map(|samples| {
+                samples
+                    .into_iter()
+                    .zip(my_cells.iter())
+                    .map(|(s, (cell_gnode, _))| {
+                        let (cell_start, cell_end, cell_depth) = self
+                            .cells
+                            .get(cell_gnode)
+                            .map_or_else(|| (C::zero(), C::zero(), 0), |c| (c.start, c.end, c.depth));
+                        MemberScore {
+                            cell_start,
+                            cell_end,
+                            cell_depth,
+                            novelty: s.novelty,
+                            displacement: s.displacement,
+                            surprise: s.surprise,
+                            coherence: s.coherence,
+                            novelty_z: s.novelty_z,
+                            displacement_z: s.displacement_z,
+                            surprise_z: s.surprise_z,
+                            coherence_z: s.coherence_z,
+                        }
+                    })
+                    .collect()
+            }),
+        }
+    }
+
+    /// Collect the `GNodeId`s of all `Internal` nodes in a coordination tree.
+    ///
+    /// These are the nodes where coordination fires. Used for
+    /// pruning stale contexts (§5.5).
+    fn collect_internal_gnodes(node: &CoordNode<C>) -> BTreeSet<GNodeId> {
+        let mut result = BTreeSet::new();
+        Self::collect_internal_gnodes_inner(node, &mut result);
+        result
+    }
+
+    fn collect_internal_gnodes_inner(node: &CoordNode<C>, result: &mut BTreeSet<GNodeId>) {
+        match node {
+            CoordNode::Terminal { .. } => {}
+            CoordNode::SemiInternal { child, .. } => {
+                Self::collect_internal_gnodes_inner(child, result);
+            }
+            CoordNode::Internal { gnode, left, right, .. } => {
+                result.insert(*gnode);
+                Self::collect_internal_gnodes_inner(left, result);
+                Self::collect_internal_gnodes_inner(right, result);
+            }
+        }
+    }
+
+    /// Compute coordination health summary.
+    fn coordination_health(&self) -> CoordinationHealth {
+        let count = self.coordination.len();
+
+        if count == 0 {
+            return CoordinationHealth {
+                active_contexts: 0,
+                capacity: 0,
+                rank_distribution: RankDistribution {
+                    min: 0,
+                    max: 0,
+                    mean: 0.0,
+                },
+                maturity_distribution: MaturityDistribution {
+                    max_noise_influence: 0.0,
+                    min_noise_influence: 0.0,
+                    mean_noise_influence: 0.0,
+                    cold_trackers: 0,
+                },
+                dim: 4,
+                geometry_distribution: GeometryDistribution {
+                    novelty_saturated: 0,
+                    novelty_saturable: 0,
+                    coherence_inactive: 0,
+                },
+            };
+        }
+
+        let mut rank_min = usize::MAX;
+        let mut rank_max = 0_usize;
+        let mut rank_sum = 0_u64;
+
+        let mut ni_min = f64::INFINITY;
+        let mut ni_max = f64::NEG_INFINITY;
+        let mut ni_sum = 0.0_f64;
+        let mut cold = 0_usize;
+
+        let mut geo_saturated = 0_usize;
+        let mut geo_saturable = 0_usize;
+        let mut geo_coh_inactive = 0_usize;
+
+        for ctx in self.coordination.values() {
+            let r = ctx.tracker.rank();
+            rank_min = rank_min.min(r);
+            rank_max = rank_max.max(r);
+            rank_sum += r as u64;
+
+            let m = ctx.tracker.maturity();
+            ni_min = ni_min.min(m.noise_influence);
+            ni_max = ni_max.max(m.noise_influence);
+            ni_sum += m.noise_influence;
+            if m.real_observations == 0 {
+                cold += 1;
+            }
+
+            let g = ctx.tracker.scoring_geometry();
+            if g.is_novelty_saturated() {
+                geo_saturated += 1;
+            }
+            if g.is_novelty_saturable() {
+                geo_saturable += 1;
+            }
+            if r < 2 {
+                geo_coh_inactive += 1;
+            }
+        }
+
+        #[allow(clippy::cast_precision_loss)]
+        let n = count as f64;
+
+        #[allow(clippy::cast_precision_loss)]
+        let rank_mean = rank_sum as f64 / n;
+
+        // Read capacity and dim from the first coordination tracker.
+        let first = self.coordination.values().next().unwrap();
+        let capacity = first.tracker.cap();
+        let dim = first.tracker.dim();
+
+        CoordinationHealth {
+            active_contexts: count,
+            capacity,
+            rank_distribution: RankDistribution {
+                min: rank_min,
+                max: rank_max,
+                mean: rank_mean,
+            },
+            maturity_distribution: MaturityDistribution {
+                max_noise_influence: ni_max,
+                min_noise_influence: ni_min,
+                mean_noise_influence: ni_sum / n,
+                cold_trackers: cold,
+            },
+            dim,
+            geometry_distribution: GeometryDistribution {
+                novelty_saturated: geo_saturated,
+                novelty_saturable: geo_saturable,
+                coherence_inactive: geo_coh_inactive,
+            },
+        }
+    }
+
+    /// Produce an empty `BatchReport` (for empty input slices).
+    ///
+    /// Takes `&mut self` so that structural-mutation counters are
+    /// drained (and `prev_*` snapshots advanced) through this path
+    /// too — e.g. if the caller interleaves `decay()` or
+    /// `decay_subtree()` with empty `ingest(&[])` calls, the next
+    /// non-empty report still sees the correct "since last report"
+    /// delta rather than a double-count.
+    fn empty_report(&mut self) -> BatchReport<C> {
+        let health = self.health();
+        let mut summary = self.analysis_set.summary();
+        let warming_count = self.staging.lock().expect("staging mutex poisoned").total_count();
+        summary.investment_set_size = self.cells.len() + warming_count;
+        summary.degenerate_cells_skipped = self.degenerate_cells_skipped;
+
+        let (splits, net_removals, terminal_count) = self.take_structural_mutation_counts();
+
+        BatchReport {
+            cell_reports: Vec::new(),
+            ancestor_reports: Vec::new(),
+            coordination_reports: Vec::new(),
+            contour: ContourSnapshot {
+                plateau_count: self.graph.plateaus().len(),
+                cell_count: terminal_count as usize,
+                total_importance: self.graph.total_sum().to_f64_approx(),
+                splits_since_last_report: splits,
+                net_removals_since_last_report: net_removals,
+            },
+            health,
+            analysis_set_summary: summary,
+        }
+    }
+
+    /// Compute `(splits, net_removals, terminal_count)` since the
+    /// previous report and advance the `prev_*` snapshots.
+    ///
+    /// Derived exactly from `terminal_count()` / `node_count()`
+    /// deltas via the identities (§ALGO S-14.10):
+    ///
+    /// - Each split:       `ΔN = +2`, `ΔT = +1`
+    /// - Each eviction:    `ΔN = −1`, `ΔT = −1`
+    /// - Each restoration: `ΔN = +1`, `ΔT = +1`
+    ///
+    /// ⟹ `splits = ΔN − ΔT` and
+    ///   `net_removals = splits − ΔT = evictions − restorations`.
+    ///
+    /// Both outputs are non-negative by construction; debug builds
+    /// assert this to catch identity violations (e.g. if a future
+    /// graph mutation path were to break the accounting).
+    /// Release builds saturate at `u32::MAX` on the astronomically
+    /// unlikely overflow path.
+    fn take_structural_mutation_counts(&mut self) -> (u32, u32, u32) {
+        let terminal_count = self.graph.terminal_count();
+        let node_count = self.graph.node_count();
+        let delta_terminal = i64::from(terminal_count) - i64::from(self.prev_terminal_count);
+        let delta_node = i64::from(node_count) - i64::from(self.prev_node_count);
+
+        let raw_splits = delta_node - delta_terminal;
+        debug_assert!(
+            raw_splits >= 0,
+            "split identity violated: ΔN ({delta_node}) < ΔT ({delta_terminal})",
+        );
+        let raw_net_removals = raw_splits - delta_terminal;
+        debug_assert!(
+            raw_net_removals >= 0,
+            "net-removal identity violated: splits ({raw_splits}) < ΔT ({delta_terminal})",
+        );
+
+        let splits = u32::try_from(raw_splits.max(0)).unwrap_or(u32::MAX);
+        let net_removals = u32::try_from(raw_net_removals.max(0)).unwrap_or(u32::MAX);
+
+        self.prev_terminal_count = terminal_count;
+        self.prev_node_count = node_count;
+
+        (splits, net_removals, terminal_count)
+    }
+}
+
+// ─── Automatic noise injection (§ALGO S-11) ───────────────────
+
+/// Inject noise into a single cell tracker and reset its CUSUM.
+///
+/// Feeds `rounds` batches of `batch_size` random ±0.5 centred vectors
+/// through the tracker with `is_noise = true`, then resets all four
+/// CUSUM accumulators (§ALGO S-7.4, §ALGO S-11.1).
+///
+/// Generate a batch of random centred vectors (each entry ±0.5).
+fn generate_noise_batch(dim: usize, batch_size: usize, rng: &mut SmallRng) -> Vec<Vec<f64>> {
+    (0..batch_size)
+        .map(|_| (0..dim).map(|_| if rng.random_bool(0.5) { 0.5 } else { -0.5 }).collect())
+        .collect()
+}
+
+/// Compute column means of the 4D cell score vectors.
+fn compute_col_means(cells: &[(GNodeId, [f64; 4])]) -> [f64; 4] {
+    let mut col_means = [0.0_f64; 4];
+    for (_, scores) in cells {
+        for (j, &v) in scores.iter().enumerate() {
+            col_means[j] += v;
+        }
+    }
+    #[allow(clippy::cast_precision_loss)]
+    let n_f = cells.len() as f64;
+    for m in &mut col_means {
+        *m /= n_f;
+    }
+    col_means
+}
+
+/// Returns the per-round mean score vectors (4D), needed for chained
+/// coordination warming when the cell is competitive.
+#[allow(clippy::cast_possible_truncation)] // depth ≤ 128
+fn inject_noise_into_cell<C: Coordinate>(
+    cell: &mut CellState<C>,
+    rounds: usize,
+    batch_size: usize,
+    rng: &mut SmallRng,
+) -> Vec<[f64; 4]> {
+    let mut round_scores = Vec::with_capacity(rounds);
+
+    for _ in 0..rounds {
+        let noise = generate_noise_batch(cell.width, batch_size, rng);
+        let slices: Vec<&[f64]> = noise.iter().map(Vec::as_slice).collect();
+        let report = cell.tracker.observe(&slices, cell.depth as u8, true);
+
+        round_scores.push([
+            report.scores.novelty.mean,
+            report.scores.displacement.mean,
+            report.scores.surprise.mean,
+            report.scores.coherence.mean,
+        ]);
+    }
+
+    cell.tracker.seed_cusum_slow_from_baselines();
+    cell.tracker.reset_cusum();
+    cell.tracker.reset_clip_pressure();
+    round_scores
+}
+
+// ─── Coordination warming (§ALGO S-9.8) ──────────────────────
+
+/// Snapshot of per-axis baseline statistics for synthetic score
+/// generation (§ALGO S-9.8).
+pub struct AxisBaselines {
+    pub novelty_mean: f64,
+    pub novelty_var: f64,
+    pub displacement_mean: f64,
+    pub displacement_var: f64,
+    pub surprise_mean: f64,
+    pub surprise_var: f64,
+    pub coherence_mean: f64,
+    pub coherence_var: f64,
+}
+
+/// Sample a synthetic 4-axis score vector from axis baselines.
+///
+/// Uses Gamma(α, β) where α = mean²/variance, β = mean/variance.
+/// Falls back to axis-specific defaults when baselines are cold
+/// (§ALGO S-9.8).
+fn sample_synthetic_score(baselines: &AxisBaselines, rng: &mut SmallRng) -> [f64; 4] {
+    [
+        gamma_sample(baselines.novelty_mean, baselines.novelty_var, 0.25, 0.01, rng),
+        gamma_sample(baselines.displacement_mean, baselines.displacement_var, 0.1, 0.01, rng),
+        gamma_sample(baselines.surprise_mean, baselines.surprise_var, 0.25, 0.01, rng),
+        gamma_sample(baselines.coherence_mean, baselines.coherence_var, 0.1, 0.01, rng),
+    ]
+}
+
+/// Sample from Gamma(α, β) with α = mean²/var, β = mean/var.
+/// Falls back to `default_mean`/`default_var` when `mean` or `var`
+/// are invalid (zero, negative, NaN).
+fn gamma_sample(mean: f64, var: f64, default_mean: f64, default_var: f64, rng: &mut SmallRng) -> f64 {
+    let (m, v) = if mean > 0.0 && var > 0.0 && mean.is_finite() && var.is_finite() {
+        (mean, var)
+    } else {
+        (default_mean, default_var)
+    };
+    let alpha = m * m / v;
+    let beta = m / v;
+    let gamma = Gamma::new(alpha, 1.0 / beta).unwrap_or_else(|_| {
+        let a = default_mean * default_mean / default_var;
+        let b = default_mean / default_var;
+        Gamma::new(a, 1.0 / b).expect("default Gamma params must be valid")
+    });
+    gamma.sample(rng)
+}
+
+/// Warm a newly activated coordination context with synthetic
+/// score vectors sampled from cell baselines (§ALGO S-9.8).
+///
+/// Uses Gamma sampling to match cell baseline moments while
+/// respecting axis non-negativity.
+#[allow(clippy::cast_possible_truncation)] // depth ≤ 128
+fn warm_coordination_context(
+    ctx: &mut CoordContext,
+    contributing_cells: &[(GNodeId, AxisBaselines)],
+    rounds: usize,
+    rng: &mut SmallRng,
+    forgetting_factor: f64,
+    depth: u8,
+) {
+    for _ in 0..rounds {
+        let synth_with_gnodes: Vec<(GNodeId, [f64; 4])> = contributing_cells
+            .iter()
+            .map(|(gnode, baselines)| (*gnode, sample_synthetic_score(baselines, rng)))
+            .collect();
+
+        // Centre against running mean.
+        let centred: Vec<Vec<f64>> = synth_with_gnodes
+            .iter()
+            .map(|(_, scores)| scores.iter().enumerate().map(|(j, &v)| v - ctx.running_mean[j]).collect())
+            .collect();
+
+        let slices: Vec<&[f64]> = centred.iter().map(Vec::as_slice).collect();
+        ctx.tracker.observe(&slices, depth, true);
+
+        // Update running mean.
+        let col_means = compute_col_means(&synth_with_gnodes);
+        let lam = forgetting_factor;
+        let alpha = 1.0 - lam;
+        if ctx.warm {
+            for (j, m) in ctx.running_mean.iter_mut().enumerate() {
+                *m = lam.mul_add(*m, alpha * col_means[j]);
+            }
+        } else {
+            ctx.running_mean = col_means;
+            ctx.warm = true;
+        }
+    }
+
+    ctx.tracker.reset_cusum();
+    ctx.tracker.reset_clip_pressure();
+}
+
+// ─── Coordination tree topology ─────────────────────────────
+
+/// Lightweight mirror of the G-tree topology, pre-collected for
+/// coordination traversal. Avoids borrowing `self.graph` during
+/// `self.coordination` mutation.
+enum CoordNode<C: Coordinate> {
+    /// Terminal node: a competitive cell with no relevant children.
+    Terminal { gnode: GNodeId },
+    /// Internal node with only one relevant child (no coordination fires here).
+    SemiInternal {
+        gnode: GNodeId,
+        is_competitive: bool,
+        child: Box<Self>,
+    },
+    /// Internal node with both relevant children: coordination fires here.
+    Internal {
+        gnode: GNodeId,
+        depth: u32,
+        start: C,
+        end: C,
+        is_competitive: bool,
+        left: Box<Self>,
+        right: Box<Self>,
+    },
+}
+
+// ─── Compile-time safety ────────────────────────────────────
+
+/// Static assertion that `SpectralSentinel` is `Send + Sync`.
+const _: () = {
+    const fn assert_send_sync<T: Send + Sync>() {}
+    assert_send_sync::<SpectralSentinel<u128, u64, 128>>();
+};
diff --git a/packages/sentinel/src/sentinel/staging.rs b/packages/sentinel/src/sentinel/staging.rs
new file mode 100644
index 000000000..2459d2c31
--- /dev/null
+++ b/packages/sentinel/src/sentinel/staging.rs
@@ -0,0 +1,774 @@
+//! Staging area for deferred cell warm-up (S1, §ALGO S-18.2).
+//!
+//! New analysis cells are enqueued here for background noise injection
+//! instead of being warmed inline during `ingest()`. This decouples
+//! cell-creation latency from the observation hot path.
+//!
+//! # Lifecycle
+//!
+//! 1. **Enqueue** — `reconcile_analysis_set()` creates a `CellState` and
+//!    enqueues it with `staging.enqueue(gnode, cell, target_rounds)`.
+//! 2. **Warm** — the background thread (or synchronous drain) picks the
+//!    highest-priority cell (by volume) and injects noise batches.
+//! 3. **Ready** — when a cell completes all its target rounds, it moves
+//!    to the ready queue.
+//! 4. **Promote** — `take_ready()` returns completed cells for insertion
+//!    into the live `cells` map.
+//!
+//! # Thread safety (Step 3)
+//!
+//! When background warming is enabled, the staging area lives behind
+//! `Arc<Mutex<StagingArea>>`. The background thread takes cells out
+//! via [`take_highest_priority`] (moving them to `in_flight`), does
+//! the expensive noise injection *without* holding the lock, then
+//! returns the cell via [`finish_warming`] or [`return_warming`].
+//! The main thread can enqueue, evict, and promote while the
+//! background thread is working.
+
+use std::collections::{BTreeMap, BTreeSet};
+
+use rand::rngs::SmallRng;
+use torrust_mudlark::{Coordinate, GNodeId, GvGraph, Inspectable};
+
+use super::{CellState, generate_noise_batch};
+
+// ─── WarmingCell ────────────────────────────────────────────
+
+/// Progress state of a cell being warmed in the background.
+pub struct WarmingCell<C: Coordinate> {
+    /// The cell under construction.
+    pub cell: CellState<C>,
+
+    /// Total noise rounds required (from `NoiseSchedule::rounds_for_depth()`).
+    pub target_rounds: u32,
+
+    /// Rounds completed so far.
+    pub completed_rounds: u32,
+
+    /// Accumulated per-round mean score vectors (4D) for coordination
+    /// warm-up at promotion time.
+    pub round_scores: Vec<[f64; 4]>,
+
+    /// Cached volume (g.sum) for priority ordering, erased to `f64`.
+    /// Updated by the main thread during `reconcile_analysis_set()` via
+    /// [`StagingArea::update_volumes`].
+    pub volume: f64,
+}
+
+impl<C: Coordinate> WarmingCell<C> {
+    /// Whether this cell has completed all its target noise rounds.
+    #[must_use]
+    pub const fn is_ready(&self) -> bool {
+        self.completed_rounds >= self.target_rounds
+    }
+}
+
+// ─── StagingArea ────────────────────────────────────────────
+
+/// Staging area for cells undergoing deferred noise warm-up.
+///
+/// In synchronous mode this is owned directly by the sentinel.
+/// In background-warming mode it lives behind `Arc<Mutex<_>>`
+/// and is shared between the main thread and the warming thread.
+///
+/// `ingest()` reads only the `ready` queue (via `take_ready()`).
+pub struct StagingArea<C: Coordinate> {
+    /// Cells currently being warmed, keyed by `GNodeId`.
+    ///
+    /// `BTreeMap` for deterministic iteration order (ADR-S-005).
+    warming: BTreeMap<GNodeId, WarmingCell<C>>,
+
+    /// Cells that completed warming since the last promotion.
+    ready: Vec<(GNodeId, CellState<C>)>,
+
+    /// Cells currently checked out by the background warming thread.
+    ///
+    /// These are logically still "in the staging area" — they have
+    /// been temporarily removed from `warming` so the thread can
+    /// work on them without holding the lock. [`contains`] and
+    /// [`retain_in_set`] account for them.
+    in_flight: BTreeSet<GNodeId>,
+}
+
+impl<C: Coordinate> StagingArea<C> {
+    /// Create an empty staging area.
+    pub const fn new() -> Self {
+        Self {
+            warming: BTreeMap::new(),
+            ready: Vec::new(),
+            in_flight: BTreeSet::new(),
+        }
+    }
+
+    // ── Enqueue / promote ───────────────────────────────
+
+    /// Enqueue a newly created cell for background warming.
+    ///
+    /// The cell is added to the warming set with zero completed rounds.
+    /// If `target_rounds` is zero, the cell goes directly to the ready
+    /// queue (no warming needed).
+    pub fn enqueue(&mut self, gnode: GNodeId, cell: CellState<C>, target_rounds: u32) {
+        if target_rounds == 0 {
+            self.ready.push((gnode, cell));
+            return;
+        }
+
+        self.warming.insert(
+            gnode,
+            WarmingCell {
+                cell,
+                target_rounds,
+                completed_rounds: 0,
+                round_scores: Vec::with_capacity(target_rounds as usize),
+                volume: 0.0,
+            },
+        );
+    }
+
+    /// Take all ready cells for promotion into the live cells map.
+    ///
+    /// The caller inserts them into `self.cells` and fires coordination
+    /// warm-up as needed.
+    pub fn take_ready(&mut self) -> Vec<(GNodeId, CellState<C>)> {
+        std::mem::take(&mut self.ready)
+    }
+
+    // ── Background-thread interface (Step 3) ────────────
+
+    /// Remove the highest-priority warming cell for background processing.
+    ///
+    /// The cell moves from `warming` to `in_flight`. The caller is
+    /// responsible for returning it via [`return_warming`] or
+    /// [`finish_warming`].
+    ///
+    /// Priority is by cached `volume` (largest first).
+    pub fn take_highest_priority(&mut self) -> Option<(GNodeId, WarmingCell<C>)> {
+        let (&gnode, _) = self
+            .warming
+            .iter()
+            .max_by(|(_, a), (_, b)| a.volume.partial_cmp(&b.volume).unwrap_or(std::cmp::Ordering::Equal))?;
+        let wc = self.warming.remove(&gnode)?;
+        self.in_flight.insert(gnode);
+        Some((gnode, wc))
+    }
+
+    /// Return an in-flight cell to the warming map (not yet ready).
+    ///
+    /// If the cell was evicted while in-flight (removed from
+    /// `in_flight` by [`retain_in_set`]), the cell is silently
+    /// discarded — the work is wasted but correctness is preserved.
+    pub fn return_warming(&mut self, gnode: GNodeId, wc: WarmingCell<C>) {
+        if self.in_flight.remove(&gnode) {
+            self.warming.insert(gnode, wc);
+        }
+        // else: evicted while in-flight — discard.
+    }
+
+    /// Complete an in-flight cell and add it to the ready queue.
+    ///
+    /// If the cell was evicted while in-flight, it is silently
+    /// discarded.
+    pub fn finish_warming(&mut self, gnode: GNodeId, cell: CellState<C>) {
+        if self.in_flight.remove(&gnode) {
+            self.ready.push((gnode, cell));
+        }
+        // else: evicted while in-flight — discard.
+    }
+
+    // ── Warm-one-batch (used by background thread) ──────
+
+    /// Pick the highest-priority warming cell (largest `volume`) and
+    /// inject one noise batch. Returns `true` if a batch was injected.
+    ///
+    /// Used by the Step 1 unit tests and by the synchronous fallback
+    /// in [`warm_one_batch`] with graph-volume updates. The background
+    /// thread uses [`take_highest_priority`] instead to avoid holding
+    /// the lock during expensive noise injection.
+    #[allow(dead_code)] // used in tests
+    pub fn warm_one_batch<V: Inspectable, const N: u32>(
+        &mut self,
+        graph: &GvGraph<C, V, N>,
+        batch_size: usize,
+        rng: &mut SmallRng,
+    ) -> bool {
+        // Find the highest-priority warming cell.
+        let Some((&gnode, _)) = self
+            .warming
+            .iter()
+            .max_by(|(_, a), (_, b)| a.volume.partial_cmp(&b.volume).unwrap_or(std::cmp::Ordering::Equal))
+        else {
+            return false;
+        };
+
+        // Remove temporarily to satisfy the borrow checker, then re-insert.
+        let Some(mut wc) = self.warming.remove(&gnode) else {
+            return false;
+        };
+
+        // Generate and inject one noise batch.
+        let noise = generate_noise_batch(wc.cell.width, batch_size, rng);
+        let slices: Vec<&[f64]> = noise.iter().map(Vec::as_slice).collect();
+        #[allow(clippy::cast_possible_truncation)] // depth ≤ 128, fits in u8
+        let report = wc.cell.tracker.observe(&slices, wc.cell.depth as u8, true);
+
+        wc.round_scores.push([
+            report.scores.novelty.mean,
+            report.scores.displacement.mean,
+            report.scores.surprise.mean,
+            report.scores.coherence.mean,
+        ]);
+        wc.completed_rounds += 1;
+
+        if wc.is_ready() {
+            // Finalize: seed CUSUM slow from baselines and reset.
+            wc.cell.tracker.seed_cusum_slow_from_baselines();
+            wc.cell.tracker.reset_cusum();
+            wc.cell.tracker.reset_clip_pressure();
+            self.ready.push((gnode, wc.cell));
+        } else {
+            self.warming.insert(gnode, wc);
+        }
+
+        // Update volumes from the graph for remaining warming cells.
+        for (&g, wc) in &mut self.warming {
+            wc.volume = graph.gnode_info(g).map_or(0.0, |info| info.sum.to_f64_approx());
+        }
+
+        true
+    }
+
+    // ── Query / eviction ────────────────────────────────
+
+    /// Check if a `GNodeId` is currently in the staging area
+    /// (warming, ready, or in-flight).
+    pub fn contains(&self, gnode: GNodeId) -> bool {
+        self.warming.contains_key(&gnode) || self.in_flight.contains(&gnode) || self.ready.iter().any(|(g, _)| *g == gnode)
+    }
+
+    /// Set of all `GNodeId`s currently in the staging area.
+    ///
+    /// Useful for diagnostics and testing.
+    #[allow(dead_code)] // used in tests; no longer needed for routing
+    pub fn gnode_set(&self) -> BTreeSet<GNodeId> {
+        let mut set: BTreeSet<GNodeId> = self.warming.keys().copied().collect();
+        set.extend(&self.in_flight);
+        for (g, _) in &self.ready {
+            set.insert(*g);
+        }
+        set
+    }
+
+    /// Remove a cell from the staging area (e.g. evicted by graph rebalance).
+    ///
+    /// Returns `true` if the cell was found and removed.
+    #[allow(dead_code)] // used in tests, will be called from Step 3 thread lifecycle
+    pub fn remove(&mut self, gnode: GNodeId) -> bool {
+        if self.warming.remove(&gnode).is_some() {
+            return true;
+        }
+        if self.in_flight.remove(&gnode) {
+            return true;
+        }
+        let before = self.ready.len();
+        self.ready.retain(|(g, _)| *g != gnode);
+        self.ready.len() < before
+    }
+
+    /// Retain only cells whose `GNodeId` is in `keep`.
+    ///
+    /// Evicts warming, ready, *and* in-flight cells that exited the
+    /// analysis set. In-flight cells evicted here will be silently
+    /// discarded when the background thread tries to return them
+    /// (the `in_flight` entry is gone, so [`return_warming`] /
+    /// [`finish_warming`] no-ops).
+    pub fn retain_in_set(&mut self, keep: &BTreeSet<GNodeId>) {
+        self.warming.retain(|gnode, _| keep.contains(gnode));
+        self.ready.retain(|(gnode, _)| keep.contains(gnode));
+        self.in_flight.retain(|gnode| keep.contains(gnode));
+    }
+
+    // ── Volume update ───────────────────────────────────
+
+    /// Update cached volumes for warming cells from the G-V Graph.
+    ///
+    /// Called by the main thread after G-V Graph observation so the
+    /// background thread can prioritise cells by current importance.
+    pub fn update_volumes<V: Inspectable, const N: u32>(&mut self, graph: &GvGraph<C, V, N>) {
+        for (&g, wc) in &mut self.warming {
+            wc.volume = graph.gnode_info(g).map_or(0.0, |info| info.sum.to_f64_approx());
+        }
+    }
+
+    // ── Synchronous drain (Step 2 fallback) ─────────────
+
+    /// Drain all warming cells to completion synchronously.
+    ///
+    /// Processes cells in descending g.sum (volume) order, matching
+    /// the background thread's priority rule (§ALGO S-11.6.2,
+    /// ADR-S-019). This ensures ancestors come online before
+    /// descendants even in synchronous mode.
+    #[allow(clippy::cast_possible_truncation)] // depth ≤ 128, fits u8
+    pub fn drain_all_synchronous(&mut self, batch_size: usize, rng: &mut SmallRng) {
+        // Sort by cached volume (g.sum) descending, then GNodeId for
+        // deterministic tie-breaking (ADR-S-005).
+        let mut gnodes: Vec<(GNodeId, f64)> = self.warming.iter().map(|(&gnode, wc)| (gnode, wc.volume)).collect();
+        gnodes.sort_by(|a, b| {
+            b.1.partial_cmp(&a.1)
+                .unwrap_or(std::cmp::Ordering::Equal)
+                .then_with(|| a.0.cmp(&b.0))
+        });
+
+        for (gnode, _) in gnodes {
+            let Some(mut wc) = self.warming.remove(&gnode) else {
+                continue;
+            };
+
+            // Warm to completion — same loop as inject_noise_into_cell().
+            while !wc.is_ready() {
+                let noise = generate_noise_batch(wc.cell.width, batch_size, rng);
+                let slices: Vec<&[f64]> = noise.iter().map(Vec::as_slice).collect();
+                let report = wc.cell.tracker.observe(&slices, wc.cell.depth as u8, true);
+
+                wc.round_scores.push([
+                    report.scores.novelty.mean,
+                    report.scores.displacement.mean,
+                    report.scores.surprise.mean,
+                    report.scores.coherence.mean,
+                ]);
+                wc.completed_rounds += 1;
+            }
+
+            // Finalize: seed CUSUM slow from baselines and reset.
+            wc.cell.tracker.seed_cusum_slow_from_baselines();
+            wc.cell.tracker.reset_cusum();
+            wc.cell.tracker.reset_clip_pressure();
+            self.ready.push((gnode, wc.cell));
+        }
+    }
+
+    // ── Count / clear ───────────────────────────────────
+
+    /// Number of cells currently warming (not yet ready, not in-flight).
+    #[allow(dead_code)] // used in tests + Step 6 health reporting
+    pub fn warming_count(&self) -> usize {
+        self.warming.len()
+    }
+
+    /// Number of cells in the ready queue awaiting promotion.
+    #[allow(dead_code)] // used in tests + Step 6 health reporting
+    pub const fn ready_count(&self) -> usize {
+        self.ready.len()
+    }
+
+    /// Number of cells currently checked out by the background thread.
+    #[allow(dead_code)] // used in tests + Step 6 health reporting
+    pub fn in_flight_count(&self) -> usize {
+        self.in_flight.len()
+    }
+
+    /// Total cells in the staging area (warming + ready + in-flight).
+    #[allow(dead_code)] // used in tests + Step 6 health reporting
+    pub fn total_count(&self) -> usize {
+        self.warming.len() + self.ready.len() + self.in_flight.len()
+    }
+
+    /// Number of warming cells (including in-flight) that are
+    /// competitive targets (not ancestor-only).
+    ///
+    /// Used to populate `HealthReport::warming_competitive_targets`
+    /// (§ALGO S-14.11, ADR-S-019).
+    pub fn warming_competitive_count(&self) -> usize {
+        self.warming.values().filter(|wc| wc.cell.is_competitive).count()
+        // Note: in-flight cells are not counted here because their
+        // competitive status may have changed since checkout. This
+        // is conservative — the count may undercount by at most the
+        // number of in-flight cells (typically 0 or 1).
+    }
+
+    /// Whether there are any cells that need warming work.
+    ///
+    /// Returns `true` if `warming` is non-empty (excludes in-flight
+    /// and ready cells — those are already being processed or done).
+    pub fn has_warming_work(&self) -> bool {
+        !self.warming.is_empty()
+    }
+
+    /// Clear all warming, ready, and in-flight cells (for `reset()`).
+    pub fn clear(&mut self) {
+        self.warming.clear();
+        self.ready.clear();
+        self.in_flight.clear();
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::config::SentinelConfig;
+    use crate::sentinel::tracker::SubspaceTracker;
+
+    fn make_cell(depth: u32) -> CellState<u128> {
+        let cfg = SentinelConfig::<u64>::default();
+        let width = 128usize.saturating_sub(depth as usize);
+        CellState {
+            tracker: SubspaceTracker::new(width, &cfg, cfg.cusum_slow_decay),
+            depth,
+            width,
+            start: 0,
+            end: u128::MAX >> depth,
+            is_competitive: false,
+        }
+    }
+
+    /// Create a graph with a split so we can extract multiple distinct `GNodeId`s.
+    ///
+    /// Returns `(graph, root, left_child, right_child)` where the children
+    /// are `Option<GNodeId>` (will be `Some` after the split).
+    fn split_graph() -> (GvGraph<u128, u64, 128>, GNodeId, GNodeId, GNodeId) {
+        use torrust_mudlark::Config as GvConfig;
+        let config = GvConfig {
+            split_threshold: 2u64,
+            depth_create: 3,
+            depth_evict: 6,
+            budget: None,
+            alpha_relax: 0.75,
+            bounded_eviction: true,
+        };
+        let mut graph: GvGraph<u128, u64, 128> = GvGraph::new(config);
+        let root = graph.g_root();
+
+        // Observe enough in both halves to trigger a split.
+        let lo = 0u128;
+        let hi = u128::MAX / 2 + 1;
+        for _ in 0..5 {
+            graph.observe(lo, 1u64);
+            graph.observe(hi, 1u64);
+        }
+
+        let children = graph.gnode_children(root).expect("root must exist");
+        let left = children.left.expect("root must have left child after split");
+        let right = children.right.expect("root must have right child after split");
+        (graph, root, left, right)
+    }
+
+    #[test]
+    fn warming_cell_is_ready_when_complete() {
+        let cell = make_cell(0);
+        let wc = WarmingCell {
+            cell,
+            target_rounds: 5,
+            completed_rounds: 4,
+            round_scores: Vec::new(),
+            volume: 100.0,
+        };
+        assert!(!wc.is_ready());
+
+        let cell2 = make_cell(0);
+        let wc2 = WarmingCell {
+            cell: cell2,
+            target_rounds: 5,
+            completed_rounds: 5,
+            round_scores: Vec::new(),
+            volume: 100.0,
+        };
+        assert!(wc2.is_ready());
+    }
+
+    #[test]
+    fn warming_cell_is_ready_when_over_target() {
+        let cell = make_cell(0);
+        let wc = WarmingCell {
+            cell,
+            target_rounds: 3,
+            completed_rounds: 10,
+            round_scores: Vec::new(),
+            volume: 0.0,
+        };
+        assert!(wc.is_ready());
+    }
+
+    #[test]
+    fn enqueue_zero_rounds_goes_directly_to_ready() {
+        let mut staging = StagingArea::<u128>::new();
+        let (_, root, _, _) = split_graph();
+        let cell = make_cell(0);
+
+        staging.enqueue(root, cell, 0);
+
+        assert_eq!(staging.warming_count(), 0);
+        assert_eq!(staging.ready_count(), 1);
+
+        let ready = staging.take_ready();
+        assert_eq!(ready.len(), 1);
+        assert_eq!(ready[0].0, root);
+    }
+
+    #[test]
+    fn enqueue_with_rounds_goes_to_warming() {
+        let mut staging = StagingArea::<u128>::new();
+        let (_, root, _, _) = split_graph();
+        let cell = make_cell(0);
+
+        staging.enqueue(root, cell, 5);
+
+        assert_eq!(staging.warming_count(), 1);
+        assert_eq!(staging.ready_count(), 0);
+        assert!(staging.contains(root));
+    }
+
+    #[test]
+    fn take_ready_empties_queue() {
+        let mut staging = StagingArea::<u128>::new();
+        let (_, root, _, _) = split_graph();
+        let cell = make_cell(0);
+
+        staging.enqueue(root, cell, 0);
+        assert_eq!(staging.ready_count(), 1);
+
+        let ready = staging.take_ready();
+        assert_eq!(ready.len(), 1);
+        assert_eq!(staging.ready_count(), 0);
+    }
+
+    #[test]
+    fn remove_from_warming() {
+        let mut staging = StagingArea::<u128>::new();
+        let (_, _, left, _) = split_graph();
+        let cell = make_cell(1);
+
+        staging.enqueue(left, cell, 10);
+        assert!(staging.contains(left));
+
+        assert!(staging.remove(left));
+        assert!(!staging.contains(left));
+        assert_eq!(staging.warming_count(), 0);
+    }
+
+    #[test]
+    fn remove_from_ready() {
+        let mut staging = StagingArea::<u128>::new();
+        let (_, root, _, _) = split_graph();
+        let cell = make_cell(0);
+
+        staging.enqueue(root, cell, 0);
+        assert!(staging.contains(root));
+
+        assert!(staging.remove(root));
+        assert!(!staging.contains(root));
+        assert_eq!(staging.ready_count(), 0);
+    }
+
+    #[test]
+    fn remove_nonexistent_returns_false() {
+        let mut staging = StagingArea::<u128>::new();
+        let (_, root, _, _) = split_graph();
+        assert!(!staging.remove(root));
+    }
+
+    #[test]
+    fn clear_empties_everything() {
+        let mut staging = StagingArea::<u128>::new();
+        let (_, _, left, right) = split_graph();
+
+        staging.enqueue(left, make_cell(1), 5);
+        staging.enqueue(right, make_cell(1), 0);
+
+        assert_eq!(staging.total_count(), 2);
+
+        staging.clear();
+        assert_eq!(staging.total_count(), 0);
+        assert_eq!(staging.warming_count(), 0);
+        assert_eq!(staging.ready_count(), 0);
+    }
+
+    #[test]
+    fn warm_one_batch_completes_single_round_cell() {
+        use rand::SeedableRng;
+        let mut staging = StagingArea::<u128>::new();
+        let (graph, _, left, _) = split_graph();
+        let cell = make_cell(1);
+
+        // Enqueue with target_rounds = 1: one batch should complete it.
+        staging.enqueue(left, cell, 1);
+        assert_eq!(staging.warming_count(), 1);
+
+        let mut rng = SmallRng::seed_from_u64(42);
+        let warmed = staging.warm_one_batch(&graph, 64, &mut rng);
+        assert!(warmed);
+
+        // Cell should have moved to ready.
+        assert_eq!(staging.warming_count(), 0);
+        assert_eq!(staging.ready_count(), 1);
+
+        let ready = staging.take_ready();
+        assert_eq!(ready.len(), 1);
+        assert_eq!(ready[0].0, left);
+    }
+
+    #[test]
+    fn warm_one_batch_returns_false_when_empty() {
+        use rand::SeedableRng;
+        let mut staging = StagingArea::<u128>::new();
+        let (graph, _, _, _) = split_graph();
+        let mut rng = SmallRng::seed_from_u64(42);
+
+        assert!(!staging.warm_one_batch(&graph, 64, &mut rng));
+    }
+
+    #[test]
+    fn warm_one_batch_incremental_progress() {
+        use rand::SeedableRng;
+        let mut staging = StagingArea::<u128>::new();
+        let (graph, _, left, _) = split_graph();
+        let cell = make_cell(1);
+
+        staging.enqueue(left, cell, 3);
+        let mut rng = SmallRng::seed_from_u64(42);
+
+        // First batch: still warming.
+        assert!(staging.warm_one_batch(&graph, 64, &mut rng));
+        assert_eq!(staging.warming_count(), 1);
+        assert_eq!(staging.ready_count(), 0);
+
+        // Second batch: still warming.
+        assert!(staging.warm_one_batch(&graph, 64, &mut rng));
+        assert_eq!(staging.warming_count(), 1);
+        assert_eq!(staging.ready_count(), 0);
+
+        // Third batch: completes.
+        assert!(staging.warm_one_batch(&graph, 64, &mut rng));
+        assert_eq!(staging.warming_count(), 0);
+        assert_eq!(staging.ready_count(), 1);
+    }
+
+    #[test]
+    fn warm_one_batch_picks_highest_volume() {
+        use rand::SeedableRng;
+        let mut staging = StagingArea::<u128>::new();
+        let (graph, _, left, right) = split_graph();
+
+        // Enqueue two cells with different target_rounds.
+        staging.enqueue(left, make_cell(1), 1);
+        staging.enqueue(right, make_cell(1), 1);
+
+        // Manually set volumes so right > left.
+        staging.warming.get_mut(&left).unwrap().volume = 10.0;
+        staging.warming.get_mut(&right).unwrap().volume = 100.0;
+
+        let mut rng = SmallRng::seed_from_u64(42);
+
+        // First warm_one_batch should pick right (higher volume).
+        assert!(staging.warm_one_batch(&graph, 64, &mut rng));
+        // right should be ready now (target_rounds=1).
+        assert!(staging.ready.iter().any(|(g, _)| *g == right));
+        assert_eq!(staging.warming_count(), 1);
+        assert!(staging.warming.contains_key(&left));
+    }
+
+    #[test]
+    fn contains_checks_both_warming_and_ready() {
+        let mut staging = StagingArea::<u128>::new();
+        let (_, _, left, right) = split_graph();
+
+        staging.enqueue(left, make_cell(1), 5); // goes to warming
+        staging.enqueue(right, make_cell(1), 0); // goes to ready
+
+        assert!(staging.contains(left));
+        assert!(staging.contains(right));
+    }
+
+    // ── Step 3 in-flight tests ──────────────────────────
+
+    #[test]
+    fn take_highest_priority_moves_to_in_flight() {
+        let mut staging = StagingArea::<u128>::new();
+        let (_, _, left, right) = split_graph();
+
+        staging.enqueue(left, make_cell(1), 3);
+        staging.enqueue(right, make_cell(1), 3);
+        staging.warming.get_mut(&left).unwrap().volume = 10.0;
+        staging.warming.get_mut(&right).unwrap().volume = 100.0;
+
+        let (gnode, _wc) = staging.take_highest_priority().unwrap();
+        assert_eq!(gnode, right); // highest volume
+        assert_eq!(staging.warming_count(), 1); // left remains
+        assert_eq!(staging.in_flight_count(), 1);
+        assert!(staging.contains(right)); // still logically present
+    }
+
+    #[test]
+    fn return_warming_restores_cell() {
+        let mut staging = StagingArea::<u128>::new();
+        let (_, _, left, _) = split_graph();
+
+        staging.enqueue(left, make_cell(1), 3);
+        let (gnode, wc) = staging.take_highest_priority().unwrap();
+        assert_eq!(staging.warming_count(), 0);
+        assert_eq!(staging.in_flight_count(), 1);
+
+        staging.return_warming(gnode, wc);
+        assert_eq!(staging.warming_count(), 1);
+        assert_eq!(staging.in_flight_count(), 0);
+    }
+
+    #[test]
+    fn finish_warming_moves_to_ready() {
+        let mut staging = StagingArea::<u128>::new();
+        let (_, _, left, _) = split_graph();
+
+        staging.enqueue(left, make_cell(1), 1);
+        let (gnode, wc) = staging.take_highest_priority().unwrap();
+
+        staging.finish_warming(gnode, wc.cell);
+        assert_eq!(staging.ready_count(), 1);
+        assert_eq!(staging.in_flight_count(), 0);
+    }
+
+    #[test]
+    fn eviction_of_in_flight_cell_discards_on_return() {
+        let mut staging = StagingArea::<u128>::new();
+        let (_, _, left, right) = split_graph();
+
+        staging.enqueue(left, make_cell(1), 3);
+        staging.enqueue(right, make_cell(1), 3);
+
+        // Set left to highest priority so take_highest_priority picks it.
+        staging.warming.get_mut(&left).unwrap().volume = 100.0;
+        staging.warming.get_mut(&right).unwrap().volume = 10.0;
+
+        // Take left for background warming.
+        let (gnode, wc) = staging.take_highest_priority().unwrap();
+        assert_eq!(gnode, left);
+
+        // Meanwhile, evict left from the analysis set.
+        let mut keep = BTreeSet::new();
+        keep.insert(right);
+        staging.retain_in_set(&keep);
+
+        // Background thread tries to return the evicted cell — silently discarded.
+        staging.return_warming(gnode, wc);
+        assert_eq!(staging.warming_count(), 1); // only right
+        assert_eq!(staging.in_flight_count(), 0);
+        assert!(!staging.contains(gnode)); // left is gone
+    }
+
+    #[test]
+    fn gnode_set_includes_all_states() {
+        let mut staging = StagingArea::<u128>::new();
+        let (_, root, left, right) = split_graph();
+
+        staging.enqueue(root, make_cell(0), 0); // → ready
+        staging.enqueue(left, make_cell(1), 3); // → warming
+        staging.enqueue(right, make_cell(1), 3); // → warming
+
+        // Take right to in-flight.
+        staging.warming.get_mut(&right).unwrap().volume = 100.0;
+        let _taken = staging.take_highest_priority(); // takes right
+
+        let set = staging.gnode_set();
+        assert!(set.contains(&root)); // ready
+        assert!(set.contains(&left)); // warming
+        assert!(set.contains(&right)); // in-flight
+        assert_eq!(set.len(), 3);
+    }
+}
diff --git a/packages/sentinel/src/sentinel/tracker.rs b/packages/sentinel/src/sentinel/tracker.rs
new file mode 100644
index 000000000..749926b7a
--- /dev/null
+++ b/packages/sentinel/src/sentinel/tracker.rs
@@ -0,0 +1,871 @@
+//! Subspace tracker — the core online SVD engine.
+//!
+//! Maintains a low-rank model of "normal" via streaming thin SVD
+//! with exponential forgetting.  Scores each new batch along four
+//! axes (novelty, displacement, surprise, coherence), then evolves
+//! the model to incorporate the new data.
+//!
+//! See `docs/algorithm.md` §4.2 for the five-phase core loop and
+//! §5 for the four scoring axes.
+//!
+//! This is the only module that depends on `faer`.
+
+use faer::Mat;
+use torrust_mudlark::Accumulator;
+
+use crate::config::SentinelConfig;
+use crate::ewma::EwmaStats;
+use crate::report::{AnomalyScores, SampleScore, ScoreDistribution, ScoringGeometry, TrackerMaturity, TrackerReport};
+use crate::sentinel::cusum::CusumAccumulator;
+
+// ─── Per-axis baseline ──────────────────────────────────────
+
+/// Fast EWMA (z-scores) + CUSUM (drift detection) for one scoring axis.
+#[derive(Debug, Clone)]
+struct AxisBaseline {
+    fast: EwmaStats,
+    cusum: CusumAccumulator,
+    /// Clip-pressure EWMA: ρ̄ ∈ [0, 1]  (§ALGO S-6.4).
+    clip_pressure: f64,
+}
+
+impl AxisBaseline {
+    const fn new(fast_decay: f64, slow_decay: f64) -> Self {
+        Self {
+            fast: EwmaStats::new(fast_decay),
+            cusum: CusumAccumulator::new(slow_decay),
+            clip_pressure: 0.0,
+        }
+    }
+
+    /// Destroy all learned state — return to the freshly-constructed
+    /// state with cold EWMA and zeroed CUSUM.
+    const fn reset_cold(&mut self) {
+        self.fast.reset_cold();
+        self.cusum.reset_cold();
+        self.clip_pressure = 0.0;
+    }
+
+    /// Seed the CUSUM's slow EWMA from this axis's fast EWMA.
+    ///
+    /// Closes the fast-slow gap after noise injection (ADR-S-013
+    /// §6b, Option C).
+    const fn seed_cusum_slow_from_fast(&mut self) {
+        self.cusum.seed_slow_from(&self.fast);
+    }
+}
+
+// ─── SubspaceTracker ────────────────────────────────────────
+
+/// Low-rank subspace model with online learning and four-axis scoring.
+///
+/// Each analysis cell gets one of these.  The tracker
+/// accepts centred suffix observation slices via [`observe`](Self::observe)
+/// and returns a [`TrackerReport`].
+///
+/// The tracker has zero knowledge of cell identity, observation types,
+/// or the host's domain.  It operates on `&[&[f64]]` — a batch of
+/// d-dimensional centred bit slices.
+#[derive(Debug, Clone)]
+pub struct SubspaceTracker {
+    /// Suffix width: dimensionality of the working space (128 − depth).
+    dim: usize,
+
+    /// Hard ceiling on rank: `min(dim, max_rank)`.
+    cap: usize,
+
+    /// Current active rank (number of basis vectors in use).
+    rank: usize,
+
+    /// Observation step counter (for rank adaptation timing).
+    step: u64,
+
+    // ── Subspace state ──────────────────────────────────
+    /// Orthonormal basis, shape `(dim, cap)`.  Only columns `[:rank]` are active.
+    basis: Mat<f64>,
+
+    /// Singular values, length `cap`.  Only `[:rank]` are meaningful.
+    sigmas: Vec<f64>,
+
+    // ── Latent distribution ─────────────────────────────
+    /// EWMA mean of latent coordinates, length `cap`.
+    lat_mean: Vec<f64>,
+
+    /// EWMA variance of latent coordinates, length `cap`.
+    lat_var: Vec<f64>,
+
+    /// Upper-triangle cross-correlation, flat-packed.
+    /// `cross_corr[tri_idx(j, l, cap)]` for `j < l` tracks EWMA of `zⱼ · zₗ`.
+    ///
+    /// Length: `cap * (cap - 1) / 2`.  When rank increases, new entries
+    /// are already zero (the full triangle is pre-allocated at construction).
+    /// When rank decreases, outer entries are ignored but preserved (§4.2 Phase 3).
+    cross_corr: Vec<f64>,
+
+    // ── Score baselines (one per axis) ──────────────────
+    novelty_bl: AxisBaseline,
+    displacement_bl: AxisBaseline,
+    surprise_bl: AxisBaseline,
+    coherence_bl: AxisBaseline,
+
+    // ── Maturity tracking ───────────────────────────────
+    real_observations: u64,
+    noise_observations: u64,
+    noise_influence: f64,
+
+    // ── Config snapshots ────────────────────────────────
+    forgetting_factor: f64,
+    energy_threshold: f64,
+    rank_update_interval: u64,
+    eps: f64,
+    per_sample_scores: bool,
+    cusum_allowance_sigmas: f64,
+    clip_sigmas: f64,
+    /// Clip-pressure EWMA decay factor (`λ_ρ`, §ALGO S-6.4).
+    clip_pressure_decay: f64,
+    svd_strategy: crate::maths::SvdStrategy,
+}
+
+impl SubspaceTracker {
+    /// Create a new tracker for the given suffix width.
+    ///
+    /// The initial basis is identity-like columns (not random).
+    /// Noise injection will diversify it before real traffic arrives.
+    pub fn new<V: Accumulator>(dim: usize, cfg: &SentinelConfig<V>, slow_decay: f64) -> Self {
+        debug_assert!(
+            dim >= crate::MIN_TRACKER_DIM,
+            "SubspaceTracker::new() called with dim={dim}, \
+             expected >= {} (caller should have filtered)",
+            crate::MIN_TRACKER_DIM,
+        );
+
+        let cap = dim.min(cfg.max_rank);
+
+        // Identity-like basis: column j has a 1.0 at row j.
+        let mut basis = Mat::zeros(dim, cap);
+        for j in 0..cap.min(dim) {
+            basis[(j, j)] = 1.0;
+        }
+
+        let fast_decay = cfg.forgetting_factor;
+
+        Self {
+            dim,
+            cap,
+            rank: 1,
+            step: 0,
+            basis,
+            sigmas: vec![0.01; cap],
+            lat_mean: vec![0.0; cap],
+            lat_var: vec![1.0; cap],
+            cross_corr: vec![0.0; cap * (cap.saturating_sub(1)) / 2],
+            novelty_bl: AxisBaseline::new(fast_decay, slow_decay),
+            displacement_bl: AxisBaseline::new(fast_decay, slow_decay),
+            surprise_bl: AxisBaseline::new(fast_decay, slow_decay),
+            coherence_bl: AxisBaseline::new(fast_decay, slow_decay),
+            real_observations: 0,
+            noise_observations: 0,
+            noise_influence: 1.0,
+            forgetting_factor: cfg.forgetting_factor,
+            energy_threshold: cfg.energy_threshold,
+            rank_update_interval: cfg.rank_update_interval,
+            eps: cfg.eps,
+            per_sample_scores: cfg.per_sample_scores,
+            cusum_allowance_sigmas: cfg.cusum_allowance_sigmas,
+            clip_sigmas: cfg.clip_sigmas,
+            clip_pressure_decay: cfg.clip_pressure_decay,
+            svd_strategy: cfg.svd_strategy,
+        }
+    }
+
+    /// Process a batch of centred observation slices and return a report.
+    ///
+    /// Each inner slice in `rows` has length `self.dim`.
+    /// Scoring happens against the *prior* model, then the model evolves.
+    ///
+    /// `is_noise` controls maturity bookkeeping — noise observations
+    /// don't count as real.
+    #[allow(clippy::many_single_char_names)] // mathematical notation matching the spec
+    pub fn observe(&mut self, rows: &[&[f64]], depth: u8, is_noise: bool) -> TrackerReport {
+        let _observe_span = tracing::debug_span!("observe", depth, is_noise, b = rows.len(), k = self.rank).entered();
+
+        let b = rows.len();
+        let d = self.dim;
+        let k = self.rank;
+        let eps = self.eps;
+
+        // Build X matrix (b × d).
+        let x = Self::build_matrix(rows, b, d);
+
+        // ── Phase 1: Score against the prior model ──────
+        let p1_guard = tracing::debug_span!("phase1_score").entered();
+        // Capture Z from this phase for Phase 3 (latent evolution uses
+        // the prior-basis projection, not the post-evolution basis).
+        let u_k = self.basis.subcols(0, k);
+        let z = &x * u_k; // (b × k)
+        let x_hat = &z * u_k.transpose(); // (b × d)
+        let residual = &x - &x_hat; // (b × d)
+
+        let (nov_scores, disp_scores, surp_scores, coh_scores) = self.compute_scores(&z, &residual, b, d, k, eps);
+
+        // Build per-sample structs only when enabled (avoids 4 z-score
+        // calls per sample in the common disabled case).
+        let per_sample = if self.per_sample_scores {
+            Some(self.build_per_sample(&nov_scores, &disp_scores, &surp_scores, &coh_scores, eps))
+        } else {
+            None
+        };
+        drop(p1_guard);
+
+        // ── Phase 2: Evolve subspace (streaming SVD) ─────
+        // Uses the maths module dispatch: the selected SvdStrategy
+        // runs, and in debug builds the other strategy also runs
+        // with results compared via debug_assert (ADR-S-016).
+        self.evolve_subspace(&z, &residual, k);
+
+        // ── Phase 3: Evolve latent distribution ─────────
+        let p3_guard = tracing::debug_span!("phase3_latent").entered();
+        // Uses Z from Phase 1 (prior basis), not the updated basis.
+        self.evolve_latent(&z, k);
+        drop(p3_guard);
+
+        // ── Phase 4: Update score baselines and CUSUM ───
+        //
+        // Unified clip + clip-pressure EWMA (§ALGO S-6.1.1, §ALGO S-6.4).
+        //
+        // Each axis computes its own effective clip width from:
+        //   p = max(η, ρ̄)           — noise influence OR clip pressure
+        //   n_σ_eff = n_σ · (1 + p / (1 − p + ε))
+        //
+        // A single shared clip filter (from the fast EWMA ceiling) is
+        // applied, and both the fast EWMA and CUSUM slow EWMA receive
+        // the same retained set.  The per-axis clip-pressure ρ̄ is
+        // updated from the fraction of samples clipped.
+        //
+        // At ρ̄ = 0 this reduces to the old η-only formula:
+        //   n_σ_eff = n_σ + n_σ · η / (1 − η + ε)
+        let allowance = self.cusum_allowance_sigmas;
+        let eta = self.noise_influence;
+        let clip_sigmas = self.clip_sigmas;
+        let cp_decay = self.clip_pressure_decay;
+
+        let novelty_dist = Self::update_axis(
+            &mut self.novelty_bl,
+            &nov_scores,
+            eps,
+            allowance,
+            clip_sigmas,
+            eta,
+            cp_decay,
+            true,
+        );
+        let displacement_dist = Self::update_axis(
+            &mut self.displacement_bl,
+            &disp_scores,
+            eps,
+            allowance,
+            clip_sigmas,
+            eta,
+            cp_decay,
+            true,
+        );
+        let surprise_dist = Self::update_axis(
+            &mut self.surprise_bl,
+            &surp_scores,
+            eps,
+            allowance,
+            clip_sigmas,
+            eta,
+            cp_decay,
+            true,
+        );
+        // Coherence does not exist at k < 2 (no pairs).  Scores are
+        // identically zero, so the baseline must not evolve — it stays
+        // cold until k reaches 2, where the first real values enter
+        // through the cold→warm path.  If rank later drops back below
+        // 2, adapt_rank() destroys the coherence baseline entirely.
+        let coherence_dist = Self::update_axis(
+            &mut self.coherence_bl,
+            &coh_scores,
+            eps,
+            allowance,
+            clip_sigmas,
+            eta,
+            cp_decay,
+            k >= 2,
+        );
+
+        // ── Phase 5: Adapt rank ─────────────────────────
+        self.step += 1;
+        if self.step.is_multiple_of(self.rank_update_interval) {
+            self.adapt_rank();
+        }
+
+        // ── Maturity bookkeeping ────────────────────────
+        self.update_maturity(b, is_noise);
+
+        TrackerReport {
+            depth,
+            rank: self.rank,
+            energy_ratio: self.energy_ratio(),
+            top_singular_value: self.sigmas.first().copied().unwrap_or(0.0),
+            scores: AnomalyScores {
+                novelty: novelty_dist,
+                displacement: displacement_dist,
+                surprise: surprise_dist,
+                coherence: coherence_dist,
+            },
+            maturity: self.maturity(),
+            geometry: self.scoring_geometry(),
+            per_sample,
+        }
+    }
+
+    /// Reset all CUSUM accumulators to zero (post-noise-injection).
+    pub const fn reset_cusum(&mut self) {
+        self.novelty_bl.cusum.reset();
+        self.displacement_bl.cusum.reset();
+        self.surprise_bl.cusum.reset();
+        self.coherence_bl.cusum.reset();
+    }
+
+    /// Zero clip-pressure EWMA on all four axes (§ALGO S-11.4).
+    ///
+    /// Prevents warm-up contamination from echoing into production
+    /// scoring.  Called after noise injection completes, alongside
+    /// [`reset_cusum`](Self::reset_cusum).
+    pub const fn reset_clip_pressure(&mut self) {
+        self.novelty_bl.clip_pressure = 0.0;
+        self.displacement_bl.clip_pressure = 0.0;
+        self.surprise_bl.clip_pressure = 0.0;
+        self.coherence_bl.clip_pressure = 0.0;
+    }
+
+    /// Seed each axis's CUSUM slow EWMA from the corresponding fast EWMA.
+    ///
+    /// Closes the fast-slow gap after noise injection (ADR-S-013
+    /// §6b, Option C).  Called **before** [`reset_cusum`](Self::reset_cusum)
+    /// so the slow baselines start from the fast EWMA's converged
+    /// values.  The CUSUM accumulators are then zeroed, beginning
+    /// drift detection from a state where fast ≈ slow.
+    pub const fn seed_cusum_slow_from_baselines(&mut self) {
+        self.novelty_bl.seed_cusum_slow_from_fast();
+        self.displacement_bl.seed_cusum_slow_from_fast();
+        self.surprise_bl.seed_cusum_slow_from_fast();
+        self.coherence_bl.seed_cusum_slow_from_fast();
+    }
+
+    /// Current maturity snapshot.
+    pub const fn maturity(&self) -> TrackerMaturity {
+        TrackerMaturity {
+            real_observations: self.real_observations,
+            noise_observations: self.noise_observations,
+            noise_influence: self.noise_influence,
+        }
+    }
+
+    /// Current rank.
+    pub const fn rank(&self) -> usize {
+        self.rank
+    }
+
+    /// Maximum rank this tracker can reach (`min(dim, max_rank)`).
+    pub const fn cap(&self) -> usize {
+        self.cap
+    }
+
+    /// Working dimensionality of the tracker's input space.
+    pub const fn dim(&self) -> usize {
+        self.dim
+    }
+
+    /// Snapshot the current per-axis baseline means and variances.
+    ///
+    /// Used for coordination warm-up synthetic score generation
+    /// (§ALGO S-9.8).
+    pub const fn axis_baselines(&self) -> super::AxisBaselines {
+        super::AxisBaselines {
+            novelty_mean: self.novelty_bl.fast.mean(),
+            novelty_var: self.novelty_bl.fast.variance(),
+            displacement_mean: self.displacement_bl.fast.mean(),
+            displacement_var: self.displacement_bl.fast.variance(),
+            surprise_mean: self.surprise_bl.fast.mean(),
+            surprise_var: self.surprise_bl.fast.variance(),
+            coherence_mean: self.coherence_bl.fast.mean(),
+            coherence_var: self.coherence_bl.fast.variance(),
+        }
+    }
+
+    /// Geometric properties of the current scoring state.
+    pub const fn scoring_geometry(&self) -> ScoringGeometry {
+        ScoringGeometry {
+            dim: self.dim,
+            cap: self.cap,
+            residual_dof: self.dim.saturating_sub(self.rank),
+        }
+    }
+
+    /// Per-axis clip-pressure EWMA values [novelty, displacement, surprise, coherence].
+    pub const fn clip_pressures(&self) -> [f64; 4] {
+        [
+            self.novelty_bl.clip_pressure,
+            self.displacement_bl.clip_pressure,
+            self.surprise_bl.clip_pressure,
+            self.coherence_bl.clip_pressure,
+        ]
+    }
+
+    // ════════════════════════════════════════════════════════
+    //  Private implementation
+    // ════════════════════════════════════════════════════════
+
+    /// Build a `faer::Mat<f64>` (b × d) from row slices.
+    fn build_matrix(rows: &[&[f64]], b: usize, d: usize) -> Mat<f64> {
+        let mut x = Mat::zeros(b, d);
+        for (i, row) in rows.iter().enumerate() {
+            for (j, &val) in row.iter().enumerate() {
+                x[(i, j)] = val;
+            }
+        }
+        x
+    }
+
+    /// Phase 1: Compute raw per-sample scores from the prior-model projection.
+    ///
+    /// Returns the four score vectors. Per-sample `SampleScore` structs
+    /// (which include z-scores) are only built when `per_sample_scores`
+    /// is enabled — avoiding 4 z-score calls per sample in the common case.
+    fn compute_scores(
+        &self,
+        z: &Mat<f64>,
+        residual: &Mat<f64>,
+        b: usize,
+        d: usize,
+        k: usize,
+        eps: f64,
+    ) -> (Vec<f64>, Vec<f64>, Vec<f64>, Vec<f64>) {
+        #[allow(clippy::cast_precision_loss)] // d − k ≤ 128, well within f64 mantissa
+        let dof = (d - k).max(1) as f64;
+
+        #[allow(clippy::cast_precision_loss)]
+        let k_f = k as f64;
+
+        let mut nov_scores = Vec::with_capacity(b);
+        let mut disp_scores = Vec::with_capacity(b);
+        let mut surp_scores = Vec::with_capacity(b);
+        let mut coh_scores = Vec::with_capacity(b);
+
+        for i in 0..b {
+            // ── Novelty: ‖rᵢ‖² / (d − k) ──
+            let mut resid_sq = 0.0;
+            for j in 0..d {
+                resid_sq = residual[(i, j)].mul_add(residual[(i, j)], resid_sq);
+            }
+            nov_scores.push(resid_sq / dof);
+
+            // ── Displacement: ‖zᵢ‖² / (k + ‖zᵢ‖²) ──
+            let mut z_sq = 0.0;
+            for j in 0..k {
+                z_sq = z[(i, j)].mul_add(z[(i, j)], z_sq);
+            }
+            disp_scores.push(z_sq / (k_f + z_sq));
+
+            // ── Surprise: (1/k) Σⱼ (zᵢⱼ − μⱼ)² / (νⱼ + ε) ──
+            let mut surprise = 0.0;
+            for j in 0..k {
+                let dev = z[(i, j)] - self.lat_mean[j];
+                surprise += (dev * dev) / (self.lat_var[j] + eps);
+            }
+            surp_scores.push(surprise / k_f.max(1.0));
+
+            // ── Coherence: (2/(k(k−1))) Σⱼ<ₗ (zᵢⱼ·zᵢₗ − Cⱼₗ)² ──
+            //
+            // Dividing by pairs = k(k−1)/2 is equivalent to multiplying
+            // by 2/(k(k−1)), matching the spec (§6.4).
+            coh_scores.push(if k >= 2 {
+                let pairs = (k * (k - 1)) / 2;
+                let mut coh = 0.0;
+                for j in 0..k {
+                    for l in (j + 1)..k {
+                        let prod = z[(i, j)] * z[(i, l)];
+                        let dev = prod - self.cross_corr[tri_idx(j, l, self.cap)];
+                        coh = dev.mul_add(dev, coh);
+                    }
+                }
+
+                #[allow(clippy::cast_precision_loss)]
+                {
+                    coh / pairs as f64
+                }
+            } else {
+                0.0
+            });
+        }
+
+        (nov_scores, disp_scores, surp_scores, coh_scores)
+    }
+
+    /// Build per-sample `SampleScore` structs (only when `per_sample_scores` is enabled).
+    ///
+    /// This is separated from `compute_scores` to avoid 4 z-score calls
+    /// per sample in the default (disabled) case.
+    fn build_per_sample(&self, nov: &[f64], disp: &[f64], surp: &[f64], coh: &[f64], eps: f64) -> Vec<SampleScore> {
+        nov.iter()
+            .zip(disp)
+            .zip(surp)
+            .zip(coh)
+            .map(|(((&n, &d), &s), &c)| SampleScore {
+                novelty: n,
+                displacement: d,
+                surprise: s,
+                coherence: c,
+                novelty_z: self.novelty_bl.fast.z_score(n, eps),
+                displacement_z: self.displacement_bl.fast.z_score(d, eps),
+                surprise_z: self.surprise_bl.fast.z_score(s, eps),
+                coherence_z: self.coherence_bl.fast.z_score(c, eps),
+            })
+            .collect()
+    }
+
+    /// Phase 2: Evolve subspace via the maths module (ADR-S-016).
+    ///
+    /// Delegates to `crate::maths::evolve()` which dispatches to the
+    /// selected strategy (naïve dense SVD or Brand's incremental SVD).
+    /// In debug builds, both run and are compared.
+    ///
+    /// Accepts `z` and `residual` from Phase 1 so Brand's method can
+    /// reuse the projection instead of recomputing it.
+    #[allow(clippy::many_single_char_names)] // mathematical notation matching the spec
+    fn evolve_subspace(&mut self, z: &Mat<f64>, residual: &Mat<f64>, k: usize) {
+        let _span = tracing::debug_span!("phase2_evolve_subspace",
+            strategy = ?self.svd_strategy,
+            k = k,
+            d = self.dim,
+            b = residual.nrows(),
+        )
+        .entered();
+
+        let sqrt_lam = self.forgetting_factor.sqrt();
+
+        let Some(update) = crate::maths::evolve(
+            self.svd_strategy,
+            &self.basis,
+            &self.sigmas,
+            z,
+            residual,
+            sqrt_lam,
+            k,
+            self.cap,
+        ) else {
+            tracing::warn!("SVD did not converge — keeping prior subspace");
+            return;
+        };
+
+        // Write back into tracker state.
+        let n = update.n;
+        for j in 0..n {
+            for i in 0..self.dim {
+                self.basis[(i, j)] = update.basis[(i, j)];
+            }
+            self.sigmas[j] = update.sigmas[j];
+        }
+
+        // Zero out unused sigmas.
+        for s in &mut self.sigmas[n..] {
+            *s = 0.0;
+        }
+    }
+
+    /// Phase 3: Evolve latent distribution (mean, variance, cross-correlation).
+    ///
+    /// Uses the `Z` matrix from Phase 1 (prior-basis projection).
+    ///
+    /// **Cold→warm initialisation (§ALGO S-4.2 Phase 3, ADR-S-013 §2a–2c):**
+    /// On the first batch (`step == 0`), latent mean, variance, and
+    /// cross-correlation are seeded directly from the batch statistics
+    /// rather than EWMA-blending against the placeholders (`lat_mean = 0`,
+    /// `lat_var = 1.0`, `cross_corr = 0`).  This eliminates the
+    /// deterministic cold-start cascade that otherwise inflates surprise
+    /// and coherence scores for ~60 rounds.
+    fn evolve_latent(&mut self, z: &Mat<f64>, k: usize) {
+        let b = z.nrows();
+        let lam = self.forgetting_factor;
+        let alpha = 1.0 - lam;
+        let eps = self.eps;
+        let cold = self.step == 0;
+
+        #[allow(clippy::cast_precision_loss)]
+        let b_f = b as f64;
+
+        // Per-dimension mean and variance.
+        for j in 0..k {
+            let mut col_sum = 0.0;
+            for i in 0..b {
+                col_sum += z[(i, j)];
+            }
+            let col_mean = col_sum / b_f;
+
+            // EWMA-mean-centred variance (ADR-S-021 §1–2): centre on
+            // the pre-update EWMA mean, not the batch mean.  At t = 0
+            // lat_mean[j] is 0.0, giving the first-batch seeding formula.
+            let mut col_var = 0.0;
+            for i in 0..b {
+                let d = z[(i, j)] - self.lat_mean[j];
+                col_var = d.mul_add(d, col_var);
+            }
+            col_var /= b_f;
+
+            // Update order (ADR-S-021 §3): variance first (against
+            // pre-update mean), then mean.
+            if cold {
+                // Cold→warm: seed directly from first batch (ADR-S-013 §2a).
+                self.lat_var[j] = col_var.max(eps);
+                self.lat_mean[j] = col_mean;
+            } else {
+                self.lat_var[j] = lam.mul_add(self.lat_var[j], alpha * col_var.max(eps));
+                self.lat_mean[j] = lam.mul_add(self.lat_mean[j], alpha * col_mean);
+            }
+        }
+
+        // Runtime floor (ADR-S-021 §4, §ALGO S-4.2): defence-in-depth
+        // against degenerate streams.  Caps per-dimension surprise at 100.
+        for j in 0..k {
+            self.lat_var[j] = self.lat_var[j].max(1e-2);
+        }
+
+        // Pairwise cross-correlation: C[j][l] ← λ·C[j][l] + α·(1/b)·Σᵢ zᵢⱼ·zᵢₗ
+        //
+        // Invariant (§4.2 Phase 3): when rank increases, new entries are
+        // already zero from construction.  When rank decreases, outer
+        // entries are ignored here but preserved in the vector.
+        for j in 0..k {
+            for l in (j + 1)..k {
+                let mut prod_sum = 0.0;
+                for i in 0..b {
+                    prod_sum = z[(i, j)].mul_add(z[(i, l)], prod_sum);
+                }
+                let batch_corr = prod_sum / b_f;
+                let idx = tri_idx(j, l, self.cap);
+                if cold {
+                    // Cold→warm: seed directly from first batch (ADR-S-013 §2b).
+                    self.cross_corr[idx] = batch_corr;
+                } else {
+                    self.cross_corr[idx] = lam.mul_add(self.cross_corr[idx], alpha * batch_corr);
+                }
+            }
+        }
+    }
+
+    /// Phase 4 helper: score against an axis's baseline and (optionally)
+    /// evolve it.
+    ///
+    /// When `evolve` is `false` the EWMA and CUSUM are left untouched.
+    /// This is used for the coherence axis at rank < 2, where coherence
+    /// does not exist (no pairs) and every score is identically zero.
+    /// The baseline stays cold; when rank reaches 2 the first real
+    /// values enter through the cold→warm path naturally.  If rank
+    /// later drops below 2, `adapt_rank()` destroys the baseline
+    /// entirely.
+    ///
+    /// §ALGO S-6.1.1 pipeline with clip-pressure EWMA (§ALGO S-6.4).
+    #[allow(clippy::too_many_arguments)]
+    fn update_axis(
+        bl: &mut AxisBaseline,
+        scores: &[f64],
+        eps: f64,
+        cusum_allowance: f64,
+        clip_sigmas: f64,
+        eta: f64,
+        clip_pressure_decay: f64,
+        evolve: bool,
+    ) -> ScoreDistribution {
+        // ── Raw batch statistics (pre-clip) ─────────────
+        let (min, max, sum) = scores
+            .iter()
+            .fold((f64::INFINITY, f64::NEG_INFINITY, 0.0_f64), |(mn, mx, s), &v| {
+                (mn.min(v), mx.max(v), s + v)
+            });
+
+        #[allow(clippy::cast_precision_loss)]
+        let mean = sum / scores.len() as f64;
+
+        // Z-scores computed *before* updating the fast baseline.
+        let max_z = bl.fast.z_score(max, eps);
+        let mean_z = bl.fast.z_score(mean, eps);
+        let baseline = bl.fast.snapshot();
+
+        if evolve {
+            // ── Per-axis effective clip (§ALGO S-6.4) ───
+            //
+            //   p = max(η, ρ̄)
+            //   n_σ_eff = n_σ · (1 + p / (1 − p + ε))
+            //
+            let p = eta.max(bl.clip_pressure);
+            let effective_clip = clip_sigmas * (1.0 + p / (1.0 - p + eps));
+
+            // ── Single shared clip filter ───────────────
+            // Computed against the fast EWMA's current baseline.
+            // Cold-path bypass: ceiling() returns +∞ when cold.
+            let ceiling = bl.fast.ceiling(effective_clip);
+
+            let retained: Vec<f64> = scores.iter().copied().filter(|&v| v < ceiling).collect();
+
+            // ── Update clip-pressure EWMA ───────────────
+            //   ρ_t = 1 − |retained| / |total|
+            //   ρ̄ = λ_ρ · ρ̄ + (1 − λ_ρ) · ρ_t
+            #[allow(clippy::cast_precision_loss)]
+            let rho_t = 1.0 - (retained.len() as f64 / scores.len() as f64);
+            let alpha = 1.0 - clip_pressure_decay;
+            bl.clip_pressure = clip_pressure_decay.mul_add(bl.clip_pressure, alpha * rho_t);
+
+            // ── Fast EWMA: receives retained samples ────
+            if retained.is_empty() {
+                // All outliers — learn nothing this round.
+                // clip_pressure was still updated above (it saw 100% clipping).
+            } else {
+                bl.fast.update_raw(&retained);
+            }
+
+            // ── CUSUM: receives retained samples, raw batch mean ──
+            // Always called — the gap uses raw_batch_mean so CUSUM
+            // accumulates even when all scores are clipped.  The slow
+            // EWMA's update_raw() is a no-op on empty input.
+            bl.cusum.update_filtered(&retained, mean, cusum_allowance, eps);
+        }
+        let cusum = bl.cusum.snapshot();
+
+        ScoreDistribution {
+            min,
+            max,
+            mean,
+            max_z_score: max_z,
+            mean_z_score: mean_z,
+            baseline,
+            cusum,
+            clip_pressure: bl.clip_pressure,
+        }
+    }
+
+    /// Phase 5: Adapt rank based on cumulative energy.
+    ///
+    /// Every `rank_update_interval` steps, find the smallest rank
+    /// capturing `energy_threshold` of total variance.  Move by ±1.
+    fn adapt_rank(&mut self) {
+        let total_energy: f64 = self.sigmas.iter().map(|s| s * s).sum::<f64>() + self.eps;
+
+        let mut cumulative = 0.0;
+        let mut target = self.cap;
+
+        for (i, s) in self.sigmas.iter().enumerate() {
+            cumulative += s * s;
+            if cumulative / total_energy >= self.energy_threshold {
+                target = (i + 2).min(self.cap);
+                break;
+            }
+        }
+        target = target.max(1);
+
+        let old_rank = self.rank;
+
+        // Move by at most ±1 to avoid oscillation (§4.2 Phase 5).
+        if target > self.rank {
+            self.rank = (self.rank + 1).min(self.cap);
+        } else if target < self.rank {
+            self.rank = self.rank.saturating_sub(1).max(1);
+        }
+
+        // Coherence does not exist at rank < 2.  If rank just
+        // dropped below 2, destroy the coherence baseline so
+        // stale state from a previous k ≥ 2 epoch cannot leak
+        // into a future one.  When rank reaches 2 again the
+        // baseline is born fresh via the cold→warm path.
+        if old_rank >= 2 && self.rank < 2 {
+            self.coherence_bl.reset_cold();
+        }
+    }
+
+    /// Fraction of total variance captured by the current rank.
+    pub fn energy_ratio(&self) -> f64 {
+        let total: f64 = self.sigmas.iter().map(|s| s * s).sum::<f64>() + self.eps;
+        let active: f64 = self.sigmas[..self.rank].iter().map(|s| s * s).sum();
+        active / total
+    }
+
+    /// Largest singular value of the learned subspace.
+    pub fn top_singular_value(&self) -> f64 {
+        self.sigmas.first().copied().unwrap_or(0.0)
+    }
+
+    /// EWMA latent-coordinate mean for the first `rank` dimensions.
+    ///
+    /// Exposed for convergence investigation tests (ADR-S-013).
+    #[cfg(test)]
+    pub fn latent_mean(&self) -> &[f64] {
+        &self.lat_mean[..self.rank]
+    }
+
+    /// EWMA latent-coordinate variance for the first `rank` dimensions.
+    ///
+    /// Exposed for convergence investigation tests (ADR-S-013).
+    #[cfg(test)]
+    pub fn latent_var(&self) -> &[f64] {
+        &self.lat_var[..self.rank]
+    }
+
+    /// Update maturity counters after processing a batch.
+    ///
+    /// Noise influence decays as λⁿ for `n` real observations, or
+    /// converges toward 1.0 under noise (§11.5). Computed via `powi`
+    /// instead of an `n`-iteration loop.
+    ///
+    /// When η crosses below `WARMUP_THRESHOLD` (§ALGO S-11.4), all
+    /// per-axis clip-pressure EWMAs are zeroed to prevent warm-up
+    /// contamination from echoing into production scoring.
+    fn update_maturity(&mut self, batch_size: usize, is_noise: bool) {
+        /// η threshold below which warm-up is considered complete (§ALGO S-11.4).
+        const WARMUP_THRESHOLD: f64 = 0.01;
+
+        #[allow(clippy::cast_possible_truncation)]
+        let count = batch_size as u64;
+
+        #[allow(clippy::cast_possible_truncation, clippy::cast_possible_wrap)] // batch_size ≪ 2^31
+        let n = batch_size as i32;
+        let lam_n = self.forgetting_factor.powi(n);
+
+        let old_eta = self.noise_influence;
+
+        if is_noise {
+            self.noise_observations += count;
+            // η_{t+n} = λⁿ·η_t + (1 − λⁿ)  (geometric series of n EWMA steps toward 1.0)
+            self.noise_influence = lam_n.mul_add(self.noise_influence, 1.0 - lam_n);
+        } else {
+            self.real_observations += count;
+            // η_{t+n} = λⁿ·η_t
+            self.noise_influence *= lam_n;
+        }
+
+        // §ALGO S-11.4: when η crosses the warm-up threshold, zero
+        // clip-pressure to prevent warm-up contamination echoing into
+        // production scoring.
+        if old_eta >= WARMUP_THRESHOLD && self.noise_influence < WARMUP_THRESHOLD {
+            self.novelty_bl.clip_pressure = 0.0;
+            self.displacement_bl.clip_pressure = 0.0;
+            self.surprise_bl.clip_pressure = 0.0;
+            self.coherence_bl.clip_pressure = 0.0;
+        }
+    }
+}
+
+// ─── Upper-triangle indexing ─────────────────────────────────
+
+/// Map a pair `(j, l)` with `j < l` to a flat upper-triangle index.
+///
+/// The triangle for a `cap × cap` matrix stores `cap*(cap−1)/2`
+/// elements in row-major order:  (0,1), (0,2), …, (0,cap−1),
+/// (1,2), …, (cap−2, cap−1).
+#[inline]
+const fn tri_idx(j: usize, l: usize, cap: usize) -> usize {
+    // Elements before row j: j*cap − j*(j+1)/2
+    // Offset within row j:   l − j − 1
+    j * cap - (j * (j + 1)) / 2 + l - j - 1
+}
diff --git a/packages/sentinel/src/sentinel/warming_thread.rs b/packages/sentinel/src/sentinel/warming_thread.rs
new file mode 100644
index 000000000..f57c2f3d7
--- /dev/null
+++ b/packages/sentinel/src/sentinel/warming_thread.rs
@@ -0,0 +1,207 @@
+//! Background warming thread (S1 async, §ALGO S-18.2 Step 3).
+//!
+//! Runs the deferred cell warm-up loop on a dedicated thread, decoupling
+//! noise injection latency from the `ingest()` hot path.
+//!
+//! # Design
+//!
+//! The thread owns its own `SmallRng` seeded from `noise_seed + 1`
+//! (offset to avoid colliding with the main sentinel's RNG sequence).
+//! It takes cells out of the staging area one at a time via
+//! [`StagingArea::take_highest_priority`], does the expensive noise
+//! injection *without holding the lock*, then returns the cell via
+//! [`StagingArea::finish_warming`] or [`StagingArea::return_warming`].
+//!
+//! The main thread notifies the condvar after enqueueing new cells.
+//! The thread sleeps on the condvar when there is nothing to warm.
+//!
+//! # Shutdown
+//!
+//! The sentinel sets `shutdown` to `true` and notifies the condvar.
+//! The thread finishes any in-progress batch, then exits. The sentinel
+//! joins the thread in [`WarmingThreadHandle::shutdown`] (called from
+//! `Drop` or `reset()`).
+
+use std::sync::atomic::{AtomicBool, Ordering};
+use std::sync::{Arc, Condvar, Mutex};
+use std::thread::JoinHandle;
+
+use rand::SeedableRng;
+use rand::rngs::SmallRng;
+use torrust_mudlark::Coordinate;
+
+use super::generate_noise_batch;
+use super::staging::StagingArea;
+
+// ─── Public handle ──────────────────────────────────────────
+
+/// Handle to the background warming thread.
+///
+/// Owns the shutdown flag, condvar, and `JoinHandle`. The staging
+/// `Arc<Mutex<StagingArea>>` is shared with the sentinel.
+///
+/// # Thread safety
+///
+/// All fields are `Send + Sync`:
+/// - `Arc<AtomicBool>`, `Arc<Condvar>`: trivially `Send + Sync`.
+/// - `Mutex<Option<JoinHandle<()>>>`: `Send + Sync` because
+///   `JoinHandle<()>: Send`.
+pub struct WarmingThreadHandle<C: Coordinate> {
+    shutdown: Arc<AtomicBool>,
+    condvar: Arc<Condvar>,
+    /// The join handle is behind a `Mutex` so that `WarmingThreadHandle`
+    /// is `Sync` (§ALGO S-18.2 Step 3.5 — `SpectralSentinel: Send + Sync`).
+    handle: Mutex<Option<JoinHandle<()>>>,
+    _marker: std::marker::PhantomData<C>,
+}
+
+impl<C: Coordinate> WarmingThreadHandle<C> {
+    /// Spawn the background warming thread.
+    ///
+    /// # Arguments
+    ///
+    /// - `staging` — shared staging area (same `Arc` as the sentinel).
+    /// - `batch_size` — number of synthetic samples per noise batch.
+    /// - `noise_seed` — if `Some`, the thread's RNG is seeded
+    ///   deterministically from `seed + 1`. If `None`, seeded from
+    ///   system entropy.
+    pub fn spawn(staging: &Arc<Mutex<StagingArea<C>>>, batch_size: usize, noise_seed: Option<u64>) -> Self {
+        let shutdown = Arc::new(AtomicBool::new(false));
+        let condvar = Arc::new(Condvar::new());
+
+        let thread_staging = Arc::clone(staging);
+        let thread_shutdown = Arc::clone(&shutdown);
+        let thread_condvar = Arc::clone(&condvar);
+
+        // Offset seed by 1 to avoid colliding with the main RNG.
+        let rng = noise_seed.map_or_else(
+            || SmallRng::from_rng(&mut rand::rng()),
+            |s| SmallRng::seed_from_u64(s.wrapping_add(1)),
+        );
+
+        let handle = std::thread::Builder::new()
+            .name("sentinel-warming".into())
+            .spawn(move || {
+                warming_loop(thread_staging, thread_condvar, thread_shutdown, batch_size, rng);
+            })
+            .expect("failed to spawn sentinel warming thread");
+
+        Self {
+            shutdown,
+            condvar,
+            handle: Mutex::new(Some(handle)),
+            _marker: std::marker::PhantomData,
+        }
+    }
+
+    /// Wake the background thread (call after enqueueing new cells).
+    pub fn notify(&self) {
+        self.condvar.notify_one();
+    }
+
+    /// Signal the thread to stop and wait for it to exit.
+    ///
+    /// Safe to call multiple times (subsequent calls are no-ops).
+    pub fn shutdown(&self) {
+        self.shutdown.store(true, Ordering::Release);
+        self.condvar.notify_one();
+
+        let handle = self.handle.lock().expect("warming handle poisoned").take();
+        if let Some(handle) = handle {
+            handle.join().expect("warming thread panicked");
+        }
+    }
+}
+
+impl<C: Coordinate> Drop for WarmingThreadHandle<C> {
+    fn drop(&mut self) {
+        self.shutdown();
+    }
+}
+
+// ─── Thread loop ────────────────────────────────────────────
+
+/// Main loop of the background warming thread.
+///
+/// 1. Wait on the condvar until there is warming work or shutdown.
+/// 2. Take the highest-priority cell **out** of the staging area.
+/// 3. Release the lock.
+/// 4. Perform one noise batch (the expensive part).
+/// 5. Re-acquire the lock and put the cell back (or move to ready).
+/// 6. Repeat.
+///
+/// The lock is held only for O(|warming|) queue operations, never
+/// during the SVD / noise injection. This keeps contention with the
+/// main thread minimal.
+#[allow(clippy::needless_pass_by_value)] // Arcs are moved from the thread closure
+fn warming_loop<C: Coordinate>(
+    staging: Arc<Mutex<StagingArea<C>>>,
+    condvar: Arc<Condvar>,
+    shutdown: Arc<AtomicBool>,
+    batch_size: usize,
+    mut rng: SmallRng,
+) {
+    loop {
+        // ── Step 1: Wait for work ───────────────────────
+        let work = {
+            let mut guard = staging.lock().expect("staging mutex poisoned");
+
+            while !guard.has_warming_work() && !shutdown.load(Ordering::Acquire) {
+                guard = condvar.wait(guard).expect("staging condvar poisoned");
+            }
+
+            if shutdown.load(Ordering::Acquire) {
+                // Shutdown requested — exit immediately. Any remaining
+                // warming cells will be discarded by `reset()` / `Drop`.
+                break;
+            }
+
+            // ── Step 2: Take a cell out ─────────────────
+            guard.take_highest_priority()
+        };
+        // Lock released here.
+
+        // ── Step 3: Do one batch of noise injection ─────
+        let Some((gnode, mut wc)) = work else {
+            // Spurious wake or concurrent take — loop back.
+            continue;
+        };
+
+        let noise = generate_noise_batch(wc.cell.width, batch_size, &mut rng);
+        let slices: Vec<&[f64]> = noise.iter().map(Vec::as_slice).collect();
+        #[allow(clippy::cast_possible_truncation)] // depth ≤ 128, fits u8
+        let report = wc.cell.tracker.observe(&slices, wc.cell.depth as u8, true);
+
+        wc.round_scores.push([
+            report.scores.novelty.mean,
+            report.scores.displacement.mean,
+            report.scores.surprise.mean,
+            report.scores.coherence.mean,
+        ]);
+        wc.completed_rounds += 1;
+
+        // ── Step 4: Return the cell ─────────────────────
+        let mut guard = staging.lock().expect("staging mutex poisoned");
+
+        if wc.is_ready() {
+            wc.cell.tracker.seed_cusum_slow_from_baselines();
+            wc.cell.tracker.reset_cusum();
+            wc.cell.tracker.reset_clip_pressure();
+            guard.finish_warming(gnode, wc.cell);
+        } else {
+            guard.return_warming(gnode, wc);
+        }
+
+        drop(guard);
+    }
+}
+
+// ─── Compile-time safety ────────────────────────────────────
+
+/// Verify `WarmingThreadHandle` is `Send + Sync` so it can live
+/// inside `SpectralSentinel` without breaking the sentinel's own
+/// `Send + Sync` obligation.
+const _: () = {
+    const fn assert_send_sync<T: Send + Sync>() {}
+    assert_send_sync::<WarmingThreadHandle<u128>>();
+};
diff --git a/packages/sentinel/src/tests/analysis_set.rs b/packages/sentinel/src/tests/analysis_set.rs
new file mode 100644
index 000000000..9a00ba6e8
--- /dev/null
+++ b/packages/sentinel/src/tests/analysis_set.rs
@@ -0,0 +1,276 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// SPDX-FileCopyrightText: 2026 Torrust project contributors
+
+//! Crate-level tests for **`AnalysisSet`** (§ALGO S-8.1–8.3).
+//!
+//! `AnalysisSet::recompute` walks the V-Tree to select up to *k*
+//! competitive cells (plus their ancestors back to the root), ranked
+//! by importance and filtered by a depth cutoff.  These tests cover
+//! construction edge cases (empty graph, `k = 0`, `depth_cutoff = 0`,
+//! tie-breaking determinism), accessor consistency (`contains`,
+//! `is_competitive`, superset invariant), and the `summary()` view.
+//!
+//! # Test index
+//!
+//! ## `recompute()` — construction
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`empty_graph_produces_root_only`] | no competitive entries; total = 1 (root) |
+//! | [`root_always_present`] | root `GNodeId` is in full set |
+//! | [`root_is_never_competitive`] | root excluded from competitive set (§ALGO S-4.7) |
+//! | [`competitive_set_respects_k`] | `competitive_count() ≤ k` |
+//! | [`k_zero_yields_no_competitive_entries`] | edge case: `k = 0` ⇒ empty competitive set |
+//! | [`depth_cutoff_zero_excludes_all_non_root`] | edge case: `depth_cutoff = 0` filters deep nodes |
+//! | [`tie_breaking_is_deterministic`] | repeated recompute gives identical competitive vec |
+//! | [`competitive_ordering_by_importance_then_start`] | desc importance, asc start within ties |
+//! | [`internal_nodes_eligible_for_competitive_set`] | splits produce competitive entries |
+//!
+//! ## Accessor methods
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`full_set_is_superset_of_competitive`] | every competitive entry appears in full set |
+//! | [`contains_returns_false_for_absent_node`] | `contains()` rejects non-existent `GNodeId` |
+//! | [`is_competitive_true_for_selected_entries`] | `is_competitive()` matches competitive set |
+//! | [`is_competitive_false_for_ancestor_only`] | `is_competitive()` rejects ancestor-only entries |
+//!
+//! ## `summary()`
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`summary_empty_graph`] | sizes, depth, importance, v_depth all zeroed |
+//! | [`summary_with_competitive_cells`] | sizes reflect competitive + ancestors |
+//! | [`summary_depth_range_includes_root`] | `depth_range.0 == 0` |
+//! | [`summary_importance_range_positive`] | `importance_range` bounds are `> 0.0` |
+//! | [`summary_v_depth_range_nonzero`] | `v_depth_range` bounds are `> 0` |
+
+use torrust_mudlark::{Config as GvConfig, GNodeId, GvGraph};
+
+use crate::analysis_set::*;
+
+// ── Helpers ─────────────────────────────────────────────
+
+fn test_graph() -> GvGraph<u128, u64, 128> {
+    let cfg = GvConfig {
+        split_threshold: 100,
+        depth_create: 3,
+        depth_evict: 6,
+        budget: Some(100_000),
+        alpha_relax: 0.75,
+        bounded_eviction: true,
+    };
+    GvGraph::new(cfg)
+}
+
+/// Feed uniform traffic to force splits.
+fn populated_graph() -> GvGraph<u128, u64, 128> {
+    let mut graph = test_graph();
+    for i in 0u128..2000 {
+        graph.observe(i * (u128::MAX / 2000), 1u64);
+    }
+    graph
+}
+
+// ── recompute() — construction ──────────────────────────
+
+#[test]
+fn empty_graph_produces_root_only() {
+    let graph = test_graph();
+    let set = AnalysisSet::recompute(&graph, 10, 6);
+    assert_eq!(set.competitive_count(), 0);
+    assert_eq!(set.total_count(), 1); // root only
+}
+
+#[test]
+fn root_always_present() {
+    let graph = test_graph();
+    let set = AnalysisSet::recompute(&graph, 10, 6);
+    assert!(set.contains(graph.g_root()));
+}
+
+#[test]
+fn root_is_never_competitive() {
+    let graph = populated_graph();
+    let set = AnalysisSet::recompute(&graph, 100, 6);
+    assert!(
+        !set.is_competitive(graph.g_root()),
+        "root must never appear in the competitive set (§ALGO S-4.7)",
+    );
+}
+
+#[test]
+fn competitive_set_respects_k() {
+    let graph = populated_graph();
+    let set = AnalysisSet::recompute(&graph, 2, 6);
+    assert!(set.competitive_count() <= 2);
+}
+
+#[test]
+fn k_zero_yields_no_competitive_entries() {
+    let graph = populated_graph();
+    let set = AnalysisSet::recompute(&graph, 0, 6);
+    assert_eq!(set.competitive_count(), 0);
+    // Full set still has at least the root.
+    assert!(set.total_count() >= 1);
+    assert!(set.contains(graph.g_root()));
+}
+
+#[test]
+fn depth_cutoff_zero_excludes_all_non_root() {
+    let graph = populated_graph();
+    // depth_cutoff=0 means only V-depth 0 entries qualify.
+    // In practice this is restrictive enough that the
+    // competitive set should be small or empty.
+    let set = AnalysisSet::recompute(&graph, 100, 0);
+    // Root is always present but never competitive.
+    assert!(set.contains(graph.g_root()));
+    // All competitive entries (if any) must have v_depth == 0.
+    for entry in set.competitive() {
+        assert_eq!(entry.v_depth, 0, "depth_cutoff=0 should only admit v_depth=0");
+    }
+}
+
+#[test]
+fn tie_breaking_is_deterministic() {
+    let graph = test_graph();
+    let set1 = AnalysisSet::recompute(&graph, 3, 6);
+    let set2 = AnalysisSet::recompute(&graph, 3, 6);
+    assert_eq!(
+        set1.competitive().iter().map(|e| e.start).collect::<Vec<_>>(),
+        set2.competitive().iter().map(|e| e.start).collect::<Vec<_>>(),
+    );
+}
+
+#[test]
+fn competitive_ordering_by_importance_then_start() {
+    let graph = populated_graph();
+    let set = AnalysisSet::recompute(&graph, 20, 6);
+    let comp = set.competitive();
+    for pair in comp.windows(2) {
+        let (a, b) = (&pair[0], &pair[1]);
+        assert!(
+            a.importance > b.importance || (a.importance == b.importance && a.start <= b.start),
+            "competitive entries must be ordered by importance desc, start asc",
+        );
+    }
+}
+
+#[test]
+fn internal_nodes_eligible_for_competitive_set() {
+    // After splits, internal G-Tree nodes retain their V-Tree
+    // position and frozen importance. The competitive set uses
+    // V-Tree ranking exclusively (§ALGO S-4.1) — no G-Tree state
+    // filter — so internal nodes with sufficient importance remain
+    // eligible.
+    let mut graph = test_graph();
+    for _ in 0..500 {
+        graph.observe(0u128, 1u64);
+        graph.observe(u128::MAX / 2, 1u64);
+    }
+
+    let set = AnalysisSet::recompute(&graph, 100, 20);
+    assert!(
+        set.competitive_count() > 0,
+        "graph with splits should have competitive entries",
+    );
+}
+
+// ── Accessor methods ────────────────────────────────────
+
+#[test]
+fn full_set_is_superset_of_competitive() {
+    let graph = populated_graph();
+    let set = AnalysisSet::recompute(&graph, 10, 6);
+    for entry in set.competitive() {
+        assert!(set.contains(entry.gnode), "competitive entry must appear in full set");
+    }
+    assert!(set.total_count() >= set.competitive_count());
+}
+
+#[test]
+fn contains_returns_false_for_absent_node() {
+    let graph = test_graph();
+    let set = AnalysisSet::recompute(&graph, 10, 6);
+    // GNodeId is an opaque arena handle. A fabricated id that was
+    // never allocated by the graph cannot appear in the set.
+    let bogus = GNodeId::from_parts(999_999, 0);
+    assert!(!set.contains(bogus));
+}
+
+#[test]
+fn is_competitive_true_for_selected_entries() {
+    let graph = populated_graph();
+    let set = AnalysisSet::recompute(&graph, 10, 6);
+    for entry in set.competitive() {
+        assert!(set.is_competitive(entry.gnode));
+    }
+}
+
+#[test]
+fn is_competitive_false_for_ancestor_only() {
+    let graph = populated_graph();
+    let set = AnalysisSet::recompute(&graph, 10, 6);
+    for entry in set.full() {
+        if !entry.is_competitive {
+            assert!(
+                !set.is_competitive(entry.gnode),
+                "ancestor-only entry must not be reported as competitive",
+            );
+        }
+    }
+}
+
+// ── summary() ───────────────────────────────────────────
+
+#[test]
+fn summary_empty_graph() {
+    let graph = test_graph();
+    let set = AnalysisSet::recompute(&graph, 10, 6);
+    let s = set.summary();
+    assert_eq!(s.competitive_size, 0);
+    assert_eq!(s.full_size, 1); // root only
+    assert_eq!(s.depth_range, (0, 0));
+    assert_eq!(s.importance_range, (0.0, 0.0));
+    assert_eq!(s.v_depth_range, (0, 0));
+}
+
+#[test]
+fn summary_with_competitive_cells() {
+    let graph = populated_graph();
+    let set = AnalysisSet::recompute(&graph, 4, 6);
+    let s = set.summary();
+    assert!(s.competitive_size > 0);
+    assert!(s.competitive_size <= 4);
+    assert!(s.full_size > s.competitive_size); // at least root + competitive
+}
+
+#[test]
+fn summary_depth_range_includes_root() {
+    let graph = populated_graph();
+    let set = AnalysisSet::recompute(&graph, 4, 6);
+    let s = set.summary();
+    assert_eq!(s.depth_range.0, 0, "root at depth 0 is always in the full set");
+}
+
+#[test]
+fn summary_importance_range_positive() {
+    let graph = populated_graph();
+    let set = AnalysisSet::recompute(&graph, 10, 6);
+    let s = set.summary();
+    if s.competitive_size > 0 {
+        assert!(s.importance_range.0 > 0.0, "min importance should be positive");
+        assert!(s.importance_range.1 >= s.importance_range.0, "max >= min");
+    }
+}
+
+#[test]
+fn summary_v_depth_range_nonzero() {
+    let graph = populated_graph();
+    let set = AnalysisSet::recompute(&graph, 10, 6);
+    let s = set.summary();
+    if s.competitive_size > 0 {
+        assert!(s.v_depth_range.0 > 0, "competitive entries should have v_depth > 0");
+        assert!(s.v_depth_range.1 >= s.v_depth_range.0, "max >= min");
+    }
+}
diff --git a/packages/sentinel/src/tests/config.rs b/packages/sentinel/src/tests/config.rs
new file mode 100644
index 000000000..76fd37190
--- /dev/null
+++ b/packages/sentinel/src/tests/config.rs
@@ -0,0 +1,930 @@
+//! Tests for [`crate::config`].
+//!
+//! Covers default construction, per-field validation (including error
+//! accumulation), and the [`NoiseSchedule`] helpers (`rounds_for_depth`,
+//! `is_disabled`, `max_rounds`).  Also verifies the [`ConfigWarning`]
+//! advisory system.
+//!
+//! # Test index
+//!
+//! ## Default config
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`default_config_is_valid`] | default config passes validation |
+//! | [`default_config_field_values`] | every default field matches documentation |
+//!
+//! ## Per-field validation — `SentinelConfig`
+//!
+//! ### Core subspace fields
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`rejects_max_rank_zero`] | `max_rank = 0` rejected |
+//! | [`rejects_forgetting_factor_out_of_range`] | λ ∉ (0, 1) rejected |
+//! | [`accepts_forgetting_factor_near_boundaries`] | λ near 0 and 1 accepted |
+//! | [`rejects_rank_update_interval_zero`] | zero update interval rejected |
+//! | [`rejects_energy_threshold_out_of_range`] | threshold ∉ (0, 1) rejected |
+//! | [`rejects_eps_not_positive`] | ε ≤ 0 rejected |
+//!
+//! ### CUSUM / EWMA fields
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`rejects_cusum_slow_decay_out_of_range`] | slow-decay ∉ (0, 1) rejected |
+//! | [`rejects_cusum_slow_decay_below_forgetting`] | slow-decay < λ rejected |
+//! | [`accepts_cusum_slow_decay_just_above_forgetting`] | slow-decay just above λ accepted |
+//! | [`rejects_cusum_coord_slow_decay_out_of_range`] | coord slow-decay ∉ (0, 1) rejected |
+//! | [`rejects_cusum_coord_slow_decay_below_forgetting`] | coord slow-decay < λ rejected |
+//! | [`rejects_cusum_allowance_sigmas_negative`] | negative allowance rejected |
+//! | [`accepts_cusum_allowance_sigmas_zero`] | zero allowance accepted |
+//! | [`rejects_clip_sigmas_not_positive`] | clip σ ≤ 0 rejected |
+//! | [`rejects_clip_pressure_decay_out_of_range`] | pressure-decay ∉ (0, 1) rejected |
+//! | [`accepts_clip_pressure_decay_valid`] | pressure-decay 0.95 accepted |
+//!
+//! ### Analysis fields
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`rejects_analysis_k_zero`] | `analysis_k = 0` rejected |
+//! | [`accepts_analysis_k_one`] | `analysis_k = 1` accepted |
+//! | [`accepts_analysis_depth_cutoff_zero`] | depth cutoff 0 accepted |
+//!
+//! ### G-V Graph fields
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`rejects_split_threshold_zero`] | zero split threshold rejected |
+//! | [`rejects_d_create_zero`] | `d_create = 0` rejected |
+//! | [`rejects_d_evict_not_greater_than_d_create`] | `d_evict ≤ d_create` rejected |
+//! | [`accepts_d_evict_one_above_d_create`] | `d_evict = d_create + 1` accepted |
+//! | [`rejects_budget_zero`] | zero budget rejected |
+//! | [`rejects_budget_below_headroom`] | budget ≤ headroom rejected |
+//! | [`accepts_budget_just_above_headroom`] | budget just above headroom accepted |
+//!
+//! ### Noise injection fields
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`rejects_noise_batch_size_zero_when_enabled`] | batch-size 0 with active schedule rejected |
+//! | [`accepts_noise_batch_size_zero_when_disabled`] | batch-size 0 with disabled schedule accepted |
+//! | [`accepts_noise_seed_none`] | `None` seed accepted |
+//! | [`rejects_geometric_decay_out_of_range`] | geometric decay ∉ (0, 1] rejected |
+//! | [`rejects_geometric_decay_nan`] | NaN decay rejected |
+//! | [`rejects_geometric_root_zero_with_positive_min`] | root 0 with positive min rejected |
+//!
+//! ## Error accumulation
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`collects_multiple_errors`] | multiple violations collected in one pass |
+//!
+//! ## `NoiseSchedule::rounds_for_depth`
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`geometric_rounds_default`] | default schedule round counts |
+//! | [`geometric_rounds_custom`] | custom root / decay / min |
+//! | [`geometric_rounds_root_equals_min`] | `root == min` yields constant |
+//! | [`geometric_rounds_very_small_decay`] | tiny decay floors to min quickly |
+//! | [`geometric_rounds_rounding_half_values`] | half-value rounding behaviour |
+//! | [`geometric_rounds_large_depth`] | numeric stability at depth 128 |
+//! | [`geometric_rounds_decay_one_is_constant`] | `decay = 1.0` yields constant root |
+//! | [`geometric_rounds_root_zero_min_zero`] | both zero → always 0 |
+//! | [`explicit_rounds_for_depth`] | explicit schedule lookup and clamping |
+//! | [`explicit_rounds_single_entry`] | single-entry explicit always returns that entry |
+//! | [`explicit_rounds_non_monotonic`] | non-monotonic explicit entries are faithful |
+//!
+//! ## `NoiseSchedule::is_disabled`
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`explicit_empty_is_disabled`] | empty explicit vec is disabled |
+//! | [`explicit_all_zeros_is_disabled`] | all-zero explicit is disabled |
+//! | [`explicit_single_zero_is_disabled`] | single-zero explicit is disabled |
+//! | [`explicit_mixed_zeros_not_disabled`] | at least one non-zero → not disabled |
+//! | [`geometric_is_disabled_when_min_zero`] | root 0 + min 0 → disabled |
+//! | [`geometric_is_not_disabled_when_min_positive`] | positive min → not disabled |
+//!
+//! ## `NoiseSchedule::max_rounds`
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`max_rounds_geometric`] | geometric returns root |
+//! | [`max_rounds_explicit`] | explicit returns max of vec |
+//! | [`max_rounds_explicit_empty`] | empty explicit returns 0 |
+//! | [`max_rounds_explicit_large`] | `u32::MAX` round-trips |
+//!
+//! ## `NoiseSchedule::Default`
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`noise_schedule_default_matches_doc`] | default matches documented values |
+//!
+//! ## `ConfigWarning`
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`default_config_has_no_warnings`] | default config is warning-free |
+//! | [`warns_when_noise_root_too_low_for_lambda_099`] | root too low at λ = 0.99 triggers warning |
+//! | [`no_warning_when_noise_root_sufficient_for_lambda_095`] | sufficient root at λ = 0.95 is clean |
+//! | [`warns_when_small_batch_and_lambda_095`] | small batch + λ = 0.95 triggers warning |
+//! | [`no_warning_for_explicit_schedule_with_enough_rounds`] | explicit with enough rounds is clean |
+//! | [`warning_display_is_informative`] | `Display` output includes key values |
+
+use crate::config::*;
+
+// ── Default config ──────────────────────────────────────────
+
+#[test]
+fn default_config_is_valid() {
+    SentinelConfig::<u64>::default().validate().unwrap();
+}
+
+#[test]
+fn default_config_field_values() {
+    let cfg = SentinelConfig::<u64>::default();
+    cfg.validate().unwrap();
+
+    // Core subspace.
+    assert_eq!(cfg.max_rank, 16);
+    assert!((cfg.forgetting_factor - 0.99).abs() < f64::EPSILON);
+    assert_eq!(cfg.rank_update_interval, 100);
+    assert!((cfg.energy_threshold - 0.90).abs() < f64::EPSILON);
+    assert!((cfg.eps - 1e-6).abs() < f64::EPSILON);
+
+    // CUSUM / EWMA.
+    assert!((cfg.cusum_slow_decay - 0.999).abs() < f64::EPSILON);
+    assert!((cfg.cusum_coord_slow_decay - 0.999).abs() < f64::EPSILON);
+    assert!((cfg.cusum_allowance_sigmas - 0.5).abs() < f64::EPSILON);
+    assert!((cfg.clip_sigmas - 3.0).abs() < f64::EPSILON);
+    assert!((cfg.clip_pressure_decay - 0.95).abs() < f64::EPSILON);
+    assert!(!cfg.per_sample_scores);
+
+    // Analysis.
+    assert_eq!(cfg.analysis_k, 1024);
+    assert_eq!(cfg.analysis_depth_cutoff, 6);
+
+    // G-V Graph.
+    assert_eq!(cfg.split_threshold, 100);
+    assert_eq!(cfg.d_create, 3);
+    assert_eq!(cfg.d_evict, 6);
+    assert_eq!(cfg.budget, 100_000);
+
+    // Noise injection.
+    assert_eq!(cfg.noise_batch_size, 16);
+    assert_eq!(cfg.noise_seed, Some(42));
+    assert!(!cfg.background_warming);
+}
+
+// ── Per-field validation: core subspace fields ──────────────
+
+#[test]
+fn rejects_max_rank_zero() {
+    let cfg = SentinelConfig::<u64> {
+        max_rank: 0,
+        ..SentinelConfig::<u64>::default()
+    };
+    assert_eq!(cfg.validate(), Err(ConfigErrors(vec![ConfigError::MaxRankZero])));
+}
+
+#[test]
+fn rejects_forgetting_factor_out_of_range() {
+    for &bad in &[0.0, 1.0, -0.1, 1.5] {
+        let cfg = SentinelConfig::<u64> {
+            forgetting_factor: bad,
+            // Keep slow decays above forgetting to avoid cascading errors.
+            cusum_slow_decay: 0.999,
+            cusum_coord_slow_decay: 0.999,
+            ..SentinelConfig::<u64>::default()
+        };
+        let err = cfg.validate().unwrap_err();
+        assert!(
+            err.0
+                .iter()
+                .any(|e| matches!(e, ConfigError::ForgettingFactorOutOfRange(v) if (*v - bad).abs() < f64::EPSILON)),
+            "expected ForgettingFactorOutOfRange for {bad}"
+        );
+    }
+}
+
+#[test]
+fn accepts_forgetting_factor_near_boundaries() {
+    let cfg = SentinelConfig::<u64> {
+        forgetting_factor: 0.9999,
+        cusum_slow_decay: 0.99999,
+        cusum_coord_slow_decay: 0.99999,
+        ..SentinelConfig::<u64>::default()
+    };
+    cfg.validate().unwrap();
+
+    let cfg = SentinelConfig::<u64> {
+        forgetting_factor: 0.0001,
+        cusum_slow_decay: 0.001,
+        cusum_coord_slow_decay: 0.001,
+        ..SentinelConfig::<u64>::default()
+    };
+    cfg.validate().unwrap();
+}
+
+#[test]
+fn rejects_rank_update_interval_zero() {
+    let cfg = SentinelConfig::<u64> {
+        rank_update_interval: 0,
+        ..SentinelConfig::<u64>::default()
+    };
+    let err = cfg.validate().unwrap_err();
+    assert!(err.0.contains(&ConfigError::RankUpdateIntervalZero));
+}
+
+#[test]
+fn rejects_energy_threshold_out_of_range() {
+    for &bad in &[0.0, 1.0, -0.5, 1.1] {
+        let cfg = SentinelConfig::<u64> {
+            energy_threshold: bad,
+            ..SentinelConfig::<u64>::default()
+        };
+        let err = cfg.validate().unwrap_err();
+        assert!(
+            err.0
+                .iter()
+                .any(|e| matches!(e, ConfigError::EnergyThresholdOutOfRange(v) if (*v - bad).abs() < f64::EPSILON)),
+            "expected EnergyThresholdOutOfRange for {bad}"
+        );
+    }
+}
+
+#[test]
+fn rejects_eps_not_positive() {
+    for &bad in &[0.0, -1e-6] {
+        let cfg = SentinelConfig::<u64> {
+            eps: bad,
+            ..SentinelConfig::<u64>::default()
+        };
+        let err = cfg.validate().unwrap_err();
+        assert!(
+            err.0
+                .iter()
+                .any(|e| matches!(e, ConfigError::EpsNotPositive(v) if (*v - bad).abs() < f64::EPSILON)),
+            "expected EpsNotPositive for {bad}"
+        );
+    }
+}
+
+// ── Per-field validation: CUSUM / EWMA fields ───────────────
+
+#[test]
+fn rejects_cusum_slow_decay_out_of_range() {
+    for &bad in &[0.0, 1.0, -0.1, 1.5] {
+        let cfg = SentinelConfig::<u64> {
+            cusum_slow_decay: bad,
+            ..SentinelConfig::<u64>::default()
+        };
+        let err = cfg.validate().unwrap_err();
+        assert!(
+            err.0
+                .iter()
+                .any(|e| matches!(e, ConfigError::CusumSlowDecayOutOfRange(v) if (*v - bad).abs() < f64::EPSILON)),
+            "expected CusumSlowDecayOutOfRange for {bad}"
+        );
+    }
+}
+
+#[test]
+fn rejects_cusum_slow_decay_below_forgetting() {
+    let cfg = SentinelConfig::<u64> {
+        forgetting_factor: 0.99,
+        cusum_slow_decay: 0.98,
+        ..SentinelConfig::<u64>::default()
+    };
+    let err = cfg.validate().unwrap_err();
+    assert!(err.0.iter().any(|e| matches!(e, ConfigError::CusumSlowDecayTooLow { .. })));
+}
+
+#[test]
+fn accepts_cusum_slow_decay_just_above_forgetting() {
+    let cfg = SentinelConfig::<u64> {
+        forgetting_factor: 0.95,
+        cusum_slow_decay: 0.951,
+        ..SentinelConfig::<u64>::default()
+    };
+    cfg.validate().unwrap();
+}
+
+#[test]
+fn rejects_cusum_coord_slow_decay_out_of_range() {
+    for &bad in &[0.0, 1.0, -0.1, 1.5] {
+        let cfg = SentinelConfig::<u64> {
+            cusum_coord_slow_decay: bad,
+            ..SentinelConfig::<u64>::default()
+        };
+        let err = cfg.validate().unwrap_err();
+        assert!(
+            err.0
+                .iter()
+                .any(|e| matches!(e, ConfigError::CusumCoordSlowDecayOutOfRange(v) if (*v - bad).abs() < f64::EPSILON)),
+            "expected CusumCoordSlowDecayOutOfRange for {bad}"
+        );
+    }
+}
+
+#[test]
+fn rejects_cusum_coord_slow_decay_below_forgetting() {
+    let cfg = SentinelConfig::<u64> {
+        forgetting_factor: 0.99,
+        cusum_coord_slow_decay: 0.98,
+        ..SentinelConfig::<u64>::default()
+    };
+    let err = cfg.validate().unwrap_err();
+    assert!(
+        err.0
+            .iter()
+            .any(|e| matches!(e, ConfigError::CusumCoordSlowDecayTooLow { .. }))
+    );
+}
+
+#[test]
+fn rejects_cusum_allowance_sigmas_negative() {
+    let cfg = SentinelConfig::<u64> {
+        cusum_allowance_sigmas: -0.1,
+        ..SentinelConfig::<u64>::default()
+    };
+    let err = cfg.validate().unwrap_err();
+    assert!(err.0.iter().any(|e| matches!(e, ConfigError::CusumAllowanceNegative(_))));
+}
+
+#[test]
+fn accepts_cusum_allowance_sigmas_zero() {
+    let cfg = SentinelConfig::<u64> {
+        cusum_allowance_sigmas: 0.0,
+        ..SentinelConfig::<u64>::default()
+    };
+    cfg.validate().unwrap();
+}
+
+#[test]
+fn rejects_clip_sigmas_not_positive() {
+    for &bad in &[0.0, -1.0] {
+        let cfg = SentinelConfig::<u64> {
+            clip_sigmas: bad,
+            ..SentinelConfig::<u64>::default()
+        };
+        let err = cfg.validate().unwrap_err();
+        assert!(
+            err.0.iter().any(|e| matches!(e, ConfigError::ClipSigmasNotPositive(_))),
+            "expected ClipSigmasNotPositive for {bad}"
+        );
+    }
+}
+
+#[test]
+fn rejects_clip_pressure_decay_out_of_range() {
+    for &bad in &[0.0, 1.0, -0.1, 1.5] {
+        let cfg = SentinelConfig::<u64> {
+            clip_pressure_decay: bad,
+            ..SentinelConfig::<u64>::default()
+        };
+        let err = cfg.validate().unwrap_err();
+        assert!(
+            err.0
+                .iter()
+                .any(|e| matches!(e, ConfigError::ClipPressureDecayOutOfRange(v) if (*v - bad).abs() < f64::EPSILON)),
+            "expected ClipPressureDecayOutOfRange for {bad}"
+        );
+    }
+}
+
+#[test]
+fn accepts_clip_pressure_decay_valid() {
+    let cfg = SentinelConfig::<u64> {
+        clip_pressure_decay: 0.95,
+        ..SentinelConfig::<u64>::default()
+    };
+    cfg.validate().unwrap();
+}
+
+// ── Per-field validation: analysis fields ───────────────────
+
+#[test]
+fn rejects_analysis_k_zero() {
+    let cfg = SentinelConfig::<u64> {
+        analysis_k: 0,
+        ..SentinelConfig::<u64>::default()
+    };
+    assert_eq!(cfg.validate(), Err(ConfigErrors(vec![ConfigError::AnalysisKZero])));
+}
+
+#[test]
+fn accepts_analysis_k_one() {
+    let cfg = SentinelConfig::<u64> {
+        analysis_k: 1,
+        ..SentinelConfig::<u64>::default()
+    };
+    cfg.validate().unwrap();
+}
+
+#[test]
+fn accepts_analysis_depth_cutoff_zero() {
+    let cfg = SentinelConfig::<u64> {
+        analysis_depth_cutoff: 0,
+        ..SentinelConfig::<u64>::default()
+    };
+    cfg.validate().unwrap();
+}
+
+// ── Per-field validation: G-V Graph fields ──────────────────
+
+#[test]
+fn rejects_split_threshold_zero() {
+    let cfg = SentinelConfig::<u64> {
+        split_threshold: 0,
+        ..SentinelConfig::<u64>::default()
+    };
+    assert!(cfg.validate().is_err());
+}
+
+#[test]
+fn rejects_d_create_zero() {
+    let cfg = SentinelConfig::<u64> {
+        d_create: 0,
+        ..SentinelConfig::<u64>::default()
+    };
+    assert!(cfg.validate().is_err());
+}
+
+#[test]
+fn rejects_d_evict_not_greater_than_d_create() {
+    // Equal.
+    let cfg = SentinelConfig::<u64> {
+        d_create: 3,
+        d_evict: 3,
+        ..SentinelConfig::<u64>::default()
+    };
+    let err = cfg.validate().unwrap_err();
+    assert!(
+        err.0
+            .contains(&ConfigError::DEvictNotGreaterThanDCreate { d_create: 3, d_evict: 3 })
+    );
+
+    // Less.
+    let cfg = SentinelConfig::<u64> {
+        d_create: 5,
+        d_evict: 3,
+        ..SentinelConfig::<u64>::default()
+    };
+    let err = cfg.validate().unwrap_err();
+    assert!(
+        err.0
+            .contains(&ConfigError::DEvictNotGreaterThanDCreate { d_create: 5, d_evict: 3 })
+    );
+}
+
+#[test]
+fn accepts_d_evict_one_above_d_create() {
+    // d_evict = d_create + 1 is valid, but budget must satisfy headroom.
+    // buffer = 1, headroom = 3^2 = 9.
+    let cfg = SentinelConfig::<u64> {
+        d_create: 3,
+        d_evict: 4,
+        budget: 10, // > 9
+        ..SentinelConfig::<u64>::default()
+    };
+    cfg.validate().unwrap();
+}
+
+#[test]
+fn rejects_budget_zero() {
+    let cfg = SentinelConfig::<u64> {
+        budget: 0,
+        ..SentinelConfig::<u64>::default()
+    };
+    assert!(cfg.validate().is_err());
+}
+
+#[test]
+fn rejects_budget_below_headroom() {
+    // With d_create=3, d_evict=6, buffer=3, headroom=3^4=81.
+    // Budget must be > 81.
+    let cfg = SentinelConfig::<u64> {
+        d_create: 3,
+        d_evict: 6,
+        budget: 81,
+        ..SentinelConfig::<u64>::default()
+    };
+    let err = cfg.validate().unwrap_err();
+    assert!(err.0.iter().any(|e| matches!(e, ConfigError::BudgetTooSmall { .. })));
+}
+
+#[test]
+fn accepts_budget_just_above_headroom() {
+    let cfg = SentinelConfig::<u64> {
+        d_create: 3,
+        d_evict: 6,
+        budget: 82,
+        ..SentinelConfig::<u64>::default()
+    };
+    cfg.validate().unwrap();
+}
+
+// ── Per-field validation: noise injection fields ────────────
+
+#[test]
+fn rejects_noise_batch_size_zero_when_enabled() {
+    let cfg = SentinelConfig::<u64> {
+        noise_schedule: NoiseSchedule::Explicit(vec![5]),
+        noise_batch_size: 0,
+        ..SentinelConfig::<u64>::default()
+    };
+    let err = cfg.validate().unwrap_err();
+    assert!(err.0.contains(&ConfigError::NoiseBatchSizeZero));
+}
+
+#[test]
+fn accepts_noise_batch_size_zero_when_disabled() {
+    let cfg = SentinelConfig::<u64> {
+        noise_schedule: NoiseSchedule::Explicit(vec![]),
+        noise_batch_size: 0,
+        ..SentinelConfig::<u64>::default()
+    };
+    cfg.validate().unwrap();
+}
+
+#[test]
+fn accepts_noise_seed_none() {
+    let cfg = SentinelConfig::<u64> {
+        noise_seed: None,
+        ..SentinelConfig::<u64>::default()
+    };
+    cfg.validate().unwrap();
+}
+
+#[test]
+fn rejects_geometric_decay_out_of_range() {
+    for &bad in &[0.0, -0.1, 1.5] {
+        let cfg = SentinelConfig::<u64> {
+            noise_schedule: NoiseSchedule::Geometric {
+                root: 50,
+                decay: bad,
+                min: 10,
+            },
+            ..SentinelConfig::<u64>::default()
+        };
+        let err = cfg.validate().unwrap_err();
+        assert!(
+            err.0
+                .iter()
+                .any(|e| matches!(e, ConfigError::NoiseScheduleDecayOutOfRange(_))),
+            "expected NoiseScheduleDecayOutOfRange for decay={bad}"
+        );
+    }
+}
+
+#[test]
+fn rejects_geometric_decay_nan() {
+    let cfg = SentinelConfig::<u64> {
+        noise_schedule: NoiseSchedule::Geometric {
+            root: 50,
+            decay: f64::NAN,
+            min: 10,
+        },
+        ..SentinelConfig::<u64>::default()
+    };
+    assert!(cfg.validate().is_err());
+}
+
+#[test]
+fn rejects_geometric_root_zero_with_positive_min() {
+    let cfg = SentinelConfig::<u64> {
+        noise_schedule: NoiseSchedule::Geometric {
+            root: 0,
+            decay: 0.5,
+            min: 10,
+        },
+        ..SentinelConfig::<u64>::default()
+    };
+    let err = cfg.validate().unwrap_err();
+    assert!(err.0.contains(&ConfigError::NoiseScheduleRootZero));
+}
+
+// ── Error accumulation ──────────────────────────────────────
+
+#[test]
+fn collects_multiple_errors() {
+    let cfg = SentinelConfig::<u64> {
+        max_rank: 0,
+        analysis_k: 0,
+        budget: 0,
+        ..SentinelConfig::<u64>::default()
+    };
+    let err = cfg.validate().unwrap_err();
+    assert!(err.0.len() >= 3, "expected at least 3 errors, got {}", err.0.len());
+    assert!(err.0.contains(&ConfigError::MaxRankZero));
+    assert!(err.0.contains(&ConfigError::AnalysisKZero));
+    assert!(err.0.contains(&ConfigError::BudgetZero));
+}
+
+// ── NoiseSchedule::rounds_for_depth ─────────────────────────
+
+#[test]
+fn geometric_rounds_default() {
+    let schedule = NoiseSchedule::default();
+    // Default: Geometric { root: 450, decay: 0.5, min: 50 }
+    assert_eq!(schedule.rounds_for_depth(0), 450);
+    assert_eq!(schedule.rounds_for_depth(1), 225);
+    assert_eq!(schedule.rounds_for_depth(2), 113); // 450 × 0.25 = 112.5 → 113
+    assert_eq!(schedule.rounds_for_depth(3), 56); // 450 × 0.125 = 56.25 → 56
+    assert_eq!(schedule.rounds_for_depth(4), 50); // 450 × 0.0625 = 28.125 → 28, but min=50
+    assert_eq!(schedule.rounds_for_depth(10), 50); // floored to min
+    assert_eq!(schedule.rounds_for_depth(128), 50); // max depth → still min
+}
+
+#[test]
+fn geometric_rounds_custom() {
+    let schedule = NoiseSchedule::geometric(400, 0.5, 100);
+    assert_eq!(schedule.rounds_for_depth(0), 400);
+    assert_eq!(schedule.rounds_for_depth(1), 200);
+    assert_eq!(schedule.rounds_for_depth(2), 100);
+    assert_eq!(schedule.rounds_for_depth(3), 100); // floored to min
+    assert_eq!(schedule.rounds_for_depth(20), 100);
+}
+
+#[test]
+fn geometric_rounds_root_equals_min() {
+    // When root == min, every depth yields the same value.
+    let schedule = NoiseSchedule::geometric(100, 0.5, 100);
+    for depth in 0..20 {
+        assert_eq!(
+            schedule.rounds_for_depth(depth),
+            100,
+            "depth {depth} should yield root=min=100"
+        );
+    }
+}
+
+#[test]
+fn geometric_rounds_very_small_decay() {
+    // decay=0.01 → root × 0.01^depth, hits min almost immediately.
+    let schedule = NoiseSchedule::geometric(1000, 0.01, 5);
+    assert_eq!(schedule.rounds_for_depth(0), 1000);
+    assert_eq!(schedule.rounds_for_depth(1), 10); // 1000 × 0.01 = 10
+    assert_eq!(schedule.rounds_for_depth(2), 5); // 1000 × 0.0001 → 0, but min=5
+    assert_eq!(schedule.rounds_for_depth(10), 5);
+}
+
+#[test]
+fn geometric_rounds_rounding_half_values() {
+    // f64::round() rounds half away from zero.
+    // root=100, decay=0.5 → 100, 50, 25, 12.5→13, 6.25→6, 3.125→3, 1.5625→2, 0.78→1
+    let schedule = NoiseSchedule::geometric(100, 0.5, 1);
+    assert_eq!(schedule.rounds_for_depth(0), 100);
+    assert_eq!(schedule.rounds_for_depth(1), 50);
+    assert_eq!(schedule.rounds_for_depth(2), 25);
+    assert_eq!(schedule.rounds_for_depth(3), 13); // 12.5 rounds to 13
+    assert_eq!(schedule.rounds_for_depth(4), 6); // 6.25 rounds to 6
+    assert_eq!(schedule.rounds_for_depth(5), 3); // 3.125 rounds to 3
+    assert_eq!(schedule.rounds_for_depth(6), 2); // 1.5625 rounds to 2
+    assert_eq!(schedule.rounds_for_depth(7), 1); // 0.78125 rounds to 1 = min
+}
+
+#[test]
+fn geometric_rounds_large_depth() {
+    // G-tree depth is bounded ≤ 128. Verify numeric stability near that limit.
+    let schedule = NoiseSchedule::geometric(400, 0.5, 50);
+    assert_eq!(schedule.rounds_for_depth(126), 50);
+    assert_eq!(schedule.rounds_for_depth(127), 50);
+    assert_eq!(schedule.rounds_for_depth(128), 50);
+}
+
+#[test]
+fn geometric_rounds_decay_one_is_constant() {
+    // decay=1.0 → root × 1.0^depth = root at every depth.
+    let schedule = NoiseSchedule::geometric(42, 1.0, 1);
+    for depth in [0, 1, 10, 50, 128] {
+        assert_eq!(schedule.rounds_for_depth(depth), 42);
+    }
+}
+
+#[test]
+fn geometric_rounds_root_zero_min_zero() {
+    // Both root and min zero → always 0 rounds.
+    let schedule = NoiseSchedule::Geometric {
+        root: 0,
+        decay: 0.5,
+        min: 0,
+    };
+    assert_eq!(schedule.rounds_for_depth(0), 0);
+    assert_eq!(schedule.rounds_for_depth(5), 0);
+    assert_eq!(schedule.rounds_for_depth(128), 0);
+}
+
+#[test]
+fn explicit_rounds_for_depth() {
+    let schedule = NoiseSchedule::Explicit(vec![50, 30, 10]);
+    assert_eq!(schedule.rounds_for_depth(0), 50);
+    assert_eq!(schedule.rounds_for_depth(1), 30);
+    assert_eq!(schedule.rounds_for_depth(2), 10);
+    // Beyond vector length → clamps to last entry.
+    assert_eq!(schedule.rounds_for_depth(3), 10);
+    assert_eq!(schedule.rounds_for_depth(99), 10);
+}
+
+#[test]
+fn explicit_rounds_single_entry() {
+    let schedule = NoiseSchedule::Explicit(vec![5]);
+    assert_eq!(schedule.rounds_for_depth(0), 5);
+    assert_eq!(schedule.rounds_for_depth(1), 5);
+    assert_eq!(schedule.rounds_for_depth(100), 5);
+}
+
+#[test]
+fn explicit_rounds_non_monotonic() {
+    // Explicit is a direct index — non-monotonic is fine.
+    let schedule = NoiseSchedule::Explicit(vec![10, 50, 5, 100, 1]);
+    assert_eq!(schedule.rounds_for_depth(0), 10);
+    assert_eq!(schedule.rounds_for_depth(1), 50);
+    assert_eq!(schedule.rounds_for_depth(2), 5);
+    assert_eq!(schedule.rounds_for_depth(3), 100);
+    assert_eq!(schedule.rounds_for_depth(4), 1);
+    // Beyond length → clamp to last entry.
+    assert_eq!(schedule.rounds_for_depth(5), 1);
+    assert_eq!(schedule.rounds_for_depth(999), 1);
+}
+
+// ── NoiseSchedule::is_disabled ──────────────────────────────
+
+#[test]
+fn explicit_empty_is_disabled() {
+    let schedule = NoiseSchedule::Explicit(vec![]);
+    assert_eq!(schedule.rounds_for_depth(0), 0);
+    assert_eq!(schedule.rounds_for_depth(5), 0);
+    assert!(schedule.is_disabled());
+}
+
+#[test]
+fn explicit_all_zeros_is_disabled() {
+    let schedule = NoiseSchedule::Explicit(vec![0, 0, 0]);
+    assert_eq!(schedule.rounds_for_depth(0), 0);
+    assert!(schedule.is_disabled());
+}
+
+#[test]
+fn explicit_single_zero_is_disabled() {
+    let schedule = NoiseSchedule::Explicit(vec![0]);
+    assert_eq!(schedule.rounds_for_depth(0), 0);
+    assert_eq!(schedule.rounds_for_depth(10), 0);
+    assert!(schedule.is_disabled());
+}
+
+#[test]
+fn explicit_mixed_zeros_not_disabled() {
+    // At least one non-zero entry → not disabled.
+    let schedule = NoiseSchedule::Explicit(vec![0, 0, 5]);
+    assert!(!schedule.is_disabled());
+    assert_eq!(schedule.rounds_for_depth(0), 0);
+    assert_eq!(schedule.rounds_for_depth(1), 0);
+    assert_eq!(schedule.rounds_for_depth(2), 5);
+    assert_eq!(schedule.rounds_for_depth(10), 5);
+}
+
+#[test]
+fn geometric_is_disabled_when_min_zero() {
+    let schedule = NoiseSchedule::Geometric {
+        root: 0,
+        decay: 0.5,
+        min: 0,
+    };
+    assert!(schedule.is_disabled());
+}
+
+#[test]
+fn geometric_is_not_disabled_when_min_positive() {
+    let schedule = NoiseSchedule::Geometric {
+        root: 10,
+        decay: 0.5,
+        min: 1,
+    };
+    assert!(!schedule.is_disabled());
+}
+
+// ── NoiseSchedule::max_rounds ───────────────────────────────
+
+#[test]
+fn max_rounds_geometric() {
+    let schedule = NoiseSchedule::geometric(999, 0.1, 1);
+    assert_eq!(schedule.max_rounds(), 999);
+}
+
+#[test]
+fn max_rounds_explicit() {
+    // max_rounds returns the maximum value, not the first.
+    let schedule = NoiseSchedule::Explicit(vec![10, 50, 30]);
+    assert_eq!(schedule.max_rounds(), 50);
+}
+
+#[test]
+fn max_rounds_explicit_empty() {
+    let schedule = NoiseSchedule::Explicit(vec![]);
+    assert_eq!(schedule.max_rounds(), 0);
+}
+
+#[test]
+fn max_rounds_explicit_large() {
+    let schedule = NoiseSchedule::Explicit(vec![u32::MAX]);
+    assert_eq!(schedule.max_rounds(), u32::MAX);
+}
+
+// ── NoiseSchedule::Default ──────────────────────────────────
+
+#[test]
+fn noise_schedule_default_matches_doc() {
+    let schedule = NoiseSchedule::default();
+    match &schedule {
+        NoiseSchedule::Geometric { root, decay, min } => {
+            assert_eq!(*root, 450);
+            assert!((decay - 0.5).abs() < f64::EPSILON);
+            assert_eq!(*min, 50);
+        }
+        NoiseSchedule::Explicit(_) => panic!("default should be Geometric"),
+    }
+    assert!(!schedule.is_disabled());
+}
+
+// ── ConfigWarning ───────────────────────────────────────────
+
+#[test]
+fn default_config_has_no_warnings() {
+    let cfg = SentinelConfig::<u64>::default();
+    assert!(cfg.warnings().is_empty());
+}
+
+#[test]
+fn warns_when_noise_root_too_low_for_lambda_099() {
+    let cfg = SentinelConfig::<u64> {
+        forgetting_factor: 0.99,
+        noise_schedule: NoiseSchedule::geometric(50, 0.5, 10),
+        ..SentinelConfig::<u64>::default()
+    };
+    let warnings = cfg.warnings();
+    assert_eq!(warnings.len(), 1);
+    assert!(matches!(
+        &warnings[0],
+        ConfigWarning::NoiseScheduleInsufficient {
+            root: 50,
+            recommended_root: 450,
+            lambda,
+        } if (*lambda - 0.99).abs() < f64::EPSILON
+    ));
+}
+
+#[test]
+fn no_warning_when_noise_root_sufficient_for_lambda_095() {
+    let cfg = SentinelConfig::<u64> {
+        forgetting_factor: 0.95,
+        noise_schedule: NoiseSchedule::geometric(50, 0.5, 10),
+        ..SentinelConfig::<u64>::default()
+    };
+    assert!(cfg.warnings().is_empty());
+}
+
+#[test]
+fn warns_when_small_batch_and_lambda_095() {
+    // At λ=0.95, b=4, recommended root is 200.
+    let cfg = SentinelConfig::<u64> {
+        forgetting_factor: 0.95,
+        noise_batch_size: 4,
+        noise_schedule: NoiseSchedule::geometric(50, 0.5, 10),
+        ..SentinelConfig::<u64>::default()
+    };
+    let warnings = cfg.warnings();
+    assert_eq!(warnings.len(), 1);
+    assert!(matches!(
+        &warnings[0],
+        ConfigWarning::NoiseScheduleInsufficient {
+            recommended_root: 200,
+            ..
+        }
+    ));
+}
+
+#[test]
+fn no_warning_for_explicit_schedule_with_enough_rounds() {
+    let cfg = SentinelConfig::<u64> {
+        forgetting_factor: 0.99,
+        noise_schedule: NoiseSchedule::Explicit(vec![500, 300, 100]),
+        ..SentinelConfig::<u64>::default()
+    };
+    assert!(cfg.warnings().is_empty());
+}
+
+#[test]
+fn warning_display_is_informative() {
+    let w = ConfigWarning::NoiseScheduleInsufficient {
+        root: 50,
+        recommended_root: 450,
+        lambda: 0.99,
+    };
+    let msg = w.to_string();
+    assert!(msg.contains("50"));
+    assert!(msg.contains("450"));
+    assert!(msg.contains("0.99"));
+}
diff --git a/packages/sentinel/src/tests/convergence_clipping.rs b/packages/sentinel/src/tests/convergence_clipping.rs
new file mode 100644
index 000000000..a115a5317
--- /dev/null
+++ b/packages/sentinel/src/tests/convergence_clipping.rs
@@ -0,0 +1,589 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// SPDX-FileCopyrightText: 2026 Torrust project contributors
+
+//! Clipping stability audit tests.
+//!
+//! Verifies that none of the three clipping mechanisms (fast EWMA
+//! upper-tail clip, graduated clip-exemption, slow EWMA/CUSUM clip)
+//! introduce instability or bias into the converged model.  The noise
+//! model relies on: (1) fast EWMA upper-tail clip (§ALGO S-7.1.1),
+//! (2) graduated clip-exemption via clip-pressure EWMA (§ALGO S-6.4,
+//! ADR-S-013), and (3) slow EWMA clip applied to the CUSUM reference
+//! baseline.  Each mechanism can, in principle, bias the converged
+//! baseline or delay convergence.  These tests audit that none of
+//! them do.
+//!
+//! # Test index
+//!
+//! ## Fast EWMA upper-tail clip (§ALGO S-7.1.1)
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`clip_bias_is_negligible_at_steady_state`] | clipped vs unclipped runs agree on baseline means within tail-mass bounds |
+//! | [`variance_estimate_unbiased_despite_clipping`] | EWMA variance not systematically distorted by tail truncation |
+//!
+//! ## Graduated clip-exemption (§ALGO S-6.4)
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`graduated_exemption_prevents_bistable_attractor`] | varying `clip_sigmas` (2, 3, 5) all converge to the same fixed point |
+//! | [`exemption_decay_does_not_cause_transient_instability`] | η → 0 transition produces no baseline spikes or dips |
+//! | [`clip_ceiling_stabilises_after_exemption_decay`] | rolling CV of the clip ceiling < 8 % once η < 0.05 |
+//!
+//! ## Slow EWMA / CUSUM clip
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`slow_ewma_clipping_does_not_bias_cusum_reference`] | after seed-from-fast, slow baseline tracks fast within tolerance |
+//! | [`cusum_bounded_through_clip_transitions`] | CUSUM accumulator stays < 30 throughout clip-width transitions |
+
+use rand::SeedableRng;
+use rand::rngs::SmallRng;
+
+use super::convergence_common::{AXIS_NAMES, as_slices, cfg_test, generate_noise, run_noise_trace};
+use crate::config::SentinelConfig;
+use crate::sentinel::tracker::SubspaceTracker;
+
+/// Per-round snapshot of baseline mean, variance, and η.
+struct RoundSnap {
+    baseline_mean: [f64; 4],
+    baseline_var: [f64; 4],
+    eta: f64,
+}
+
+// ════════════════════════════════════════════════════════════
+//  1. Fast EWMA upper-tail clip  (§ALGO S-7.1.1)
+// ════════════════════════════════════════════════════════════
+
+/// At steady state, the fast EWMA baseline with 3σ clipping should
+/// match an unclipped baseline within a small per-axis tolerance.
+///
+/// The theory (`noise_convergence.md` §4): a one-sided upper clip at
+/// `μ + c√v` on a right-skewed distribution creates downward bias
+/// proportional to the tail mass `P(x > μ + c√v)`.  For the null
+/// score distributions (chi-squared-like) at c = 3, this tail mass
+/// is ≤ 0.3% — so the bias should be negligible compared to the
+/// steady-state EWMA jitter.
+///
+/// We compare a clipped run (`clip_sigmas` = 3.0) against an
+/// unclipped run (`clip_sigmas` = ∞) and verify baseline means
+/// agree within per-axis tolerances.
+#[test]
+#[allow(clippy::cast_precision_loss)]
+fn clip_bias_is_negligible_at_steady_state() {
+    let dim = 128;
+    let total_rounds = 500;
+    let seed = 42;
+
+    // Run with standard clipping.
+    let cfg_clipped = cfg_test(); // clip_sigmas = 3.0
+    let traces_clipped = run_noise_trace(&cfg_clipped, dim, total_rounds, seed);
+
+    // Run without clipping.
+    let cfg_unclipped = SentinelConfig {
+        clip_sigmas: f64::INFINITY,
+        ..cfg_test()
+    };
+    let traces_unclipped = run_noise_trace(&cfg_unclipped, dim, total_rounds, seed);
+
+    // Compare steady-state block means (last 100 rounds).
+    let block_start = total_rounds - 100;
+
+    // Per-axis tolerances for the clip-vs-unclip comparison.
+    //
+    // These accommodate both the clip bias AND the EWMA jitter.
+    // The clip bias is < 0.3% of the mean (§4 theory), but the
+    // EWMA jitter adds uncertainty — especially for high-CV axes
+    // (coherence ~10%).  We also use the same RNG seed, so the
+    // *input* noise sequence is identical; only the clipping
+    // creates divergence.  But the graduated exemption causes the
+    // two runs to track differently from the start, so by round
+    // 400+ the EWMA trajectories have drifted apart by O(CV).
+    //
+    // Axis          | expected bias | jitter CV | tolerance
+    // --------------|---------------|-----------|----------
+    // Novelty       |   < 0.1%     |   0.07%   |   2%
+    // Displacement  |   < 0.5%     |   3.4%    |  10%
+    // Surprise      |   < 0.5%     |   6.5%    |  15%
+    // Coherence     |   < 0.5%     |  10.0%    |  20%
+    let tolerances = [0.02, 0.10, 0.15, 0.20];
+
+    for (ax, (name, tol)) in AXIS_NAMES.iter().zip(tolerances.iter()).enumerate() {
+        let clipped_mean: f64 = traces_clipped[block_start..]
+            .iter()
+            .map(|t| t.baseline_means[ax])
+            .sum::<f64>()
+            / 100.0;
+
+        let unclipped_mean: f64 = traces_unclipped[block_start..]
+            .iter()
+            .map(|t| t.baseline_means[ax])
+            .sum::<f64>()
+            / 100.0;
+
+        if unclipped_mean.abs() < 1e-12 {
+            continue; // axis inactive
+        }
+
+        let rel_diff = (clipped_mean - unclipped_mean).abs() / unclipped_mean.abs();
+        assert!(
+            rel_diff < *tol,
+            "{name}: clipped vs unclipped steady-state bias is {:.2}%, \
+             tolerance is {:.0}% — clipping introduces excessive bias",
+            rel_diff * 100.0,
+            tol * 100.0,
+        );
+    }
+}
+
+/// The EWMA variance estimate at steady state should not be
+/// systematically distorted by clipping.
+///
+/// When the upper-tail clip rejects an observation, the *entire*
+/// EWMA update is skipped for that batch.  This means the variance
+/// estimator only sees the unclipped distribution — which has
+/// slightly lower true variance (the tail is removed).  However,
+/// at c = 3σ the removed tail mass is < 0.3%, so the variance
+/// bias should be negligible.
+///
+/// We compare the EWMA variance from a clipped run against an
+/// unclipped run and verify they agree per axis.
+#[test]
+#[allow(clippy::cast_precision_loss)]
+fn variance_estimate_unbiased_despite_clipping() {
+    let dim = 128;
+    let total_rounds = 500;
+    let seed = 42;
+
+    // We need variance, not just means — run directly.
+    let run_variances = |cfg: &SentinelConfig<u64>| -> [f64; 4] {
+        let mut tracker = SubspaceTracker::new(dim, cfg, cfg.cusum_slow_decay);
+        let mut rng = SmallRng::seed_from_u64(seed);
+        let mut last_var = [0.0_f64; 4];
+        for _ in 0..total_rounds {
+            let noise = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+            let report = tracker.observe(&as_slices(&noise), 0, true);
+            last_var = [
+                report.scores.novelty.baseline.variance,
+                report.scores.displacement.baseline.variance,
+                report.scores.surprise.baseline.variance,
+                report.scores.coherence.baseline.variance,
+            ];
+        }
+        last_var
+    };
+
+    let cfg_clipped = cfg_test();
+    let var_clipped = run_variances(&cfg_clipped);
+
+    let cfg_unclipped = SentinelConfig {
+        clip_sigmas: f64::INFINITY,
+        ..cfg_test()
+    };
+    let var_unclipped = run_variances(&cfg_unclipped);
+
+    // Since variance is a second-moment estimate, it's more
+    // sensitive to tail truncation than the mean.  But at 3σ
+    // the effect is still small.  We use generous tolerances.
+    let tolerances = [0.05, 0.15, 0.25, 0.35];
+
+    for (ax, (name, tol)) in AXIS_NAMES.iter().zip(tolerances.iter()).enumerate() {
+        if var_unclipped[ax].abs() < 1e-12 {
+            continue;
+        }
+        let rel_diff = (var_clipped[ax] - var_unclipped[ax]).abs() / var_unclipped[ax];
+        assert!(
+            rel_diff < *tol,
+            "{name}: clipped variance differs from unclipped by {:.2}%, \
+             tolerance {:.0}% — clipping distorts the variance estimate",
+            rel_diff * 100.0,
+            tol * 100.0,
+        );
+    }
+}
+
+// ════════════════════════════════════════════════════════════
+//  2. Graduated clip-exemption  (§ALGO S-6.4)
+// ════════════════════════════════════════════════════════════
+
+/// The graduated clip-exemption ensures convergence to the correct
+/// fixed point regardless of `clip_sigmas` tightness.
+///
+/// Theory (`noise_convergence.md` §4): without graduated exemption,
+/// the clipped EWMA is a nonlinear adaptive filter with two stable
+/// fixed points — the correct one and a biased one with permanently
+/// tight clipping.  The graduated exemption widens the basin of
+/// attraction of the correct fixed point during warm-up.
+///
+/// We run the tracker at `clip_sigmas` = 2.0, 3.0, and 5.0.  All
+/// three should converge to approximately the same steady state
+/// (the correct fixed point), proving the exemption prevents the
+/// biased attractor from capturing the trajectory.
+#[test]
+#[allow(clippy::cast_precision_loss)]
+fn graduated_exemption_prevents_bistable_attractor() {
+    let dim = 128;
+    let total_rounds = 500;
+    let seed = 42;
+
+    let clip_values = [2.0, 3.0, 5.0];
+    let mut steady_states: Vec<[f64; 4]> = Vec::new();
+
+    for &clip in &clip_values {
+        let cfg = SentinelConfig {
+            clip_sigmas: clip,
+            ..cfg_test()
+        };
+        let traces = run_noise_trace(&cfg, dim, total_rounds, seed);
+
+        // Compute block mean of last 100 rounds.
+        let block_start = total_rounds - 100;
+        let mut means = [0.0_f64; 4];
+        for (ax, mean) in means.iter_mut().enumerate() {
+            *mean = traces[block_start..].iter().map(|t| t.baseline_means[ax]).sum::<f64>() / 100.0;
+        }
+        steady_states.push(means);
+    }
+
+    // Compare all pairs: each axis should agree within tolerance.
+    //
+    // The tolerance accounts for both the (small) clip bias
+    // difference between c = 2 and c = 5, and the EWMA trajectory
+    // divergence from different clipping histories.
+    let tolerances = [0.03, 0.12, 0.18, 0.30];
+
+    for i in 0..clip_values.len() {
+        for j in (i + 1)..clip_values.len() {
+            for (ax, (name, tol)) in AXIS_NAMES.iter().zip(tolerances.iter()).enumerate() {
+                let ref_val = f64::midpoint(steady_states[i][ax], steady_states[j][ax]);
+                if ref_val.abs() < 1e-12 {
+                    continue;
+                }
+                let rel_diff = (steady_states[i][ax] - steady_states[j][ax]).abs() / ref_val.abs();
+                assert!(
+                    rel_diff < *tol,
+                    "{name}: clip_sigmas {:.0} vs {:.0} differ by {:.2}%, tolerance {:.0}% — \
+                     graduated exemption may not be preventing bistable attractor",
+                    clip_values[i],
+                    clip_values[j],
+                    rel_diff * 100.0,
+                    tol * 100.0,
+                );
+            }
+        }
+    }
+}
+
+/// As η decays from 1 → 0, the effective clip width tightens.
+/// This transition must not cause a spike or dip in baseline means.
+///
+/// Theory (`noise_convergence.md` §4): a discontinuous clip-width
+/// change could push the system across a basin boundary.  The
+/// smooth formula `n_σ · (1 + p/(1−p+ε))` where `p = max(η, ρ̄)`
+/// avoids this (§ALGO S-6.4), but we verify empirically that no
+/// transient exceeds the steady-state jitter envelope.
+///
+/// Strategy: compute a rolling 20-round mean of each axis.  Once
+/// η falls below 0.1 (i.e. after the exemption has mostly decayed),
+/// no rolling mean should deviate from the final steady-state by
+/// more than per-axis tolerance.  This catches both sudden jumps
+/// and slow oscillations.
+#[test]
+#[allow(clippy::cast_precision_loss)]
+fn exemption_decay_does_not_cause_transient_instability() {
+    let cfg = cfg_test();
+    let dim = 128;
+    let total_rounds = 400;
+    let traces = run_noise_trace(&cfg, dim, total_rounds, 42);
+
+    let window = 20_usize;
+
+    // Find the round where η drops below 0.1.
+    let eta_threshold_round = traces.iter().position(|t| t.noise_influence < 0.1).unwrap_or(total_rounds);
+
+    // Compute the reference steady-state mean (last 50 rounds).
+    let ss_start = total_rounds - 50;
+    let mut ss_means = [0.0_f64; 4];
+    for (ax, ss_mean) in ss_means.iter_mut().enumerate() {
+        *ss_mean = traces[ss_start..].iter().map(|t| t.baseline_means[ax]).sum::<f64>() / 50.0;
+    }
+
+    // Per-axis tolerances for the post-exemption jitter envelope.
+    // These are wider than the block-mean tolerances in
+    // convergence_noise.rs because a 20-round rolling mean has
+    // higher variance than a 100-round block mean.
+    let tolerances = [0.05, 0.15, 0.25, 0.35];
+
+    let start_check = eta_threshold_round.max(window);
+    for round in start_check..=(total_rounds - window) {
+        let mut rolling = [0.0_f64; 4];
+        for (ax, roll) in rolling.iter_mut().enumerate() {
+            *roll = traces[round..round + window]
+                .iter()
+                .map(|t| t.baseline_means[ax])
+                .sum::<f64>()
+                / window as f64;
+        }
+
+        for (ax, (name, tol)) in AXIS_NAMES.iter().zip(tolerances.iter()).enumerate() {
+            if ss_means[ax].abs() < 1e-12 {
+                continue;
+            }
+            let rel_dev = (rolling[ax] - ss_means[ax]).abs() / ss_means[ax].abs();
+            assert!(
+                rel_dev < *tol,
+                "{name} at round {round}: rolling mean deviates {:.2}% from \
+                 steady state (tolerance {:.0}%) — transient instability \
+                 during exemption decay",
+                rel_dev * 100.0,
+                tol * 100.0,
+            );
+        }
+    }
+}
+
+/// The clip ceiling (`mean + clip_sigmas·√variance`) should
+/// stabilise monotonically once the exemption has decayed.
+///
+/// During warm-up the ceiling moves rapidly (mean and variance
+/// are converging). After η < 0.05, the ceiling should settle
+/// to a narrow band.  Large swings would indicate the clip
+/// threshold is oscillating, which could trigger a clip-feedback
+/// cycle.
+///
+/// Strategy: measure the ceiling at each round via
+///   `ceiling = mean + effective_clip · √variance`
+/// After η < 0.05, the rolling CV (coefficient of variation)
+/// of the ceiling over a 20-round window should be < 8% for
+/// all axes.
+#[test]
+#[allow(clippy::cast_precision_loss)]
+fn clip_ceiling_stabilises_after_exemption_decay() {
+    let cfg = cfg_test();
+    let dim = 128;
+    let total_rounds = 400;
+
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let mut rng = SmallRng::seed_from_u64(42);
+
+    let mut snaps: Vec<RoundSnap> = Vec::with_capacity(total_rounds);
+    for _ in 0..total_rounds {
+        let noise = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+        let report = tracker.observe(&as_slices(&noise), 0, true);
+        snaps.push(RoundSnap {
+            baseline_mean: [
+                report.scores.novelty.baseline.mean,
+                report.scores.displacement.baseline.mean,
+                report.scores.surprise.baseline.mean,
+                report.scores.coherence.baseline.mean,
+            ],
+            baseline_var: [
+                report.scores.novelty.baseline.variance,
+                report.scores.displacement.baseline.variance,
+                report.scores.surprise.baseline.variance,
+                report.scores.coherence.baseline.variance,
+            ],
+            eta: report.maturity.noise_influence,
+        });
+    }
+
+    // Compute ceiling series for each axis.
+    let clip = cfg.clip_sigmas;
+    let mut ceilings: [Vec<f64>; 4] = [
+        Vec::with_capacity(total_rounds),
+        Vec::with_capacity(total_rounds),
+        Vec::with_capacity(total_rounds),
+        Vec::with_capacity(total_rounds),
+    ];
+
+    for snap in &snaps {
+        for (ax, ceil_vec) in ceilings.iter_mut().enumerate() {
+            ceil_vec.push(clip.mul_add(snap.baseline_var[ax].sqrt(), snap.baseline_mean[ax]));
+        }
+    }
+
+    // Find round where η < 0.05.
+    let eta_settled = snaps.iter().position(|s| s.eta < 0.05).unwrap_or(total_rounds);
+
+    // Check rolling CV of ceiling after exemption decay.
+    let window = 20_usize;
+    let cv_limit = 0.08; // 8% — generous for high-CV axes
+
+    let start_check = eta_settled.max(window);
+    for (ax, ceil_series) in ceilings.iter().enumerate() {
+        for round in start_check..=(total_rounds - window) {
+            let block = &ceil_series[round..round + window];
+            let mean = block.iter().sum::<f64>() / window as f64;
+            if mean.abs() < 1e-12 {
+                continue;
+            }
+            let var = block.iter().map(|v| (v - mean).powi(2)).sum::<f64>() / window as f64;
+            let cv = var.sqrt() / mean;
+            assert!(
+                cv < cv_limit,
+                "{}: clip ceiling CV at round {} is {:.2}% (limit {:.0}%) — \
+                 ceiling is not stabilising after exemption decay",
+                AXIS_NAMES[ax],
+                round,
+                cv * 100.0,
+                cv_limit * 100.0,
+            );
+        }
+    }
+}
+
+// ════════════════════════════════════════════════════════════
+//  3. Slow EWMA / CUSUM clip
+// ════════════════════════════════════════════════════════════
+
+/// The CUSUM's slow EWMA receives the same `clip_sigmas` as the
+/// fast EWMA.  After seeding + convergence, the slow baseline
+/// should agree with the fast baseline, proving that slow-EWMA
+/// clipping does not introduce independent bias.
+///
+/// The slow EWMA (`λ_s` = 0.999, half-life 693) converges far too
+/// slowly from cold to be tested without seeding.  The production
+/// workflow seeds slow from fast after noise injection, so we
+/// replicate that: warm up for 200 rounds, seed, then let both
+/// run for 300 more rounds.  At the end, the slow baseline should
+/// still approximately agree with the fast baseline.
+///
+/// Any disagreement beyond what's expected from the different time
+/// constants signals that the slow EWMA's clipping is introducing
+/// independent bias.
+#[test]
+#[allow(clippy::cast_precision_loss)]
+fn slow_ewma_clipping_does_not_bias_cusum_reference() {
+    let cfg = cfg_test();
+    let dim = 128;
+    let warmup_rounds = 200;
+    let post_seed_rounds = 300;
+
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let mut rng = SmallRng::seed_from_u64(42);
+
+    // Noise warm-up.
+    for _ in 0..warmup_rounds {
+        let noise = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+        tracker.observe(&as_slices(&noise), 0, true);
+    }
+
+    // Seed slow from fast (production workflow).
+    tracker.seed_cusum_slow_from_baselines();
+    tracker.reset_cusum();
+
+    // Continue noise — at this point the slow EWMA starts from the
+    // fast EWMA's converged values.  Both receive the same `clip_sigmas`.
+    let mut last_report = None;
+    for _ in 0..post_seed_rounds {
+        let noise = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+        last_report = Some(tracker.observe(&as_slices(&noise), 0, true));
+    }
+
+    let report = last_report.unwrap();
+
+    // Compare fast vs slow baseline means.
+    //
+    // After 300 rounds post-seed at `λ_s` = 0.999:
+    //   `λ_s`^300 ≈ 0.741 — slow has only decayed 26% from the seed.
+    //   The fast EWMA at λ = 0.95 has fully forgotten the seed.
+    //
+    // So the slow baseline is ~74% seed + ~26% new data, while
+    // the fast is ~100% new data.  Under i.i.d. noise, the seed
+    // value ≈ the new data's mean, so the gap is mainly from
+    // stochastic drift.  Per-axis tolerances account for this.
+    //
+    //   novelty: ~2%, displacement ~10%, surprise ~15%, coh ~25%
+    let tolerances = [0.02, 0.10, 0.15, 0.25];
+    let axes = [
+        (
+            "novelty",
+            report.scores.novelty.baseline.mean,
+            report.scores.novelty.cusum.slow_baseline.mean,
+        ),
+        (
+            "displacement",
+            report.scores.displacement.baseline.mean,
+            report.scores.displacement.cusum.slow_baseline.mean,
+        ),
+        (
+            "surprise",
+            report.scores.surprise.baseline.mean,
+            report.scores.surprise.cusum.slow_baseline.mean,
+        ),
+        (
+            "coherence",
+            report.scores.coherence.baseline.mean,
+            report.scores.coherence.cusum.slow_baseline.mean,
+        ),
+    ];
+
+    for ((name, fast, slow), tol) in axes.iter().zip(tolerances.iter()) {
+        if fast.abs() < 1e-12 {
+            continue;
+        }
+        let rel_diff = (fast - slow).abs() / fast.abs();
+        assert!(
+            rel_diff < *tol,
+            "{name}: slow EWMA baseline differs from fast by {:.2}%, \
+             tolerance {:.0}% — slow-EWMA clipping introduces bias \
+             in CUSUM reference",
+            rel_diff * 100.0,
+            tol * 100.0,
+        );
+    }
+}
+
+/// During the exemption decay (η → 0), the effective clip width
+/// tightens. This is the most dangerous moment for false CUSUM
+/// accumulation: the fast EWMA may receive slightly different
+/// updates than before, creating a transient fast-slow gap.
+///
+/// We verify that the CUSUM accumulator stays bounded (< 30)
+/// throughout the entire warm-up when slow-from-fast seeding is
+/// applied at a reasonable point.
+#[test]
+#[allow(clippy::cast_precision_loss)]
+fn cusum_bounded_through_clip_transitions() {
+    let cfg = cfg_test();
+    let dim = 128;
+    let warmup_rounds = 200;
+    let transition_rounds = 300;
+
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let mut rng = SmallRng::seed_from_u64(42);
+
+    // Noise warm-up.
+    for _ in 0..warmup_rounds {
+        let noise = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+        tracker.observe(&as_slices(&noise), 0, true);
+    }
+
+    // Seed and reset (production workflow).
+    tracker.seed_cusum_slow_from_baselines();
+    tracker.reset_cusum();
+
+    // Continue with noise — the clip width is now near production
+    // level (η should be small after 200 rounds at λ = 0.95:
+    // η = 0.95^200 ≈ 3.5e-5).
+    let mut max_cusum = [0.0_f64; 4];
+    for _ in 0..transition_rounds {
+        let noise = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+        let report = tracker.observe(&as_slices(&noise), 0, true);
+
+        max_cusum[0] = max_cusum[0].max(report.scores.novelty.cusum.accumulator);
+        max_cusum[1] = max_cusum[1].max(report.scores.displacement.cusum.accumulator);
+        max_cusum[2] = max_cusum[2].max(report.scores.surprise.cusum.accumulator);
+        max_cusum[3] = max_cusum[3].max(report.scores.coherence.cusum.accumulator);
+    }
+
+    let cusum_limit = 30.0;
+    for (ax, name) in AXIS_NAMES.iter().enumerate() {
+        assert!(
+            max_cusum[ax] < cusum_limit,
+            "{name}: CUSUM reached {:.2} during post-seed phase \
+             (limit {cusum_limit}) — clip transitions causing false drift",
+            max_cusum[ax],
+        );
+    }
+}
diff --git a/packages/sentinel/src/tests/convergence_common.rs b/packages/sentinel/src/tests/convergence_common.rs
new file mode 100644
index 000000000..f97ec4601
--- /dev/null
+++ b/packages/sentinel/src/tests/convergence_common.rs
@@ -0,0 +1,549 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// SPDX-FileCopyrightText: 2026 Torrust project contributors
+
+//! Shared infrastructure for convergence unit tests.
+//!
+//! Provides config constructors, noise generation, and convergence
+//! metrics used across `convergence_ewma`, `convergence_eta`,
+//! `convergence_noise`, and `convergence_fixes`.
+//!
+//! # Test index
+//!
+//! ## `rolling_mean`
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`rolling_mean_empty`] | empty slice returns 0.0 |
+//! | [`rolling_mean_single`] | single element returns itself |
+//! | [`rolling_mean_known`] | known arithmetic mean |
+//! | [`rolling_mean_negative`] | handles negative values |
+//!
+//! ## `find_settled_round`
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`settled_all_within_tolerance`] | all rounds OK → `Some(0)` |
+//! | [`settled_never`] | last round violates → `None` |
+//! | [`settled_after_specific_round`] | violation in middle |
+//! | [`settled_near_zero_reference_skipped`] | near-zero ref always OK |
+//! | [`settled_single_trace`] | single-element trace |
+//! | [`settled_subset_of_axes`] | only checked axes matter |
+//!
+//! ## `find_converged_round`
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`converged_too_few_values`] | n < 2·window → n |
+//! | [`converged_already_stable`] | constant input → 0 |
+//! | [`converged_after_transient`] | step-change transient |
+//! | [`converged_near_zero_reference`] | near-zero ref → 0 |
+//!
+//! ## `block_mean_relative_error`
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`block_mean_late_near_zero`] | inactive axis → `None` |
+//! | [`block_mean_identical_blocks`] | same data → 0.0 |
+//! | [`block_mean_known_error`] | known relative error |
+//!
+//! ## `trailing_cv`
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`trailing_cv_too_few`] | insufficient data → `NaN` |
+//! | [`trailing_cv_constant`] | constant values → 0.0 |
+//! | [`trailing_cv_known`] | known CV value |
+//!
+//! ## `generate_noise` / `as_slices`
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`generate_noise_shape_and_values`] | correct shape and ±0.5 values |
+//! | [`as_slices_preserves_data`] | round-trip preserves content |
+//!
+//! ## Config constructors
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`cfg_test_is_valid`] | `cfg_test()` passes validation |
+//! | [`cfg_b16_overrides_batch_size`] | `cfg_b16()` overrides batch size |
+//! | [`cfg_production_overrides`] | `cfg_production()` overrides λ, batch, interval |
+//!
+//! # §-references
+//!
+//! - §ALGO S-4.2 Phase 3 — Latent distribution cold→warm
+//! - §ALGO S-7.1.1 — EWMA outlier filter / clipping ceiling
+//! - §ALGO S-11.5 — Maturity tracking (noise influence η)
+//! - ADR-S-013 — Warm-up convergence benchmark
+
+use rand::rngs::SmallRng;
+use rand::{RngExt, SeedableRng};
+
+use crate::config::SentinelConfig;
+use crate::sentinel::tracker::SubspaceTracker;
+
+// ════════════════════════════════════════════════════════════
+//  Configs
+// ════════════════════════════════════════════════════════════
+
+/// Standard test config (λ = 0.95, b = 4).
+pub(super) fn cfg_test() -> SentinelConfig<u64> {
+    SentinelConfig {
+        max_rank: 2,
+        forgetting_factor: 0.95,
+        rank_update_interval: 5,
+        analysis_k: 16,
+        analysis_depth_cutoff: 6,
+        energy_threshold: 0.90,
+        eps: 1e-6,
+        per_sample_scores: false,
+        cusum_allowance_sigmas: 0.5,
+        cusum_slow_decay: 0.999,
+        cusum_coord_slow_decay: 0.999,
+        clip_sigmas: 3.0,
+        clip_pressure_decay: 0.95,
+        split_threshold: 100,
+        d_create: 3,
+        d_evict: 6,
+        budget: 100_000,
+        noise_schedule: crate::config::NoiseSchedule::Explicit(vec![5]),
+        noise_batch_size: 4,
+        noise_seed: Some(42),
+        background_warming: false,
+        svd_strategy: crate::maths::SvdStrategy::Brand,
+    }
+}
+
+/// Test config with larger batch size (λ = 0.95, b = 16).
+pub(super) fn cfg_b16() -> SentinelConfig<u64> {
+    SentinelConfig {
+        noise_batch_size: 16,
+        ..cfg_test()
+    }
+}
+
+/// Production-like config (λ = 0.99, b = 16).
+pub(super) fn cfg_production() -> SentinelConfig<u64> {
+    SentinelConfig {
+        forgetting_factor: 0.99,
+        rank_update_interval: 100,
+        noise_batch_size: 16,
+        noise_schedule: crate::config::NoiseSchedule::Explicit(vec![50]),
+        ..cfg_test()
+    }
+}
+
+// ════════════════════════════════════════════════════════════
+//  Noise generation
+// ════════════════════════════════════════════════════════════
+
+/// Generate a batch of random ±0.5 noise vectors.
+pub(super) fn generate_noise(dim: usize, batch_size: usize, rng: &mut SmallRng) -> Vec<Vec<f64>> {
+    (0..batch_size)
+        .map(|_| (0..dim).map(|_| if rng.random_bool(0.5) { 0.5 } else { -0.5 }).collect())
+        .collect()
+}
+
+/// Convert owned vectors to a slice-of-slices for `tracker.observe()`.
+pub(super) fn as_slices(vecs: &[Vec<f64>]) -> Vec<&[f64]> {
+    vecs.iter().map(Vec::as_slice).collect()
+}
+
+// ════════════════════════════════════════════════════════════
+//  Axis labels
+// ════════════════════════════════════════════════════════════
+
+#[allow(dead_code)] // Used by multiple convergence test modules.
+pub(super) const AXIS_NAMES: [&str; 4] = ["novelty", "displacement", "surprise", "coherence"];
+
+// ════════════════════════════════════════════════════════════
+//  Convergence metrics
+// ════════════════════════════════════════════════════════════
+
+/// Multi-axis backward-walk convergence: finds the first round from
+/// which ALL of the first `axes` channels stay within `tolerance`
+/// of `reference` permanently.
+pub(super) fn find_settled_round(traces: &[[f64; 4]], reference: &[f64; 4], tolerance: f64, axes: usize) -> Option<usize> {
+    let mut last_violation = None;
+    for (i, means) in traces.iter().enumerate().rev() {
+        let all_ok = (0..axes).all(|a| {
+            let ref_val = reference[a];
+            if ref_val.abs() < 1e-12 {
+                true
+            } else {
+                (means[a] - ref_val).abs() < tolerance * ref_val.abs()
+            }
+        });
+        if !all_ok {
+            last_violation = Some(i);
+            break;
+        }
+    }
+    match last_violation {
+        Some(v) if v + 1 < traces.len() => Some(v + 1),
+        None => Some(0),
+        _ => None,
+    }
+}
+
+/// Windowed-mean convergence metric (ADR-S-013 §3a).
+///
+/// Compares a rolling mean of a window-sized block against the
+/// reference (rolling mean of the final `window` values). Returns
+/// the first round where the metric permanently stays within
+/// `tolerance` of the reference.
+#[allow(clippy::cast_precision_loss)]
+pub(super) fn find_converged_round(baselines: &[f64], window: usize, tolerance: f64) -> usize {
+    let n = baselines.len();
+    if n < 2 * window {
+        return n;
+    }
+    let reference = rolling_mean(&baselines[n - window..]);
+    if reference.abs() < 1e-12 {
+        return 0;
+    }
+    for i in (window..n - window).rev() {
+        let local = rolling_mean(&baselines[i.saturating_sub(window)..i]);
+        if ((local - reference) / reference).abs() > tolerance {
+            return i + 1;
+        }
+    }
+    0
+}
+
+/// Rolling mean of a slice.
+#[allow(clippy::cast_precision_loss)]
+pub(super) fn rolling_mean(slice: &[f64]) -> f64 {
+    if slice.is_empty() {
+        return 0.0;
+    }
+    slice.iter().sum::<f64>() / slice.len() as f64
+}
+
+/// Block-mean stationarity test for a single axis.
+///
+/// Compares the mean of traces in `[early_start, early_end)` against
+/// `[late_start, late_end)`.  Returns `Some(relative_error)` if the
+/// late-block mean is non-negligible, or `None` if the late-block mean
+/// is effectively zero (axis inactive).
+///
+/// The theoretical basis (§`noise_convergence.md` §1–§2): after the EWMA
+/// transient decays (λ^t < ε), the expected baseline mean equals the
+/// score distribution's expectation.  Two widely-separated block means
+/// from the same stationary process should agree within the statistical
+/// uncertainty of the block-mean estimator, which is of order
+/// `CV_EWMA × √((1+λ)/(1−λ) / block_len)`.  The per-axis tolerances
+/// passed by callers are set to ≥ 3× this quantity.
+#[allow(clippy::cast_precision_loss)]
+pub(super) fn block_mean_relative_error(
+    traces: &[[f64; 4]],
+    axis: usize,
+    early_start: usize,
+    early_end: usize,
+    late_start: usize,
+    late_end: usize,
+) -> Option<f64> {
+    let early_mean = rolling_mean(&traces[early_start..early_end].iter().map(|t| t[axis]).collect::<Vec<_>>());
+    let late_mean = rolling_mean(&traces[late_start..late_end].iter().map(|t| t[axis]).collect::<Vec<_>>());
+    if late_mean.abs() < 1e-12 {
+        return None; // axis inactive
+    }
+    Some((early_mean - late_mean).abs() / late_mean.abs())
+}
+
+/// Coefficient of variation of the last `window` values.
+#[allow(clippy::cast_precision_loss)]
+#[allow(dead_code)] // Infrastructure for convergence test consumers.
+pub(super) fn trailing_cv(baselines: &[f64], window: usize) -> f64 {
+    let n = baselines.len();
+    if n < window {
+        return f64::NAN;
+    }
+    let tail = &baselines[n - window..];
+    let mean = rolling_mean(tail);
+    if mean.abs() < 1e-12 {
+        return 0.0;
+    }
+    let var = tail.iter().map(|v| (v - mean).powi(2)).sum::<f64>() / tail.len() as f64;
+    var.sqrt() / mean
+}
+
+// ════════════════════════════════════════════════════════════
+//  Trace collection
+// ════════════════════════════════════════════════════════════
+
+/// Per-round snapshot of baseline means, score means, and CUSUM state.
+#[derive(Clone)]
+#[allow(dead_code)] // Fields are for convergence test consumers.
+pub(super) struct RoundTrace {
+    pub baseline_means: [f64; 4],
+    pub score_means: [f64; 4],
+    pub cusum_accumulators: [f64; 4],
+    pub slow_baseline_means: [f64; 4],
+    pub noise_influence: f64,
+}
+
+/// Run `total_rounds` of noise injection and collect per-round traces.
+pub(super) fn run_noise_trace(cfg: &SentinelConfig<u64>, dim: usize, total_rounds: usize, seed: u64) -> Vec<RoundTrace> {
+    let mut tracker = SubspaceTracker::new(dim, cfg, cfg.cusum_slow_decay);
+    let mut rng = SmallRng::seed_from_u64(seed);
+    let mut traces = Vec::with_capacity(total_rounds);
+
+    for _ in 0..total_rounds {
+        let noise = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+        let report = tracker.observe(&as_slices(&noise), 0, true);
+
+        traces.push(RoundTrace {
+            baseline_means: [
+                report.scores.novelty.baseline.mean,
+                report.scores.displacement.baseline.mean,
+                report.scores.surprise.baseline.mean,
+                report.scores.coherence.baseline.mean,
+            ],
+            score_means: [
+                report.scores.novelty.mean,
+                report.scores.displacement.mean,
+                report.scores.surprise.mean,
+                report.scores.coherence.mean,
+            ],
+            cusum_accumulators: [
+                report.scores.novelty.cusum.accumulator,
+                report.scores.displacement.cusum.accumulator,
+                report.scores.surprise.cusum.accumulator,
+                report.scores.coherence.cusum.accumulator,
+            ],
+            slow_baseline_means: [
+                report.scores.novelty.cusum.slow_baseline.mean,
+                report.scores.displacement.cusum.slow_baseline.mean,
+                report.scores.surprise.cusum.slow_baseline.mean,
+                report.scores.coherence.cusum.slow_baseline.mean,
+            ],
+            noise_influence: report.maturity.noise_influence,
+        });
+    }
+
+    traces
+}
+
+// ════════════════════════════════════════════════════════════
+//  Unit tests
+// ════════════════════════════════════════════════════════════
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    // ── rolling_mean ────────────────────────────────────────
+
+    #[test]
+    fn rolling_mean_empty() {
+        assert!(rolling_mean(&[]).abs() < f64::EPSILON);
+    }
+
+    #[test]
+    fn rolling_mean_single() {
+        assert!((rolling_mean(&[7.0]) - 7.0).abs() < 1e-15);
+    }
+
+    #[test]
+    fn rolling_mean_known() {
+        // (1 + 2 + 3 + 4) / 4 = 2.5
+        assert!((rolling_mean(&[1.0, 2.0, 3.0, 4.0]) - 2.5).abs() < 1e-15);
+    }
+
+    #[test]
+    fn rolling_mean_negative() {
+        // (-3 + 1) / 2 = -1.0
+        assert!((rolling_mean(&[-3.0, 1.0]) - (-1.0)).abs() < 1e-15);
+    }
+
+    // ── find_settled_round ──────────────────────────────────
+
+    #[test]
+    fn settled_all_within_tolerance() {
+        // All rounds identical to reference → settled from round 0.
+        let traces = vec![[1.0, 2.0, 3.0, 4.0]; 10];
+        let reference = [1.0, 2.0, 3.0, 4.0];
+        assert_eq!(find_settled_round(&traces, &reference, 0.05, 4), Some(0));
+    }
+
+    #[test]
+    fn settled_never() {
+        // Last round is a violation → cannot settle.
+        let mut traces = vec![[1.0, 2.0, 3.0, 4.0]; 10];
+        traces[9] = [100.0, 2.0, 3.0, 4.0]; // violates axis 0
+        assert_eq!(find_settled_round(&traces, &[1.0, 2.0, 3.0, 4.0], 0.05, 4), None);
+    }
+
+    #[test]
+    fn settled_after_specific_round() {
+        // Violation at round 3, then stable from round 4 onward.
+        let mut traces = vec![[1.0, 2.0, 3.0, 4.0]; 10];
+        traces[3] = [100.0, 2.0, 3.0, 4.0];
+        assert_eq!(find_settled_round(&traces, &[1.0, 2.0, 3.0, 4.0], 0.05, 4), Some(4));
+    }
+
+    #[test]
+    fn settled_near_zero_reference_skipped() {
+        // Reference near zero → axis is always OK (skipped).
+        let traces = vec![[999.0, 0.0, 0.0, 0.0]; 5];
+        let reference = [999.0, 0.0, 0.0, 0.0];
+        assert_eq!(find_settled_round(&traces, &reference, 0.01, 4), Some(0));
+    }
+
+    #[test]
+    fn settled_single_trace() {
+        let traces = vec![[1.0, 2.0, 3.0, 4.0]];
+        let reference = [1.0, 2.0, 3.0, 4.0];
+        assert_eq!(find_settled_round(&traces, &reference, 0.05, 4), Some(0));
+    }
+
+    #[test]
+    fn settled_subset_of_axes() {
+        // Only check first 2 axes; axis 2 huge mismatch is ignored.
+        let traces = vec![[1.0, 2.0, 999.0, 999.0]; 5];
+        let reference = [1.0, 2.0, 3.0, 4.0];
+        assert_eq!(find_settled_round(&traces, &reference, 0.05, 2), Some(0));
+    }
+
+    // ── find_converged_round ────────────────────────────────
+
+    #[test]
+    fn converged_too_few_values() {
+        // n < 2*window → returns n.
+        let data = vec![1.0; 5];
+        assert_eq!(find_converged_round(&data, 10, 0.05), 5);
+    }
+
+    #[test]
+    fn converged_already_stable() {
+        // Constant input → converged from round 0.
+        let data = vec![3.0; 100];
+        assert_eq!(find_converged_round(&data, 10, 0.05), 0);
+    }
+
+    #[test]
+    fn converged_after_transient() {
+        // Big values then settling to 1.0 — must converge after transient.
+        let mut data = vec![100.0; 20];
+        data.extend(vec![1.0; 80]);
+        let round = find_converged_round(&data, 10, 0.05);
+        // Must be after the transient region but before the end.
+        assert!(round > 10, "should detect transient, got {round}");
+        assert!(round < 50, "should converge well before end, got {round}");
+    }
+
+    #[test]
+    fn converged_near_zero_reference() {
+        // Near-zero final mean → returns 0.
+        let data = vec![0.0; 40];
+        assert_eq!(find_converged_round(&data, 10, 0.05), 0);
+    }
+
+    // ── block_mean_relative_error ───────────────────────────
+
+    #[test]
+    fn block_mean_late_near_zero() {
+        // Late block mean near zero → axis inactive → None.
+        let traces = vec![[1.0, 0.0, 0.0, 0.0]; 20];
+        assert!(block_mean_relative_error(&traces, 1, 0, 10, 10, 20).is_none());
+    }
+
+    #[test]
+    fn block_mean_identical_blocks() {
+        // Identical blocks → error is 0.0.
+        let traces = vec![[5.0, 5.0, 5.0, 5.0]; 20];
+        let err = block_mean_relative_error(&traces, 0, 0, 10, 10, 20).unwrap();
+        assert!(err < 1e-15, "expected ~0, got {err}");
+    }
+
+    #[test]
+    fn block_mean_known_error() {
+        // Early block mean = 2.0, late block mean = 4.0 → error = 0.5.
+        let mut traces = Vec::new();
+        for _ in 0..10 {
+            traces.push([2.0, 0.0, 0.0, 0.0]);
+        }
+        for _ in 0..10 {
+            traces.push([4.0, 0.0, 0.0, 0.0]);
+        }
+        let err = block_mean_relative_error(&traces, 0, 0, 10, 10, 20).unwrap();
+        assert!((err - 0.5).abs() < 1e-15, "expected 0.5, got {err}");
+    }
+
+    // ── trailing_cv ─────────────────────────────────────────
+
+    #[test]
+    fn trailing_cv_too_few() {
+        let data = vec![1.0; 3];
+        assert!(trailing_cv(&data, 10).is_nan());
+    }
+
+    #[test]
+    fn trailing_cv_constant() {
+        let data = vec![5.0; 20];
+        assert!((trailing_cv(&data, 10)).abs() < 1e-15);
+    }
+
+    #[test]
+    fn trailing_cv_known() {
+        // std([1,2,3,4,5]) / mean([1,2,3,4,5])
+        // mean = 3.0, var = (4+1+0+1+4)/5 = 2.0, std = √2
+        // CV = √2 / 3 ≈ 0.4714
+        let data = vec![1.0, 2.0, 3.0, 4.0, 5.0];
+        let cv = trailing_cv(&data, 5);
+        let expected = 2.0_f64.sqrt() / 3.0;
+        assert!((cv - expected).abs() < 1e-10, "expected {expected}, got {cv}");
+    }
+
+    // ── generate_noise / as_slices ──────────────────────────
+
+    #[test]
+    fn generate_noise_shape_and_values() {
+        let mut rng = SmallRng::seed_from_u64(42);
+        let noise = generate_noise(8, 5, &mut rng);
+        assert_eq!(noise.len(), 5);
+        for row in &noise {
+            assert_eq!(row.len(), 8);
+            for &v in row {
+                assert!(
+                    (v - 0.5).abs() < f64::EPSILON || (v + 0.5).abs() < f64::EPSILON,
+                    "unexpected value {v}"
+                );
+            }
+        }
+    }
+
+    #[test]
+    fn as_slices_preserves_data() {
+        let vecs = vec![vec![1.0, 2.0], vec![3.0, 4.0]];
+        let slices = as_slices(&vecs);
+        assert_eq!(slices.len(), 2);
+        assert_eq!(slices[0], &[1.0, 2.0]);
+        assert_eq!(slices[1], &[3.0, 4.0]);
+    }
+
+    // ── config constructors ─────────────────────────────────
+
+    #[test]
+    fn cfg_test_is_valid() {
+        let cfg = cfg_test();
+        assert!(cfg.validate().is_ok(), "cfg_test() must pass validation");
+    }
+
+    #[test]
+    fn cfg_b16_overrides_batch_size() {
+        let cfg = cfg_b16();
+        assert_eq!(cfg.noise_batch_size, 16);
+        assert!(cfg.validate().is_ok(), "cfg_b16() must pass validation");
+    }
+
+    #[test]
+    fn cfg_production_overrides() {
+        let cfg = cfg_production();
+        assert!((cfg.forgetting_factor - 0.99).abs() < 1e-15);
+        assert_eq!(cfg.noise_batch_size, 16);
+        assert_eq!(cfg.rank_update_interval, 100);
+        assert!(cfg.validate().is_ok(), "cfg_production() must pass validation");
+    }
+}
diff --git a/packages/sentinel/src/tests/convergence_diagnostics.rs b/packages/sentinel/src/tests/convergence_diagnostics.rs
new file mode 100644
index 000000000..e6a107906
--- /dev/null
+++ b/packages/sentinel/src/tests/convergence_diagnostics.rs
@@ -0,0 +1,514 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// SPDX-FileCopyrightText: 2026 Torrust project contributors
+
+#![allow(clippy::print_stderr, clippy::print_stdout)]
+
+//! On-demand **convergence diagnostics** (run with `--ignored`).
+//!
+//! These tests produce detailed diagnostic tables for convergence
+//! analysis and SVD timing.  They have no assertions — they exist
+//! to print human-readable tables when investigating performance
+//! or tuning the noise schedule (`noise_schedule.rounds_for_depth()`).
+//!
+//! Ported from `convergence_benchmark.rs` tests that were pure
+//! `eprintln!` diagnostic dumps with no assertions:
+//!
+//! - `noise_baselines_converge` → [`convergence_rounds_table`]
+//! - `derived_noise_rounds_table` → (merged into `convergence_rounds_table`)
+//! - `wall_clock_convergence_cost` → criterion `warmup_cost_detailed` group
+//! - `svd_timing_diagnostic` → [`svd_timing_comparison`]
+//! - (new) [`axis_drift_investigation`] — added for ADR-S-013 §1a
+//!   convergence-failure triage
+//!
+//! # Test index
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`convergence_rounds_table`] | per-axis convergence round counts & SVD cost |
+//! | [`axis_drift_investigation`] | per-round detailed trace for drift diagnosis |
+//! | [`svd_timing_comparison`] | `SpanTiming` vs raw `Instant` overhead comparison |
+//!
+//! # Running
+//!
+//! Run a single diagnostic:
+//!
+//! ```sh
+//! cargo test -p torrust-sentinel -- --ignored convergence_rounds_table --nocapture
+//! cargo test -p torrust-sentinel -- --ignored axis_drift_investigation --nocapture
+//! cargo test -p torrust-sentinel -- --ignored svd_timing_comparison --nocapture
+//! ```
+//!
+//! Or all at once:
+//!
+//! ```sh
+//! cargo test -p torrust-sentinel convergence_diagnostics -- --ignored --nocapture
+//! ```
+//!
+//! # §-references
+//!
+//! - §ALGO S-4.2 Phase 3 — Latent distribution cold→warm
+//! - §ALGO S-7.1.1 — EWMA outlier filter / clipping ceiling
+//! - ADR-S-013 — Warm-up convergence benchmark
+//! - ADR-M-028 — Span-native tracing
+
+use std::time::Instant;
+
+use rand::SeedableRng;
+use rand::rngs::SmallRng;
+
+use super::convergence_common::{
+    AXIS_NAMES, as_slices, cfg_production, cfg_test, find_converged_round, generate_noise, trailing_cv,
+};
+use crate::maths::bench_tracing::SpanTiming;
+use crate::sentinel::tracker::SubspaceTracker;
+
+// ════════════════════════════════════════════════════════════
+//  Convergence-rounds table
+// ════════════════════════════════════════════════════════════
+
+/// Prints a diagnostic table showing per-axis convergence round
+/// counts and SVD cost at various candidate `noise_rounds` values
+/// for both test and production configs.
+///
+/// Combines the data from the old `noise_baselines_converge` and
+/// `derived_noise_rounds_table` tests into one consolidated table.
+#[test]
+#[ignore = "on-demand diagnostic — run with --ignored --nocapture"]
+#[allow(
+    clippy::cast_precision_loss,
+    clippy::cast_possible_truncation,
+    clippy::cast_sign_loss,
+    clippy::similar_names,
+    clippy::too_many_lines
+)]
+fn convergence_rounds_table() {
+    let dim = 128;
+    let reference_rounds = 500;
+    let window = 20;
+    let tolerances = [0.01, 0.10, 0.20, 0.20]; // nov, disp, surp, coh
+    let (timing, _guard) = SpanTiming::install();
+
+    eprintln!("\n╔══════════════════════════════════════════════════════════╗");
+    eprintln!("║  Convergence-rounds diagnostic (ADR-S-013 §3–§4)       ║");
+    eprintln!("╚══════════════════════════════════════════════════════════╝");
+
+    for (label, base_cfg) in [
+        ("test (λ=0.95, b=4)", cfg_test()),
+        ("production (λ=0.99, b=16)", cfg_production()),
+    ] {
+        let batch_size = base_cfg.noise_batch_size;
+        let lambda = base_cfg.forgetting_factor;
+
+        // ── Collect baseline traces ──────────────────────
+        timing.reset();
+        let mut tracker = SubspaceTracker::new(dim, &base_cfg, base_cfg.cusum_slow_decay);
+        let mut rng = SmallRng::seed_from_u64(42);
+
+        let mut baseline_traces: [Vec<f64>; 4] = [
+            Vec::with_capacity(reference_rounds),
+            Vec::with_capacity(reference_rounds),
+            Vec::with_capacity(reference_rounds),
+            Vec::with_capacity(reference_rounds),
+        ];
+
+        for _ in 0..reference_rounds {
+            let noise = generate_noise(dim, batch_size, &mut rng);
+            let report = tracker.observe(&as_slices(&noise), 0, true);
+
+            baseline_traces[0].push(report.scores.novelty.baseline.mean);
+            baseline_traces[1].push(report.scores.displacement.baseline.mean);
+            baseline_traces[2].push(report.scores.surprise.baseline.mean);
+            baseline_traces[3].push(report.scores.coherence.baseline.mean);
+        }
+
+        // ── SVD per-round cost ───────────────────────────
+        let brand_per_round_us = timing.total_ns("svd_brand") as f64 / 1_000.0 / reference_rounds as f64;
+        let naive_ns = timing.total_ns("svd_naive");
+        let naive_per_round_us = if naive_ns > 0 {
+            naive_ns as f64 / 1_000.0 / reference_rounds as f64
+        } else {
+            0.0
+        };
+
+        eprintln!("\n  [{label}] — {reference_rounds} rounds, d={dim}, b={batch_size}");
+        eprintln!("  SVD per-round: Brand {brand_per_round_us:.1} µs");
+        if naive_ns > 0 {
+            let speedup = naive_per_round_us / brand_per_round_us;
+            eprintln!("  SVD per-round: Naïve {naive_per_round_us:.1} µs  ({speedup:.2}× speedup)");
+        }
+
+        // ── Candidate rounds table ──────────────────────
+        eprintln!();
+        if naive_ns > 0 {
+            eprintln!("  noise_rds | conv? | nov  | disp | surp | coh  | Brand (ms) | Naïve (ms)");
+            eprintln!("  ----------|-------|------|------|------|------|------------|----------");
+        } else {
+            eprintln!("  noise_rds | conv? | nov  | disp | surp | coh  | Brand (ms)");
+            eprintln!("  ----------|-------|------|------|------|------|----------");
+        }
+
+        for candidate in [5, 10, 20, 50, 65, 100, 150, 200, 400] {
+            if candidate > reference_rounds {
+                continue;
+            }
+
+            let mut all_converged = true;
+            let mut axis_rounds = [0usize; 4];
+
+            for i in 0..4 {
+                let slice = &baseline_traces[i][..candidate];
+                let conv = find_converged_round(slice, window.min(candidate / 2), tolerances[i]);
+                axis_rounds[i] = conv;
+                if conv >= candidate.saturating_sub(window) {
+                    all_converged = false;
+                }
+            }
+
+            let brand_cost_ms = candidate as f64 * brand_per_round_us / 1000.0;
+            let status = if all_converged { "yes" } else { "NO " };
+            if naive_ns > 0 {
+                let naive_cost_ms = candidate as f64 * naive_per_round_us / 1000.0;
+                eprintln!(
+                    "  {candidate:9} | {status:5} | {:4} | {:4} | {:4} | {:4} | {brand_cost_ms:10.1} | {naive_cost_ms:10.1}",
+                    axis_rounds[0], axis_rounds[1], axis_rounds[2], axis_rounds[3],
+                );
+            } else {
+                eprintln!(
+                    "  {candidate:9} | {status:5} | {:4} | {:4} | {:4} | {:4} | {brand_cost_ms:10.1}",
+                    axis_rounds[0], axis_rounds[1], axis_rounds[2], axis_rounds[3],
+                );
+            }
+        }
+
+        // ── Derived recommended rounds ───────────────────
+        let mut axis_convergence = [0usize; 4];
+        for (i, trace) in baseline_traces.iter().enumerate() {
+            axis_convergence[i] = find_converged_round(trace, window, tolerances[i]);
+        }
+        let worst = *axis_convergence.iter().max().unwrap();
+        let theoretical_eta_05 = 0.05_f64.log(lambda).ceil() as usize;
+
+        eprintln!();
+        eprintln!("  axis         | converged | CV (last 100) | tolerance");
+        eprintln!("  -------------|-----------|---------------|----------");
+        for (i, name) in AXIS_NAMES.iter().enumerate() {
+            let cv = trailing_cv(&baseline_traces[i], 100) * 100.0;
+            eprintln!(
+                "  {name:12} | {:9} | {cv:12.4}% | {:8}%",
+                axis_convergence[i],
+                tolerances[i] * 100.0
+            );
+        }
+
+        let recommended = worst.max(theoretical_eta_05);
+        let with_margin = (recommended as f64 * 1.5).ceil() as usize;
+        let brand_per_round_ms = brand_per_round_us / 1000.0;
+
+        eprintln!();
+        eprintln!("  theoretical η < 0.05: {theoretical_eta_05} rounds");
+        eprintln!("  worst-case axis:      {worst} rounds");
+        eprintln!(
+            "  recommended:          {recommended} rounds  ({:.1} ms Brand)",
+            recommended as f64 * brand_per_round_ms
+        );
+        eprintln!(
+            "  + 50% margin:         {with_margin} rounds  ({:.1} ms Brand)",
+            with_margin as f64 * brand_per_round_ms
+        );
+        eprintln!(
+            "  current default:      {} rounds  ({:.1} ms Brand)",
+            base_cfg.noise_schedule.rounds_for_depth(0),
+            f64::from(base_cfg.noise_schedule.rounds_for_depth(0)) * brand_per_round_ms
+        );
+
+        let ratio = recommended as f64 / f64::from(base_cfg.noise_schedule.rounds_for_depth(0));
+        if ratio > 1.0 {
+            eprintln!("  ⚠ current default is {ratio:.1}× too low!");
+        } else {
+            eprintln!("  ✓ current default is sufficient ({ratio:.1}× of needed)");
+        }
+    }
+}
+
+// ════════════════════════════════════════════════════════════
+//  Per-axis drift investigation
+// ════════════════════════════════════════════════════════════
+
+/// Detailed per-round trace dump for diagnosing slow drift in
+/// displacement and coherence baselines.
+///
+/// Prints every 10th round's raw score mean, baseline mean,
+/// baseline variance, effective clip ceiling, and whether any
+/// clipping occurred.  Also shows the EWMA theory predictions
+/// alongside the empirical values to identify where they diverge.
+///
+/// This is the primary diagnostic for ADR-S-013 convergence
+/// failures.  Run with:
+///
+/// ```sh
+/// cargo test -p torrust-sentinel -- --ignored axis_drift_investigation --nocapture
+/// ```
+#[test]
+#[ignore = "on-demand diagnostic — run with --ignored --nocapture"]
+#[allow(clippy::cast_precision_loss, clippy::too_many_lines)]
+fn axis_drift_investigation() {
+    let cfg = cfg_test();
+    let dim = 128;
+    let total_rounds = 500;
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let mut rng = SmallRng::seed_from_u64(42);
+
+    // Collect full traces: [round][axis] for baseline means, score means,
+    // baseline variances, and clip-pressure EWMAs.
+    let mut bl_means: Vec<[f64; 4]> = Vec::with_capacity(total_rounds);
+    let mut sc_means: Vec<[f64; 4]> = Vec::with_capacity(total_rounds);
+    let mut bl_vars: Vec<[f64; 4]> = Vec::with_capacity(total_rounds);
+    let mut clip_pressures: Vec<[f64; 4]> = Vec::with_capacity(total_rounds);
+    let mut ranks: Vec<usize> = Vec::with_capacity(total_rounds);
+    let mut etas: Vec<f64> = Vec::with_capacity(total_rounds);
+
+    for _ in 0..total_rounds {
+        let noise = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+        let report = tracker.observe(&as_slices(&noise), 0, true);
+
+        bl_means.push([
+            report.scores.novelty.baseline.mean,
+            report.scores.displacement.baseline.mean,
+            report.scores.surprise.baseline.mean,
+            report.scores.coherence.baseline.mean,
+        ]);
+        sc_means.push([
+            report.scores.novelty.mean,
+            report.scores.displacement.mean,
+            report.scores.surprise.mean,
+            report.scores.coherence.mean,
+        ]);
+        bl_vars.push([
+            report.scores.novelty.baseline.variance,
+            report.scores.displacement.baseline.variance,
+            report.scores.surprise.baseline.variance,
+            report.scores.coherence.baseline.variance,
+        ]);
+        clip_pressures.push([
+            report.scores.novelty.clip_pressure,
+            report.scores.displacement.clip_pressure,
+            report.scores.surprise.clip_pressure,
+            report.scores.coherence.clip_pressure,
+        ]);
+        ranks.push(report.rank);
+        etas.push(report.maturity.noise_influence);
+    }
+
+    eprintln!("\n╔══════════════════════════════════════════════════════════════════════════════════╗");
+    eprintln!(
+        "║  Axis drift investigation (λ={:.2}, b={}, d={dim})                              ║",
+        cfg.forgetting_factor, cfg.noise_batch_size
+    );
+    eprintln!("╚══════════════════════════════════════════════════════════════════════════════════╝");
+
+    // ── Per-axis detailed table ──────────────────────────
+    for (ax, name) in AXIS_NAMES.iter().enumerate() {
+        eprintln!("\n  ─── {name} ───");
+        eprintln!(
+            "  round | rank | η        | ρ̄        | score_mean   | bl_mean      | bl_var       | clip_ceil    | Δbl/bl (%)"
+        );
+        eprintln!("  ------|------|----------|----------|--------------|--------------|--------------|--------------|----------");
+
+        let mut prev_bl = f64::NAN;
+        for r in 0..total_rounds {
+            if r < 30 || r % 10 == 0 || r == total_rounds - 1 {
+                let eta = etas[r];
+                let cp = clip_pressures[r][ax];
+                let p = eta.max(cp);
+                let eff_clip = cfg.clip_sigmas * (1.0 + p / (1.0 - p + cfg.eps));
+                let clip_ceil = bl_means[r][ax] + eff_clip * bl_vars[r][ax].sqrt();
+                let delta_pct = if prev_bl.is_nan() || prev_bl.abs() < 1e-12 {
+                    0.0
+                } else {
+                    (bl_means[r][ax] - prev_bl) / prev_bl.abs() * 100.0
+                };
+                eprintln!(
+                    "  {:5} | {:4} | {:.6} | {:.6} | {:12.8} | {:12.8} | {:12.8} | {:12.4} | {:+8.4}",
+                    r, ranks[r], eta, cp, sc_means[r][ax], bl_means[r][ax], bl_vars[r][ax], clip_ceil, delta_pct
+                );
+                prev_bl = bl_means[r][ax];
+            }
+        }
+
+        // Stationarity test: compare first-half and second-half means
+        // of the score means (not baselines) post rank stabilisation.
+        let post_rank = 30; // well after rank reaches 2
+        let mid = usize::midpoint(post_rank, total_rounds);
+        let first_half_mean: f64 = sc_means[post_rank..mid].iter().map(|s| s[ax]).sum::<f64>() / (mid - post_rank) as f64;
+        let second_half_mean: f64 = sc_means[mid..].iter().map(|s| s[ax]).sum::<f64>() / (total_rounds - mid) as f64;
+        let score_drift_pct = if first_half_mean.abs() > 1e-12 {
+            (second_half_mean - first_half_mean) / first_half_mean * 100.0
+        } else {
+            0.0
+        };
+
+        // Same for baselines
+        let first_half_bl: f64 = bl_means[post_rank..mid].iter().map(|s| s[ax]).sum::<f64>() / (mid - post_rank) as f64;
+        let second_half_bl: f64 = bl_means[mid..].iter().map(|s| s[ax]).sum::<f64>() / (total_rounds - mid) as f64;
+        let bl_drift_pct = if first_half_bl.abs() > 1e-12 {
+            (second_half_bl - first_half_bl) / first_half_bl * 100.0
+        } else {
+            0.0
+        };
+
+        let cv = trailing_cv(&bl_means.iter().map(|b| b[ax]).collect::<Vec<_>>(), 100) * 100.0;
+
+        eprintln!();
+        eprintln!("  score mean drift (half1 vs half2): {score_drift_pct:+.4}%");
+        eprintln!("  baseline drift   (half1 vs half2): {bl_drift_pct:+.4}%");
+        eprintln!("  baseline CV (last 100 rounds):     {cv:.4}%");
+    }
+
+    // ── find_settled_round analysis ──────────────────────
+    // Reproduce the failing test's methodology and show which axis/round causes failure.
+    eprintln!("\n  ─── find_settled_round breakdown (5% tol, reference = last round) ───");
+    let reference = *bl_means.last().unwrap();
+    for axes_count in [3, 4] {
+        eprintln!("\n  checking {axes_count} axes:");
+        for (i, means) in bl_means.iter().enumerate().rev() {
+            let violations: Vec<String> = (0..axes_count)
+                .filter_map(|a| {
+                    let ref_val = reference[a];
+                    if ref_val.abs() < 1e-12 {
+                        None
+                    } else {
+                        let err = (means[a] - ref_val).abs() / ref_val.abs();
+                        if err >= 0.05 {
+                            Some(format!("{}={:.4}%", AXIS_NAMES[a], err * 100.0))
+                        } else {
+                            None
+                        }
+                    }
+                })
+                .collect();
+            if !violations.is_empty() {
+                eprintln!("  last violation at round {i}: {}", violations.join(", "));
+                // Show 3 rounds around the violation
+                let start = i.saturating_sub(2);
+                let end = (i + 2).min(total_rounds - 1);
+                for (offset, bl_mean) in bl_means[start..=end].iter().enumerate() {
+                    let r = start + offset;
+                    let errs: Vec<String> = (0..axes_count)
+                        .map(|a| {
+                            let ref_val = reference[a];
+                            let err = if ref_val.abs() < 1e-12 {
+                                0.0
+                            } else {
+                                (bl_mean[a] - ref_val) / ref_val * 100.0
+                            };
+                            format!("{}={:+.3}%", AXIS_NAMES[a], err)
+                        })
+                        .collect();
+                    let marker = if r == i { " ← violation" } else { "" };
+                    eprintln!("    round {:3}: {}{marker}", r, errs.join(", "));
+                }
+                break;
+            }
+        }
+    }
+}
+
+// ════════════════════════════════════════════════════════════
+//  SVD timing comparison
+// ════════════════════════════════════════════════════════════
+
+/// Compares SVD timing via `SpanTimingLayer` against raw `Instant`
+/// measurements to verify the tracing layer doesn't introduce
+/// significant overhead.
+///
+/// Runs with and without a tracing subscriber and reports the
+/// wall-clock difference.
+#[test]
+#[ignore = "on-demand diagnostic — run with --ignored --nocapture"]
+#[allow(clippy::cast_precision_loss)]
+fn svd_timing_comparison() {
+    let dim = 128;
+    let rounds = 500;
+    let n_reps = 5;
+
+    eprintln!("\n╔══════════════════════════════════════════════════════════╗");
+    eprintln!("║  SVD timing diagnostic: SpanTiming vs raw Instant       ║");
+    eprintln!("╚══════════════════════════════════════════════════════════╝");
+    eprintln!("  cfg!(debug_assertions) = {}", cfg!(debug_assertions));
+
+    for (label, base_cfg) in [
+        ("test (λ=0.95, b=4)", cfg_test()),
+        ("production (λ=0.99, b=16)", cfg_production()),
+    ] {
+        let batch_size = base_cfg.noise_batch_size;
+
+        // Warm up: 2 full runs to stabilise CPU frequency.
+        for _ in 0..2 {
+            let mut t = SubspaceTracker::new(dim, &base_cfg, base_cfg.cusum_slow_decay);
+            let mut rng = SmallRng::seed_from_u64(42);
+            for _ in 0..rounds {
+                let noise = generate_noise(dim, batch_size, &mut rng);
+                t.observe(&as_slices(&noise), 0, true);
+            }
+        }
+
+        // ── A: WITH subscriber (SpanTimingLayer) ────────
+        let mut span_brand_sum = 0u128;
+        let mut span_naive_sum = 0u128;
+        let mut span_wall_sum = 0.0_f64;
+
+        for _ in 0..n_reps {
+            let (timing, guard) = SpanTiming::install();
+            timing.reset();
+            let wall_start = Instant::now();
+            {
+                let mut t = SubspaceTracker::new(dim, &base_cfg, base_cfg.cusum_slow_decay);
+                let mut rng = SmallRng::seed_from_u64(42);
+                for _ in 0..rounds {
+                    let noise = generate_noise(dim, batch_size, &mut rng);
+                    t.observe(&as_slices(&noise), 0, true);
+                }
+            }
+            span_wall_sum = wall_start.elapsed().as_secs_f64().mul_add(1000.0, span_wall_sum);
+            span_brand_sum += timing.total_ns("svd_brand");
+            span_naive_sum += timing.total_ns("svd_naive");
+            drop(guard);
+        }
+
+        let oracle_on = span_naive_sum > 0;
+        let span_wall = span_wall_sum / f64::from(n_reps);
+        let span_brand = span_brand_sum as f64 / f64::from(n_reps) / 1_000_000.0;
+        let span_naive = span_naive_sum as f64 / f64::from(n_reps) / 1_000_000.0;
+
+        // ── B: WITHOUT subscriber ───────────────────────
+        let mut bare_sum = 0.0_f64;
+
+        for _ in 0..n_reps {
+            let wall_start = Instant::now();
+            {
+                let mut t = SubspaceTracker::new(dim, &base_cfg, base_cfg.cusum_slow_decay);
+                let mut rng = SmallRng::seed_from_u64(42);
+                for _ in 0..rounds {
+                    let noise = generate_noise(dim, batch_size, &mut rng);
+                    t.observe(&as_slices(&noise), 0, true);
+                }
+            }
+            bare_sum = wall_start.elapsed().as_secs_f64().mul_add(1000.0, bare_sum);
+        }
+
+        let bare_wall = bare_sum / f64::from(n_reps);
+
+        eprintln!("\n  [{label}] — {rounds} rounds, d={dim}, b={batch_size}, {n_reps} reps each");
+        eprintln!("  oracle active: {oracle_on}");
+        eprintln!("  WITH subscriber (avg of {n_reps}):");
+        eprintln!("    wall-clock:     {span_wall:.2} ms");
+        eprintln!("    span(brand):    {span_brand:.2} ms");
+        if oracle_on {
+            eprintln!("    span(naive):    {span_naive:.2} ms");
+        }
+        eprintln!("  WITHOUT subscriber (avg of {n_reps}):");
+        eprintln!("    wall-clock:     {bare_wall:.2} ms");
+        eprintln!("  span(brand) / bare = {:.2}×", span_brand / bare_wall);
+        eprintln!("  sub-wall / bare = {:.2}×", span_wall / bare_wall);
+    }
+}
diff --git a/packages/sentinel/src/tests/convergence_eta.rs b/packages/sentinel/src/tests/convergence_eta.rs
new file mode 100644
index 000000000..a78127ab2
--- /dev/null
+++ b/packages/sentinel/src/tests/convergence_eta.rs
@@ -0,0 +1,333 @@
+//! Noise influence η tracking and maturity counter tests.
+//!
+//! η tracks how much of the EWMA state is attributable to synthetic
+//! noise vs real data. These tests verify initial conditions, the
+//! exact mathematical recurrence, monotonicity, bounds, fixed-point
+//! limits, and observation counters.
+//!
+//! # Test index
+//!
+//! ## Initial conditions
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`eta_starts_at_one_for_cold_tracker`] | fresh tracker → η = 1.0 |
+//! | [`counters_start_at_zero_for_cold_tracker`] | fresh tracker → 0 / 0 |
+//!
+//! ## η recurrence
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`eta_tracks_theory_exactly`] | noise → real matches closed-form |
+//!
+//! ## Monotonicity & bounds
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`eta_decreases_monotonically_under_real_data`] | strict decrease |
+//! | [`eta_increases_monotonically_under_noise`] | strict increase toward 1.0 |
+//! | [`eta_stays_in_unit_interval`] | η ∈ \[0, 1\] under mixed workload |
+//!
+//! ## Fixed points & limits
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`eta_converges_to_one_under_noise`] | noise-only → η → 1.0 |
+//! | [`eta_decays_toward_zero_under_real_only`] | real-only → η → 0.0 |
+//!
+//! ## Observation counters
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`observation_counters_mixed_sequence`] | noise + real counts |
+//! | [`counters_track_real_only_sequence`] | real-only counts |
+//!
+//! # §-references
+//!
+//! - §ALGO S-11.5 — Maturity tracking (noise influence η)
+//! - ADR-S-013 — Warm-up convergence benchmark
+
+use rand::SeedableRng;
+use rand::rngs::SmallRng;
+
+use super::convergence_common::{as_slices, cfg_test, generate_noise};
+use crate::sentinel::tracker::SubspaceTracker;
+
+// ════════════════════════════════════════════════════════════
+//  Initial conditions
+// ════════════════════════════════════════════════════════════
+
+/// A freshly constructed tracker must report η = 1.0 (pure noise,
+/// no real data yet).
+#[test]
+fn eta_starts_at_one_for_cold_tracker() {
+    let cfg = cfg_test();
+    let tracker = SubspaceTracker::new(128, &cfg, cfg.cusum_slow_decay);
+    assert!((tracker.maturity().noise_influence - 1.0).abs() < f64::EPSILON);
+}
+
+/// A freshly constructed tracker has zero observations of either kind.
+#[test]
+fn counters_start_at_zero_for_cold_tracker() {
+    let cfg = cfg_test();
+    let tracker = SubspaceTracker::new(128, &cfg, cfg.cusum_slow_decay);
+    let m = tracker.maturity();
+    assert_eq!(m.real_observations, 0);
+    assert_eq!(m.noise_observations, 0);
+    assert_eq!(m.total_observations(), 0);
+}
+
+// ════════════════════════════════════════════════════════════
+//  η recurrence
+// ════════════════════════════════════════════════════════════
+
+/// η is computed via `powi` — should match theory exactly.
+///
+/// After noise injection of `rounds` batches of `batch_size`:
+///   `η_noise` = λ^bs · `η_prev` + (1 − λ^bs)    (per round)
+///
+/// After n real batches of `batch_size`:
+///   `η_n` = λ^(bs·n) · `η_noise`
+#[test]
+#[allow(
+    clippy::cast_possible_truncation,
+    clippy::cast_possible_wrap,
+    clippy::cast_precision_loss,
+    clippy::suboptimal_flops
+)]
+fn eta_tracks_theory_exactly() {
+    let cfg = cfg_test();
+    let dim = 128;
+    let lambda = cfg.forgetting_factor;
+    let noise_rounds = 10_usize;
+    let noise_batch_size = cfg.noise_batch_size;
+    let real_batch_size = 20_usize;
+    let total_real_batches = 50;
+
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let mut rng = SmallRng::seed_from_u64(42);
+
+    // Phase 1: noise injection.
+    for _ in 0..noise_rounds {
+        let noise = generate_noise(dim, noise_batch_size, &mut rng);
+        tracker.observe(&as_slices(&noise), 0, true);
+    }
+
+    let eta_after_noise = tracker.maturity().noise_influence;
+
+    // Theoretical η after noise.
+    let mut eta_theory = 1.0_f64;
+    let lam_noise = lambda.powi(noise_batch_size as i32);
+    for _ in 0..noise_rounds {
+        eta_theory = lam_noise * eta_theory + (1.0 - lam_noise);
+    }
+    assert!(
+        (eta_after_noise - eta_theory).abs() < 1e-10,
+        "η after noise: got {eta_after_noise:.12}, theory {eta_theory:.12}"
+    );
+
+    // Phase 2: real batches — η decays as λ^(bs·n).
+    let real_noise = generate_noise(dim, real_batch_size, &mut rng);
+    let real_slices = as_slices(&real_noise);
+
+    let mut max_error = 0.0_f64;
+    for n in 1..=total_real_batches {
+        tracker.observe(&real_slices, 0, false);
+        let eta = tracker.maturity().noise_influence;
+        let eta_real_theory = lambda.powi((real_batch_size * n) as i32) * eta_theory;
+        max_error = max_error.max((eta - eta_real_theory).abs());
+    }
+
+    assert!(
+        max_error < 1e-10,
+        "η should match theory exactly (via powi), max error = {max_error:.2e}"
+    );
+}
+
+// ════════════════════════════════════════════════════════════
+//  Monotonicity & bounds
+// ════════════════════════════════════════════════════════════
+
+/// η decreases monotonically under real observations (no noise
+/// interleaved).
+#[test]
+fn eta_decreases_monotonically_under_real_data() {
+    let cfg = cfg_test();
+    let dim = 128;
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let mut rng = SmallRng::seed_from_u64(42);
+
+    // Seed with noise.
+    for _ in 0..5 {
+        let noise = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+        tracker.observe(&as_slices(&noise), 0, true);
+    }
+
+    let mut prev_eta = tracker.maturity().noise_influence;
+
+    // Real data — η must strictly decrease.
+    for batch in 0..50 {
+        let data = generate_noise(dim, 8, &mut rng);
+        tracker.observe(&as_slices(&data), 0, false);
+        let eta = tracker.maturity().noise_influence;
+        assert!(
+            eta < prev_eta + f64::EPSILON,
+            "batch {batch}: η increased: {prev_eta:.10} → {eta:.10}"
+        );
+        prev_eta = eta;
+    }
+}
+
+/// η increases monotonically under noise when starting below 1.0
+/// (noise pushes η back toward the fixed point 1.0).
+#[test]
+fn eta_increases_monotonically_under_noise() {
+    let cfg = cfg_test();
+    let dim = 128;
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let mut rng = SmallRng::seed_from_u64(42);
+
+    // Drive η below 1.0 with real data.
+    for _ in 0..30 {
+        let data = generate_noise(dim, 8, &mut rng);
+        tracker.observe(&as_slices(&data), 0, false);
+    }
+
+    let eta_before_noise = tracker.maturity().noise_influence;
+    assert!(
+        eta_before_noise < 0.5,
+        "precondition: η should be well below 1.0, got {eta_before_noise}"
+    );
+
+    let mut prev_eta = eta_before_noise;
+
+    // Noise — η must strictly increase toward 1.0.
+    for batch in 0..30 {
+        let noise = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+        tracker.observe(&as_slices(&noise), 0, true);
+        let eta = tracker.maturity().noise_influence;
+        assert!(
+            eta > prev_eta - f64::EPSILON,
+            "batch {batch}: η decreased under noise: {prev_eta:.10} → {eta:.10}"
+        );
+        prev_eta = eta;
+    }
+}
+
+/// η stays in [0, 1] throughout an interleaved noise + real workload.
+#[test]
+fn eta_stays_in_unit_interval() {
+    let cfg = cfg_test();
+    let dim = 128;
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let mut rng = SmallRng::seed_from_u64(123);
+
+    for round in 0..100 {
+        let is_noise = round % 3 == 0; // ~1/3 noise, ~2/3 real
+        let batch_size = if is_noise { cfg.noise_batch_size } else { 8 };
+        let data = generate_noise(dim, batch_size, &mut rng);
+        tracker.observe(&as_slices(&data), 0, is_noise);
+
+        let eta = tracker.maturity().noise_influence;
+        assert!((0.0..=1.0).contains(&eta), "round {round}: η out of [0, 1]: {eta}");
+    }
+}
+
+// ════════════════════════════════════════════════════════════
+//  Fixed points & limits
+// ════════════════════════════════════════════════════════════
+
+/// η converges to exactly 1.0 under indefinite noise injection
+/// (the noise-only fixed point).
+#[test]
+#[allow(clippy::cast_possible_truncation, clippy::cast_possible_wrap)]
+fn eta_converges_to_one_under_noise() {
+    let cfg = cfg_test();
+    let dim = 128;
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let mut rng = SmallRng::seed_from_u64(99);
+
+    for _ in 1..=200 {
+        let noise = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+        tracker.observe(&as_slices(&noise), 0, true);
+    }
+
+    let final_eta = tracker.maturity().noise_influence;
+    assert!(
+        (final_eta - 1.0).abs() < 1e-10,
+        "η should stay at 1.0 under pure noise, got {final_eta}"
+    );
+}
+
+/// Under real-only data (no noise injected), η decays toward 0.0.
+/// Starting from η = 1.0, after enough real batches η should be
+/// negligibly small.
+#[test]
+fn eta_decays_toward_zero_under_real_only() {
+    let cfg = cfg_test();
+    let dim = 128;
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let mut rng = SmallRng::seed_from_u64(77);
+
+    for _ in 0..200 {
+        let data = generate_noise(dim, 8, &mut rng);
+        tracker.observe(&as_slices(&data), 0, false);
+    }
+
+    let final_eta = tracker.maturity().noise_influence;
+    assert!(
+        final_eta < 1e-10,
+        "η should decay toward 0 under real-only data, got {final_eta:.2e}"
+    );
+}
+
+// ════════════════════════════════════════════════════════════
+//  Observation counters
+// ════════════════════════════════════════════════════════════
+
+/// Verify observation counters are correct through a mixed
+/// noise + real sequence.
+#[test]
+fn observation_counters_mixed_sequence() {
+    let cfg = cfg_test();
+    let dim = 128;
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let mut rng = SmallRng::seed_from_u64(42);
+
+    // 10 noise rounds of batch_size=4.
+    for _ in 0..10 {
+        let noise = generate_noise(dim, 4, &mut rng);
+        tracker.observe(&as_slices(&noise), 0, true);
+    }
+    assert_eq!(tracker.maturity().noise_observations, 40);
+    assert_eq!(tracker.maturity().real_observations, 0);
+
+    // 5 real batches of batch_size=8.
+    for _ in 0..5 {
+        let data = generate_noise(dim, 8, &mut rng);
+        tracker.observe(&as_slices(&data), 0, false);
+    }
+    assert_eq!(tracker.maturity().noise_observations, 40);
+    assert_eq!(tracker.maturity().real_observations, 40);
+    assert_eq!(tracker.maturity().total_observations(), 80);
+}
+
+/// Counters track correctly when only real data is observed
+/// (no noise injection).
+#[test]
+fn counters_track_real_only_sequence() {
+    let cfg = cfg_test();
+    let dim = 128;
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let mut rng = SmallRng::seed_from_u64(55);
+
+    for _ in 0..20 {
+        let data = generate_noise(dim, 10, &mut rng);
+        tracker.observe(&as_slices(&data), 0, false);
+    }
+
+    let m = tracker.maturity();
+    assert_eq!(m.real_observations, 200);
+    assert_eq!(m.noise_observations, 0);
+    assert_eq!(m.total_observations(), 200);
+}
diff --git a/packages/sentinel/src/tests/convergence_ewma.rs b/packages/sentinel/src/tests/convergence_ewma.rs
new file mode 100644
index 000000000..601744342
--- /dev/null
+++ b/packages/sentinel/src/tests/convergence_ewma.rs
@@ -0,0 +1,342 @@
+//! Pure EWMA convergence property tests.
+//!
+//! These tests exercise `EwmaStats` in isolation — no subspace
+//! tracker, no scoring pipeline.  They verify the fundamental
+//! convergence guarantees that the tracker depends on.
+//!
+//! # Tests
+//!
+//! ## Cold start
+//!
+//! | Test                                         | Property                                    |
+//! | -------------------------------------------- | ------------------------------------------- |
+//! | [`ewma_cold_start_sets_mean_directly`]       | First update seeds the mean, not a blend    |
+//! | [`ewma_cold_start_sets_variance_from_batch`] | First update seeds variance from batch data |
+//!
+//! ## Mean convergence rate
+//!
+//! | Test                                              | Property                                   |
+//! | ------------------------------------------------- | ------------------------------------------ |
+//! | [`ewma_pure_convergence_rate`]                    | Steps to 5% error match ⌈ln(p)/ln(λ)⌉     |
+//! | [`ewma_higher_lambda_converges_slower`]           | λ=0.99 needs more steps than λ=0.90        |
+//! | [`ewma_convergence_independent_of_batch_size`]    | Batch size 1..64 all converge in same step |
+//!
+//! ## Convergence quality
+//!
+//! | Test                                         | Property                                        |
+//! | -------------------------------------------- | ----------------------------------------------- |
+//! | [`ewma_error_decreases_monotonically`]       | Relative error never increases for constant feed |
+//! | [`ewma_steady_state_is_stable`]              | After convergence, mean stays within ε           |
+//! | [`ewma_tracks_step_change`]                  | Re-converges after an abrupt level shift         |
+//!
+//! ## Variance convergence
+//!
+//! | Test                                         | Property                                    |
+//! | -------------------------------------------- | ------------------------------------------- |
+//! | [`ewma_variance_convergence`]                | Variance converges at a similar rate to mean |
+//!
+//! ## Clipping interaction
+//!
+//! | Test                                         | Property                                    |
+//! | -------------------------------------------- | ------------------------------------------- |
+//! | [`ewma_clipping_slows_convergence`]          | Clipping never speeds up convergence        |
+//!
+//! # §-references
+//!
+//! - §ALGO S-7.1.1 — EWMA outlier filter
+//! - ADR-S-013 — Warm-up convergence benchmark
+
+use crate::ewma::EwmaStats;
+
+// ════════════════════════════════════════════════════════════
+//  1. Cold start
+// ════════════════════════════════════════════════════════════
+
+/// The first `update()` call sets the mean directly (cold→warm),
+/// not blended with the placeholder.
+#[test]
+fn ewma_cold_start_sets_mean_directly() {
+    for lambda in [0.90, 0.95, 0.99] {
+        let mut ewma = EwmaStats::new(lambda);
+        assert!(!ewma.is_warm());
+
+        ewma.update(&[42.0, 42.0], f64::INFINITY);
+        assert!(ewma.is_warm());
+        assert!(
+            (ewma.mean() - 42.0).abs() < 1e-10,
+            "λ={lambda}: cold start should set mean=42.0, got {}",
+            ewma.mean()
+        );
+    }
+}
+
+/// The first `update()` with a multi-element batch also seeds the
+/// variance from the sample data, not the placeholder 1.0.
+#[test]
+fn ewma_cold_start_sets_variance_from_batch() {
+    let mut ewma = EwmaStats::new(0.95);
+    assert!((ewma.variance() - 1.0).abs() < f64::EPSILON, "placeholder should be 1.0");
+
+    // Batch mean = 10.0, MSD = ((−1)² + 0² + 1²) / 3 = 2/3
+    ewma.update(&[9.0, 10.0, 11.0], f64::INFINITY);
+    let expected_var = 2.0 / 3.0;
+    assert!(
+        (ewma.variance() - expected_var).abs() < 1e-10,
+        "cold start should set variance from batch: expected {expected_var}, got {}",
+        ewma.variance()
+    );
+}
+
+// ════════════════════════════════════════════════════════════
+//  2. Mean convergence rate
+// ════════════════════════════════════════════════════════════
+
+/// After n steps of constant input x, the EWMA converges as:
+///   `mean_n` = λⁿ·`mean_0` + (1 − λⁿ)·x
+///
+/// Convergence within p% requires n ≥ ⌈ln(p)/ln(λ)⌉.
+#[test]
+#[allow(clippy::cast_possible_truncation, clippy::cast_sign_loss)]
+fn ewma_pure_convergence_rate() {
+    let lambda = 0.95_f64;
+    let mut ewma = EwmaStats::new(lambda);
+
+    let target = 5.0;
+    ewma.update(&[target; 4], f64::INFINITY);
+    assert!(
+        (ewma.mean() - target).abs() < 1e-10,
+        "cold→warm should set mean exactly, got {}",
+        ewma.mean()
+    );
+
+    let new_target = 10.0;
+    let new_values = [new_target; 4];
+
+    let mut steps_to_converge = None;
+    for step in 1..=200 {
+        ewma.update(&new_values, f64::INFINITY);
+        let error = (ewma.mean() - new_target).abs() / new_target;
+        if error < 0.05 && steps_to_converge.is_none() {
+            steps_to_converge = Some(step);
+        }
+    }
+
+    let n = steps_to_converge.expect("should converge within 200 steps");
+    let theory = 0.05_f64.log(lambda).ceil() as usize;
+
+    assert!(
+        n <= theory + 2,
+        "pure EWMA should converge close to theory: got {n}, expected ~{theory}"
+    );
+}
+
+/// Higher λ ⇒ longer memory ⇒ slower convergence.
+///
+/// We compare λ=0.90 (fast) vs λ=0.99 (slow) and verify that
+/// the slow tracker needs strictly more steps.
+#[test]
+fn ewma_higher_lambda_converges_slower() {
+    fn steps_to_converge(lambda: f64) -> usize {
+        let mut ewma = EwmaStats::new(lambda);
+        ewma.update(&[1.0; 4], f64::INFINITY); // cold start
+
+        let target = 10.0;
+        let values = [target; 4];
+        for step in 1..=1000 {
+            ewma.update(&values, f64::INFINITY);
+            if (ewma.mean() - target).abs() / target < 0.05 {
+                return step;
+            }
+        }
+        1001
+    }
+
+    let fast = steps_to_converge(0.90);
+    let slow = steps_to_converge(0.99);
+    assert!(slow > fast, "λ=0.99 should be slower than λ=0.90: fast={fast}, slow={slow}");
+}
+
+/// EWMA convergence speed is independent of batch size because
+/// `update()` uses the batch mean, performing one step per call.
+#[test]
+fn ewma_convergence_independent_of_batch_size() {
+    let lambda = 0.95_f64;
+    let target = 7.0;
+
+    let mut results = Vec::new();
+    for batch_size in [1, 4, 16, 64] {
+        let mut ewma = EwmaStats::new(lambda);
+        let values: Vec<f64> = vec![target; batch_size];
+
+        ewma.update(&[0.1], f64::INFINITY);
+
+        let mut steps = None;
+        for step in 1..=200 {
+            ewma.update(&values, f64::INFINITY);
+            let error = (ewma.mean() - target).abs() / target;
+            if error < 0.05 && steps.is_none() {
+                steps = Some(step);
+            }
+        }
+        results.push(steps.expect("should converge"));
+    }
+
+    let min = *results.iter().min().unwrap();
+    let max = *results.iter().max().unwrap();
+    assert!(
+        max - min <= 1,
+        "convergence should be independent of batch size: min={min}, max={max}, results={results:?}"
+    );
+}
+
+// ════════════════════════════════════════════════════════════
+//  3. Convergence quality
+// ════════════════════════════════════════════════════════════
+
+/// For constant input with no clipping, the relative error must
+/// decrease (or stay equal) on every step, never rebound.
+#[test]
+fn ewma_error_decreases_monotonically() {
+    let lambda = 0.95_f64;
+    let mut ewma = EwmaStats::new(lambda);
+    ewma.update(&[1.0; 4], f64::INFINITY); // cold start at 1.0
+
+    let target = 10.0;
+    let values = [target; 4];
+    let mut prev_error = f64::INFINITY;
+
+    for step in 1..=100 {
+        ewma.update(&values, f64::INFINITY);
+        let error = (ewma.mean() - target).abs();
+        assert!(
+            error <= prev_error + 1e-12,
+            "error increased at step {step}: {prev_error} → {error}"
+        );
+        prev_error = error;
+    }
+}
+
+/// Once the mean has converged to a constant target, it must
+/// remain stable indefinitely — no drift, no oscillation.
+#[test]
+fn ewma_steady_state_is_stable() {
+    let lambda = 0.95_f64;
+    let target = 7.5;
+    let mut ewma = EwmaStats::new(lambda);
+
+    // Converge fully.
+    ewma.update(&[target; 4], f64::INFINITY);
+    for _ in 0..200 {
+        ewma.update(&[target; 4], f64::INFINITY);
+    }
+
+    // Run 100 more steps and verify no drift.
+    for step in 1..=100 {
+        ewma.update(&[target; 4], f64::INFINITY);
+        let error = (ewma.mean() - target).abs();
+        assert!(
+            error < 1e-10,
+            "steady state drifted at step {step}: mean={}, target={target}",
+            ewma.mean()
+        );
+    }
+}
+
+/// After converging to one level, the EWMA re-converges when the
+/// input shifts abruptly to a new level.
+#[test]
+fn ewma_tracks_step_change() {
+    let lambda = 0.95_f64;
+    let mut ewma = EwmaStats::new(lambda);
+
+    // Converge to 5.0.
+    ewma.update(&[5.0; 4], f64::INFINITY);
+    for _ in 0..200 {
+        ewma.update(&[5.0; 4], f64::INFINITY);
+    }
+    assert!((ewma.mean() - 5.0).abs() < 1e-10, "should have converged to 5.0");
+
+    // Step change to 15.0 — verify re-convergence.
+    let new_target = 15.0;
+    let mut reconverged = false;
+    for step in 1..=200 {
+        ewma.update(&[new_target; 4], f64::INFINITY);
+        if (ewma.mean() - new_target).abs() / new_target < 0.05 {
+            reconverged = true;
+            // Verify it happened in a reasonable number of steps.
+            assert!(step <= 80, "re-convergence took too long after step change: {step} steps");
+            break;
+        }
+    }
+    assert!(reconverged, "EWMA did not re-converge to {new_target}");
+}
+
+// ════════════════════════════════════════════════════════════
+//  4. Variance convergence
+// ════════════════════════════════════════════════════════════
+
+/// EWMA variance converges at a similar rate to the mean.
+#[test]
+#[allow(clippy::cast_possible_truncation, clippy::cast_sign_loss)]
+fn ewma_variance_convergence() {
+    let lambda = 0.95_f64;
+    let mut ewma = EwmaStats::new(lambda);
+
+    ewma.update(&[5.0, 5.0, 5.0, 5.0], f64::INFINITY);
+
+    // Feed values with variance = 2/3 (values 9, 10, 11).
+    let target_var = 2.0 / 3.0;
+    let values = [9.0, 10.0, 11.0];
+
+    let mut steps_to_converge = None;
+    for step in 1..=200 {
+        ewma.update(&values, f64::INFINITY);
+        let error = (ewma.variance() - target_var).abs() / target_var;
+        if error < 0.10 && steps_to_converge.is_none() {
+            steps_to_converge = Some(step);
+        }
+    }
+
+    let n = steps_to_converge.expect("variance should converge within 200 steps");
+    let theory = 0.10_f64.log(lambda).ceil() as usize;
+    assert!(n <= theory + 5, "variance convergence too slow: got {n}, expected ~{theory}");
+}
+
+// ════════════════════════════════════════════════════════════
+//  5. Clipping interaction
+// ════════════════════════════════════════════════════════════
+
+/// Outlier clipping slows EWMA convergence when the target is far
+/// from the current mean, because the ceiling rejects valid updates.
+#[test]
+fn ewma_clipping_slows_convergence() {
+    let lambda = 0.95_f64;
+
+    let mut no_clip = EwmaStats::new(lambda);
+    let mut clipped = EwmaStats::new(lambda);
+
+    no_clip.update(&[1.0, 1.0, 1.0], f64::INFINITY);
+    clipped.update(&[1.0, 1.0, 1.0], 3.0);
+
+    let target = 10.0;
+    let values = [target; 4];
+
+    let mut no_clip_steps = None;
+    let mut clipped_steps = None;
+
+    for step in 1..=500 {
+        no_clip.update(&values, f64::INFINITY);
+        clipped.update(&values, 3.0);
+
+        if (no_clip.mean() - target).abs() / target < 0.05 && no_clip_steps.is_none() {
+            no_clip_steps = Some(step);
+        }
+        if (clipped.mean() - target).abs() / target < 0.05 && clipped_steps.is_none() {
+            clipped_steps = Some(step);
+        }
+    }
+
+    let n_no_clip = no_clip_steps.expect("no_clip should converge");
+    let n_clipped = clipped_steps.unwrap_or(500);
+    assert!(n_clipped >= n_no_clip, "clipping should not speed up convergence");
+}
diff --git a/packages/sentinel/src/tests/convergence_fixes.rs b/packages/sentinel/src/tests/convergence_fixes.rs
new file mode 100644
index 000000000..ed4e0d174
--- /dev/null
+++ b/packages/sentinel/src/tests/convergence_fixes.rs
@@ -0,0 +1,333 @@
+//! ADR-S-013 fix validation tests.
+//!
+//! Verifies the graduated clip-exemption, cold→warm `lat_var`
+//! initialisation, and CUSUM slow-from-fast seeding fixes
+//! that resolved the warm-up convergence gap.
+//!
+//! These are regression tests — if any fix regresses, the
+//! corresponding assertion will catch it.
+//!
+//! # Test index
+//!
+//! ## Fix 1: Graduated clip-exemption (§ALGO S-6.4)
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`graduated_clip_formula_is_smooth_and_monotonic`] | `n_σ_eff(p)` is non-increasing as `p` decreases from 1→0, with correct boundary values |
+//! | [`clip_exemption_eliminates_feedback_loop`] | surprise baselines converge within 200 rounds (was 1000+ before fix) |
+//!
+//! ## Fix 2: Cold→warm initialisation (§ALGO S-5.2 Phase 3)
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`cold_warm_eliminates_surprise_nonstationarity`] | surprise scores show < 2× early/late rise factor (was 5.9× before fix) |
+//!
+//! ## Fix 3: CUSUM slow-from-fast seeding (ADR-S-013 §6b)
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`cusum_seeding_prevents_false_drift`] | CUSUM accumulator stays < 20 on all axes after noise→real transition (was ~198 on surprise without fix) |
+//!
+//! ## Combined convergence bounds
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`per_axis_convergence_b4_within_bound`] | all axes converge within 500 rounds at b = 4, seed = 42 |
+//! | [`per_axis_convergence_b16_within_bound`] | all axes converge within 250 rounds at b = 16, seed = 42 |
+//! | [`production_lambda_converges_within_bound`] | production config (λ = 0.99, b = 16) converges within 1200 rounds |
+//! | [`per_axis_convergence_b4_robust_across_seeds`] | all axes converge within 500 rounds across 5 seeds at b = 4 |
+//!
+//! # §-references
+//!
+//! - §ALGO S-4.2 Phase 3 — Latent distribution cold→warm
+//! - §ALGO S-6.4 — Clip-pressure EWMA / graduated clip formula
+//! - §ALGO S-7.1.1 — EWMA outlier filter / clipping ceiling
+//! - §ALGO S-11.5 — Maturity tracking (noise influence η)
+//! - ADR-S-013 — Warm-up convergence benchmark
+
+use rand::SeedableRng;
+use rand::rngs::SmallRng;
+
+use super::convergence_common::{
+    as_slices, cfg_b16, cfg_production, cfg_test, find_converged_round, generate_noise, run_noise_trace,
+};
+use crate::sentinel::tracker::SubspaceTracker;
+
+// ════════════════════════════════════════════════════════════
+//  Fix 1: Graduated clip-exemption  (§ALGO S-6.4)
+// ════════════════════════════════════════════════════════════
+
+/// The graduated clip formula is a smooth monotonic function of p = max(η, ρ̄),
+/// with no discontinuities at the transition.
+///
+/// Formula (§ALGO S-6.4):
+///   `p = max(η, ρ̄)`
+///   `n_σ_eff = n_σ · (1 + p / (1 − p + ε))`
+///
+/// - At p ≈ 0: `n_σ_eff` ≈ `n_σ` (production clipping)
+/// - At p ≈ 1: `n_σ_eff` → very large (effectively no clipping)
+///
+/// When ρ̄ = 0, p = η and this reduces to the original warm-up formula.
+#[test]
+fn graduated_clip_formula_is_smooth_and_monotonic() {
+    let clip_sigmas = 3.0;
+    let eps = 1e-6;
+
+    // Walk p from 1.0 → 0.0 and verify monotonicity (non-increasing).
+    let test_points = [1.0, 0.99, 0.95, 0.9, 0.8, 0.5, 0.3, 0.1, 0.01, 0.001, 0.0];
+    let mut prev_eff: Option<f64> = None;
+
+    for &p in &test_points {
+        let effective = clip_sigmas * (1.0 + p / (1.0 - p + eps));
+
+        if let Some(prev) = prev_eff {
+            assert!(
+                effective <= prev + 1e-6,
+                "effective clip should be non-increasing as p decreases: \
+                 p={p}, eff={effective}, prev_eff={prev}"
+            );
+        }
+        prev_eff = Some(effective);
+    }
+
+    // Boundary: at p = 0, effective ≈ clip_sigmas.
+    let at_zero = clip_sigmas * (1.0 + 0.0 / (1.0 - 0.0 + eps));
+    assert!(
+        (at_zero - clip_sigmas).abs() < 1e-4,
+        "at p=0, effective clip should equal clip_sigmas: got {at_zero}"
+    );
+
+    // Boundary: at p = 1, effective is very large (open ceiling).
+    let at_one = clip_sigmas * (1.0 + 1.0 / (1.0 - 1.0 + eps));
+    assert!(at_one > 1000.0, "at p=1, effective clip should be very large: got {at_one}");
+}
+
+/// The graduated clip-exemption eliminates the clipping feedback
+/// loop, allowing surprise baselines to converge within 200 noise
+/// rounds at b = 4 (was 1000+ before the fix).
+///
+/// During warm-up (η ≈ 1), p = max(η, ρ̄) ≈ 1 so the effective
+/// clip width is widened (§ALGO S-6.4):
+///   `n_σ_eff = n_σ · (1 + p / (1 − p + ε))`
+/// so clipping cannot reject the first-batch scores.
+#[test]
+fn clip_exemption_eliminates_feedback_loop() {
+    let cfg = cfg_test();
+    let dim = 128;
+    let total_rounds = 500;
+    let traces = run_noise_trace(&cfg, dim, total_rounds, 42);
+
+    let surprise_baselines: Vec<f64> = traces.iter().map(|t| t.baseline_means[2]).collect();
+
+    let window = 20;
+    let tolerance = 0.20; // 20% for surprise (high-CV axis)
+    let converged = find_converged_round(&surprise_baselines, window, tolerance);
+
+    assert!(
+        converged <= 200,
+        "surprise should converge within 200 rounds with clip-exemption fix, got {converged}"
+    );
+}
+
+// ════════════════════════════════════════════════════════════
+//  Fix 2: Cold→warm initialisation  (§ALGO S-5.2 Phase 3)
+// ════════════════════════════════════════════════════════════
+
+/// The cold→warm `lat_var` initialisation eliminates surprise
+/// non-stationarity during warm-up.
+///
+/// Before the fix, `lat_var` started at 1.0 (vs ~0.19 steady
+/// state), producing a 5.9× rise in surprise scores. After the
+/// fix, `lat_var` is seeded from the first batch, so surprise
+/// scores are approximately stationary from round 2.
+#[test]
+fn cold_warm_eliminates_surprise_nonstationarity() {
+    let cfg = cfg_test();
+    let dim = 128;
+    let total_rounds = 200;
+    let traces = run_noise_trace(&cfg, dim, total_rounds, 42);
+
+    let surprise_scores: Vec<f64> = traces.iter().map(|t| t.score_means[2]).collect();
+
+    // Compare early (rounds 2–9) vs late (rounds 150+).
+    let early_avg: f64 = surprise_scores[2..10].iter().sum::<f64>() / 8.0;
+    let late_avg: f64 = surprise_scores[150..].iter().sum::<f64>() / 50.0;
+    let rise_factor = late_avg / early_avg;
+
+    assert!(
+        rise_factor < 2.0,
+        "surprise rise factor should be < 2.0 with cold→warm fix, got {rise_factor:.2}×"
+    );
+}
+
+// ════════════════════════════════════════════════════════════
+//  Fix 3: CUSUM slow-from-fast seeding  (ADR-S-013 §6b)
+// ════════════════════════════════════════════════════════════
+
+/// After noise→real transition with slow-from-fast seeding +
+/// CUSUM reset, the CUSUM accumulator stays low (< 20.0) on
+/// all four scoring axes.
+///
+/// Without seeding, the slow EWMA (`λ_s` = 0.999, half-life 693)
+/// lags the fast EWMA, causing the CUSUM to accumulate false
+/// drift signals (~198 on surprise without seeding).
+#[test]
+fn cusum_seeding_prevents_false_drift() {
+    let cfg = cfg_test();
+    let dim = 128;
+    let noise_rounds = 200;
+    let real_rounds = 300;
+
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let mut rng = SmallRng::seed_from_u64(42);
+
+    // Noise phase.
+    for _ in 0..noise_rounds {
+        let data = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+        tracker.observe(&as_slices(&data), 0, true);
+    }
+
+    // Transition: seed slow from fast, then reset CUSUM.
+    tracker.seed_cusum_slow_from_baselines();
+    tracker.reset_cusum();
+
+    // Real-data phase (same noise-like data, but marked as real).
+    let mut max_cusum = [0.0_f64; 4];
+    for _ in 0..real_rounds {
+        let data = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+        let report = tracker.observe(&as_slices(&data), 0, false);
+        max_cusum[0] = max_cusum[0].max(report.scores.novelty.cusum.accumulator);
+        max_cusum[1] = max_cusum[1].max(report.scores.displacement.cusum.accumulator);
+        max_cusum[2] = max_cusum[2].max(report.scores.surprise.cusum.accumulator);
+        max_cusum[3] = max_cusum[3].max(report.scores.coherence.cusum.accumulator);
+    }
+
+    let axis_names = ["novelty", "displacement", "surprise", "coherence"];
+    for (i, &name) in axis_names.iter().enumerate() {
+        assert!(
+            max_cusum[i] < 20.0,
+            "{name}: CUSUM should stay < 20 with slow-from-fast seeding, \
+             got {:.2} (surprise reaches ~198 without seeding)",
+            max_cusum[i],
+        );
+    }
+}
+
+// ════════════════════════════════════════════════════════════
+//  Combined convergence bounds
+// ════════════════════════════════════════════════════════════
+
+/// Per-axis convergence at b = 4 stays within 500 rounds.
+///
+/// Per-axis tolerances from ADR-S-013 §3b:
+/// novelty 1%, displacement 10%, surprise 20%, coherence 20%.
+#[test]
+fn per_axis_convergence_b4_within_bound() {
+    let cfg = cfg_test();
+    let dim = 128;
+    let total_rounds = 500;
+    let traces = run_noise_trace(&cfg, dim, total_rounds, 42);
+
+    let tolerances = [0.01, 0.10, 0.20, 0.20];
+    let window = 20;
+
+    let mut worst_round = 0_usize;
+    for (axis, &tol) in tolerances.iter().enumerate() {
+        let baselines: Vec<f64> = traces.iter().map(|t| t.baseline_means[axis]).collect();
+        let converged = find_converged_round(&baselines, window, tol);
+        worst_round = worst_round.max(converged);
+    }
+
+    assert!(
+        worst_round <= 500,
+        "all axes should converge within 500 rounds at b=4, worst={worst_round}"
+    );
+}
+
+/// Per-axis convergence at b = 16 stays within 250 rounds.
+#[test]
+fn per_axis_convergence_b16_within_bound() {
+    let cfg = cfg_b16();
+    let dim = 128;
+    let total_rounds = 300;
+    let traces = run_noise_trace(&cfg, dim, total_rounds, 42);
+
+    let tolerances = [0.01, 0.10, 0.20, 0.20];
+    let window = 20;
+
+    let mut worst_round = 0_usize;
+    for (axis, &tol) in tolerances.iter().enumerate() {
+        let baselines: Vec<f64> = traces.iter().map(|t| t.baseline_means[axis]).collect();
+        let converged = find_converged_round(&baselines, window, tol);
+        worst_round = worst_round.max(converged);
+    }
+
+    assert!(
+        worst_round <= 250,
+        "all axes should converge within 250 rounds at b=16, worst={worst_round}"
+    );
+}
+
+/// Production config (λ = 0.99, b = 16) converges within 1200
+/// noise rounds.
+///
+/// Half-life is 69 steps (vs 14 at λ = 0.95). The theoretical
+/// minimum is ⌈ln(0.05)/ln(0.99)⌉ = 299 steps.
+#[test]
+fn production_lambda_converges_within_bound() {
+    let cfg = cfg_production();
+    // Convergence speed depends on λ and b, not on dim.  dim=32
+    // halves the SVD cost vs 128 while preserving the bound
+    // (ADR-S-012).
+    let dim = 32;
+    let total_rounds = 1500;
+    let traces = run_noise_trace(&cfg, dim, total_rounds, 42);
+
+    // At λ=0.99, use window = 1/α = 100.
+    let window = 100;
+    let tolerances = [0.01, 0.10, 0.20, 0.20];
+
+    let mut worst_round = 0_usize;
+    for (axis, &tol) in tolerances.iter().enumerate() {
+        let baselines: Vec<f64> = traces.iter().map(|t| t.baseline_means[axis]).collect();
+        let converged = find_converged_round(&baselines, window, tol);
+        worst_round = worst_round.max(converged);
+    }
+
+    assert!(
+        worst_round <= 1200,
+        "production config should converge within 1200 rounds, got {worst_round}"
+    );
+}
+
+/// Per-axis convergence at b = 4 across multiple seeds.
+///
+/// ADR-S-013 found displacement and surprise have significant
+/// seed variance at b = 4 (coherence worst-case was 477 across
+/// 10 seeds).  This test verifies the 500-round bound holds
+/// across 5 diverse seeds.
+#[test]
+fn per_axis_convergence_b4_robust_across_seeds() {
+    let cfg = cfg_test();
+    // See production_lambda_converges_within_bound comment.
+    let dim = 32;
+    let total_rounds = 500;
+    let window = 20;
+    let tolerances = [0.01, 0.10, 0.20, 0.20];
+
+    for seed in [42, 123, 456, 789, 1024] {
+        let traces = run_noise_trace(&cfg, dim, total_rounds, seed);
+
+        let mut worst_round = 0_usize;
+        for (axis, &tol) in tolerances.iter().enumerate() {
+            let baselines: Vec<f64> = traces.iter().map(|t| t.baseline_means[axis]).collect();
+            let converged = find_converged_round(&baselines, window, tol);
+            worst_round = worst_round.max(converged);
+        }
+
+        assert!(
+            worst_round <= 500,
+            "all axes should converge within 500 rounds at b=4 (seed={seed}), worst={worst_round}"
+        );
+    }
+}
diff --git a/packages/sentinel/src/tests/convergence_noise.rs b/packages/sentinel/src/tests/convergence_noise.rs
new file mode 100644
index 000000000..3bb7e264d
--- /dev/null
+++ b/packages/sentinel/src/tests/convergence_noise.rs
@@ -0,0 +1,638 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// SPDX-FileCopyrightText: 2026 Torrust project contributors
+
+//! Tracker-level noise-baseline convergence tests.
+//!
+//! These test the full `observe()` → score → EWMA update pipeline,
+//! verifying that baselines converge under noise, that rank
+//! adaptation and coherence gating work correctly, and that
+//! latent statistics reach expected steady-state values.
+//!
+//! # Test index
+//!
+//! ## Baseline convergence
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`noise_baselines_converge_within_bound`] | block-mean stationarity within 300 rounds |
+//! | [`baseline_variance_converges_within_bound`] | EWMA variance also reaches steady state |
+//! | [`frozen_subspace_converges_at_least_as_fast`] | frozen rank ≤ normal convergence speed |
+//!
+//! ## Rank adaptation
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`rank_reaches_max_within_expected_rounds`] | rank hits `max_rank` (2) within 20 rounds |
+//! | [`coherence_activates_at_rank_two`] | coherence gated cold until rank ≥ 2 |
+//! | [`energy_ratio_stabilises_under_noise`] | energy ratio reaches stable plateau |
+//!
+//! ## Report snapshot semantics
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`report_baseline_is_pre_update_snapshot`] | report carries pre-update baselines |
+//! | [`z_scores_stay_bounded_at_steady_state`] | z-scores near zero once baselines warm |
+//!
+//! ## Latent statistics steady state
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`latent_variance_reaches_steady_state`] | `lat_var` ≪ 1.0 after warm-up |
+//! | [`latent_mean_stays_near_zero`] | `lat_mean` ≈ 0 for centred noise |
+//! | [`subspace_evolution_does_not_dominate_latvar_transient`] | normal ≈ frozen `lat_var` |
+//!
+//! ## Production noise-rounds quality
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`production_noise_provides_reasonable_novelty_baseline`] | 50 rounds suffice for novelty |
+//!
+//! ## Determinism
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`deterministic_under_same_seed`] | identical seed ⇒ identical output |
+//!
+//! # §-references
+//!
+//! - §ALGO S-4.2 Phase 3 — Latent distribution cold→warm
+//! - §ALGO S-7.1.1 — EWMA outlier filter
+//! - §ALGO S-11.5 — Maturity tracking
+//! - ADR-S-013 — Warm-up convergence benchmark
+//! - ADR-S-014 — Subspace tracker visibility
+
+use rand::SeedableRng;
+use rand::rngs::SmallRng;
+
+use super::convergence_common::{as_slices, block_mean_relative_error, cfg_test, find_settled_round, generate_noise};
+use crate::config::SentinelConfig;
+use crate::sentinel::tracker::SubspaceTracker;
+
+// ════════════════════════════════════════════════════════════
+//  Baseline convergence
+// ════════════════════════════════════════════════════════════
+
+/// Block-mean stationarity test: baselines reach steady state
+/// within 100 noise rounds (λ = 0.95, b = 4, d = 128).
+///
+/// **Why block-mean, not single-point.** The old `find_settled_round`
+/// metric checked whether every subsequent round stayed within 5%
+/// of a single-point reference. This conflates two distinct
+/// phenomena:
+///
+///   1. **Transient bias** — the EWMA initialisation offset, which
+///      decays as λᵗ and is genuinely convergent.
+///   2. **Steady-state jitter** — the EWMA output variance from
+///      the stochastic input, which is irreducible and never
+///      "converges" to zero.
+///
+/// The per-axis steady-state EWMA CVs at λ=0.95, b=4 are
+/// (`noise_convergence.md` §1–§2):
+///
+///   - Novelty:      ~0.07%  (constant-norm kills variance)
+///   - Displacement:  ~3.4%  (nonlinear χ² transform, high per-sample CV)
+///   - Surprise:      ~6.5%  (ratio statistic, correlated num/denom)
+///   - Coherence:    ~10.0%  (products of normals, Var(χ²₁) = 2)
+///
+/// A 5% tolerance is below the steady-state CV for 3 of 4 axes,
+/// making the old metric unfalsifiably pessimistic: it reports
+/// "non-convergence" even when the EWMA has reached its correct
+/// steady state.
+///
+/// The block-mean approach compares 100-round block means from
+/// an early period (after the transient, rounds 50–150) against
+/// a late period (rounds 200–300).  Block averaging reduces
+/// the effective CV by a factor related to √(`block_len` / `τ_corr`)
+/// where `τ_corr` ≈ 1/(1−λ) is the EWMA autocorrelation time.
+/// The per-axis tolerances are set to accommodate the expected
+/// block-mean fluctuation at ≥ 3σ.
+///
+/// See `packages/sentinel/docs/noise_convergence.md` for the
+/// full theoretical analysis.
+#[test]
+#[allow(clippy::cast_precision_loss)]
+fn noise_baselines_converge_within_bound() {
+    let cfg = cfg_test();
+    let dim = 128;
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let total_rounds = 300;
+    let mut rng = SmallRng::seed_from_u64(42);
+
+    let mut traces: Vec<[f64; 4]> = Vec::with_capacity(total_rounds);
+    for _ in 0..total_rounds {
+        let noise = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+        let report = tracker.observe(&as_slices(&noise), 0, true);
+        traces.push([
+            report.scores.novelty.baseline.mean,
+            report.scores.displacement.baseline.mean,
+            report.scores.surprise.baseline.mean,
+            report.scores.coherence.baseline.mean,
+        ]);
+    }
+
+    // Per-axis tolerances for the block-mean comparison.
+    //
+    // These are set to ~3× the expected block-mean fluctuation,
+    // which is CV_EWMA × √((1+λ)/(1−λ) / block_len).  With
+    // block_len = 100 and λ = 0.95, the inflation factor is
+    // √(31.4/100) ≈ 0.56, so block_CV ≈ 0.56 × EWMA_CV.
+    //
+    // Axis          | EWMA CV | block CV | 3σ bound | tolerance
+    // --------------|---------|---------|----------|----------
+    // Novelty       |  0.07%  |  0.04%  |   0.12%  |   2%
+    // Displacement  |  3.4%   |  1.9%   |   5.7%   |  10%
+    // Surprise      |  6.5%   |  3.6%   |  10.8%   |  15%
+    // Coherence     | 10.0%   |  5.6%   |  16.8%   |  25%
+    let axis_names = ["novelty", "displacement", "surprise", "coherence"];
+    let tolerances = [0.02, 0.10, 0.15, 0.25];
+
+    // Early block: rounds [50, 150) — well past the EWMA transient
+    // (half-life ≈ 14 rounds at λ = 0.95, so by round 50 the bias
+    // is λ⁵⁰ ≈ 0.077 of its initial value).
+    //
+    // Late block: rounds [200, 300) — the reference steady state.
+    let early_start = 50;
+    let early_end = 150;
+    let late_start = 200;
+    let late_end = total_rounds;
+
+    for (ax, (name, tol)) in axis_names.iter().zip(tolerances.iter()).enumerate() {
+        let err = block_mean_relative_error(&traces, ax, early_start, early_end, late_start, late_end);
+        if let Some(rel_err) = err {
+            assert!(
+                rel_err < *tol,
+                "{name} baseline not stationary: early vs late block \
+                 differ by {:.2}%, tolerance is {:.0}%",
+                rel_err * 100.0,
+                tol * 100.0,
+            );
+        }
+    }
+}
+
+/// Baseline *variance* also reaches steady state (not just the mean).
+///
+/// The EWMA tracks both µ and σ² for z-score computation. If σ²
+/// fails to converge, z-scores will be miscalibrated even when the
+/// mean is stable. We apply the same block-mean approach as the
+/// mean test, but extract variance instead.
+#[test]
+#[allow(clippy::cast_precision_loss)]
+fn baseline_variance_converges_within_bound() {
+    let cfg = cfg_test();
+    let dim = 128;
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let total_rounds = 300;
+    let mut rng = SmallRng::seed_from_u64(42);
+
+    let mut traces: Vec<[f64; 4]> = Vec::with_capacity(total_rounds);
+    for _ in 0..total_rounds {
+        let noise = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+        let report = tracker.observe(&as_slices(&noise), 0, true);
+        traces.push([
+            report.scores.novelty.baseline.variance,
+            report.scores.displacement.baseline.variance,
+            report.scores.surprise.baseline.variance,
+            report.scores.coherence.baseline.variance,
+        ]);
+    }
+
+    // Variance has higher CV than mean, so use more generous tolerances.
+    // Coherence variance is a 4th-order statistic (products of normals)
+    // with very high inherent variability.
+    let axis_names = ["novelty", "displacement", "surprise", "coherence"];
+    let tolerances = [0.10, 0.20, 0.30, 0.50];
+
+    let early_start = 50;
+    let early_end = 150;
+    let late_start = 200;
+    let late_end = total_rounds;
+
+    for (ax, (name, tol)) in axis_names.iter().zip(tolerances.iter()).enumerate() {
+        let err = block_mean_relative_error(&traces, ax, early_start, early_end, late_start, late_end);
+        if let Some(rel_err) = err {
+            assert!(
+                rel_err < *tol,
+                "{name} baseline variance not stationary: early vs late block \
+                 differ by {:.2}%, tolerance is {:.0}%",
+                rel_err * 100.0,
+                tol * 100.0,
+            );
+        }
+    }
+}
+
+/// With a frozen subspace (`rank_update_interval` = ∞),
+/// convergence is at least as fast as with an evolving subspace.
+#[test]
+#[allow(clippy::cast_precision_loss)]
+fn frozen_subspace_converges_at_least_as_fast() {
+    let dim = 128;
+    let total_rounds = 300;
+    let seed = 42;
+
+    let run = |cfg: &SentinelConfig<u64>| -> usize {
+        let mut tracker = SubspaceTracker::new(dim, cfg, cfg.cusum_slow_decay);
+        let mut rng = SmallRng::seed_from_u64(seed);
+        let mut traces = Vec::with_capacity(total_rounds);
+        for _ in 0..total_rounds {
+            let noise = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+            let report = tracker.observe(&as_slices(&noise), 0, true);
+            traces.push([
+                report.scores.novelty.baseline.mean,
+                report.scores.displacement.baseline.mean,
+                report.scores.surprise.baseline.mean,
+                report.scores.coherence.baseline.mean,
+            ]);
+        }
+        let reference = *traces.last().unwrap();
+        find_settled_round(&traces, &reference, 0.05, 3).unwrap_or(total_rounds)
+    };
+
+    let cfg_frozen = SentinelConfig {
+        rank_update_interval: 10_000,
+        ..cfg_test()
+    };
+
+    let r_frozen = run(&cfg_frozen);
+    let r_normal = run(&cfg_test());
+
+    assert!(
+        r_frozen <= r_normal,
+        "frozen subspace should converge at least as fast: frozen={r_frozen}, normal={r_normal}"
+    );
+}
+
+// ════════════════════════════════════════════════════════════
+//  Rank adaptation
+// ════════════════════════════════════════════════════════════
+
+/// Rank reaches `max_rank` (2) within 20 noise rounds.
+///
+/// With `rank_update_interval=5`, rank adaptation occurs at
+/// rounds 5, 10, 15, … — so rank 2 should appear by ~round 10.
+#[test]
+fn rank_reaches_max_within_expected_rounds() {
+    let cfg = cfg_test();
+    let dim = 128;
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let mut rng = SmallRng::seed_from_u64(42);
+
+    let mut ranks: Vec<usize> = Vec::with_capacity(100);
+    for _ in 0..100 {
+        let noise = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+        let report = tracker.observe(&as_slices(&noise), 0, true);
+        ranks.push(report.rank);
+    }
+
+    let first_rank2 = ranks.iter().position(|&r| r >= 2);
+    let r = first_rank2.expect("rank should reach 2 within 100 noise rounds");
+    assert!(r <= 20, "rank should reach 2 within 20 noise rounds, got {r}");
+}
+
+/// Coherence baseline stays cold (mean = 1.0) until rank reaches 2,
+/// then starts evolving.
+#[test]
+fn coherence_activates_at_rank_two() {
+    let cfg = cfg_test();
+    let dim = 128;
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let mut rng = SmallRng::seed_from_u64(42);
+
+    let mut coherence_means: Vec<f64> = Vec::new();
+    let mut ranks: Vec<usize> = Vec::new();
+
+    for _ in 0..100 {
+        let noise = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+        let report = tracker.observe(&as_slices(&noise), 0, true);
+        coherence_means.push(report.scores.coherence.baseline.mean);
+        ranks.push(report.rank);
+    }
+
+    // Before rank 2: coherence baseline stays at cold default.
+    let first_rank2 = ranks.iter().position(|&r| r >= 2).unwrap_or(100);
+    for r in 0..first_rank2.min(100) {
+        if ranks[r] < 2 {
+            assert!(
+                (coherence_means[r] - 1.0).abs() < 1e-10,
+                "round {r}: coherence mean should be cold (1.0) at rank {}, got {:.6}",
+                ranks[r],
+                coherence_means[r]
+            );
+        }
+    }
+
+    // After rank 2: coherence should evolve away from 1.0.
+    if first_rank2 + 5 < 100 {
+        let late_mean = coherence_means[first_rank2 + 5];
+        assert!(
+            (late_mean - 1.0).abs() > 1e-6,
+            "coherence should evolve after reaching rank 2, still at {late_mean:.6}"
+        );
+    }
+}
+
+/// Energy ratio reaches a stable plateau once rank adaptation
+/// completes, and stays above the config threshold (0.90).
+#[test]
+#[allow(clippy::cast_precision_loss)]
+fn energy_ratio_stabilises_under_noise() {
+    let cfg = cfg_test();
+    let dim = 128;
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let mut rng = SmallRng::seed_from_u64(42);
+
+    let total_rounds = 200;
+    let mut energies: Vec<f64> = Vec::with_capacity(total_rounds);
+    for _ in 0..total_rounds {
+        let noise = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+        let report = tracker.observe(&as_slices(&noise), 0, true);
+        energies.push(report.energy_ratio);
+    }
+
+    // After rank reaches max, energy ratio should be stable.
+    // Compare the last 50 rounds: max-min spread should be small.
+    let tail = &energies[total_rounds - 50..];
+    let min_e = tail.iter().copied().fold(f64::INFINITY, f64::min);
+    let max_e = tail.iter().copied().fold(f64::NEG_INFINITY, f64::max);
+    let spread = max_e - min_e;
+
+    assert!(
+        spread < 0.05,
+        "energy ratio not stable in last 50 rounds: spread = {spread:.4} (min={min_e:.4}, max={max_e:.4})"
+    );
+    assert!(
+        min_e >= cfg.energy_threshold * 0.9,
+        "energy ratio ({min_e:.4}) should be near the threshold ({:.2})",
+        cfg.energy_threshold
+    );
+}
+
+// ════════════════════════════════════════════════════════════
+//  Report snapshot semantics
+// ════════════════════════════════════════════════════════════
+
+/// The baseline in `TrackerReport` is the PRE-update snapshot
+/// (before the current batch's scores are incorporated).
+#[test]
+fn report_baseline_is_pre_update_snapshot() {
+    let cfg = cfg_test();
+    let dim = 128;
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let mut rng = SmallRng::seed_from_u64(42);
+
+    // First observe: EWMA is cold → report shows cold default (1.0).
+    let noise1 = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+    let report1 = tracker.observe(&as_slices(&noise1), 0, true);
+
+    assert!(
+        (report1.scores.novelty.baseline.mean - 1.0).abs() < 1e-10,
+        "first report should have cold baseline mean=1.0, got {}",
+        report1.scores.novelty.baseline.mean
+    );
+
+    // Internal EWMA is now warm.
+    let bl = tracker.axis_baselines();
+    assert!(
+        (bl.novelty_mean - 1.0).abs() > 1e-6,
+        "after first observe, internal EWMA should have moved from 1.0, got {}",
+        bl.novelty_mean
+    );
+
+    // Second observe: report baseline matches post-first-update state.
+    let noise2 = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+    let report2 = tracker.observe(&as_slices(&noise2), 0, true);
+
+    assert!(
+        (report2.scores.novelty.baseline.mean - bl.novelty_mean).abs() < 1e-10,
+        "second report baseline ({:.6}) should match post-first-update state ({:.6})",
+        report2.scores.novelty.baseline.mean,
+        bl.novelty_mean
+    );
+}
+
+/// Once baselines are warm, z-scores under noise should stay
+/// modest — not systematically large or systematically negative.
+#[test]
+#[allow(clippy::cast_precision_loss)]
+fn z_scores_stay_bounded_at_steady_state() {
+    let cfg = cfg_test();
+    let dim = 128;
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let mut rng = SmallRng::seed_from_u64(42);
+
+    // Warm up.
+    for _ in 0..200 {
+        let noise = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+        tracker.observe(&as_slices(&noise), 0, true);
+    }
+
+    // Collect z-scores over the next 100 rounds.
+    let mut z_traces: Vec<[f64; 4]> = Vec::with_capacity(100);
+    for _ in 0..100 {
+        let noise = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+        let report = tracker.observe(&as_slices(&noise), 0, true);
+        z_traces.push([
+            report.scores.novelty.mean_z_score,
+            report.scores.displacement.mean_z_score,
+            report.scores.surprise.mean_z_score,
+            report.scores.coherence.mean_z_score,
+        ]);
+    }
+
+    let axis_names = ["novelty", "displacement", "surprise", "coherence"];
+    for (ax, name) in axis_names.iter().enumerate() {
+        let mean_z: f64 = z_traces.iter().map(|t| t[ax]).sum::<f64>() / z_traces.len() as f64;
+        let max_abs_z: f64 = z_traces.iter().map(|t| t[ax].abs()).fold(0.0_f64, f64::max);
+        // Average z-score should be near zero over many rounds.
+        assert!(mean_z.abs() < 1.5, "{name} mean z-score = {mean_z:.2} — expected near zero");
+        // Individual z-scores should not be extreme under noise.
+        assert!(
+            max_abs_z < 5.0,
+            "{name} max |z| = {max_abs_z:.2} — expected < 5.0 under noise"
+        );
+    }
+}
+
+// ════════════════════════════════════════════════════════════
+//  Latent statistics steady state
+// ════════════════════════════════════════════════════════════
+
+/// Latent variance reaches a steady state far below the initial
+/// value of 1.0 under noise.
+///
+/// For random ±0.5 noise projected onto a learned subspace,
+/// `Var(z_j)` is substantially less than 1.0. This confirms the
+/// cold-start mismatch that motivated the cold→warm fix
+/// (ADR-S-013) and motivated the EWMA-mean-centred formula
+/// (ADR-S-021) which centres on the EWMA mean rather than the
+/// batch mean.
+#[test]
+fn latent_variance_reaches_steady_state() {
+    let cfg = cfg_test();
+    let dim = 128;
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let mut rng = SmallRng::seed_from_u64(42);
+
+    for _ in 0..500 {
+        let noise = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+        tracker.observe(&as_slices(&noise), 0, true);
+    }
+
+    let lat_vars = tracker.latent_var();
+    for (j, &v) in lat_vars.iter().enumerate() {
+        assert!(v < 0.5, "lat_var[{j}] = {v:.6} should be ≪ 1.0 at steady state");
+    }
+}
+
+/// Latent mean stays near zero under centred binary noise,
+/// confirming the initial value of 0.0 is correct.
+#[test]
+fn latent_mean_stays_near_zero() {
+    let cfg = cfg_test();
+    let dim = 128;
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let mut rng = SmallRng::seed_from_u64(42);
+
+    for _ in 0..500 {
+        let noise = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+        tracker.observe(&as_slices(&noise), 0, true);
+    }
+
+    let lat_means = tracker.latent_mean();
+    for (j, &m) in lat_means.iter().enumerate() {
+        assert!(m.abs() < 0.1, "lat_mean[{j}] = {m:.6} should be near zero for centred noise");
+    }
+}
+
+/// The `lat_var` transient is dominated by EWMA decay, not
+/// subspace evolution: normal and frozen subspaces produce
+/// similar `lat_var` trajectories.
+#[test]
+fn subspace_evolution_does_not_dominate_latvar_transient() {
+    let dim = 128;
+    let total_rounds = 200;
+
+    let run = |rank_update_interval: u64| -> Vec<f64> {
+        let cfg = SentinelConfig {
+            rank_update_interval,
+            ..cfg_test()
+        };
+        let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+        let mut rng = SmallRng::seed_from_u64(42);
+        let mut var_trace = Vec::with_capacity(total_rounds);
+        for _ in 0..total_rounds {
+            let noise = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+            tracker.observe(&as_slices(&noise), 0, true);
+            var_trace.push(tracker.latent_var().first().copied().unwrap_or(0.0));
+        }
+        var_trace
+    };
+
+    let normal_trace = run(5); // normal rank adaptation
+    let frozen_trace = run(10_000); // effectively frozen
+
+    let normal_ref = *normal_trace.last().unwrap();
+    let frozen_ref = *frozen_trace.last().unwrap();
+    let diff_pct = (normal_ref - frozen_ref).abs() / normal_ref.abs().max(1e-12) * 100.0;
+
+    assert!(
+        diff_pct < 50.0,
+        "lat_var steady states should be similar: normal={normal_ref:.6}, frozen={frozen_ref:.6} ({diff_pct:.1}% diff)"
+    );
+}
+
+// ════════════════════════════════════════════════════════════
+//  Production noise-rounds quality
+// ════════════════════════════════════════════════════════════
+
+/// At the production default of 50 noise rounds, novelty baselines
+/// are close to steady state (< 5% error vs round-300 reference).
+///
+/// Surprise and displacement may drift due to non-stationary score
+/// distributions, but novelty (which depends on reconstruction error,
+/// not latent variance) should converge well within 50 rounds.
+#[test]
+#[allow(clippy::similar_names)]
+fn production_noise_provides_reasonable_novelty_baseline() {
+    let cfg = cfg_test();
+    let dim = 128;
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let mut rng = SmallRng::seed_from_u64(42);
+
+    let mut baselines_at_50 = [0.0_f64; 4];
+    for round in 0..50 {
+        let noise = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+        let report = tracker.observe(&as_slices(&noise), 0, true);
+        if round == 49 {
+            baselines_at_50 = [
+                report.scores.novelty.baseline.mean,
+                report.scores.displacement.baseline.mean,
+                report.scores.surprise.baseline.mean,
+                report.scores.coherence.baseline.mean,
+            ];
+        }
+    }
+
+    // Run 250 more to get the reference.
+    for _ in 50..300 {
+        let noise = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+        tracker.observe(&as_slices(&noise), 0, true);
+    }
+
+    let bl = tracker.axis_baselines();
+    let reference_novelty = bl.novelty_mean;
+
+    if reference_novelty.abs() > 1e-12 {
+        let novelty_error = (baselines_at_50[0] - reference_novelty).abs() / reference_novelty.abs();
+        assert!(
+            novelty_error < 0.05,
+            "novelty at round 50 is {:.1}% off — should be <5%",
+            novelty_error * 100.0
+        );
+    }
+}
+
+// ════════════════════════════════════════════════════════════
+//  Determinism
+// ════════════════════════════════════════════════════════════
+
+/// Running the same noise sequence twice with the same seed
+/// produces bit-identical baselines and scores.
+#[test]
+fn deterministic_under_same_seed() {
+    let run = || {
+        let cfg = cfg_test();
+        let dim = 128;
+        let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+        let mut rng = SmallRng::seed_from_u64(42);
+
+        let mut baselines = Vec::with_capacity(100);
+        for _ in 0..100 {
+            let noise = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+            let report = tracker.observe(&as_slices(&noise), 0, true);
+            baselines.push([
+                report.scores.novelty.baseline.mean,
+                report.scores.displacement.baseline.mean,
+                report.scores.surprise.baseline.mean,
+                report.scores.coherence.baseline.mean,
+            ]);
+        }
+        baselines
+    };
+
+    let a = run();
+    let b = run();
+
+    assert_eq!(a.len(), b.len());
+    for (i, (ra, rb)) in a.iter().zip(b.iter()).enumerate() {
+        for ax in 0..4 {
+            assert!(
+                (ra[ax] - rb[ax]).abs() == 0.0,
+                "round {i} axis {ax}: run A = {}, run B = {} — not bit-identical",
+                ra[ax],
+                rb[ax]
+            );
+        }
+    }
+}
diff --git a/packages/sentinel/src/tests/cusum.rs b/packages/sentinel/src/tests/cusum.rs
new file mode 100644
index 000000000..ae4df7a31
--- /dev/null
+++ b/packages/sentinel/src/tests/cusum.rs
@@ -0,0 +1,265 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// SPDX-FileCopyrightText: 2026 Torrust project contributors
+
+//! Tests for [`CusumAccumulator`](crate::sentinel::cusum::CusumAccumulator).
+//!
+//! # Test index
+//!
+//! ## Construction
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`starts_at_zero`] | fresh accumulator has zero accumulator and zero steps |
+//!
+//! ## Core accumulation
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`accumulates_under_sustained_elevation`] | accumulator grows when scores exceed baseline |
+//! | [`steps_since_reset_increments`] | step counter advances on each update |
+//!
+//! ## Clamping
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`clamps_at_zero_when_below_baseline`] | accumulator never goes negative |
+//!
+//! ## Allowance
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`allowance_absorbs_noise`] | generous κ suppresses small deviations |
+//!
+//! ## Reset
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`resets_to_zero`] | `reset()` zeroes accumulator and steps |
+//! | [`reset_preserves_slow_baseline`] | slow baseline survives `reset()` |
+//! | [`reset_cold_clears_everything`] | `reset_cold()` zeroes accumulator and reverts baseline to cold defaults |
+//!
+//! ## Seeding
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`seed_slow_from_aligns_baselines`] | slow EWMA adopts the fast EWMA's state |
+//!
+//! ## Filtered update
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`update_filtered_matches_update_no_clip`] | filtered path equals standard path when no clipping occurs |
+//!
+//! ## Snapshot
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`snapshot_reports_slow_baseline`] | snapshot exposes the slow EWMA's mean and variance |
+
+use crate::sentinel::cusum::*;
+
+// ─── Construction ───────────────────────────────────────────
+
+#[test]
+fn starts_at_zero() {
+    let c = CusumAccumulator::new(0.999);
+    let snap = c.snapshot();
+    assert!((snap.accumulator).abs() < f64::EPSILON);
+    assert_eq!(snap.steps_since_reset, 0);
+}
+
+// ─── Core accumulation ─────────────────────────────────────
+
+#[test]
+fn accumulates_under_sustained_elevation() {
+    let mut c = CusumAccumulator::new(0.999);
+    // Warm the slow baseline with normal-ish scores.
+    for _ in 0..20 {
+        c.update(&[1.0, 1.0, 1.0], 1.0, 0.5, 1e-6, 3.0);
+    }
+    let before = c.snapshot().accumulator;
+
+    // Now feed consistently elevated scores.
+    for _ in 0..10 {
+        c.update(&[5.0, 5.0, 5.0], 5.0, 0.5, 1e-6, 3.0);
+    }
+    assert!(
+        c.snapshot().accumulator > before,
+        "accumulator should grow under sustained elevation"
+    );
+}
+
+#[test]
+fn steps_since_reset_increments() {
+    let mut c = CusumAccumulator::new(0.999);
+    for i in 1..=5 {
+        c.update(&[1.0], 1.0, 0.5, 1e-6, 3.0);
+        assert_eq!(c.snapshot().steps_since_reset, i);
+    }
+}
+
+// ─── Clamping ───────────────────────────────────────────────
+
+#[test]
+fn clamps_at_zero_when_below_baseline() {
+    let mut c = CusumAccumulator::new(0.999);
+    // Warm with high values.
+    for _ in 0..20 {
+        c.update(&[10.0, 10.0], 10.0, 0.5, 1e-6, 3.0);
+    }
+    c.reset();
+
+    // Feed low values — gap is negative, accumulator stays at zero.
+    for _ in 0..10 {
+        c.update(&[0.1, 0.1], 0.1, 0.5, 1e-6, 3.0);
+    }
+    assert!(
+        (c.snapshot().accumulator).abs() < f64::EPSILON,
+        "accumulator should not go below zero"
+    );
+}
+
+// ─── Allowance ──────────────────────────────────────────────
+
+#[test]
+fn allowance_absorbs_noise() {
+    // With a large allowance, small deviations should not accumulate
+    // when the slow baseline has meaningful variance.
+    let mut c = CusumAccumulator::new(0.999);
+
+    // Warm with varied data so the slow baseline has real variance.
+    for _ in 0..20 {
+        c.update(&[0.5, 1.0, 1.5], 1.0, 2.0, 1e-6, 3.0);
+    }
+    c.reset();
+
+    // Feed slightly elevated scores — allowance should absorb them.
+    for _ in 0..10 {
+        c.update(&[1.1, 1.2, 1.3], 1.2, 2.0, 1e-6, 3.0);
+    }
+    assert!(
+        c.snapshot().accumulator < 0.1,
+        "generous allowance should absorb small deviations, got {}",
+        c.snapshot().accumulator,
+    );
+}
+
+// ─── Reset ──────────────────────────────────────────────────
+
+#[test]
+fn resets_to_zero() {
+    let mut c = CusumAccumulator::new(0.999);
+    c.update(&[5.0, 5.0], 5.0, 0.0, 1e-6, 3.0);
+    c.update(&[5.0, 5.0], 5.0, 0.0, 1e-6, 3.0);
+    assert!(c.snapshot().accumulator > 0.0);
+
+    c.reset();
+    assert!((c.snapshot().accumulator).abs() < f64::EPSILON);
+    assert_eq!(c.snapshot().steps_since_reset, 0);
+}
+
+#[test]
+fn reset_preserves_slow_baseline() {
+    let mut c = CusumAccumulator::new(0.999);
+    for _ in 0..20 {
+        c.update(&[5.0, 5.0], 5.0, 0.5, 1e-6, 3.0);
+    }
+    let baseline_before = c.snapshot().slow_baseline;
+
+    c.reset();
+
+    let baseline_after = c.snapshot().slow_baseline;
+    assert!(
+        (baseline_before.mean - baseline_after.mean).abs() < f64::EPSILON,
+        "reset() should preserve the slow baseline mean"
+    );
+    assert!(
+        (baseline_before.variance - baseline_after.variance).abs() < f64::EPSILON,
+        "reset() should preserve the slow baseline variance"
+    );
+}
+
+#[test]
+fn reset_cold_clears_everything() {
+    let mut c = CusumAccumulator::new(0.999);
+    for _ in 0..20 {
+        c.update(&[5.0, 5.0], 5.0, 0.0, 1e-6, 3.0);
+    }
+    assert!(c.snapshot().accumulator > 0.0);
+    // Slow baseline should have drifted away from the cold defaults.
+    assert!((c.snapshot().slow_baseline.mean - 1.0).abs() > 0.1);
+
+    c.reset_cold();
+
+    let snap = c.snapshot();
+    assert!((snap.accumulator).abs() < f64::EPSILON);
+    assert_eq!(snap.steps_since_reset, 0);
+    // Cold defaults: mean = 1.0, variance = 1.0.
+    assert!(
+        (snap.slow_baseline.mean - 1.0).abs() < f64::EPSILON,
+        "reset_cold should restore cold-default mean"
+    );
+    assert!(
+        (snap.slow_baseline.variance - 1.0).abs() < f64::EPSILON,
+        "reset_cold should restore cold-default variance"
+    );
+}
+
+// ─── Seeding ────────────────────────────────────────────────
+
+#[test]
+fn seed_slow_from_aligns_baselines() {
+    use crate::ewma::EwmaStats;
+
+    let mut fast = EwmaStats::new(0.95);
+    for _ in 0..20 {
+        fast.update(&[3.0, 3.5, 2.5], 3.0);
+    }
+
+    let mut c = CusumAccumulator::new(0.999);
+    c.seed_slow_from(&fast);
+
+    let snap = c.snapshot();
+    assert!(
+        (snap.slow_baseline.mean - fast.mean()).abs() < f64::EPSILON,
+        "seed_slow_from should copy mean from fast EWMA"
+    );
+    assert!(
+        (snap.slow_baseline.variance - fast.variance()).abs() < f64::EPSILON,
+        "seed_slow_from should copy variance from fast EWMA"
+    );
+}
+
+// ─── Filtered update ────────────────────────────────────────
+
+#[test]
+fn update_filtered_matches_update_no_clip() {
+    let mut a = CusumAccumulator::new(0.999);
+    let mut b = CusumAccumulator::new(0.999);
+    let scores = &[1.0, 1.1, 0.9, 1.05, 0.95];
+    #[allow(clippy::cast_precision_loss)]
+    let mean = scores.iter().sum::<f64>() / scores.len() as f64;
+
+    a.update(scores, mean, 0.5, 1e-6, 100.0);
+    b.update_filtered(scores, mean, 0.5, 1e-6);
+
+    assert!((a.snapshot().accumulator - b.snapshot().accumulator).abs() < 1e-12);
+}
+
+// ─── Snapshot ───────────────────────────────────────────────
+
+#[test]
+fn snapshot_reports_slow_baseline() {
+    let mut c = CusumAccumulator::new(0.999);
+    for _ in 0..20 {
+        c.update(&[4.0, 4.0], 4.0, 0.5, 1e-6, 3.0);
+    }
+
+    let snap = c.snapshot();
+    // After warming, baseline mean should be near 4.0.
+    assert!(
+        (snap.slow_baseline.mean - 4.0).abs() < 0.5,
+        "slow baseline mean should track input; got {}",
+        snap.slow_baseline.mean,
+    );
+}
diff --git a/packages/sentinel/src/tests/ewma.rs b/packages/sentinel/src/tests/ewma.rs
new file mode 100644
index 000000000..1df502115
--- /dev/null
+++ b/packages/sentinel/src/tests/ewma.rs
@@ -0,0 +1,406 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// SPDX-FileCopyrightText: 2026 Torrust project contributors
+
+//! Unit tests for [`EwmaStats`](crate::ewma::EwmaStats).
+//!
+//! These tests cover the full lifecycle of an EWMA tracker:
+//! construction in cold state, warm-up via the first update, state
+//! manipulation (`reset_cold`, `seed_from`), clipped and unclipped
+//! update paths, exponential decay behaviour, derived statistics
+//! (`z_score`, `ceiling`, `snapshot`), and the variance floor guard.
+//!
+//! # Test index
+//!
+//! ## Construction & initial state
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`starts_cold`] | fresh tracker is cold with placeholder values |
+//!
+//! ## Cold → warm transition
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`first_update_warms`] | single `update` makes `is_warm` true |
+//! | [`first_update_sets_mean_to_batch_mean`] | cold-path seeds mean exactly |
+//! | [`first_update_single_value_keeps_unit_variance`] | single-element batch keeps variance = 1e-4 floor |
+//! | [`first_update_multi_value_sets_sample_variance`] | multi-element batch computes variance |
+//!
+//! ## `reset_cold()`
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`reset_cold_restores_placeholder_state`] | reverts mean/variance/warm |
+//! | [`reset_cold_allows_re_warming`] | can warm again after reset |
+//!
+//! ## `seed_from()`
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`seed_from_copies_warm_state`] | copies mean, variance, warm flag |
+//! | [`seed_from_cold_source_does_not_warm_target`] | cold source leaves target cold |
+//!
+//! ## `update()` — clipped updates
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`update_empty_is_noop`] | empty slice changes nothing |
+//! | [`outliers_are_rejected`] | extreme values beyond clip ceiling are filtered |
+//! | [`update_single_value_does_not_update_variance`] | single-element batch skips variance |
+//! | [`update_skips_clipping_when_cold`] | cold-path accepts all values |
+//!
+//! ## `update_raw()` — unclipped updates
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`update_raw_empty_is_noop`] | empty slice changes nothing |
+//! | [`update_raw_matches_update_when_no_clipping`] | identical to `update` with huge `clip_sigmas` |
+//!
+//! ## Decay behaviour
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`decays_toward_new_data`] | mean converges toward repeated new data |
+//! | [`higher_decay_forgets_slower`] | λ=0.99 retains old data longer than λ=0.90 |
+//!
+//! ## `z_score()`
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`z_score_is_zero_at_mean`] | value equal to mean yields ~0 |
+//! | [`z_score_positive_above_mean`] | above mean yields positive z |
+//! | [`z_score_negative_below_mean`] | below mean yields negative z |
+//!
+//! ## `ceiling()`
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`ceiling_returns_infinity_when_cold`] | no meaningful ceiling before warm-up |
+//! | [`ceiling_returns_mean_plus_sigmas_when_warm`] | correct formula after warm-up |
+//! | [`clip_sigmas_affects_ceiling`] | wider σ lets more values through |
+//!
+//! ## `snapshot()`
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`snapshot_matches_state`] | snapshot fields equal accessor values |
+//!
+//! ## Variance floor
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`variance_floor_is_respected`] | identical values clamp variance to 1e-4 |
+
+use crate::ewma::*;
+
+// ── Construction & initial state ────────────────────────────
+
+#[test]
+fn starts_cold() {
+    let stats = EwmaStats::new(0.99);
+    assert!(!stats.is_warm());
+    assert!((stats.mean() - 1.0).abs() < f64::EPSILON);
+    assert!((stats.variance() - 1.0).abs() < f64::EPSILON);
+}
+
+// ── Cold → warm transition ──────────────────────────────────
+
+#[test]
+fn first_update_warms() {
+    let mut stats = EwmaStats::new(0.99);
+    stats.update(&[2.0, 3.0, 4.0], 3.0);
+    assert!(stats.is_warm());
+}
+
+#[test]
+fn first_update_sets_mean_to_batch_mean() {
+    let mut stats = EwmaStats::new(0.99);
+    stats.update(&[2.0, 4.0, 6.0], 3.0);
+    // Cold-path sets mean = batch mean = (2+4+6)/3 = 4.0
+    assert!((stats.mean() - 4.0).abs() < f64::EPSILON);
+}
+
+#[test]
+fn first_update_single_value_keeps_unit_variance() {
+    let mut stats = EwmaStats::new(0.99);
+    stats.update(&[5.0], 3.0);
+    assert!(stats.is_warm());
+    assert!((stats.mean() - 5.0).abs() < f64::EPSILON);
+    // Single-element cold-path: variance stays at the 1.0 initial
+    // (the code only computes variance when normals.len() > 1).
+    assert!((stats.variance() - 1.0).abs() < f64::EPSILON);
+}
+
+#[test]
+fn first_update_multi_value_sets_sample_variance() {
+    let mut stats = EwmaStats::new(0.99);
+    stats.update(&[0.0, 10.0], 3.0);
+    // mean = 5.0, variance = ((0-5)² + (10-5)²) / 2 = 25.0
+    assert!((stats.mean() - 5.0).abs() < f64::EPSILON);
+    assert!((stats.variance() - 25.0).abs() < 1e-12);
+}
+
+// ── reset_cold() ────────────────────────────────────────────
+
+#[test]
+fn reset_cold_restores_placeholder_state() {
+    let mut stats = EwmaStats::new(0.95);
+    stats.update(&[10.0, 20.0, 30.0], 3.0);
+    assert!(stats.is_warm());
+
+    stats.reset_cold();
+    assert!(!stats.is_warm());
+    assert!((stats.mean() - 1.0).abs() < f64::EPSILON);
+    assert!((stats.variance() - 1.0).abs() < f64::EPSILON);
+}
+
+#[test]
+fn reset_cold_allows_re_warming() {
+    let mut stats = EwmaStats::new(0.95);
+    stats.update(&[10.0, 10.0, 10.0], 3.0);
+    stats.reset_cold();
+    stats.update(&[42.0, 42.0, 42.0], 3.0);
+    assert!(stats.is_warm());
+    assert!((stats.mean() - 42.0).abs() < f64::EPSILON);
+}
+
+// ── seed_from() ─────────────────────────────────────────────
+
+#[test]
+fn seed_from_copies_warm_state() {
+    let mut source = EwmaStats::new(0.99);
+    source.update(&[5.0, 10.0, 15.0], 3.0);
+
+    let mut target = EwmaStats::new(0.99);
+    assert!(!target.is_warm());
+    target.seed_from(&source);
+
+    assert!(target.is_warm());
+    assert!((target.mean() - source.mean()).abs() < f64::EPSILON);
+    assert!((target.variance() - source.variance()).abs() < f64::EPSILON);
+}
+
+#[test]
+fn seed_from_cold_source_does_not_warm_target() {
+    let source = EwmaStats::new(0.99); // never updated — cold
+    let mut target = EwmaStats::new(0.99);
+    target.seed_from(&source);
+
+    assert!(!target.is_warm());
+    // Still copies the placeholder values
+    assert!((target.mean() - 1.0).abs() < f64::EPSILON);
+    assert!((target.variance() - 1.0).abs() < f64::EPSILON);
+}
+
+// ── update() — clipped updates ──────────────────────────────
+
+#[test]
+fn update_empty_is_noop() {
+    let mut stats = EwmaStats::new(0.99);
+    stats.update(&[5.0, 5.0, 5.0], 3.0);
+    let mean_before = stats.mean();
+    let var_before = stats.variance();
+
+    stats.update(&[], 3.0);
+    assert!((stats.mean() - mean_before).abs() < f64::EPSILON);
+    assert!((stats.variance() - var_before).abs() < f64::EPSILON);
+}
+
+#[test]
+fn outliers_are_rejected() {
+    let mut stats = EwmaStats::new(0.99);
+    // Warm up with small values
+    stats.update(&[1.0, 1.0, 1.0], 3.0);
+    let mean_before = stats.mean();
+
+    // Feed extreme outlier — should be rejected
+    stats.update(&[1000.0], 3.0);
+    assert!(
+        (stats.mean() - mean_before).abs() < f64::EPSILON,
+        "mean should not change when all values are outliers"
+    );
+}
+
+#[test]
+fn update_single_value_does_not_update_variance() {
+    let mut stats = EwmaStats::new(0.95);
+    stats.update(&[5.0, 5.0, 5.0], 3.0);
+    let var_before = stats.variance();
+
+    // Single accepted value — variance path is skipped (normals.len() == 1).
+    stats.update(&[5.0], 3.0);
+    assert!(
+        (stats.variance() - var_before).abs() < f64::EPSILON,
+        "variance should not change from a single-element update"
+    );
+}
+
+#[test]
+fn update_skips_clipping_when_cold() {
+    let mut stats = EwmaStats::new(0.99);
+    // Even though 1000.0 is far from initial mean=1.0, cold-path accepts it.
+    stats.update(&[1000.0, 1000.0], 3.0);
+    assert!(stats.is_warm());
+    assert!((stats.mean() - 1000.0).abs() < f64::EPSILON);
+}
+
+// ── update_raw() — unclipped updates ────────────────────────
+
+#[test]
+fn update_raw_empty_is_noop() {
+    let mut stats = EwmaStats::new(0.99);
+    stats.update_raw(&[5.0, 5.0, 5.0]);
+    let mean_before = stats.mean();
+    let var_before = stats.variance();
+
+    stats.update_raw(&[]);
+    assert!((stats.mean() - mean_before).abs() < f64::EPSILON);
+    assert!((stats.variance() - var_before).abs() < f64::EPSILON);
+}
+
+#[test]
+fn update_raw_matches_update_when_no_clipping() {
+    let mut a = EwmaStats::new(0.95);
+    let mut b = EwmaStats::new(0.95);
+    let values = &[1.0, 1.2, 0.8, 1.1, 0.9];
+
+    a.update(values, 100.0); // clip_sigmas so high nothing is clipped
+    b.update_raw(values);
+
+    assert!((a.mean() - b.mean()).abs() < 1e-12);
+    assert!((a.variance() - b.variance()).abs() < 1e-12);
+}
+
+// ── Decay behaviour ─────────────────────────────────────────
+
+#[test]
+fn decays_toward_new_data() {
+    let mut stats = EwmaStats::new(0.90); // fast decay
+    stats.update(&[10.0, 10.0, 10.0], 3.0);
+    assert!((stats.mean() - 10.0).abs() < f64::EPSILON);
+
+    // Push toward 0.0
+    for _ in 0..50 {
+        stats.update(&[0.0, 0.0, 0.0], 3.0);
+    }
+    assert!(stats.mean() < 1.0, "mean should have decayed toward 0.0");
+}
+
+#[test]
+fn higher_decay_forgets_slower() {
+    let mut slow = EwmaStats::new(0.99); // slow decay (long memory)
+    let mut fast = EwmaStats::new(0.90); // fast decay (short memory)
+
+    // Both start at 10.0
+    slow.update(&[10.0, 10.0, 10.0], 5.0);
+    fast.update(&[10.0, 10.0, 10.0], 5.0);
+
+    // Push both toward 0.0
+    for _ in 0..20 {
+        slow.update(&[0.0, 0.0, 0.0], 5.0);
+        fast.update(&[0.0, 0.0, 0.0], 5.0);
+    }
+
+    // Slow retains more of the original 10.0
+    assert!(
+        slow.mean() > fast.mean(),
+        "slow (λ=0.99) should retain more: slow={}, fast={}",
+        slow.mean(),
+        fast.mean()
+    );
+}
+
+// ── z_score() ───────────────────────────────────────────────
+
+#[test]
+fn z_score_is_zero_at_mean() {
+    let mut stats = EwmaStats::new(0.99);
+    stats.update(&[5.0, 5.0, 5.0, 5.0], 3.0);
+    let z = stats.z_score(5.0, 1e-6);
+    assert!(z.abs() < 0.01);
+}
+
+#[test]
+fn z_score_positive_above_mean() {
+    let mut stats = EwmaStats::new(0.99);
+    stats.update(&[5.0, 5.0, 5.0], 3.0);
+    let z = stats.z_score(10.0, 1e-6);
+    assert!(z > 0.0, "z-score should be positive above mean, got {z}");
+}
+
+#[test]
+fn z_score_negative_below_mean() {
+    let mut stats = EwmaStats::new(0.99);
+    stats.update(&[5.0, 5.0, 5.0], 3.0);
+    let z = stats.z_score(1.0, 1e-6);
+    assert!(z < 0.0, "z-score should be negative below mean, got {z}");
+}
+
+// ── ceiling() ───────────────────────────────────────────────
+
+#[test]
+fn ceiling_returns_infinity_when_cold() {
+    let stats = EwmaStats::new(0.95);
+    assert!(stats.ceiling(3.0).is_infinite());
+}
+
+#[test]
+fn ceiling_returns_mean_plus_sigmas_when_warm() {
+    let mut stats = EwmaStats::new(0.95);
+    stats.update(&[2.0, 2.0, 2.0], 3.0);
+    let ceil = stats.ceiling(3.0);
+    let expected = 3.0_f64.mul_add(stats.variance().sqrt(), stats.mean());
+    assert!((ceil - expected).abs() < 1e-12);
+}
+
+#[test]
+fn clip_sigmas_affects_ceiling() {
+    // With tight clip (1σ), more values are rejected.
+    let mut tight = EwmaStats::new(0.99);
+    tight.update(&[1.0, 1.0, 1.0], 1.0);
+
+    // With wide clip (5σ), fewer values are rejected.
+    let mut wide = EwmaStats::new(0.99);
+    wide.update(&[1.0, 1.0, 1.0], 5.0);
+
+    // Feed a moderately elevated value.
+    let elevated = &[3.0];
+    let mean_before_tight = tight.mean();
+    let mean_before_wide = wide.mean();
+    tight.update(elevated, 1.0);
+    wide.update(elevated, 5.0);
+
+    // Wide should have moved toward 3.0 more than tight
+    // (tight may reject 3.0 if it's beyond 1σ from mean ~1.0).
+    let tight_delta = (tight.mean() - mean_before_tight).abs();
+    let wide_delta = (wide.mean() - mean_before_wide).abs();
+    assert!(
+        wide_delta >= tight_delta,
+        "wide clip should accept more: wide_delta={wide_delta}, tight_delta={tight_delta}"
+    );
+}
+
+// ── snapshot() ──────────────────────────────────────────────
+
+#[test]
+fn snapshot_matches_state() {
+    let mut stats = EwmaStats::new(0.99);
+    stats.update(&[2.0, 4.0, 6.0], 3.0);
+    let snap = stats.snapshot();
+    assert!((snap.mean - stats.mean()).abs() < f64::EPSILON);
+    assert!((snap.variance - stats.variance()).abs() < f64::EPSILON);
+}
+
+// ── Variance floor ──────────────────────────────────────────
+
+#[test]
+fn variance_floor_is_respected() {
+    let mut stats = EwmaStats::new(0.95);
+    // All identical values → zero deviation, but floor should apply.
+    stats.update(&[7.0, 7.0, 7.0, 7.0], 3.0);
+    assert!(
+        stats.variance() >= 1e-4,
+        "variance should be clamped to floor 1e-4, got {}",
+        stats.variance()
+    );
+}
diff --git a/packages/sentinel/src/tests/mod.rs b/packages/sentinel/src/tests/mod.rs
new file mode 100644
index 000000000..242aa3291
--- /dev/null
+++ b/packages/sentinel/src/tests/mod.rs
@@ -0,0 +1,24 @@
+//! Collected unit tests for the sentinel crate.
+//!
+//! Tests that formerly lived as inline `#[cfg(test)] mod tests { … }`
+//! blocks inside their parent modules are refactored here so the
+//! production source files stay focused on production code.
+//!
+//! The convergence characterisation suite (ADR-S-013) also lives
+//! under this tree.
+
+mod analysis_set;
+mod config;
+mod convergence_clipping;
+mod convergence_common;
+mod convergence_diagnostics;
+mod convergence_eta;
+mod convergence_ewma;
+mod convergence_fixes;
+mod convergence_noise;
+mod cusum;
+mod ewma;
+mod observation;
+mod report;
+mod tracker;
+mod variance_formula;
diff --git a/packages/sentinel/src/tests/observation.rs b/packages/sentinel/src/tests/observation.rs
new file mode 100644
index 000000000..5c83ba7b4
--- /dev/null
+++ b/packages/sentinel/src/tests/observation.rs
@@ -0,0 +1,192 @@
+//! Tests for [`crate::observation`] — centred-bit representation.
+//!
+//! # Test index
+//!
+//! ## `CentredBits::from_u128` (128-bit convenience)
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`from_u128_zero_all_minus_half`] | zero maps every bit to −0.5 |
+//! | [`from_u128_max_all_plus_half`] | `u128::MAX` maps every bit to +0.5 |
+//! | [`from_u128_one_only_lsb_set`] | value `1` sets only the LSB to +0.5 |
+//! | [`from_u128_msb_first_ordering`] | MSB-first storage order |
+//!
+//! ## `CentredBitSource` for `u128` — custom width
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`u128_custom_width_populates_n_bits`] | `n` requested bits populated, rest zero |
+//! | [`u128_zero_width_gives_empty`] | width 0 yields empty suffix |
+//!
+//! ## `CentredBitSource` for `u64`
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`u64_max_all_plus_half`] | `u64::MAX` with width 64 → all +0.5 |
+//! | [`u64_width_capped_at_64`] | requesting width > 64 caps at 64 |
+//!
+//! ## `CentredBits::from_coord`
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`from_coord_delegates_correctly`] | delegates to `to_centred_bits` |
+//!
+//! ## `CentredBits::suffix`
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`suffix_zero_is_full_vector`] | `suffix(0)` returns the full bit vector |
+//! | [`suffix_intermediate_depth`] | `suffix(8)` returns last 120 bits |
+//! | [`suffix_at_len_is_empty`] | `suffix(len)` is empty |
+//! | [`suffix_panics_beyond_len`] | `suffix(len+1)` panics |
+//! | [`suffix_norm_squared_is_width_over_four`] | ‖suffix‖² = width / 4 for all depths |
+
+use crate::observation::*;
+
+// ─── CentredBits::from_u128 ────────────────────────────────
+
+#[test]
+fn from_u128_zero_all_minus_half() {
+    let cb = CentredBits::from_u128(0);
+    for (i, &b) in cb.bits.iter().enumerate() {
+        assert!((b - (-0.5)).abs() < f64::EPSILON, "bit {i}: expected -0.5, got {b}");
+    }
+}
+
+#[test]
+fn from_u128_max_all_plus_half() {
+    let cb = CentredBits::from_u128(u128::MAX);
+    for (i, &b) in cb.bits.iter().enumerate() {
+        assert!((b - 0.5).abs() < f64::EPSILON, "bit {i}: expected +0.5, got {b}");
+    }
+}
+
+#[test]
+fn from_u128_one_only_lsb_set() {
+    let cb = CentredBits::from_u128(1);
+    for &b in &cb.bits[..127] {
+        assert!((b - (-0.5)).abs() < f64::EPSILON, "expected -0.5, got {b}");
+    }
+    assert!((cb.bits[127] - 0.5).abs() < f64::EPSILON);
+}
+
+#[test]
+fn from_u128_msb_first_ordering() {
+    // 0x80…0 has only the MSB set — index 0 should be +0.5.
+    let cb = CentredBits::from_u128(1_u128 << 127);
+    assert!((cb.bits[0] - 0.5).abs() < f64::EPSILON, "MSB should be at index 0");
+    for &b in &cb.bits[1..] {
+        assert!((b - (-0.5)).abs() < f64::EPSILON, "remaining bits should be -0.5");
+    }
+}
+
+// ─── CentredBitSource for u128 — custom width ──────────────
+
+#[test]
+fn u128_custom_width_populates_n_bits() {
+    // 0xFF = 8 one-bits; ask for n=8 → first 8 slots are +0.5, rest 0.0.
+    let cb = 0xFF_u128.to_centred_bits(8);
+    for (i, &b) in cb.bits[..8].iter().enumerate() {
+        assert!((b - 0.5).abs() < f64::EPSILON, "bit {i}: expected +0.5, got {b}");
+    }
+    for (i, &b) in cb.bits[8..].iter().enumerate() {
+        assert!(b.abs() < f64::EPSILON, "bit {}: expected 0.0, got {b}", i + 8);
+    }
+    // suffix(0) should return exactly 8 elements, not 128.
+    assert_eq!(cb.suffix(0).len(), 8);
+}
+
+#[test]
+fn u128_zero_width_gives_empty() {
+    let cb = 42_u128.to_centred_bits(0);
+    assert_eq!(cb.suffix(0).len(), 0);
+    for &b in &cb.bits {
+        assert!(b.abs() < f64::EPSILON, "all slots should be 0.0");
+    }
+}
+
+// ─── CentredBitSource for u64 ──────────────────────────────
+
+#[test]
+fn u64_max_all_plus_half() {
+    let cb = u64::MAX.to_centred_bits(64);
+    assert_eq!(cb.suffix(0).len(), 64);
+    for (i, &b) in cb.bits[..64].iter().enumerate() {
+        assert!((b - 0.5).abs() < f64::EPSILON, "bit {i}: expected +0.5, got {b}");
+    }
+}
+
+#[test]
+fn u64_width_capped_at_64() {
+    // Requesting n=128 for a u64 should cap at 64.
+    let cb = u64::MAX.to_centred_bits(128);
+    assert_eq!(cb.suffix(0).len(), 64);
+}
+
+// ─── CentredBits::from_coord ───────────────────────────────
+
+#[test]
+fn from_coord_delegates_correctly() {
+    let value = 0xDEAD_BEEF_u128;
+    let direct = value.to_centred_bits(128);
+    let via_coord = CentredBits::from_coord(&value, 128);
+    for (a, b) in direct.bits.iter().zip(&via_coord.bits) {
+        assert!((a - b).abs() < f64::EPSILON);
+    }
+    assert_eq!(direct.suffix(0).len(), via_coord.suffix(0).len());
+}
+
+// ─── CentredBits::suffix ───────────────────────────────────
+
+#[test]
+fn suffix_zero_is_full_vector() {
+    let cb = CentredBits::from_u128(0xDEAD_BEEF);
+    let s = cb.suffix(0);
+    assert_eq!(s.len(), 128);
+    assert_eq!(s, &cb.bits[..]);
+}
+
+#[test]
+fn suffix_intermediate_depth() {
+    let cb = CentredBits::from_u128(1);
+    let s = cb.suffix(8);
+    assert_eq!(s.len(), 120);
+    assert_eq!(s, &cb.bits[8..128]);
+}
+
+#[test]
+fn suffix_at_len_is_empty() {
+    let cb = CentredBits::from_u128(42);
+    assert_eq!(cb.suffix(128).len(), 0);
+
+    // Also verify for a smaller width.
+    let cb_small = 0xFF_u128.to_centred_bits(16);
+    assert_eq!(cb_small.suffix(16).len(), 0);
+}
+
+#[test]
+#[should_panic(expected = "slice index starts at 17 but ends at 16")]
+fn suffix_panics_beyond_len() {
+    let cb = 0xFF_u128.to_centred_bits(16);
+    let _ = cb.suffix(17); // depth > len → panic
+}
+
+#[test]
+fn suffix_norm_squared_is_width_over_four() {
+    let values: [u128; 5] = [0, 1, u128::MAX, 0xDEAD_BEEF, 0x1234_5678_9ABC_DEF0];
+
+    for &v in &values {
+        let cb = CentredBits::from_u128(v);
+        for d in 0..128_u8 {
+            let s = cb.suffix(d);
+            let w = s.len();
+            let norm_sq: f64 = s.iter().map(|x| x * x).sum();
+            #[allow(clippy::cast_precision_loss)] // width ≤ 128, well within f64 precision
+            let expected = w as f64 / 4.0;
+            assert!(
+                (norm_sq - expected).abs() < 1e-10,
+                "v={v:#x}, depth={d}: suffix norm²={norm_sq}, expected w/4={expected}"
+            );
+        }
+    }
+}
diff --git a/packages/sentinel/src/tests/report.rs b/packages/sentinel/src/tests/report.rs
new file mode 100644
index 000000000..0e951ee51
--- /dev/null
+++ b/packages/sentinel/src/tests/report.rs
@@ -0,0 +1,191 @@
+//! Tests for [`crate::report`] types.
+//!
+//! # Index
+//!
+//! ## `TrackerMaturity`
+//!
+//! - [`cold_maturity_is_fully_noisy`] — `cold()` returns max noise, zero observations
+//! - [`total_observations_sums_real_and_noise`] — `total_observations()` adds both counters
+//!
+//! ## `ScoringGeometry`
+//!
+//! - [`novelty_not_saturated_when_residual_dof_positive`]
+//! - [`novelty_saturated_when_residual_dof_zero`]
+//! - [`novelty_saturable_when_cap_ge_dim`]
+//! - [`novelty_not_saturable_when_cap_lt_dim`]
+//!
+//! ## `ContourSnapshot`
+//!
+//! - [`contour_snapshot_fields`] — round-trip field construction
+//!
+//! ## `AnalysisSetSummary`
+//!
+//! - [`analysis_set_summary_empty`] — zero-sized summary
+//! - [`analysis_set_summary_populated`] — populated ranges
+//! - [`analysis_set_summary_with_degenerate_skips`]
+//!
+//! ## `MemberScore`
+//!
+//! - [`member_score_has_cell_identity`] — cell coordinates and all four axes
+
+use crate::report::*;
+
+// ── TrackerMaturity ─────────────────────────────────────────
+
+#[test]
+fn cold_maturity_is_fully_noisy() {
+    let m = TrackerMaturity::cold();
+    assert_eq!(m.real_observations, 0);
+    assert_eq!(m.noise_observations, 0);
+    assert!((m.noise_influence - 1.0).abs() < f64::EPSILON);
+    assert_eq!(m.total_observations(), 0);
+}
+
+#[test]
+fn total_observations_sums_real_and_noise() {
+    let m = TrackerMaturity {
+        real_observations: 100,
+        noise_observations: 25,
+        noise_influence: 0.2,
+    };
+    assert_eq!(m.total_observations(), 125);
+}
+
+// ── ScoringGeometry ─────────────────────────────────────────
+
+#[test]
+fn novelty_not_saturated_when_residual_dof_positive() {
+    let g = ScoringGeometry {
+        dim: 16,
+        cap: 8,
+        residual_dof: 12,
+    };
+    assert!(!g.is_novelty_saturated());
+}
+
+#[test]
+fn novelty_saturated_when_residual_dof_zero() {
+    let g = ScoringGeometry {
+        dim: 16,
+        cap: 16,
+        residual_dof: 0,
+    };
+    assert!(g.is_novelty_saturated());
+}
+
+#[test]
+fn novelty_saturable_when_cap_ge_dim() {
+    let g = ScoringGeometry {
+        dim: 8,
+        cap: 8,
+        residual_dof: 4,
+    };
+    assert!(g.is_novelty_saturable());
+}
+
+#[test]
+fn novelty_not_saturable_when_cap_lt_dim() {
+    let g = ScoringGeometry {
+        dim: 16,
+        cap: 8,
+        residual_dof: 8,
+    };
+    assert!(!g.is_novelty_saturable());
+}
+
+// ── ContourSnapshot ─────────────────────────────────────────
+
+#[test]
+fn contour_snapshot_fields() {
+    let cs = ContourSnapshot {
+        plateau_count: 3,
+        cell_count: 12,
+        total_importance: 42_000.0,
+        splits_since_last_report: 0,
+        net_removals_since_last_report: 0,
+    };
+    assert_eq!(cs.plateau_count, 3);
+    assert_eq!(cs.cell_count, 12);
+    assert!((cs.total_importance - 42_000.0).abs() < f64::EPSILON);
+    assert_eq!(cs.splits_since_last_report, 0);
+    assert_eq!(cs.net_removals_since_last_report, 0);
+}
+
+// ── AnalysisSetSummary ──────────────────────────────────────
+
+#[test]
+fn analysis_set_summary_empty() {
+    let summary = AnalysisSetSummary {
+        competitive_size: 0,
+        full_size: 1,
+        investment_set_size: 1,
+        depth_range: (0, 0),
+        importance_range: (0.0, 0.0),
+        v_depth_range: (0, 0),
+        degenerate_cells_skipped: 0,
+    };
+    assert_eq!(summary.competitive_size, 0);
+    assert_eq!(summary.full_size, 1);
+    assert_eq!(summary.investment_set_size, 1);
+    assert_eq!(summary.depth_range, (0, 0));
+    assert_eq!(summary.importance_range, (0.0, 0.0));
+    assert_eq!(summary.v_depth_range, (0, 0));
+    assert_eq!(summary.degenerate_cells_skipped, 0);
+}
+
+#[test]
+fn analysis_set_summary_populated() {
+    let summary = AnalysisSetSummary {
+        competitive_size: 5,
+        full_size: 12,
+        investment_set_size: 15,
+        depth_range: (0, 4),
+        importance_range: (100.0, 5000.0),
+        v_depth_range: (1, 3),
+        degenerate_cells_skipped: 0,
+    };
+    assert_eq!(summary.competitive_size, 5);
+    assert_eq!(summary.full_size, 12);
+    assert_eq!(summary.investment_set_size, 15);
+    assert!(summary.depth_range.0 < summary.depth_range.1);
+    assert!(summary.importance_range.0 < summary.importance_range.1);
+}
+
+#[test]
+fn analysis_set_summary_with_degenerate_skips() {
+    let summary = AnalysisSetSummary {
+        competitive_size: 3,
+        full_size: 8,
+        investment_set_size: 10,
+        depth_range: (0, 6),
+        importance_range: (50.0, 2000.0),
+        v_depth_range: (1, 2),
+        degenerate_cells_skipped: 4,
+    };
+    assert_eq!(summary.degenerate_cells_skipped, 4);
+}
+
+// ── MemberScore ─────────────────────────────────────────────
+
+#[test]
+fn member_score_has_cell_identity() {
+    let ms = MemberScore::<u128> {
+        cell_start: 0,
+        cell_end: u128::MAX / 2,
+        cell_depth: 1,
+        novelty: 0.5,
+        displacement: 0.3,
+        surprise: 0.7,
+        coherence: 0.9,
+        novelty_z: 1.0,
+        displacement_z: -0.5,
+        surprise_z: 2.1,
+        coherence_z: 0.0,
+    };
+    assert!(ms.cell_start < ms.cell_end);
+    assert_eq!(ms.cell_depth, 1);
+    assert!(!ms.novelty.is_nan());
+    assert!(!ms.displacement.is_nan());
+    assert!(!ms.surprise.is_nan());
+    assert!(!ms.coherence.is_nan());
+}
diff --git a/packages/sentinel/src/tests/tracker.rs b/packages/sentinel/src/tests/tracker.rs
new file mode 100644
index 000000000..ee082ab38
--- /dev/null
+++ b/packages/sentinel/src/tests/tracker.rs
@@ -0,0 +1,613 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// SPDX-FileCopyrightText: 2026 Torrust project contributors
+
+//! Crate-level tests for [`SubspaceTracker`](crate::sentinel::tracker::SubspaceTracker).
+//!
+//! These tests exercise construction, observation reports, maturity
+//! tracking (noise-influence η), scoring behaviour (novelty /
+//! coherence), rank adaptation, and the CUSUM / clip-pressure
+//! lifecycle.  Centred bit-vector helpers produce deterministic inputs
+//! so that every assertion is reproducible without randomness.
+//!
+//! # Test index
+//!
+//! ## Construction & accessors
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`new_starts_at_rank_one`] | fresh tracker starts at rank 1 with zero maturity |
+//! | [`new_accepts_min_tracker_dim`] | minimum allowed dimension is accepted |
+//! | [`new_panics_on_zero_dim_debug`] | dim = 0 panics in debug builds |
+//! | [`new_panics_on_dim_one_debug`] | dim = 1 panics in debug builds |
+//! | [`dim_and_cap_reflect_construction`] | `dim()` / `cap()` mirror constructor args |
+//! | [`scoring_geometry_matches_state`] | `scoring_geometry()` reflects dim, cap, residual dof |
+//!
+//! ## Observe — report structure
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`observe_returns_correct_depth`] | report depth equals input depth |
+//! | [`observe_per_sample_when_enabled`] | `per_sample` is `Some` when config enables it |
+//! | [`observe_no_per_sample_when_disabled`] | `per_sample` is `None` when config disables it |
+//! | [`observe_report_batch_size_matches`] | per-sample vec length equals batch size |
+//!
+//! ## Maturity tracking
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`maturity_noise_only`] | noise-only batches increment `noise_observations` |
+//! | [`maturity_real_only`] | real-only batches increment `real_observations` |
+//! | [`maturity_mixed_real_and_noise`] | mixed batches update both counters and decay η |
+//! | [`noise_influence_decays_toward_zero_for_real`] | η → 0 under sustained real traffic |
+//! | [`noise_influence_converges_toward_one_for_noise`] | η recovers toward 1 under noise after real data |
+//!
+//! ## Scoring behaviour
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`novelty_low_for_repeated_pattern`] | learned pattern produces low novelty |
+//! | [`novelty_high_for_unseen_pattern`] | unseen pattern produces higher novelty than learned |
+//! | [`coherence_cold_at_rank_one`] | coherence is zero when rank = 1 (no pairs) |
+//!
+//! ## Rank adaptation
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`rank_stays_bounded_by_max_rank`] | rank never exceeds `max_rank` |
+//! | [`rank_acquires_buffer_dimension`] | rank grows to include +1 buffer dimension |
+//! | [`energy_ratio_and_top_singular_value_evolve`] | energy ratio ∈ (0, 1] and top σ > 0 after training |
+//!
+//! ## CUSUM & clip-pressure lifecycle
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`cusum_reset_zeroes_steps`] | `reset_cusum()` resets step counter to 0 |
+//! | [`seed_cusum_slow_from_baselines_then_reset`] | warm-up completion sequence: seed + reset |
+//! | [`explicit_reset_clip_pressure`] | `reset_clip_pressure()` zeroes all axes |
+//! | [`eta_threshold_crossing_zeros_clip_pressure`] | η crossing below 0.01 auto-zeroes clip-pressure |
+
+use crate::config::SentinelConfig;
+use crate::sentinel::tracker::SubspaceTracker;
+
+// ════════════════════════════════════════════════════════════
+//  Helpers
+// ════════════════════════════════════════════════════════════
+
+/// Config used by most tests: fast adaptation, per-sample scores on.
+fn cfg_per_sample() -> SentinelConfig<u64> {
+    SentinelConfig {
+        max_rank: 4,
+        forgetting_factor: 0.95,
+        rank_update_interval: 10,
+        energy_threshold: 0.90,
+        eps: 1e-6,
+        per_sample_scores: true,
+        cusum_allowance_sigmas: 0.5,
+        ..SentinelConfig::default()
+    }
+}
+
+/// Same as [`cfg_per_sample`] but with per-sample scores *disabled*.
+fn cfg_no_per_sample() -> SentinelConfig<u64> {
+    SentinelConfig {
+        per_sample_scores: false,
+        ..cfg_per_sample()
+    }
+}
+
+/// Generate a batch of centred bit vectors from `u128` values.
+fn centred_rows(values: &[u128], depth: usize) -> Vec<Vec<f64>> {
+    values
+        .iter()
+        .map(|&v| {
+            (0..depth)
+                .map(|i| if (v >> (127 - i)) & 1 == 1 { 0.5 } else { -0.5 })
+                .collect()
+        })
+        .collect()
+}
+
+fn as_slices(vecs: &[Vec<f64>]) -> Vec<&[f64]> {
+    vecs.iter().map(Vec::as_slice).collect()
+}
+
+// ════════════════════════════════════════════════════════════
+//  Construction & accessors
+// ════════════════════════════════════════════════════════════
+
+#[test]
+fn new_starts_at_rank_one() {
+    let cfg = cfg_per_sample();
+    let t = SubspaceTracker::new(8, &cfg, 0.999);
+
+    assert_eq!(t.rank(), 1);
+    assert_eq!(t.maturity().real_observations, 0);
+    assert_eq!(t.maturity().noise_observations, 0);
+    assert!((t.maturity().noise_influence - 1.0).abs() < f64::EPSILON);
+}
+
+#[test]
+fn new_accepts_min_tracker_dim() {
+    let cfg = cfg_per_sample();
+    let t = SubspaceTracker::new(crate::MIN_TRACKER_DIM, &cfg, 0.999);
+    assert_eq!(t.rank(), 1);
+}
+
+#[test]
+#[cfg(debug_assertions)]
+#[should_panic(expected = "SubspaceTracker::new() called with dim=0")]
+fn new_panics_on_zero_dim_debug() {
+    let cfg = cfg_per_sample();
+    drop(SubspaceTracker::new(0, &cfg, 0.999));
+}
+
+#[test]
+#[cfg(debug_assertions)]
+#[should_panic(expected = "SubspaceTracker::new() called with dim=1")]
+fn new_panics_on_dim_one_debug() {
+    let cfg = cfg_per_sample();
+    drop(SubspaceTracker::new(1, &cfg, 0.999));
+}
+
+#[test]
+fn dim_and_cap_reflect_construction() {
+    let cfg = SentinelConfig {
+        max_rank: 6,
+        ..cfg_per_sample()
+    };
+
+    // When dim > max_rank, cap = max_rank.
+    let t = SubspaceTracker::new(16, &cfg, 0.999);
+    assert_eq!(t.dim(), 16);
+    assert_eq!(t.cap(), 6);
+
+    // When dim < max_rank, cap = dim.
+    let t2 = SubspaceTracker::new(4, &cfg, 0.999);
+    assert_eq!(t2.dim(), 4);
+    assert_eq!(t2.cap(), 4);
+}
+
+#[test]
+fn scoring_geometry_matches_state() {
+    let cfg = SentinelConfig {
+        max_rank: 4,
+        ..cfg_per_sample()
+    };
+    let t = SubspaceTracker::new(16, &cfg, 0.999);
+    let g = t.scoring_geometry();
+
+    assert_eq!(g.dim, 16);
+    assert_eq!(g.cap, 4);
+    // rank starts at 1, so residual_dof = dim - rank = 15.
+    assert_eq!(g.residual_dof, 15);
+}
+
+// ════════════════════════════════════════════════════════════
+//  Observe — report structure
+// ════════════════════════════════════════════════════════════
+
+#[test]
+fn observe_returns_correct_depth() {
+    let cfg = cfg_per_sample();
+    let mut t = SubspaceTracker::new(8, &cfg, 0.999);
+
+    let rows = centred_rows(&[0x0123_4567_89AB_CDEF_0123_4567_89AB_CDEF], 8);
+    let report = t.observe(&as_slices(&rows), 8, false);
+
+    assert_eq!(report.depth, 8);
+    assert_eq!(report.rank, 1); // hasn't adapted yet
+}
+
+#[test]
+fn observe_per_sample_when_enabled() {
+    let cfg = cfg_per_sample();
+    let mut t = SubspaceTracker::new(8, &cfg, 0.999);
+
+    let rows = centred_rows(&[1, 2, 3], 8);
+    let report = t.observe(&as_slices(&rows), 8, false);
+
+    let ps = report.per_sample.as_ref().expect("per_sample should be Some when enabled");
+    assert_eq!(ps.len(), 3, "one SampleScore per input row");
+}
+
+#[test]
+fn observe_no_per_sample_when_disabled() {
+    let cfg = cfg_no_per_sample();
+    let mut t = SubspaceTracker::new(8, &cfg, 0.999);
+
+    let rows = centred_rows(&[1, 2, 3], 8);
+    let report = t.observe(&as_slices(&rows), 8, false);
+
+    assert!(report.per_sample.is_none(), "per_sample should be None when disabled");
+}
+
+#[test]
+fn observe_report_batch_size_matches() {
+    let cfg = cfg_per_sample();
+    let mut t = SubspaceTracker::new(8, &cfg, 0.999);
+
+    // Single sample.
+    let rows1 = centred_rows(&[1], 8);
+    let r1 = t.observe(&as_slices(&rows1), 8, false);
+    assert_eq!(r1.per_sample.as_ref().unwrap().len(), 1);
+
+    // Larger batch.
+    let rows8 = centred_rows(&[1, 2, 3, 4, 5, 6, 7, 8], 8);
+    let r8 = t.observe(&as_slices(&rows8), 8, false);
+    assert_eq!(r8.per_sample.as_ref().unwrap().len(), 8);
+}
+
+// ════════════════════════════════════════════════════════════
+//  Maturity tracking
+// ════════════════════════════════════════════════════════════
+
+#[test]
+fn maturity_noise_only() {
+    let cfg = cfg_per_sample();
+    let mut t = SubspaceTracker::new(8, &cfg, 0.999);
+
+    let rows = centred_rows(&[1, 2, 3], 8);
+    t.observe(&as_slices(&rows), 8, true);
+
+    assert_eq!(t.maturity().noise_observations, 3);
+    assert_eq!(t.maturity().real_observations, 0);
+}
+
+#[test]
+fn maturity_real_only() {
+    let cfg = cfg_per_sample();
+    let mut t = SubspaceTracker::new(8, &cfg, 0.999);
+
+    let rows = centred_rows(&[10, 20], 8);
+    t.observe(&as_slices(&rows), 8, false);
+
+    assert_eq!(t.maturity().real_observations, 2);
+    assert_eq!(t.maturity().noise_observations, 0);
+}
+
+#[test]
+fn maturity_mixed_real_and_noise() {
+    let cfg = cfg_per_sample();
+    let mut t = SubspaceTracker::new(8, &cfg, 0.999);
+
+    let rows = centred_rows(&[1, 2, 3], 8);
+    let slices = as_slices(&rows);
+
+    t.observe(&slices, 8, true); // noise
+    assert_eq!(t.maturity().noise_observations, 3);
+    assert_eq!(t.maturity().real_observations, 0);
+
+    t.observe(&slices, 8, false); // real
+    assert_eq!(t.maturity().real_observations, 3);
+    assert!(t.maturity().noise_influence < 1.0);
+}
+
+#[test]
+fn noise_influence_decays_toward_zero_for_real() {
+    let cfg = cfg_per_sample();
+    let mut t = SubspaceTracker::new(8, &cfg, 0.999);
+
+    let rows = centred_rows(&[1, 2, 3, 4], 8);
+    let slices = as_slices(&rows);
+
+    // η starts at 1.0; each real batch decays it by λⁿ.
+    for _ in 0..50 {
+        t.observe(&slices, 8, false);
+    }
+
+    assert!(
+        t.maturity().noise_influence < 0.01,
+        "η should decay toward 0 after many real batches, got {}",
+        t.maturity().noise_influence,
+    );
+}
+
+#[test]
+fn noise_influence_converges_toward_one_for_noise() {
+    let cfg = cfg_per_sample();
+    let mut t = SubspaceTracker::new(8, &cfg, 0.999);
+
+    // Start by feeding real data to push η below 1.0.
+    let rows = centred_rows(&[1, 2, 3, 4], 8);
+    let slices = as_slices(&rows);
+    for _ in 0..10 {
+        t.observe(&slices, 8, false);
+    }
+    let eta_after_real = t.maturity().noise_influence;
+    assert!(eta_after_real < 1.0);
+
+    // Now feed noise — η should move back toward 1.0.
+    for _ in 0..30 {
+        t.observe(&slices, 8, true);
+    }
+
+    assert!(
+        t.maturity().noise_influence > eta_after_real,
+        "η should increase toward 1 under noise, was {eta_after_real}, now {}",
+        t.maturity().noise_influence,
+    );
+}
+
+// ════════════════════════════════════════════════════════════
+//  Scoring behaviour
+// ════════════════════════════════════════════════════════════
+
+#[test]
+fn novelty_low_for_repeated_pattern() {
+    let cfg = cfg_per_sample();
+    let mut t = SubspaceTracker::new(16, &cfg, 0.999);
+
+    let pattern: u128 = 0xAAAA_0000_0000_0000_0000_0000_0000_0000;
+    let rows = centred_rows(&[pattern; 8], 16);
+    let slices = as_slices(&rows);
+
+    // Train the subspace on a single repeated pattern.
+    for _ in 0..20 {
+        t.observe(&slices, 16, false);
+    }
+
+    // Score the same pattern — novelty should be low.
+    let report = t.observe(&slices, 16, false);
+    assert!(
+        report.scores.novelty.mean < 1.0,
+        "novelty should be low for a learned pattern, got {}",
+        report.scores.novelty.mean,
+    );
+}
+
+#[test]
+fn novelty_high_for_unseen_pattern() {
+    let cfg = cfg_per_sample();
+    let mut t = SubspaceTracker::new(16, &cfg, 0.999);
+
+    // Train on pattern A.
+    let pattern_a: u128 = 0xAAAA_0000_0000_0000_0000_0000_0000_0000;
+    let rows_a = centred_rows(&[pattern_a; 8], 16);
+    let slices_a = as_slices(&rows_a);
+    for _ in 0..20 {
+        t.observe(&slices_a, 16, false);
+    }
+
+    // Novelty for trained pattern A.
+    let report_a = t.observe(&slices_a, 16, false);
+
+    // Score a completely different pattern B (without further training).
+    let pattern_b: u128 = 0x5555_FFFF_0000_0000_0000_0000_0000_0000;
+    let rows_b = centred_rows(&[pattern_b; 8], 16);
+    let report_b = t.observe(&as_slices(&rows_b), 16, false);
+
+    assert!(
+        report_b.scores.novelty.mean > report_a.scores.novelty.mean,
+        "unseen pattern should produce higher novelty ({}) than learned pattern ({})",
+        report_b.scores.novelty.mean,
+        report_a.scores.novelty.mean,
+    );
+}
+
+#[test]
+fn coherence_cold_at_rank_one() {
+    // At rank 1 there are no pairs, so coherence should be zero.
+    let cfg = SentinelConfig {
+        max_rank: 1,
+        rank_update_interval: 1,
+        ..cfg_per_sample()
+    };
+    let mut t = SubspaceTracker::new(8, &cfg, 0.999);
+
+    let rows = centred_rows(&[0xFF00_0000_0000_0000_0000_0000_0000_0000; 4], 8);
+    let slices = as_slices(&rows);
+
+    for _ in 0..10 {
+        let report = t.observe(&slices, 8, false);
+        assert!(
+            report.scores.coherence.mean.abs() < f64::EPSILON,
+            "coherence should be zero at rank 1, got {}",
+            report.scores.coherence.mean,
+        );
+    }
+}
+
+// ════════════════════════════════════════════════════════════
+//  Rank adaptation
+// ════════════════════════════════════════════════════════════
+
+#[test]
+fn rank_stays_bounded_by_max_rank() {
+    let cfg = SentinelConfig {
+        max_rank: 3,
+        rank_update_interval: 1,
+        ..cfg_per_sample()
+    };
+    let mut t = SubspaceTracker::new(8, &cfg, 0.999);
+
+    let rows = centred_rows(&[0xFF00_0000_0000_0000_0000_0000_0000_0000; 8], 8);
+    let slices = as_slices(&rows);
+
+    for _ in 0..50 {
+        t.observe(&slices, 8, false);
+    }
+
+    assert!(t.rank() >= 1);
+    assert!(t.rank() <= 3, "rank should not exceed max_rank, got {}", t.rank());
+}
+
+#[test]
+fn rank_acquires_buffer_dimension() {
+    // With one dominant pattern capturing ≥ 90% energy, the target is
+    // min{i : c_i ≥ 0.90} + 1 = 0 + 1 = 1 → +1 buffer → 2.
+    let cfg = SentinelConfig::<u64> {
+        max_rank: 8,
+        rank_update_interval: 1,
+        energy_threshold: 0.90,
+        ..SentinelConfig::default()
+    };
+    let mut t = SubspaceTracker::new(16, &cfg, 0.999);
+
+    let pattern: u128 = 0xAAAA_BBBB_0000_0000_0000_0000_0000_0000;
+    let rows = centred_rows(&[pattern; 16], 16);
+    let slices = as_slices(&rows);
+
+    for _ in 0..100 {
+        t.observe(&slices, 16, false);
+    }
+
+    assert!(
+        t.rank() >= 2,
+        "rank should be at least 2 (buffer dimension), got {}",
+        t.rank(),
+    );
+}
+
+#[test]
+fn energy_ratio_and_top_singular_value_evolve() {
+    let cfg = cfg_per_sample();
+    let mut t = SubspaceTracker::new(8, &cfg, 0.999);
+
+    let rows = centred_rows(&[0xABCD_EF01_2345_6789_ABCD_EF01_2345_6789; 4], 8);
+    let slices = as_slices(&rows);
+
+    for _ in 0..20 {
+        t.observe(&slices, 8, false);
+    }
+
+    let report = t.observe(&slices, 8, false);
+
+    // After learning, the energy ratio should be positive and ≤ 1.
+    assert!(report.energy_ratio > 0.0, "energy_ratio should be positive");
+    assert!(report.energy_ratio <= 1.0, "energy_ratio should be at most 1.0");
+
+    // Top singular value should be positive after observations.
+    assert!(
+        report.top_singular_value > 0.0,
+        "top_singular_value should be positive after training",
+    );
+}
+
+// ════════════════════════════════════════════════════════════
+//  CUSUM & clip-pressure lifecycle
+// ════════════════════════════════════════════════════════════
+
+#[test]
+fn cusum_reset_zeroes_steps() {
+    let cfg = cfg_per_sample();
+    let mut t = SubspaceTracker::new(8, &cfg, 0.999);
+
+    let rows = centred_rows(&[1, 2, 3, 4], 8);
+    let slices = as_slices(&rows);
+
+    for _ in 0..5 {
+        t.observe(&slices, 8, false);
+    }
+
+    t.reset_cusum();
+
+    let report = t.observe(&slices, 8, false);
+    assert_eq!(report.scores.novelty.cusum.steps_since_reset, 1);
+}
+
+#[test]
+fn seed_cusum_slow_from_baselines_then_reset() {
+    let cfg = cfg_per_sample();
+    let mut t = SubspaceTracker::new(8, &cfg, 0.999);
+
+    let rows = centred_rows(&[0xFF00_FF00_FF00_FF00_FF00_FF00_FF00_FF00; 8], 8);
+    let slices = as_slices(&rows);
+
+    // Build up baselines with noise.
+    for _ in 0..20 {
+        t.observe(&slices, 8, true);
+    }
+
+    // Seed slow from fast, then reset — the canonical warm-up
+    // completion sequence.
+    t.seed_cusum_slow_from_baselines();
+    t.reset_cusum();
+
+    // After reset + one real step, CUSUM steps = 1 and accumulator
+    // should be close to zero (fast ≈ slow, so no drift).
+    let report = t.observe(&slices, 8, false);
+    assert_eq!(report.scores.novelty.cusum.steps_since_reset, 1);
+}
+
+#[test]
+fn explicit_reset_clip_pressure() {
+    let cfg = SentinelConfig::<u64> {
+        max_rank: 4,
+        forgetting_factor: 0.95,
+        rank_update_interval: 10,
+        energy_threshold: 0.90,
+        clip_pressure_decay: 0.95,
+        ..SentinelConfig::default()
+    };
+    let mut t = SubspaceTracker::new(8, &cfg, 0.999);
+
+    // Inject noise to potentially build clip-pressure.
+    let rows = centred_rows(&[0xFF00_FF00_FF00_FF00_FF00_FF00_FF00_FF00; 8], 8);
+    let slices = as_slices(&rows);
+    for _ in 0..20 {
+        t.observe(&slices, 8, true);
+    }
+
+    assert!(
+        t.maturity().noise_influence > 0.5,
+        "η should be high after noise injection, got {}",
+        t.maturity().noise_influence,
+    );
+
+    // Explicit reset.
+    t.seed_cusum_slow_from_baselines();
+    t.reset_cusum();
+    t.reset_clip_pressure();
+
+    let cp = t.clip_pressures();
+    for (i, &v) in cp.iter().enumerate() {
+        assert!(
+            v.abs() < f64::EPSILON,
+            "axis {i} clip_pressure should be 0 after reset_clip_pressure(), got {v}",
+        );
+    }
+}
+
+#[test]
+fn eta_threshold_crossing_zeros_clip_pressure() {
+    // When η crosses below WARMUP_THRESHOLD (0.01), clip-pressure
+    // is automatically zeroed even without an explicit reset call.
+    let cfg = SentinelConfig::<u64> {
+        max_rank: 4,
+        forgetting_factor: 0.95,
+        rank_update_interval: 10,
+        energy_threshold: 0.90,
+        clip_pressure_decay: 0.95,
+        ..SentinelConfig::default()
+    };
+    let mut t = SubspaceTracker::new(8, &cfg, 0.999);
+
+    // Drive η high via noise.
+    let rows = centred_rows(&[0xABCD_EF01_2345_6789_ABCD_EF01_2345_6789; 4], 8);
+    let slices = as_slices(&rows);
+    for _ in 0..30 {
+        t.observe(&slices, 8, true);
+    }
+    assert!(t.maturity().noise_influence > 0.5);
+
+    // Feed real traffic step-by-step, watching for the η threshold crossing.
+    let mut crossed = false;
+    for _ in 0..100 {
+        let old_eta = t.maturity().noise_influence;
+        t.observe(&slices, 8, false);
+        let new_eta = t.maturity().noise_influence;
+
+        if old_eta >= 0.01 && new_eta < 0.01 {
+            let cp = t.clip_pressures();
+            for (i, &v) in cp.iter().enumerate() {
+                assert!(
+                    v.abs() < f64::EPSILON,
+                    "axis {i} clip_pressure should be 0 at η threshold crossing, got {v}",
+                );
+            }
+            crossed = true;
+            break;
+        }
+    }
+    assert!(crossed, "η never crossed the 0.01 threshold");
+}
diff --git a/packages/sentinel/src/tests/variance_formula.rs b/packages/sentinel/src/tests/variance_formula.rs
new file mode 100644
index 000000000..00286b7bc
--- /dev/null
+++ b/packages/sentinel/src/tests/variance_formula.rs
@@ -0,0 +1,392 @@
+//! EWMA-mean-centred variance formula tests (ADR-S-021).
+//!
+//! Validates the properties mandated by ADR-S-021: correct cold-start
+//! seeding, batch-size–invariant surprise ratios, runtime-floor
+//! protection against degenerate collapse, adaptivity to distribution
+//! shifts, and correct update ordering (variance before mean).
+//!
+//! # Test index
+//!
+//! ## Cold-start seeding
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`cold_start_seeds_variance_from_first_batch`] | first batch directly seeds `lat_var` (not blended) |
+//!
+//! ## Batch-size correctness
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`b1_surprise_bounded`] | b=1 surprise scores stay bounded (no O(10⁵) blow-up) |
+//! | [`b1_latent_variance_stable`] | b=1 `lat_var` converges to ≈0.25, not ε |
+//! | [`b2_no_systematic_surprise_inflation`] | b=2 surprise ratio near 1.0 (no 2× inflation) |
+//! | [`batch_size_invariant_surprise_ratio`] | surprise ≈1.0 at b∈{1,2,4,16,64} (self-consistency) |
+//!
+//! ## Robustness & adaptivity
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`runtime_floor_prevents_degenerate_collapse`] | constant observations: `lat_var` ≥ 10⁻² |
+//! | [`variance_adapts_to_distribution_shift`] | `lat_var` tracks upward after 4× input scale change |
+//!
+//! ## Update order (white-box)
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`update_order_variance_before_mean`] | variance uses pre-update mean (white-box) |
+//!
+//! # §-references
+//!
+//! - §ALGO S-4.2 Phase 3 — Evolve Latent Distribution
+//! - §ALGO S-5.4 — Surprise scoring
+//! - §ALGO S-11.2 — Cold-start latent seeding
+//! - ADR-S-021 — EWMA-mean-centred latent variance
+
+use rand::SeedableRng;
+use rand::rngs::SmallRng;
+
+use super::convergence_common::{as_slices, cfg_test, generate_noise};
+use crate::config::SentinelConfig;
+use crate::sentinel::tracker::SubspaceTracker;
+
+// ════════════════════════════════════════════════════════════
+//  Helpers
+// ════════════════════════════════════════════════════════════
+
+/// Build a config with a specific `noise_batch_size`.
+fn cfg_with_batch_size(b: usize) -> SentinelConfig<u64> {
+    SentinelConfig {
+        noise_batch_size: b,
+        ..cfg_test()
+    }
+}
+
+// ════════════════════════════════════════════════════════════
+//  Cold-start seeding
+// ════════════════════════════════════════════════════════════
+
+/// Cold-start seeding produces correct initial variance.
+///
+/// After exactly one batch (the cold → warm transition), `lat_var`
+/// is seeded directly from the first batch's projected statistics
+/// rather than EWMA-blended against the placeholder.  For isotropic
+/// ±0.5 noise the expected per-coordinate projected variance is
+/// ≈ 0.25.  A b = 16 batch keeps sampling noise manageable.
+#[test]
+fn cold_start_seeds_variance_from_first_batch() {
+    let cfg = cfg_with_batch_size(16);
+    let dim = 128;
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let mut rng = SmallRng::seed_from_u64(42);
+
+    let noise = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+    tracker.observe(&as_slices(&noise), 0, true);
+
+    let lat_vars = tracker.latent_var();
+    for (j, &v) in lat_vars.iter().enumerate() {
+        // Should be near 0.25; upper bound excludes the 1.0 placeholder.
+        assert!(
+            (1e-2..0.8).contains(&v),
+            "cold-start lat_var[{j}] = {v:.6} should be near 0.25, not the 1.0 placeholder"
+        );
+    }
+}
+
+// ════════════════════════════════════════════════════════════
+//  b = 1 tests
+// ════════════════════════════════════════════════════════════
+
+/// At b = 1, surprise scores stay bounded (no O(10⁵) explosion).
+///
+/// Under the old within-batch variance formula, b = 1 collapsed
+/// `lat_var` to ε every batch, inflating surprise to O(10⁵).
+/// The EWMA-mean-centred formula produces correctly-scaled
+/// variance at every batch size.
+#[test]
+fn b1_surprise_bounded() {
+    let cfg = cfg_with_batch_size(1);
+    let dim = 128;
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let mut rng = SmallRng::seed_from_u64(42);
+    let mut max_surprise = 0.0_f64;
+
+    for _ in 0..200 {
+        let noise = generate_noise(dim, 1, &mut rng);
+        let report = tracker.observe(&as_slices(&noise), 0, true);
+        max_surprise = max_surprise.max(report.scores.surprise.mean);
+    }
+
+    assert!(
+        max_surprise < 50.0,
+        "b=1 surprise should stay bounded; got max {max_surprise:.1} (old formula would exceed 10⁴)"
+    );
+}
+
+/// At b = 1, `lat_var` converges to ≈ 0.25, not ε.
+///
+/// The theoretical steady-state variance for centred ±0.5 binary
+/// inputs projected onto an orthonormal basis is σ² ≈ 0.25.
+/// The EWMA-mean-centred formula at b = 1 produces unbiased
+/// per-batch inputs E\[(z − μ)²\] = σ², so the EWMA converges
+/// to 0.25.
+#[test]
+fn b1_latent_variance_stable() {
+    let cfg = cfg_with_batch_size(1);
+    let dim = 128;
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let mut rng = SmallRng::seed_from_u64(42);
+
+    for _ in 0..500 {
+        let noise = generate_noise(dim, 1, &mut rng);
+        tracker.observe(&as_slices(&noise), 0, true);
+    }
+
+    let lat_vars = tracker.latent_var();
+    for (j, &v) in lat_vars.iter().enumerate() {
+        assert!(
+            (0.05..=0.5).contains(&v),
+            "b=1: lat_var[{j}] = {v:.6} should be in [0.05, 0.5] (≈0.25 expected)"
+        );
+    }
+}
+
+// ════════════════════════════════════════════════════════════
+//  b = 2 test
+// ════════════════════════════════════════════════════════════
+
+/// At b = 2, surprise ratio near 1.0 (no 2× inflation).
+///
+/// Under the old formula, b = 2 had a −50% variance bias,
+/// doubling the surprise ratio.  The EWMA-mean-centred formula
+/// eliminates this.
+#[test]
+fn b2_no_systematic_surprise_inflation() {
+    let cfg = cfg_with_batch_size(2);
+    let dim = 128;
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let mut rng = SmallRng::seed_from_u64(42);
+
+    // Warm up.
+    for _ in 0..200 {
+        let noise = generate_noise(dim, 2, &mut rng);
+        tracker.observe(&as_slices(&noise), 0, true);
+    }
+
+    // Measure: collect surprise means over 200 more rounds.
+    let mut surprise_sum = 0.0;
+    let measurement_rounds: u32 = 200;
+    for _ in 0..measurement_rounds {
+        let noise = generate_noise(dim, 2, &mut rng);
+        let report = tracker.observe(&as_slices(&noise), 0, true);
+        surprise_sum += report.scores.surprise.mean;
+    }
+    let mean_surprise = surprise_sum / f64::from(measurement_rounds);
+
+    assert!(
+        (0.5..=2.0).contains(&mean_surprise),
+        "b=2: mean surprise = {mean_surprise:.3} should be near 1.0 (old formula would give ≈2.0)"
+    );
+}
+
+// ════════════════════════════════════════════════════════════
+//  Batch-size invariance
+// ════════════════════════════════════════════════════════════
+
+/// Surprise ratio ≈ 1.0 at b ∈ {1, 2, 4, 16, 64}.
+///
+/// The EWMA-mean-centred formula's defining property: the expected
+/// surprise ratio is 1 + O(α²) regardless of batch size.  This
+/// test verifies the property empirically.
+#[test]
+fn batch_size_invariant_surprise_ratio() {
+    let batch_sizes = [1, 2, 4, 16, 64];
+    // Surprise-ratio invariance is per-axis; dim=32 validates the
+    // property at ~4× less SVD cost than dim=128 (ADR-S-012).
+    let dim = 32;
+    let warmup_rounds = 100;
+    let measurement_rounds: u32 = 100;
+
+    let mut ratios = Vec::with_capacity(batch_sizes.len());
+
+    for &b in &batch_sizes {
+        let cfg = cfg_with_batch_size(b);
+        let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+        let mut rng = SmallRng::seed_from_u64(42);
+
+        // Warm up.
+        for _ in 0..warmup_rounds {
+            let noise = generate_noise(dim, b, &mut rng);
+            tracker.observe(&as_slices(&noise), 0, true);
+        }
+
+        // Measure.
+        let mut surprise_sum = 0.0;
+        for _ in 0..measurement_rounds {
+            let noise = generate_noise(dim, b, &mut rng);
+            let report = tracker.observe(&as_slices(&noise), 0, true);
+            surprise_sum += report.scores.surprise.mean;
+        }
+        let mean_surprise = surprise_sum / f64::from(measurement_rounds);
+        ratios.push((b, mean_surprise));
+    }
+
+    // Each ratio should be in a reasonable band around 1.0.
+    for &(b, ratio) in &ratios {
+        assert!(
+            (0.7..=1.5).contains(&ratio),
+            "b={b}: surprise ratio = {ratio:.3} should be in [0.7, 1.5]"
+        );
+    }
+
+    // Self-consistency: max - min < 0.5.
+    let max_r = ratios.iter().map(|(_, r)| *r).fold(f64::NEG_INFINITY, f64::max);
+    let min_r = ratios.iter().map(|(_, r)| *r).fold(f64::INFINITY, f64::min);
+    let spread = max_r - min_r;
+
+    assert!(
+        spread < 0.5,
+        "surprise ratios should be self-consistent across batch sizes: \
+         spread = {spread:.3} (ratios: {ratios:?})"
+    );
+}
+
+// ════════════════════════════════════════════════════════════
+//  Runtime floor
+// ════════════════════════════════════════════════════════════
+
+/// Constant-observation stream: `lat_var` ≥ 10⁻².
+///
+/// When every observation in every batch is identical, the
+/// within-batch variance is zero.  Under the EWMA-mean-centred
+/// formula, the variance contribution is `(z − μ)²`, which is
+/// also zero once μ converges to z.  The runtime floor prevents
+/// collapse.
+#[test]
+fn runtime_floor_prevents_degenerate_collapse() {
+    let cfg = cfg_test(); // b = 4
+    let dim = 128;
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let mut rng = SmallRng::seed_from_u64(42);
+
+    // One batch of noise to initialise the basis.
+    let noise = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+    tracker.observe(&as_slices(&noise), 0, true);
+
+    // 200 batches of identical observations (all −0.5).
+    let constant_row: Vec<f64> = vec![-0.5; dim];
+    let constant_batch: Vec<Vec<f64>> = (0..cfg.noise_batch_size).map(|_| constant_row.clone()).collect();
+
+    for _ in 0..200 {
+        tracker.observe(&as_slices(&constant_batch), 0, true);
+    }
+
+    let lat_vars = tracker.latent_var();
+    for (j, &v) in lat_vars.iter().enumerate() {
+        assert!(v >= 1e-2, "lat_var[{j}] = {v:.6e} should be ≥ 1e-2 (runtime floor)");
+    }
+}
+
+// ════════════════════════════════════════════════════════════
+//  Distribution-shift adaptivity
+// ════════════════════════════════════════════════════════════
+
+/// Variance adapts when the input distribution changes.
+///
+/// After warm-up on ±0.5 noise (`lat_var` ≈ 0.25), switch to
+/// ±2.0 noise (4× scale → 16× projected variance).  The EWMA
+/// variance should track upward toward the new distribution's
+/// variance.
+#[test]
+fn variance_adapts_to_distribution_shift() {
+    let cfg = cfg_with_batch_size(4);
+    let dim = 128;
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let mut rng = SmallRng::seed_from_u64(42);
+
+    // Warm up on standard ±0.5 noise.
+    for _ in 0..200 {
+        let noise = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+        tracker.observe(&as_slices(&noise), 0, true);
+    }
+
+    let pre_shift_var: Vec<f64> = tracker.latent_var().to_vec();
+
+    // Switch to 4× scaled noise (±2.0).
+    for _ in 0..100 {
+        let noise: Vec<Vec<f64>> = generate_noise(dim, cfg.noise_batch_size, &mut rng)
+            .into_iter()
+            .map(|row| row.into_iter().map(|x| x * 4.0).collect())
+            .collect();
+        tracker.observe(&as_slices(&noise), 0, true);
+    }
+
+    let post_shift_var: Vec<f64> = tracker.latent_var().to_vec();
+
+    // lat_var should have increased substantially (16× theoretical).
+    for (j, (&pre, &post)) in pre_shift_var.iter().zip(post_shift_var.iter()).enumerate() {
+        assert!(
+            post > pre * 2.0,
+            "lat_var[{j}]: post-shift {post:.4} should be > 2× pre-shift {pre:.4}"
+        );
+    }
+}
+
+// ════════════════════════════════════════════════════════════
+//  Update order (white-box)
+// ════════════════════════════════════════════════════════════
+
+/// Variance update uses pre-update mean (white-box).
+///
+/// Inject two batches with known noise seeds.  After batch 2,
+/// verify that `lat_var` reflects deviations from the pre-batch-2
+/// `lat_mean`, not from the post-update `lat_mean`.
+#[test]
+fn update_order_variance_before_mean() {
+    let cfg = SentinelConfig {
+        forgetting_factor: 0.9,
+        noise_batch_size: 4,
+        max_rank: 2,
+        rank_update_interval: 5,
+        ..cfg_test()
+    };
+    let dim = 128;
+    let mut tracker = SubspaceTracker::new(dim, &cfg, cfg.cusum_slow_decay);
+    let mut rng = SmallRng::seed_from_u64(99);
+
+    // Batch 1: cold→warm seeding.
+    let noise1 = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+    tracker.observe(&as_slices(&noise1), 0, true);
+
+    // Record pre-batch-2 `lat_mean` (this is what variance should
+    // have been computed against).
+    let pre_mean: Vec<f64> = tracker.latent_mean().to_vec();
+
+    // Batch 2: non-cold path.
+    let noise2 = generate_noise(dim, cfg.noise_batch_size, &mut rng);
+    tracker.observe(&as_slices(&noise2), 0, true);
+
+    let post_mean: Vec<f64> = tracker.latent_mean().to_vec();
+    let post_var: Vec<f64> = tracker.latent_var().to_vec();
+
+    // The key invariant: with variance-before-mean order, the variance
+    // was computed against `pre_mean`, then the mean was updated.
+    // If the order were reversed, the variance would be computed against
+    // `post_mean` (which would be slightly different).
+    //
+    // Verify indirectly: `pre_mean` ≠ `post_mean` (the mean moved),
+    // and `lat_var` is within a reasonable range (not stuck at ε or 1.0).
+    let mean_shifted = pre_mean.iter().zip(post_mean.iter()).any(|(&a, &b)| (a - b).abs() > 1e-8);
+
+    assert!(mean_shifted, "lat_mean should have changed between batches 1 and 2");
+
+    for (j, &v) in post_var.iter().enumerate() {
+        // After batch 2, `lat_var` should be λ·seed_var + α·col_var
+        // where `col_var` was centred on `pre_mean` (≈ batch-1 mean).
+        // It should be in a reasonable range — not ε (which the old
+        // formula would give at b=1) and not wildly inflated.
+        assert!(
+            (1e-2..2.0).contains(&v),
+            "lat_var[{j}] = {v:.6} should be reasonable after 2 batches"
+        );
+    }
+}
diff --git a/packages/sentinel/tests/ancestor_chain.rs b/packages/sentinel/tests/ancestor_chain.rs
new file mode 100644
index 000000000..813461843
--- /dev/null
+++ b/packages/sentinel/tests/ancestor_chain.rs
@@ -0,0 +1,317 @@
+//! §9.6 — Multi-scale and ancestor chain tests.
+//!
+//! Validates the ancestor chain properties described in
+//! §ALGO S-4.4–4.9. Closes gaps G7 and G8.
+//!
+//! # Test index
+//!
+//! ## Hierarchy invariants
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`empty_batch_produces_no_ancestor_reports`] | No observations ⇒ no ancestor reports |
+//! | [`root_ancestor_present_after_warm_up`] | Root (depth 0) appears in ancestor reports |
+//! | [`root_sample_count_equals_batch_size`] | Root aggregates every observation |
+//! | [`ancestor_width_increases_toward_root`] | Shallower depth ⇒ wider analysis width |
+//! | [`ancestor_depths_cover_path_to_root`] | Root reachable from every competitive cell |
+//! | [`shared_ancestor_aggregates_disjoint_ranges`] | Root counts combine traffic from both ranges |
+//!
+//! ## Anomaly propagation
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`local_anomaly_detectable_in_hierarchy`] | Anomaly in one range visible somewhere |
+//! | [`global_anomaly_elevates_root_scores`] | System-wide anomaly raises root z-score |
+//! | [`global_anomaly_root_z_exceeds_local`] | Global root z > localised-anomaly root z |
+
+mod common;
+
+use common::{ScenarioBuilder, anomalous_values, assert_invariants, cell_values, integration_config};
+use torrust_sentinel::{Sentinel128, SentinelConfig};
+
+// ── Helpers ─────────────────────────────────────────────────
+
+/// Warmed sentinel with a single range and low split threshold.
+fn single_range_sentinel() -> Sentinel128 {
+    ScenarioBuilder::new()
+        .config(SentinelConfig::<u64> {
+            split_threshold: 10,
+            ..integration_config()
+        })
+        .seed_range(0xA, 20)
+        .warm_batches(5)
+        .build()
+}
+
+/// Warmed sentinel with two disjoint ranges.
+fn dual_range_sentinel() -> Sentinel128 {
+    ScenarioBuilder::new()
+        .config(SentinelConfig::<u64> {
+            split_threshold: 10,
+            ..integration_config()
+        })
+        .seed_range(0xA, 20)
+        .seed_range(0xB, 20)
+        .warm_batches(5)
+        .build()
+}
+
+/// Warmed sentinel with three disjoint ranges.
+fn triple_range_sentinel() -> Sentinel128 {
+    ScenarioBuilder::new()
+        .config(SentinelConfig::<u64> {
+            split_threshold: 10,
+            ..integration_config()
+        })
+        .seed_range(0xA, 20)
+        .seed_range(0xB, 20)
+        .seed_range(0xC, 20)
+        .warm_batches(5)
+        .build()
+}
+
+/// Extract root z-score from a report, defaulting to `0.0`.
+fn root_novelty_z(report: &torrust_sentinel::BatchReport<u128>) -> f64 {
+    report
+        .ancestor_reports
+        .iter()
+        .find(|r| r.depth == 0)
+        .map_or(0.0, |r| r.scores.novelty.max_z_score)
+}
+
+// ── 1. empty_batch_produces_no_ancestor_reports ─────────────
+
+#[test]
+fn empty_batch_produces_no_ancestor_reports() {
+    let mut s = single_range_sentinel();
+    let report = s.ingest(&[]);
+
+    assert!(report.ancestor_reports.is_empty());
+    assert_invariants(&s, &report);
+}
+
+// ── 2. root_ancestor_present_after_warm_up ──────────────────
+
+#[test]
+fn root_ancestor_present_after_warm_up() {
+    let mut s = single_range_sentinel();
+    let report = s.ingest(&cell_values(0xA, 8));
+
+    let root = report.ancestor_reports.iter().find(|r| r.depth == 0);
+    assert!(root.is_some(), "root tracker must produce a report");
+
+    // There should also be competitive cells at depth > 0 (the split
+    // threshold is low enough to create them).
+    assert!(
+        report.cell_reports.iter().any(|r| r.depth > 0),
+        "should have competitive cells at depth > 0",
+    );
+    assert_invariants(&s, &report);
+}
+
+// ── 3. root_sample_count_equals_batch_size ──────────────────
+
+#[test]
+fn root_sample_count_equals_batch_size() {
+    let mut s = dual_range_sentinel();
+
+    let batch = [cell_values(0xA, 5), cell_values(0xB, 7)].concat();
+    let report = s.ingest(&batch);
+
+    let root = report.ancestor_reports.iter().find(|r| r.depth == 0).unwrap();
+    assert_eq!(root.sample_count, batch.len(), "root must see every observation");
+    assert_invariants(&s, &report);
+}
+
+// ── 4. ancestor_width_increases_toward_root ─────────────────
+
+#[test]
+fn ancestor_width_increases_toward_root() {
+    let mut s = single_range_sentinel();
+    let report = s.ingest(&cell_values(0xA, 8));
+
+    // Sort ancestors by depth ascending; width should be non-increasing
+    // (width = 128 − depth, so shallower ⇒ wider).
+    let mut ancestors: Vec<_> = report.ancestor_reports.iter().collect();
+    ancestors.sort_by_key(|r| r.depth);
+
+    for window in ancestors.windows(2) {
+        assert!(
+            window[0].analysis_width >= window[1].analysis_width,
+            "ancestor at depth {} (width {}) should be >= depth {} (width {})",
+            window[0].depth,
+            window[0].analysis_width,
+            window[1].depth,
+            window[1].analysis_width,
+        );
+    }
+    assert_invariants(&s, &report);
+}
+
+// ── 5. ancestor_depths_cover_path_to_root ───────────────────
+
+#[test]
+fn ancestor_depths_cover_path_to_root() {
+    let mut s = single_range_sentinel();
+    let report = s.ingest(&cell_values(0xA, 8));
+
+    // Root (depth 0) must always be present as an ancestor.
+    assert!(
+        report.ancestor_reports.iter().any(|a| a.depth == 0),
+        "root ancestor must be present",
+    );
+
+    // Collect depths from both competitive and ancestor reports.
+    // Intermediate nodes between root and deep cells may be competitive
+    // (in cell_reports) rather than in ancestor_reports.
+    let all_depths: std::collections::BTreeSet<u32> = report
+        .cell_reports
+        .iter()
+        .chain(report.ancestor_reports.iter())
+        .map(|r| r.depth)
+        .collect();
+
+    // The combined depths should span from 0 up to the deepest
+    // competitive cell without large gaps — the Steiner tree
+    // connects them through the hierarchy.
+    let max_depth = report.cell_reports.iter().map(|c| c.depth).max().unwrap_or(0);
+    if max_depth > 1 {
+        assert!(
+            all_depths.len() > 2,
+            "depths 0..{max_depth} should include intermediate nodes, got {all_depths:?}",
+        );
+    }
+    assert_invariants(&s, &report);
+}
+
+// ── 6. shared_ancestor_aggregates_disjoint_ranges ───────────
+
+#[test]
+fn shared_ancestor_aggregates_disjoint_ranges() {
+    let mut s = dual_range_sentinel();
+
+    let batch_a = cell_values(0xA, 4);
+    let batch_b = cell_values(0xB, 6);
+    let combined = [batch_a, batch_b].concat();
+    let report = s.ingest(&combined);
+
+    let root = report.ancestor_reports.iter().find(|r| r.depth == 0).unwrap();
+    assert_eq!(root.sample_count, 10, "root should aggregate traffic from both ranges");
+    assert_invariants(&s, &report);
+}
+
+// ── 7. local_anomaly_detectable_in_hierarchy ────────────────
+
+#[test]
+fn local_anomaly_detectable_in_hierarchy() {
+    let mut s = triple_range_sentinel();
+
+    // Normal reference: root z-score under normal traffic.
+    let normal = s.ingest(&[cell_values(0xA, 8), cell_values(0xB, 8), cell_values(0xC, 8)].concat());
+    let normal_root_z = root_novelty_z(&normal);
+    assert_invariants(&s, &normal);
+
+    // Anomaly only in range A; B and C normal.
+    let report = s.ingest(&[anomalous_values(0xA, 8), cell_values(0xB, 8), cell_values(0xC, 8)].concat());
+
+    let anomaly_root_z = root_novelty_z(&report);
+
+    let max_all_z = report
+        .cell_reports
+        .iter()
+        .chain(report.ancestor_reports.iter())
+        .map(|cr| cr.scores.novelty.max_z_score)
+        .fold(f64::NEG_INFINITY, f64::max);
+
+    assert!(
+        max_all_z > normal_root_z || anomaly_root_z > normal_root_z,
+        "local anomaly should be detectable at some hierarchical level: \
+         max_z={max_all_z:.4}, anomaly_root_z={anomaly_root_z:.4}, \
+         normal_root_z={normal_root_z:.4}",
+    );
+    assert_invariants(&s, &report);
+}
+
+// ── 8. global_anomaly_elevates_root_scores ──────────────────
+
+#[test]
+fn global_anomaly_elevates_root_scores() {
+    let mut s = dual_range_sentinel();
+
+    // Normal reference.
+    let normal = s.ingest(&[cell_values(0xA, 8), cell_values(0xB, 8)].concat());
+    let normal_root_z = root_novelty_z(&normal);
+    assert_invariants(&s, &normal);
+
+    // System-wide anomaly.
+    let anomaly = s.ingest(&[anomalous_values(0xA, 8), anomalous_values(0xB, 8)].concat());
+    let anomaly_root_z = root_novelty_z(&anomaly);
+
+    assert!(
+        anomaly_root_z > normal_root_z,
+        "global anomaly should elevate root z-score: \
+         anomaly={anomaly_root_z:.4}, normal={normal_root_z:.4}",
+    );
+    assert_invariants(&s, &anomaly);
+}
+
+// ── 9. global_anomaly_root_z_exceeds_local ──────────────────
+
+#[test]
+fn global_anomaly_root_z_exceeds_local() {
+    let mut s = ScenarioBuilder::new()
+        .config(SentinelConfig::<u64> {
+            split_threshold: 10,
+            ..integration_config()
+        })
+        .seed_range(0xA, 20)
+        .seed_range(0xB, 20)
+        .seed_range(0xC, 20)
+        .seed_range(0xD, 20)
+        .warm_batches(5)
+        .build();
+
+    // Localised anomaly: only range A.
+    let local_report = s.ingest(
+        &[
+            anomalous_values(0xA, 8),
+            cell_values(0xB, 8),
+            cell_values(0xC, 8),
+            cell_values(0xD, 8),
+        ]
+        .concat(),
+    );
+    let local_root_z = root_novelty_z(&local_report);
+    assert_invariants(&s, &local_report);
+
+    // Return to normal, then system-wide anomaly.
+    for _ in 0..3 {
+        s.ingest(
+            &[
+                cell_values(0xA, 8),
+                cell_values(0xB, 8),
+                cell_values(0xC, 8),
+                cell_values(0xD, 8),
+            ]
+            .concat(),
+        );
+    }
+
+    let global_report = s.ingest(
+        &[
+            anomalous_values(0xA, 8),
+            anomalous_values(0xB, 8),
+            anomalous_values(0xC, 8),
+            anomalous_values(0xD, 8),
+        ]
+        .concat(),
+    );
+    let global_root_z = root_novelty_z(&global_report);
+
+    assert!(
+        global_root_z > local_root_z,
+        "system-wide anomaly root z ({global_root_z:.4}) should exceed \
+         localised anomaly root z ({local_root_z:.4})",
+    );
+    assert_invariants(&s, &global_report);
+}
diff --git a/packages/sentinel/tests/api.rs b/packages/sentinel/tests/api.rs
new file mode 100644
index 000000000..8507c4a9a
--- /dev/null
+++ b/packages/sentinel/tests/api.rs
@@ -0,0 +1,288 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// SPDX-FileCopyrightText: 2026 Torrust project contributors
+
+//! Public API contract tests for [`SpectralSentinel`].
+//!
+//! These are **surface-level** tests that exercise every public method
+//! once and verify the basic return-value contract. Deeper behavioural
+//! and statistical tests live in dedicated files (`invariants`, `health`,
+//! `report_structure`, `spatial_decay`, etc.).
+//!
+//! # Test index
+//!
+//! ## Construction
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`new_validates_config`] | rejects invalid config (`max_rank = 0`) |
+//! | [`new_with_default_config_succeeds`] | `SentinelConfig::default()` validates successfully |
+//! | [`new_starts_with_root_cell`] | fresh sentinel has one cell and zero observations |
+//!
+//! ## Ingest — basic contract
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`empty_ingest_returns_empty_report`] | empty slice produces empty report with root only |
+//! | [`single_value_ingest`] | single-value ingest populates ancestor reports |
+//! | [`batch_counter_increments`] | `lifetime_observations` tracks cumulative count |
+//! | [`repeated_ingest_does_not_panic`] | 10 repeated ingests preserve invariants |
+//!
+//! ## Accessors — read-only queries
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`config_accessor_returns_construction_config`] | `config()` mirrors construction parameters |
+//! | [`graph_accessor_starts_with_single_root`] | fresh graph has 1 node, 1 terminal, zero sum |
+//! | [`analysis_set_accessible`] | `analysis_set()` includes at least the root |
+//! | [`cell_gnodes_returns_all_tracked`] | `cell_gnodes().len()` equals `cells_tracked()` |
+//! | [`cells_tracked_matches_cell_gnodes_len`] | consistency holds before and after traffic |
+//! | [`lifetime_observations_reflects_real_input`] | observation counter accumulates across batches |
+//! | [`degenerate_cells_skipped_starts_at_zero`] | counter starts at zero on a fresh sentinel |
+//! | [`health_accessible_on_fresh_sentinel`] | `health()` returns sane defaults before any ingest |
+//! | [`inspect_cell_returns_state_for_root`] | root cell is inspectable with depth 0 |
+//! | [`inspect_cell_returns_none_for_unknown_gnode`] | foreign `GNodeId` yields `None` |
+//!
+//! ## Per-sample scores
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`per_sample_scores_present_when_enabled`] | scores populated when `per_sample_scores = true` |
+//! | [`per_sample_scores_absent_when_disabled`] | scores absent when `per_sample_scores = false` |
+//!
+//! ## Reset
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`reset_restores_initial_state`] | `reset()` restores single-root / zero-observation state |
+
+mod common;
+
+use common::{assert_invariants, cell_values, seeded_sentinel, test_config};
+use torrust_sentinel::{Sentinel128, SentinelConfig};
+
+// ═══════════════════════════════════════════════════════════
+//  Construction
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn new_validates_config() {
+    let bad = SentinelConfig::<u64> {
+        max_rank: 0,
+        ..test_config()
+    };
+    assert!(Sentinel128::new(bad).is_err());
+}
+
+#[test]
+fn new_with_default_config_succeeds() {
+    // Full construction with the default noise schedule (450 root rounds)
+    // takes several seconds.  Validate instead — construction is tested
+    // end-to-end by every integration test that calls `Sentinel128::new()`.
+    let cfg = SentinelConfig::<u64>::default();
+    assert!(cfg.validate().is_ok());
+}
+
+#[test]
+fn new_starts_with_root_cell() {
+    let s = Sentinel128::new(test_config()).unwrap();
+    assert_eq!(s.cells_tracked(), 1);
+    assert_eq!(s.lifetime_observations(), 0);
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Ingest — basic contract
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn empty_ingest_returns_empty_report() {
+    let mut s = Sentinel128::new(test_config()).unwrap();
+    let report = s.ingest(&[]);
+
+    assert!(report.cell_reports.is_empty());
+    assert!(report.coordination_reports.is_empty());
+    assert_eq!(report.health.lifetime_observations, 0);
+    assert_eq!(report.health.active_trackers, 1); // root cell
+}
+
+#[test]
+fn single_value_ingest() {
+    let mut s = Sentinel128::new(test_config()).unwrap();
+    let report = s.ingest(&[0xABCD_0000_0000_0000_0000_0000_0000_0001]);
+
+    // Root cell always receives all observations (as an ancestor).
+    assert!(!report.ancestor_reports.is_empty());
+    assert_eq!(report.health.lifetime_observations, 1);
+    assert_invariants(&s, &report);
+}
+
+#[test]
+fn batch_counter_increments() {
+    let mut s = Sentinel128::new(test_config()).unwrap();
+
+    s.ingest(&[1]);
+    assert_eq!(s.lifetime_observations(), 1);
+
+    s.ingest(&[2, 3, 4]);
+    assert_eq!(s.lifetime_observations(), 4);
+}
+
+#[test]
+fn repeated_ingest_does_not_panic() {
+    let mut s = Sentinel128::new(test_config()).unwrap();
+    let values = cell_values(0xA, 20);
+
+    for _ in 0..10 {
+        let report = s.ingest(&values);
+        assert_invariants(&s, &report);
+    }
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Accessors — read-only queries
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn config_accessor_returns_construction_config() {
+    let cfg = test_config();
+    let s = Sentinel128::new(cfg.clone()).unwrap();
+
+    assert_eq!(s.config().max_rank, cfg.max_rank);
+    assert_eq!(s.config().analysis_k, cfg.analysis_k);
+    assert!((s.config().forgetting_factor - cfg.forgetting_factor).abs() < f64::EPSILON);
+}
+
+#[test]
+fn graph_accessor_starts_with_single_root() {
+    let s = Sentinel128::new(test_config()).unwrap();
+
+    assert_eq!(s.graph().node_count(), 1);
+    assert_eq!(s.graph().terminal_count(), 1);
+    assert_eq!(s.graph().total_sum(), 0u64);
+}
+
+#[test]
+fn analysis_set_accessible() {
+    let s = seeded_sentinel();
+
+    let aset = s.analysis_set();
+    // The full set always includes the root.
+    assert!(aset.total_count() >= 1);
+}
+
+#[test]
+fn cell_gnodes_returns_all_tracked() {
+    let s = seeded_sentinel();
+
+    let gnodes = s.cell_gnodes();
+    assert_eq!(gnodes.len(), s.cells_tracked());
+}
+
+#[test]
+fn cells_tracked_matches_cell_gnodes_len() {
+    let mut s = Sentinel128::new(test_config()).unwrap();
+    assert_eq!(s.cells_tracked(), s.cell_gnodes().len());
+
+    // After traffic that may create cells.
+    for _ in 0..5 {
+        s.ingest(&cell_values(0xF, 8));
+    }
+    assert_eq!(s.cells_tracked(), s.cell_gnodes().len());
+}
+
+#[test]
+fn lifetime_observations_reflects_real_input() {
+    let mut s = Sentinel128::new(test_config()).unwrap();
+    assert_eq!(s.lifetime_observations(), 0);
+
+    let batch_a = cell_values(0xA, 5);
+    s.ingest(&batch_a);
+    assert_eq!(s.lifetime_observations(), 5);
+
+    let batch_b = cell_values(0xB, 3);
+    s.ingest(&batch_b);
+    assert_eq!(s.lifetime_observations(), 8);
+}
+
+#[test]
+fn degenerate_cells_skipped_starts_at_zero() {
+    let s = Sentinel128::new(test_config()).unwrap();
+    assert_eq!(s.degenerate_cells_skipped(), 0);
+}
+
+#[test]
+fn health_accessible_on_fresh_sentinel() {
+    let s = Sentinel128::new(test_config()).unwrap();
+    let h = s.health();
+
+    assert_eq!(h.active_trackers, 1);
+    assert_eq!(h.lifetime_observations, 0);
+}
+
+#[test]
+fn inspect_cell_returns_state_for_root() {
+    let s = Sentinel128::new(test_config()).unwrap();
+    let root = s.graph().g_root();
+
+    let inspection = s.inspect_cell(root).expect("root cell should exist");
+    assert_eq!(inspection.depth, 0);
+    assert_eq!(inspection.analysis_width, 128);
+    assert!(inspection.rank >= 1);
+}
+
+#[test]
+fn inspect_cell_returns_none_for_unknown_gnode() {
+    let s = Sentinel128::new(test_config()).unwrap();
+    // Build a second sentinel so its non-root GNodeIds are foreign.
+    let mut other = Sentinel128::new(test_config()).unwrap();
+    other.ingest(&[0xF000_0000_0000_0000_0000_0000_0000_0001]);
+
+    let other_gnodes = other.cell_gnodes();
+    if other_gnodes.len() > 1 {
+        let non_root = other_gnodes.iter().find(|&&g| g != other.graph().g_root()).unwrap();
+        assert!(s.inspect_cell(*non_root).is_none());
+    }
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Per-sample scores
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn per_sample_scores_present_when_enabled() {
+    let mut s = Sentinel128::new(test_config()).unwrap(); // per_sample_scores = true
+
+    let report = s.ingest(&[0xF000_0000_0000_0000_0000_0000_0000_0001]);
+
+    let root_report = report.ancestor_reports.iter().find(|cr| cr.depth == 0).unwrap();
+    assert!(root_report.per_sample.is_some());
+    assert_eq!(root_report.per_sample.as_ref().unwrap().len(), 1);
+}
+
+#[test]
+fn per_sample_scores_absent_when_disabled() {
+    let cfg = SentinelConfig::<u64> {
+        per_sample_scores: false,
+        ..test_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    let report = s.ingest(&[0xF000_0000_0000_0000_0000_0000_0000_0001]);
+    let root_report = report.ancestor_reports.iter().find(|cr| cr.depth == 0).unwrap();
+    assert!(root_report.per_sample.is_none());
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Reset
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn reset_restores_initial_state() {
+    let mut s = seeded_sentinel();
+
+    s.reset();
+
+    assert_eq!(s.graph().node_count(), 1);
+    assert_eq!(s.graph().total_sum(), 0u64);
+    assert_eq!(s.lifetime_observations(), 0);
+    assert_eq!(s.cells_tracked(), 1);
+}
diff --git a/packages/sentinel/tests/clip_pressure.rs b/packages/sentinel/tests/clip_pressure.rs
new file mode 100644
index 000000000..5c0085df2
--- /dev/null
+++ b/packages/sentinel/tests/clip_pressure.rs
@@ -0,0 +1,386 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// SPDX-FileCopyrightText: 2026 Torrust project contributors
+
+//! §ALGO S-6.4 — **Clip-pressure EWMA** integration tests.
+//!
+//! Phase 11 of ADR-S-020: validates that sustained contamination widens
+//! the clip ceiling automatically, and that clip-pressure decays back
+//! toward its equilibrium under clean traffic.
+//!
+//! Tests use cold-start configs (no noise injection) unless warm-up
+//! behaviour is under test, so that EWMA baselines begin from actual
+//! score distributions.
+//!
+//! # Test index
+//!
+//! ## State & invariants
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`fresh_sentinel_has_zero_clip_pressure`] | all clip-pressure fields are 0.0 at birth |
+//! | [`clip_pressure_distribution_min_le_mean_le_max`] | min ≤ mean ≤ max after ingestion |
+//! | [`per_axis_clip_pressure_in_cell_reports`] | batch report exposes per-axis `clip_pressure` |
+//!
+//! ## Dynamics
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`clip_pressure_stable_under_clean_traffic`] | no upward trend under clean traffic (§11.2) |
+//! | [`contamination_elevates_clip_pressure`] | contamination raises pressure above baseline |
+//! | [`clip_pressure_decays_after_contamination`] | pressure falls when contamination stops |
+//! | [`effective_ceiling_widens_under_pressure`] | effective clip formula expands under pressure |
+//!
+//! ## Recovery & warm-up
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`faster_decay_recovers_sooner`] | lower λ_ρ → faster recovery to lower pressure |
+//! | [`warm_up_completion_resets_clip_pressure`] | warm-up crossing zeros `clip_pressure` (§11.3) |
+
+mod common;
+
+use common::{cell_values, cold_config, test_config};
+use torrust_sentinel::{Sentinel128, SentinelConfig};
+
+// ═══════════════════════════════════════════════════════════
+//  Helpers
+// ═══════════════════════════════════════════════════════════
+
+/// Maximum `clip_pressure` from a health report across all
+/// active tracker axes.
+fn max_clip_pressure(s: &Sentinel128) -> f64 {
+    s.health().clip_pressure_distribution.max
+}
+
+/// Mean `clip_pressure` from a health report.
+fn mean_clip_pressure(s: &Sentinel128) -> f64 {
+    s.health().clip_pressure_distribution.mean
+}
+
+/// Generate values routed to the same cell (leading nibble `nibble`)
+/// but with moderate structural variety in the middle bits.
+///
+/// Unlike [`cell_values()`] (sequential low bits only), this injects
+/// variation across a wider bit range, producing per-batch score
+/// variance that the EWMA can track meaningfully.
+fn diverse_cell_values(nibble: u128, count: usize, batch_id: usize) -> Vec<u128> {
+    (0..count)
+        .map(|i| {
+            // Vary bits 16–31 based on batch_id, and bits 0–15 based on i.
+            let mid = ((batch_id as u128 * 7 + 3) % 0xFFFF) << 16;
+            let low = (i as u128) | ((i as u128 * 13 + batch_id as u128) % 0xFFFF);
+            (nibble << 124) | mid | low
+        })
+        .collect()
+}
+
+/// Generate contaminated values: same leading nibble for correct
+/// routing, but with a dense middle-bit block that produces elevated
+/// anomaly scores (structurally novel relative to [`diverse_cell_values()`]).
+fn contaminated_values(nibble: u128, count: usize, offset: usize) -> Vec<u128> {
+    (0..count)
+        .map(|i| {
+            // Set bits 32–63, creating structural novelty relative to
+            // the normal diverse_cell_values pattern (which only varies 0–31).
+            let dense = 0x0000_0000_FFFF_FFFF_0000_0000_0000_0000_u128;
+            (nibble << 124) | dense | ((offset + i) as u128)
+        })
+        .collect()
+}
+
+/// Common cold-start config for clip-pressure tests: root-only cell,
+/// deterministic decay.
+fn clip_cold_config(decay: f64) -> SentinelConfig<u64> {
+    SentinelConfig::<u64> {
+        clip_pressure_decay: decay,
+        clip_sigmas: 3.0,
+        split_threshold: 100_000,
+        ..cold_config()
+    }
+}
+
+/// Warm up a sentinel with `n` batches of diverse clean traffic on
+/// the given nibble, returning the sentinel.
+fn warmed_sentinel(cfg: SentinelConfig<u64>, nibble: u128, batches: usize) -> Sentinel128 {
+    let mut s = Sentinel128::new(cfg).unwrap();
+    for batch_id in 0..batches {
+        s.ingest(&diverse_cell_values(nibble, 16, batch_id));
+    }
+    s
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Initial state
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn fresh_sentinel_has_zero_clip_pressure() {
+    let s = Sentinel128::new(cold_config()).unwrap();
+    let cp = s.health().clip_pressure_distribution;
+
+    assert!(
+        cp.min == 0.0 && cp.max == 0.0 && cp.mean == 0.0,
+        "fresh sentinel should have all-zero clip_pressure, got min={}, max={}, mean={}",
+        cp.min,
+        cp.max,
+        cp.mean,
+    );
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Distribution invariants
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn clip_pressure_distribution_min_le_mean_le_max() {
+    // Ingest enough traffic that clip_pressure is non-trivially
+    // exercised, then verify the distribution ordering invariant.
+    let mut s = warmed_sentinel(clip_cold_config(0.95), 0xA, 50);
+
+    // Inject a burst of contamination so min ≠ max is more likely
+    // when multiple axes are active.
+    for batch_id in 50..60 {
+        let mut batch = diverse_cell_values(0xA, 10, batch_id);
+        batch.extend(contaminated_values(0xA, 6, batch_id));
+        s.ingest(&batch);
+    }
+
+    let cp = s.health().clip_pressure_distribution;
+
+    assert!(
+        cp.min <= cp.mean && cp.mean <= cp.max,
+        "invariant violated: min={} ≤ mean={} ≤ max={}",
+        cp.min,
+        cp.mean,
+        cp.max,
+    );
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Per-axis visibility
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn per_axis_clip_pressure_in_cell_reports() {
+    // After ingestion, the batch report's cell_reports should
+    // carry finite, non-negative clip_pressure on every axis.
+    let mut s = warmed_sentinel(clip_cold_config(0.95), 0xA, 50);
+
+    let report = s.ingest(&diverse_cell_values(0xA, 16, 50));
+
+    for cr in &report.cell_reports {
+        for (name, cp) in [
+            ("novelty", cr.scores.novelty.clip_pressure),
+            ("displacement", cr.scores.displacement.clip_pressure),
+            ("surprise", cr.scores.surprise.clip_pressure),
+            ("coherence", cr.scores.coherence.clip_pressure),
+        ] {
+            assert!(
+                cp.is_finite() && cp >= 0.0,
+                "cell gnode {:?} axis {name}: clip_pressure should be finite and ≥ 0, got {cp}",
+                cr.gnode_id,
+            );
+        }
+    }
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Clean-traffic equilibrium (§11.2)
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn clip_pressure_stable_under_clean_traffic() {
+    let mut s = warmed_sentinel(clip_cold_config(0.95), 0xA, 200);
+
+    // Collect clip_pressure over the next 100 batches.
+    let mut cp_values = Vec::with_capacity(100);
+    for batch_id in 200..300 {
+        s.ingest(&diverse_cell_values(0xA, 16, batch_id));
+        cp_values.push(max_clip_pressure(&s));
+    }
+
+    // No upward trend: second half should not exceed first by much.
+    let first_half_mean: f64 = cp_values[..50].iter().sum::<f64>() / 50.0;
+    let second_half_mean: f64 = cp_values[50..].iter().sum::<f64>() / 50.0;
+
+    assert!(
+        second_half_mean <= first_half_mean + 0.05,
+        "clip_pressure should not trend upward under clean traffic: \
+         first_half={first_half_mean:.4}, second_half={second_half_mean:.4}"
+    );
+
+    // Mean should confirm stability.
+    let mean_cp = mean_clip_pressure(&s);
+    assert!(
+        mean_cp < 0.5,
+        "mean clip_pressure should be moderate under clean traffic, got {mean_cp:.4}"
+    );
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Contamination dynamics (§11.1)
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn contamination_elevates_clip_pressure() {
+    let mut s = warmed_sentinel(clip_cold_config(0.95), 0xA, 200);
+    let cp_baseline = max_clip_pressure(&s);
+
+    // 80 batches of mixed traffic: 60% normal + 40% outliers.
+    for batch_id in 200..280 {
+        let mut batch = diverse_cell_values(0xA, 10, batch_id);
+        batch.extend(contaminated_values(0xA, 6, batch_id));
+        s.ingest(&batch);
+    }
+
+    let cp_after = max_clip_pressure(&s);
+
+    assert!(
+        cp_after > cp_baseline + 0.05,
+        "contamination should elevate clip_pressure: \
+         baseline={cp_baseline:.4}, after={cp_after:.4}"
+    );
+}
+
+#[test]
+fn clip_pressure_decays_after_contamination() {
+    let mut s = warmed_sentinel(clip_cold_config(0.95), 0xA, 200);
+
+    // Contamination phase.
+    for batch_id in 200..280 {
+        let mut batch = diverse_cell_values(0xA, 10, batch_id);
+        batch.extend(contaminated_values(0xA, 6, batch_id));
+        s.ingest(&batch);
+    }
+    let cp_contaminated = max_clip_pressure(&s);
+
+    // Recovery: 200 batches of clean traffic.
+    for batch_id in 280..480 {
+        s.ingest(&diverse_cell_values(0xA, 16, batch_id));
+    }
+    let cp_recovered = max_clip_pressure(&s);
+
+    assert!(
+        cp_recovered < cp_contaminated,
+        "clip_pressure should decrease after contamination ends: \
+         contaminated={cp_contaminated:.4}, recovered={cp_recovered:.4}"
+    );
+}
+
+#[test]
+fn effective_ceiling_widens_under_pressure() {
+    let mut s = warmed_sentinel(clip_cold_config(0.95), 0xA, 200);
+
+    // Contamination phase to elevate pressure.
+    for batch_id in 200..280 {
+        let mut batch = diverse_cell_values(0xA, 10, batch_id);
+        batch.extend(contaminated_values(0xA, 6, batch_id));
+        s.ingest(&batch);
+    }
+
+    let p = max_clip_pressure(&s);
+    let clip_sigmas = 3.0_f64;
+    let eps = 1e-6_f64;
+
+    // §ALGO S-14.4 effective-clip formula: n_σ · (1 + ρ̄ / (1 − ρ̄ + ε))
+    let effective_clip = clip_sigmas * (1.0 + p / (1.0 - p + eps));
+
+    assert!(
+        effective_clip > clip_sigmas * 1.01,
+        "effective clip {effective_clip:.4} should exceed base clip_sigmas {clip_sigmas}"
+    );
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Decay-rate effect
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn faster_decay_recovers_sooner() {
+    // Two sentinels, identical contamination, different λ_ρ.
+    // Lower λ_ρ → faster decay → lower pressure after recovery.
+    let fast_decay = 0.85;
+    let slow_decay = 0.98;
+
+    let contaminate_and_recover = |decay: f64| -> f64 {
+        let mut s = warmed_sentinel(clip_cold_config(decay), 0xA, 200);
+
+        for batch_id in 200..280 {
+            let mut batch = diverse_cell_values(0xA, 10, batch_id);
+            batch.extend(contaminated_values(0xA, 6, batch_id));
+            s.ingest(&batch);
+        }
+
+        // Fixed recovery window: 100 batches of clean traffic.
+        for batch_id in 280..380 {
+            s.ingest(&diverse_cell_values(0xA, 16, batch_id));
+        }
+
+        max_clip_pressure(&s)
+    };
+
+    let cp_fast = contaminate_and_recover(fast_decay);
+    let cp_slow = contaminate_and_recover(slow_decay);
+
+    assert!(
+        cp_fast < cp_slow,
+        "faster decay (λ_ρ={fast_decay}) should recover to lower pressure \
+         than slow decay (λ_ρ={slow_decay}): fast={cp_fast:.4}, slow={cp_slow:.4}"
+    );
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Warm-up reset (§11.3)
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn warm_up_completion_resets_clip_pressure() {
+    // With noise injection, clip_pressure may rise during warm-up.
+    // After η crosses the warm-up threshold (0.01), the
+    // update_maturity() callback zeros clip_pressure on all axes.
+    //
+    // With λ=0.90 and batch_size=8, η < 0.01 requires ~44 batches
+    // (ln(0.01) / ln(0.90) ≈ 43.7). We run up to 100 batches.
+    let cfg = SentinelConfig::<u64> {
+        clip_pressure_decay: 0.95,
+        clip_sigmas: 3.0,
+        split_threshold: 100_000,
+        forgetting_factor: 0.90,
+        ..test_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    // Seed traffic to create the root cell.
+    s.ingest(&cell_values(0xA, 16));
+
+    // Feed real traffic until η crosses below 0.01.
+    let root = s.graph().g_root();
+    let mut crossed = false;
+    let mut post_cross_batches = 0_usize;
+
+    for batch_id in 0..100 {
+        s.ingest(&diverse_cell_values(0xA, 16, batch_id));
+        let insp = s.inspect_cell(root).unwrap();
+        if !crossed && insp.maturity.noise_influence < 0.01 {
+            crossed = true;
+        }
+        if crossed {
+            post_cross_batches += 1;
+            if post_cross_batches == 5 {
+                // Clip_pressure was zeroed at crossing, then accumulated
+                // for only 5 batches. With λ_ρ=0.95, even if every
+                // batch clips 100%, ρ̄ ≤ 1 - 0.95^5 ≈ 0.23.
+                let h = s.health();
+                assert!(
+                    h.clip_pressure_distribution.max < 0.30,
+                    "clip_pressure should be low shortly after warm-up \
+                     reset, got max={:.4} (5 batches post-crossing)",
+                    h.clip_pressure_distribution.max
+                );
+                break;
+            }
+        }
+    }
+
+    assert!(crossed, "η should have crossed below 0.01 within 100 batches");
+}
diff --git a/packages/sentinel/tests/common/assertions.rs b/packages/sentinel/tests/common/assertions.rs
new file mode 100644
index 000000000..932423138
--- /dev/null
+++ b/packages/sentinel/tests/common/assertions.rs
@@ -0,0 +1,187 @@
+//! Assertions and report-inspection helpers for tests.
+
+use std::collections::HashSet;
+
+use torrust_sentinel::{BatchReport, Sentinel128};
+
+// ─── §9.1.2 — assert_invariants() ──────────────────────────
+
+/// Assert all structural invariants on a sentinel and its last report.
+///
+/// Designed to be called after any `ingest()` in any test to verify
+/// cross-cutting invariants.
+pub fn assert_invariants(sentinel: &Sentinel128, report: &BatchReport<u128>) {
+    assert_steiner_tree_bound(report);
+    assert_competitive_cap(sentinel, report);
+    assert_root_in_full_set(report);
+    assert_cell_reports_competitive(report);
+    assert_ancestor_reports_non_competitive(report);
+    assert_cell_reports_sorted(report);
+    assert_ancestor_reports_sorted(report);
+    assert_coordination_reports_unique(report);
+    assert_no_nan_scores(report);
+    assert_analysis_widths(report);
+}
+
+/// 1. Analysis set bounds: `full_size <= 1 + K * D_max` (§ALGO S-8.2).
+///
+/// The *reduced* Steiner tree has at most `2K − 1` nodes, but the
+/// materialised investment set includes degree-2 chain intermediaries
+/// that push the count higher when competitive cells sit at varying
+/// depths.  The correct worst-case bound is `1 + K·D̄` (before
+/// sharing); we use `1 + K·D_max` as a practical upper bound.
+fn assert_steiner_tree_bound(report: &BatchReport<u128>) {
+    let summary = &report.analysis_set_summary;
+    let k = summary.competitive_size;
+    let d_max = summary.depth_range.1 as usize;
+    let bound = 1 + k * d_max;
+    assert!(
+        summary.full_size <= bound,
+        "materialised Steiner tree bound violated: full={}, competitive={}, d_max={}, bound={}",
+        summary.full_size,
+        k,
+        d_max,
+        bound,
+    );
+}
+
+/// 2. Competitive cap: `competitive_size <= analysis_k`.
+fn assert_competitive_cap(sentinel: &Sentinel128, report: &BatchReport<u128>) {
+    let summary = &report.analysis_set_summary;
+    assert!(
+        summary.competitive_size <= sentinel.config().analysis_k,
+        "competitive set {} exceeds K={}",
+        summary.competitive_size,
+        sentinel.config().analysis_k,
+    );
+}
+
+/// 3. Root at depth 0 is always in the full set.
+fn assert_root_in_full_set(report: &BatchReport<u128>) {
+    let summary = &report.analysis_set_summary;
+    if summary.full_size > 0 {
+        assert_eq!(summary.depth_range.0, 0, "root (depth 0) must be in the full analysis set");
+    }
+}
+
+/// 4. `cell_reports` entries are competitive only.
+fn assert_cell_reports_competitive(report: &BatchReport<u128>) {
+    for cr in &report.cell_reports {
+        assert!(
+            cr.is_competitive,
+            "cell_reports entry at depth {} is not competitive",
+            cr.depth
+        );
+    }
+}
+
+/// 5. `ancestor_reports` entries are non-competitive.
+fn assert_ancestor_reports_non_competitive(report: &BatchReport<u128>) {
+    for ar in &report.ancestor_reports {
+        assert!(
+            !ar.is_competitive,
+            "ancestor_reports entry at depth {} is competitive",
+            ar.depth
+        );
+    }
+}
+
+/// 6. Deterministic ordering: `cell_reports` sorted by `gnode_id` ascending.
+fn assert_cell_reports_sorted(report: &BatchReport<u128>) {
+    for window in report.cell_reports.windows(2) {
+        assert!(
+            window[0].gnode_id < window[1].gnode_id,
+            "cell_reports not sorted by GNodeId: {:?} >= {:?}",
+            window[0].gnode_id,
+            window[1].gnode_id,
+        );
+    }
+}
+
+/// 7. Deterministic ordering: `ancestor_reports` sorted by `gnode_id` ascending.
+fn assert_ancestor_reports_sorted(report: &BatchReport<u128>) {
+    for window in report.ancestor_reports.windows(2) {
+        assert!(
+            window[0].gnode_id < window[1].gnode_id,
+            "ancestor_reports not sorted by GNodeId: {:?} >= {:?}",
+            window[0].gnode_id,
+            window[1].gnode_id,
+        );
+    }
+}
+
+/// 8. Coordination reports have no duplicate `GNodeId`s.
+fn assert_coordination_reports_unique(report: &BatchReport<u128>) {
+    let mut seen = HashSet::new();
+    for cr in &report.coordination_reports {
+        assert!(
+            seen.insert(cr.gnode_id),
+            "duplicate GNodeId in coordination_reports: {:?}",
+            cr.gnode_id,
+        );
+    }
+}
+
+/// 9. No NaN in score fields.
+fn assert_no_nan_scores(report: &BatchReport<u128>) {
+    for cr in report.cell_reports.iter().chain(report.ancestor_reports.iter()) {
+        assert!(!cr.scores.novelty.mean.is_nan(), "NaN novelty mean at depth {}", cr.depth);
+        assert!(
+            !cr.scores.displacement.mean.is_nan(),
+            "NaN displacement mean at depth {}",
+            cr.depth
+        );
+        assert!(!cr.scores.surprise.mean.is_nan(), "NaN surprise mean at depth {}", cr.depth);
+    }
+}
+
+/// 10. `analysis_width == 128 - depth` for every cell report.
+fn assert_analysis_widths(report: &BatchReport<u128>) {
+    for cr in report.cell_reports.iter().chain(report.ancestor_reports.iter()) {
+        assert_eq!(
+            cr.analysis_width,
+            128 - cr.depth as usize,
+            "analysis_width mismatch at depth {}",
+            cr.depth
+        );
+    }
+}
+
+/// Extract the maximum CUSUM accumulator value across all cell and
+/// ancestor reports in a [`BatchReport`].
+pub fn max_cusum(report: &BatchReport<u128>) -> f64 {
+    report
+        .cell_reports
+        .iter()
+        .chain(report.ancestor_reports.iter())
+        .flat_map(|cr| {
+            [
+                cr.scores.novelty.cusum.accumulator,
+                cr.scores.displacement.cusum.accumulator,
+                cr.scores.surprise.cusum.accumulator,
+                cr.scores.coherence.cusum.accumulator,
+            ]
+        })
+        .fold(0.0f64, f64::max)
+}
+
+/// Extract the maximum novelty z-score across all cell and
+/// ancestor reports in a [`BatchReport`].
+pub fn max_novelty_z(report: &BatchReport<u128>) -> f64 {
+    report
+        .cell_reports
+        .iter()
+        .chain(report.ancestor_reports.iter())
+        .map(|cr| cr.scores.novelty.max_z_score)
+        .fold(0.0f64, f64::max)
+}
+
+/// Extract the novelty z-score from the root (depth 0) ancestor
+/// report, returning `0.0` if no root is present.
+pub fn root_novelty_z(report: &BatchReport<u128>) -> f64 {
+    report
+        .ancestor_reports
+        .iter()
+        .find(|r| r.depth == 0)
+        .map_or(0.0, |r| r.scores.novelty.max_z_score)
+}
diff --git a/packages/sentinel/tests/common/builders.rs b/packages/sentinel/tests/common/builders.rs
new file mode 100644
index 000000000..bcdb3cfa4
--- /dev/null
+++ b/packages/sentinel/tests/common/builders.rs
@@ -0,0 +1,91 @@
+//! Sentinel constructors and scenario builders for tests.
+
+use torrust_sentinel::{BatchReport, Sentinel128, SentinelConfig};
+
+use super::config::{integration_config, test_config};
+use super::generators::cell_values;
+
+/// Pre-populate a sentinel with two distinct value ranges so the graph
+/// creates cells and the analysis set is non-trivial.
+pub fn seeded_sentinel() -> Sentinel128 {
+    let mut s = Sentinel128::new(test_config()).unwrap();
+    // Two values with well-separated leading bits → distinct cells.
+    s.ingest(&[
+        0xF000_0000_0000_0000_0000_0000_0000_0001,
+        0x1000_0000_0000_0000_0000_0000_0000_0002,
+    ]);
+    s
+}
+
+// ─── §9.1.1 — ScenarioBuilder ──────────────────────────────
+
+/// Builder for common test scenarios.
+///
+/// Constructs a [`Sentinel128`] seeded with traffic across one
+/// or more leading-nibble ranges and optionally warmed through
+/// multiple batches.
+pub struct ScenarioBuilder {
+    config: SentinelConfig<u64>,
+    seed_ranges: Vec<(u128, usize)>,
+    warm_batches: usize,
+}
+
+impl ScenarioBuilder {
+    pub fn new() -> Self {
+        Self {
+            config: integration_config(),
+            seed_ranges: Vec::new(),
+            warm_batches: 0,
+        }
+    }
+
+    pub fn config(mut self, cfg: SentinelConfig<u64>) -> Self {
+        self.config = cfg;
+        self
+    }
+
+    /// Add a seed range: feed `count` sequential values with the
+    /// given leading `nibble` during initial seeding.
+    pub fn seed_range(mut self, nibble: u128, count: usize) -> Self {
+        self.seed_ranges.push((nibble, count));
+        self
+    }
+
+    /// Number of warm-up batches to run after seeding.
+    pub const fn warm_batches(mut self, n: usize) -> Self {
+        self.warm_batches = n;
+        self
+    }
+
+    /// Build a sentinel that has been seeded and warmed.
+    pub fn build(self) -> Sentinel128 {
+        self.build_with_reports().0
+    }
+
+    /// Build and return both the sentinel and the warm-up reports.
+    pub fn build_with_reports(self) -> (Sentinel128, Vec<BatchReport<u128>>) {
+        let batch = self.make_batch();
+        let mut s = Sentinel128::new(self.config).unwrap();
+        let mut reports = Vec::new();
+
+        if !batch.is_empty() {
+            // Seed phase.
+            reports.push(s.ingest(&batch));
+
+            // Warm-up phase.
+            for _ in 0..self.warm_batches {
+                reports.push(s.ingest(&batch));
+            }
+        }
+
+        (s, reports)
+    }
+
+    /// Flatten all seed ranges into a single batch of values.
+    fn make_batch(&self) -> Vec<u128> {
+        self.seed_ranges
+            .iter()
+            .flat_map(|&(nibble, count)| cell_values(nibble, count))
+            .collect()
+    }
+}
diff --git a/packages/sentinel/tests/common/config.rs b/packages/sentinel/tests/common/config.rs
new file mode 100644
index 000000000..969cd19d0
--- /dev/null
+++ b/packages/sentinel/tests/common/config.rs
@@ -0,0 +1,67 @@
+//! Sentinel configurations for tests.
+
+use torrust_sentinel::{NoiseSchedule, SentinelConfig, SvdStrategy};
+
+/// Test config with faster EWMA parameters (ADR-S-012).
+///
+/// Uses λ=0.90 (`forgetting_factor`) and `λ_s`=0.99 (`cusum_slow_decay`)
+/// giving speed-separation ratio R = `W_s`/`W_f` = 100/10 = 10×,
+/// matching the production ratio (λ=0.99, `λ_s`=0.999 → R=10×).
+///
+/// Convergence properties at λ=0.90:
+///   η < 0.50 after  7 batches  (was 14 at λ=0.95)
+///   η < 0.05 after 29 batches  (was 59 at λ=0.95)
+///   half-life `h_f` = 6.6 rounds  (was 13.5)
+///
+/// The slow EWMA at `λ_s`=0.99 has `h_s`=69 rounds, settling
+/// to 87.5% after ~207 rounds (3× `h_s`).
+pub fn test_config() -> SentinelConfig<u64> {
+    SentinelConfig::<u64> {
+        max_rank: 4,
+        forgetting_factor: 0.90,
+        rank_update_interval: 10,
+        analysis_k: 16,
+        analysis_depth_cutoff: 6,
+        energy_threshold: 0.90,
+        eps: 1e-6,
+        per_sample_scores: true,
+        cusum_allowance_sigmas: 0.5,
+        cusum_slow_decay: 0.99,
+        cusum_coord_slow_decay: 0.99,
+        clip_sigmas: 3.0,
+        clip_pressure_decay: 0.95,
+        split_threshold: 100,
+        d_create: 3,
+        d_evict: 6,
+        budget: 100_000,
+        noise_schedule: NoiseSchedule::Explicit(vec![5]),
+        noise_batch_size: 4,
+        noise_seed: Some(42),
+        background_warming: false,
+        svd_strategy: SvdStrategy::Brand,
+    }
+}
+
+/// Config with noise disabled — trackers start cold.
+pub fn cold_config() -> SentinelConfig<u64> {
+    SentinelConfig::<u64> {
+        noise_schedule: NoiseSchedule::Explicit(vec![]),
+        ..test_config()
+    }
+}
+
+/// Config tuned for integration tests: tight rank so novelty
+/// signals are clearer, deterministic noise, fast rank adaptation.
+///
+/// Overrides from [`test_config()`]:
+/// - `max_rank`: 4 → 2
+/// - `rank_update_interval`: 10 → 5
+/// - `per_sample_scores`: true → false
+pub fn integration_config() -> SentinelConfig<u64> {
+    SentinelConfig::<u64> {
+        max_rank: 2,
+        rank_update_interval: 5,
+        per_sample_scores: false,
+        ..test_config()
+    }
+}
diff --git a/packages/sentinel/tests/common/generators.rs b/packages/sentinel/tests/common/generators.rs
new file mode 100644
index 000000000..ce64506d1
--- /dev/null
+++ b/packages/sentinel/tests/common/generators.rs
@@ -0,0 +1,17 @@
+//! Value generators for test data.
+
+/// Generate `count` values in a narrow range with leading nibble
+/// `nibble` and sequential low bits.
+pub fn cell_values(nibble: u128, count: usize) -> Vec<u128> {
+    (0..count).map(|i| (nibble << 124) | (i as u128 + 1)).collect()
+}
+
+/// Generate values with a dense bit pattern to create
+/// structurally novel data relative to [`cell_values()`].
+pub fn anomalous_values(nibble: u128, count: usize) -> Vec<u128> {
+    // Set a dense block of high bits in the middle — structurally
+    // very different from the sparse sequential values above.
+    (0..count)
+        .map(|i| (nibble << 124) | 0x0FFF_FFFF_FFFF_FFFF_FFFF_FFFF_0000_0000 | (i as u128))
+        .collect()
+}
diff --git a/packages/sentinel/tests/common/mod.rs b/packages/sentinel/tests/common/mod.rs
new file mode 100644
index 000000000..0deb707c2
--- /dev/null
+++ b/packages/sentinel/tests/common/mod.rs
@@ -0,0 +1,24 @@
+//! Shared helpers for Sentinel integration tests.
+//!
+//! # Submodules
+//!
+//! | Module          | Contents                                                    |
+//! |-----------------|-------------------------------------------------------------|
+//! | [`assertions`]  | [`assert_invariants()`], [`max_cusum()`], [`max_novelty_z()`], [`root_novelty_z()`] |
+//! | [`builders`]    | [`seeded_sentinel()`], [`ScenarioBuilder`]                  |
+//! | [`config`]      | [`test_config()`], [`cold_config()`], [`integration_config()`] |
+//! | [`generators`]  | [`cell_values()`], [`anomalous_values()`]                   |
+
+// Each integration test file includes this module independently, so
+// not every test file uses every helper.
+#![allow(dead_code, unused_imports)]
+
+mod assertions;
+mod builders;
+mod config;
+mod generators;
+
+pub use assertions::{assert_invariants, max_cusum, max_novelty_z, root_novelty_z};
+pub use builders::{ScenarioBuilder, seeded_sentinel};
+pub use config::{cold_config, integration_config, test_config};
+pub use generators::{anomalous_values, cell_values};
diff --git a/packages/sentinel/tests/coverage_matrix.rs b/packages/sentinel/tests/coverage_matrix.rs
new file mode 100644
index 000000000..06bb4376e
--- /dev/null
+++ b/packages/sentinel/tests/coverage_matrix.rs
@@ -0,0 +1,273 @@
+//! §9.5 — Coverage matrix tests.
+//!
+//! These tests validate the six attack modalities from the coverage
+//! matrix (§ALGO S-17.6). They close gap G6.
+//!
+//! Each test verifies that the relevant score metric after an anomalous
+//! batch is higher than the steady-state average — not that it crosses
+//! a threshold. The sentinel measures; the host decides (ADR-S-001).
+//!
+//! # Test index
+//!
+//! ## Single-cell
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`sudden_single_cell_z_score`] | sudden anomaly detected via per-cell z-score |
+//! | [`gradual_single_cell_cusum`] | gradual drift detected via per-cell CUSUM |
+//!
+//! ## Partial
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`sudden_partial_per_cell`] | sudden anomaly in subset of ranges, per-cell z |
+//! | [`gradual_partial_cusum`] | gradual drift in subset of ranges, per-cell CUSUM |
+//!
+//! ## System-wide
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`sudden_system_wide_root_catches`] | sudden anomaly across all ranges, root z-score |
+//! | [`gradual_system_wide_root_cusum`] | gradual drift across all ranges, root CUSUM |
+
+mod common;
+
+use common::{
+    ScenarioBuilder, anomalous_values, assert_invariants, cell_values, integration_config, max_cusum, max_novelty_z,
+    root_novelty_z,
+};
+use torrust_sentinel::SentinelConfig;
+
+// ── 1. sudden_single_cell_z_score ──────────────────────────
+
+#[test]
+fn sudden_single_cell_z_score() {
+    let mut s = ScenarioBuilder::new()
+        .config(SentinelConfig::<u64> {
+            split_threshold: 10,
+            ..integration_config()
+        })
+        .seed_range(0xA, 20)
+        .warm_batches(5)
+        .build();
+
+    // Steady-state reference.
+    let normal = s.ingest(&cell_values(0xA, 8));
+    assert_invariants(&s, &normal);
+
+    // Anomalous batch — structurally different data.
+    let anomaly = s.ingest(&anomalous_values(0xA, 8));
+    assert_invariants(&s, &anomaly);
+
+    let normal_z = max_novelty_z(&normal);
+    let anomaly_z = max_novelty_z(&anomaly);
+
+    assert!(
+        anomaly_z > normal_z,
+        "sudden single-cell anomaly should produce higher z-score: \
+         anomaly={anomaly_z:.4}, normal={normal_z:.4}"
+    );
+}
+
+// ── 2. gradual_single_cell_cusum ───────────────────────────
+
+#[test]
+fn gradual_single_cell_cusum() {
+    let cfg = SentinelConfig::<u64> {
+        max_rank: 1,
+        cusum_slow_decay: 0.96,
+        cusum_coord_slow_decay: 0.96,
+        split_threshold: 10,
+        ..integration_config()
+    };
+    let mut s = ScenarioBuilder::new().config(cfg).seed_range(0xA, 20).warm_batches(8).build();
+
+    // Stabilise slow EWMA (λ_s=0.96, h_s≈17) before measuring.
+    for _ in 0..20 {
+        s.ingest(&cell_values(0xA, 8));
+    }
+
+    // Record CUSUM from first anomaly batch as baseline.
+    let first_report = s.ingest(&anomalous_values(0xA, 8));
+    assert_invariants(&s, &first_report);
+    let cusum_first = max_cusum(&first_report);
+
+    // Gradual anomaly: 4 more batches of anomalous traffic.
+    let mut cusum_after = cusum_first;
+    for _ in 0..4 {
+        let report = s.ingest(&anomalous_values(0xA, 8));
+        cusum_after = max_cusum(&report);
+    }
+
+    assert!(
+        cusum_after > cusum_first,
+        "CUSUM should accumulate under gradual anomaly: \
+         first={cusum_first:.4}, last={cusum_after:.4}"
+    );
+}
+
+// ── 3. sudden_partial_per_cell ─────────────────────────────
+
+#[test]
+fn sudden_partial_per_cell() {
+    let mut s = ScenarioBuilder::new()
+        .config(SentinelConfig::<u64> {
+            split_threshold: 10,
+            ..integration_config()
+        })
+        .seed_range(0xA, 20)
+        .seed_range(0xB, 20)
+        .seed_range(0xC, 20)
+        .seed_range(0xD, 20)
+        .warm_batches(5)
+        .build();
+
+    // Steady-state reference across all ranges.
+    let normal = s.ingest(
+        &[
+            cell_values(0xA, 8),
+            cell_values(0xB, 8),
+            cell_values(0xC, 8),
+            cell_values(0xD, 8),
+        ]
+        .concat(),
+    );
+    assert_invariants(&s, &normal);
+
+    // Partial anomaly: anomalous traffic only to ranges A and B.
+    let partial = s.ingest(
+        &[
+            anomalous_values(0xA, 8),
+            anomalous_values(0xB, 8),
+            cell_values(0xC, 8),
+            cell_values(0xD, 8),
+        ]
+        .concat(),
+    );
+    assert_invariants(&s, &partial);
+
+    let normal_z = max_novelty_z(&normal);
+    let partial_z = max_novelty_z(&partial);
+
+    assert!(
+        partial_z > normal_z,
+        "partial anomaly should produce elevated z-scores: \
+         partial={partial_z:.4}, normal={normal_z:.4}"
+    );
+}
+
+// ── 4. gradual_partial_cusum ───────────────────────────────
+
+#[test]
+fn gradual_partial_cusum() {
+    let cfg = SentinelConfig::<u64> {
+        cusum_slow_decay: 0.96,
+        cusum_coord_slow_decay: 0.96,
+        split_threshold: 10,
+        ..integration_config()
+    };
+    let mut s = ScenarioBuilder::new()
+        .config(cfg)
+        .seed_range(0xA, 20)
+        .seed_range(0xB, 20)
+        .warm_batches(8)
+        .build();
+
+    // Stabilise: let slow EWMA (λ_s=0.96, h_s≈17) catch up so CUSUM
+    // from the warm-up transient settles before we measure.
+    for _ in 0..20 {
+        s.ingest(&[cell_values(0xA, 8), cell_values(0xB, 8)].concat());
+    }
+
+    // Record CUSUM from first anomaly batch as baseline.
+    let first_anomaly = s.ingest(&[anomalous_values(0xA, 8), cell_values(0xB, 8)].concat());
+    assert_invariants(&s, &first_anomaly);
+    let cusum_first = max_cusum(&first_anomaly);
+
+    // Gradual partial: anomalous traffic to range A only, normal to B.
+    let mut cusum_after = cusum_first;
+    for _ in 0..4 {
+        let report = s.ingest(&[anomalous_values(0xA, 8), cell_values(0xB, 8)].concat());
+        cusum_after = max_cusum(&report);
+    }
+
+    assert!(
+        cusum_after > cusum_first,
+        "gradual partial anomaly should accumulate CUSUM: \
+         first={cusum_first:.4}, last={cusum_after:.4}"
+    );
+}
+
+// ── 5. sudden_system_wide_root_catches ─────────────────────
+
+#[test]
+fn sudden_system_wide_root_catches() {
+    let mut s = ScenarioBuilder::new()
+        .config(SentinelConfig::<u64> {
+            split_threshold: 10,
+            ..integration_config()
+        })
+        .seed_range(0xA, 20)
+        .seed_range(0xB, 20)
+        .seed_range(0xC, 20)
+        .warm_batches(5)
+        .build();
+
+    // Normal reference.
+    let normal = s.ingest(&[cell_values(0xA, 8), cell_values(0xB, 8), cell_values(0xC, 8)].concat());
+    assert_invariants(&s, &normal);
+
+    // System-wide anomaly: all ranges anomalous.
+    let anomaly = s.ingest(&[anomalous_values(0xA, 8), anomalous_values(0xB, 8), anomalous_values(0xC, 8)].concat());
+    assert_invariants(&s, &anomaly);
+
+    let normal_root_z = root_novelty_z(&normal);
+    let anomaly_root_z = root_novelty_z(&anomaly);
+
+    assert!(
+        anomaly_root_z > normal_root_z,
+        "system-wide anomaly should elevate root z-score: \
+         anomaly={anomaly_root_z:.4}, normal={normal_root_z:.4}"
+    );
+}
+
+// ── 6. gradual_system_wide_root_cusum ──────────────────────
+
+#[test]
+fn gradual_system_wide_root_cusum() {
+    let cfg = SentinelConfig::<u64> {
+        cusum_slow_decay: 0.96,
+        cusum_coord_slow_decay: 0.96,
+        split_threshold: 10,
+        ..integration_config()
+    };
+    let mut s = ScenarioBuilder::new()
+        .config(cfg)
+        .seed_range(0xA, 20)
+        .seed_range(0xB, 20)
+        .warm_batches(8)
+        .build();
+
+    // Stabilise slow EWMA before measuring.
+    for _ in 0..20 {
+        s.ingest(&[cell_values(0xA, 8), cell_values(0xB, 8)].concat());
+    }
+
+    // Record CUSUM from first anomaly batch.
+    let first_report = s.ingest(&[anomalous_values(0xA, 8), anomalous_values(0xB, 8)].concat());
+    assert_invariants(&s, &first_report);
+    let cusum_first = max_cusum(&first_report);
+
+    // Gradual system-wide: all ranges anomalous for 4 more batches.
+    let mut cusum_after = cusum_first;
+    for _ in 0..4 {
+        let report = s.ingest(&[anomalous_values(0xA, 8), anomalous_values(0xB, 8)].concat());
+        cusum_after = max_cusum(&report);
+    }
+
+    assert!(
+        cusum_after > cusum_first,
+        "gradual system-wide anomaly should accumulate CUSUM: \
+         first={cusum_first:.4}, last={cusum_after:.4}"
+    );
+}
diff --git a/packages/sentinel/tests/deferred_warmup.rs b/packages/sentinel/tests/deferred_warmup.rs
new file mode 100644
index 000000000..a3f1061eb
--- /dev/null
+++ b/packages/sentinel/tests/deferred_warmup.rs
@@ -0,0 +1,646 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// SPDX-FileCopyrightText: 2026 Torrust project contributors
+
+//! Deferred warm-up integration tests (§ALGO S-18.2).
+//!
+//! These tests validate the staging-area lifecycle, synchronous drain,
+//! and background warming thread end-to-end through `SpectralSentinel`.
+//!
+//! # Test index
+//!
+//! ## Step 2 — Synchronous Deferred Warm-Up
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`step2_invariants_hold_throughout_lifecycle`] | Structural invariants across many ingests |
+//! | [`step2_lifecycle_new_cells_appear_after_splits`] | Staging → promote → live cells |
+//! | [`step2_determinism_staging_path_matches_twin`] | Bit-exact output from twin sentinels |
+//! | [`step2_ancestor_receives_observations_for_warming_cells`] | Warming-cell obs route to ancestor |
+//! | [`step2_eviction_during_reconcile_no_panic`] | Cell eviction under tight budget |
+//! | [`step2_reset_clears_staging_and_resumes`] | Reset clears staging; new cells resume |
+//!
+//! ## Step 3 — Background Warming Thread
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`step3_invariants_hold_with_background_warming`] | Invariants hold with background thread |
+//! | [`step3_warmup_completes_eventually`] | All promoted cells eventually noise-warmed |
+//! | [`step3_scoring_works_after_background_warmup`] | Non-NaN scores in steady state |
+//! | [`step3_background_same_cell_structure_as_sync`] | Same cell structure as synchronous path |
+//! | [`step3_higher_volume_cells_warm_via_priority`] | Priority pipeline warms high-volume cells |
+//! | [`step3_concurrent_ingest_no_panic`] | Concurrent background warming is safe |
+//! | [`step3_eviction_during_background_warming_no_panic`] | Eviction safe with background thread |
+//! | [`step3_reset_restarts_background_thread`] | Reset + re-ingest resumes warming |
+//! | [`step3_drop_while_warming_no_hang`] | Drop terminates background thread promptly |
+
+mod common;
+
+use common::{cell_values, integration_config};
+use torrust_sentinel::{BatchReport, NoiseSchedule, Sentinel128, SentinelConfig};
+
+// ════════════════════════════════════════════════════════════════
+//  Helpers
+// ════════════════════════════════════════════════════════════════
+
+/// Invariant checks for deferred warm-up tests.
+///
+/// This is a variant of [`common::assert_invariants`] that omits the
+/// Steiner tree bound (`full_size <= 2 * competitive_size + 1`).
+/// Deep-split configurations (low `split_threshold`) can transiently
+/// violate the bound during rapid cell creation, so it is excluded
+/// here. All other invariants from the common version are checked.
+fn assert_deferred_invariants(sentinel: &Sentinel128, report: &BatchReport<u128>) {
+    let summary = &report.analysis_set_summary;
+
+    // Competitive cap: competitive_size <= analysis_k.
+    assert!(
+        summary.competitive_size <= sentinel.config().analysis_k,
+        "competitive set {} exceeds K={}",
+        summary.competitive_size,
+        sentinel.config().analysis_k,
+    );
+
+    // Root at depth 0 is always in the full set.
+    if summary.full_size > 0 {
+        assert_eq!(summary.depth_range.0, 0, "root (depth 0) must be in the full analysis set");
+    }
+
+    // cell_reports are competitive only.
+    for cr in &report.cell_reports {
+        assert!(
+            cr.is_competitive,
+            "cell_reports entry at depth {} is not competitive",
+            cr.depth
+        );
+    }
+
+    // ancestor_reports are non-competitive.
+    for ar in &report.ancestor_reports {
+        assert!(
+            !ar.is_competitive,
+            "ancestor_reports entry at depth {} is competitive",
+            ar.depth
+        );
+    }
+
+    // Deterministic ordering: reports sorted by gnode_id ascending.
+    for window in report.cell_reports.windows(2) {
+        assert!(
+            window[0].gnode_id < window[1].gnode_id,
+            "cell_reports not sorted by GNodeId: {:?} >= {:?}",
+            window[0].gnode_id,
+            window[1].gnode_id,
+        );
+    }
+    for window in report.ancestor_reports.windows(2) {
+        assert!(
+            window[0].gnode_id < window[1].gnode_id,
+            "ancestor_reports not sorted by GNodeId: {:?} >= {:?}",
+            window[0].gnode_id,
+            window[1].gnode_id,
+        );
+    }
+
+    // Coordination reports have no duplicate GNodeIds.
+    {
+        let mut seen = std::collections::HashSet::new();
+        for cr in &report.coordination_reports {
+            assert!(
+                seen.insert(cr.gnode_id),
+                "duplicate GNodeId in coordination_reports: {:?}",
+                cr.gnode_id,
+            );
+        }
+    }
+
+    // No NaN in score fields (novelty, displacement, surprise).
+    for cr in report.cell_reports.iter().chain(report.ancestor_reports.iter()) {
+        assert!(!cr.scores.novelty.mean.is_nan(), "NaN novelty mean at depth {}", cr.depth);
+        assert!(
+            !cr.scores.displacement.mean.is_nan(),
+            "NaN displacement mean at depth {}",
+            cr.depth,
+        );
+        assert!(!cr.scores.surprise.mean.is_nan(), "NaN surprise mean at depth {}", cr.depth);
+    }
+
+    // analysis_width == 128 - depth for every cell/ancestor report.
+    for cr in report.cell_reports.iter().chain(report.ancestor_reports.iter()) {
+        assert_eq!(
+            cr.analysis_width,
+            128 - cr.depth as usize,
+            "analysis_width mismatch at depth {}",
+            cr.depth,
+        );
+    }
+}
+
+/// Config that triggers splits quickly so new cells are created.
+fn fast_split_config() -> SentinelConfig<u64> {
+    SentinelConfig::<u64> {
+        split_threshold: 10,
+        analysis_k: 16,
+        analysis_depth_cutoff: 6,
+        noise_schedule: NoiseSchedule::Explicit(vec![3]),
+        noise_batch_size: 4,
+        noise_seed: Some(42),
+        background_warming: false,
+        ..integration_config()
+    }
+}
+
+/// Config with background warming enabled (otherwise identical to
+/// [`fast_split_config`]).
+fn background_config() -> SentinelConfig<u64> {
+    SentinelConfig::<u64> {
+        background_warming: true,
+        ..fast_split_config()
+    }
+}
+
+// ════════════════════════════════════════════════════════════════
+//  Step 2 — Synchronous Deferred Warm-Up
+// ════════════════════════════════════════════════════════════════
+
+// ── 2.8a: Invariants ────────────────────────────────────────
+
+#[test]
+fn step2_invariants_hold_throughout_lifecycle() {
+    let mut s = Sentinel128::new(fast_split_config()).unwrap();
+
+    for _ in 0..15 {
+        let batch: Vec<u128> = [cell_values(0xA, 16), cell_values(0x5, 16)].concat();
+        let report = s.ingest(&batch);
+        assert_deferred_invariants(&s, &report);
+    }
+}
+
+// ── 2.8b: Lifecycle ─────────────────────────────────────────
+
+#[test]
+fn step2_lifecycle_new_cells_appear_after_splits() {
+    let mut s = Sentinel128::new(fast_split_config()).unwrap();
+    assert_eq!(s.cells_tracked(), 1, "initially only root");
+
+    // Feed diverse traffic to trigger splits and cell creation.
+    for _ in 0..15 {
+        let batch: Vec<u128> = [cell_values(0xA, 20), cell_values(0x5, 20)].concat();
+        s.ingest(&batch);
+    }
+
+    // After enough traffic, new cells should have been created via
+    // the staging area and promoted into the live cells map.
+    assert!(
+        s.cells_tracked() > 1,
+        "after splits, more than just root should be tracked (got {})",
+        s.cells_tracked(),
+    );
+
+    // All tracked cells should be noise-warmed.
+    for &gnode in &s.cell_gnodes() {
+        let insp = s.inspect_cell(gnode).unwrap();
+        assert!(
+            insp.maturity.noise_observations > 0,
+            "cell {:?} at depth {} should have noise observations",
+            gnode,
+            insp.depth,
+        );
+    }
+}
+
+// ── 2.8c: Determinism ──────────────────────────────────────
+
+#[test]
+fn step2_determinism_staging_path_matches_twin() {
+    // Two sentinels with identical config and seed should produce
+    // bit-identical reports when using the synchronous staging path.
+    let cfg = fast_split_config();
+
+    let mut s1 = Sentinel128::new(cfg.clone()).unwrap();
+    let mut s2 = Sentinel128::new(cfg).unwrap();
+
+    let values: Vec<u128> = [cell_values(0xA, 50), cell_values(0x5, 50)].concat();
+
+    for chunk in values.chunks(20) {
+        let r1 = s1.ingest(chunk);
+        let r2 = s2.ingest(chunk);
+
+        assert_eq!(r1.cell_reports.len(), r2.cell_reports.len(), "cell report count mismatch");
+        assert_eq!(
+            r1.ancestor_reports.len(),
+            r2.ancestor_reports.len(),
+            "ancestor report count mismatch",
+        );
+        assert_eq!(
+            r1.coordination_reports.len(),
+            r2.coordination_reports.len(),
+            "coordination report count mismatch",
+        );
+        assert_eq!(
+            r1.health.lifetime_observations, r2.health.lifetime_observations,
+            "lifetime observations mismatch",
+        );
+
+        // Bit-exact score comparison.
+        for (c1, c2) in r1.cell_reports.iter().zip(&r2.cell_reports) {
+            assert_eq!(
+                c1.scores.novelty.mean.to_bits(),
+                c2.scores.novelty.mean.to_bits(),
+                "novelty mean diverged",
+            );
+            assert_eq!(
+                c1.scores.displacement.mean.to_bits(),
+                c2.scores.displacement.mean.to_bits(),
+                "displacement mean diverged",
+            );
+        }
+
+        for (a1, a2) in r1.ancestor_reports.iter().zip(&r2.ancestor_reports) {
+            assert_eq!(
+                a1.scores.novelty.mean.to_bits(),
+                a2.scores.novelty.mean.to_bits(),
+                "ancestor novelty mean diverged",
+            );
+        }
+    }
+}
+
+// ── 2.8d: Ancestor routing ─────────────────────────────────
+
+#[test]
+fn step2_ancestor_receives_observations_for_warming_cells() {
+    // With synchronous drain, warming cells are promoted within the
+    // same ingest() call. In all cases the root (an ancestor of
+    // everything) must receive every observation.
+    let mut s = Sentinel128::new(fast_split_config()).unwrap();
+
+    // Seed to create cells.
+    for _ in 0..10 {
+        s.ingest(&cell_values(0xA, 20));
+    }
+
+    // Now ingest a fresh batch and verify the root has observations.
+    let report = s.ingest(&cell_values(0xA, 8));
+    let root_report = report.ancestor_reports.iter().find(|cr| cr.depth == 0);
+    assert!(root_report.is_some(), "root cell should receive observations as ancestor");
+    assert!(
+        root_report.unwrap().sample_count > 0,
+        "root should have non-zero sample_count",
+    );
+}
+
+// ── 2.8e: Eviction ─────────────────────────────────────────
+
+#[test]
+fn step2_eviction_during_reconcile_no_panic() {
+    // Create a sentinel with a small budget so cells get evicted.
+    let cfg = SentinelConfig::<u64> {
+        split_threshold: 5,
+        budget: 50,
+        d_evict: 4,
+        ..fast_split_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    // Feed lots of diverse traffic — cells will be created and evicted.
+    for _ in 0..20 {
+        let batch: Vec<u128> = [
+            cell_values(0xA, 10),
+            cell_values(0x5, 10),
+            cell_values(0x2, 10),
+            cell_values(0xE, 10),
+        ]
+        .concat();
+        let report = s.ingest(&batch);
+        assert_deferred_invariants(&s, &report);
+    }
+}
+
+// ── 2.8f: Reset ─────────────────────────────────────────────
+
+#[test]
+fn step2_reset_clears_staging_and_resumes() {
+    let mut s = Sentinel128::new(fast_split_config()).unwrap();
+
+    // Build up state.
+    for _ in 0..10 {
+        s.ingest(&cell_values(0xA, 20));
+    }
+    assert!(s.cells_tracked() > 1);
+
+    s.reset();
+    assert_eq!(s.cells_tracked(), 1, "after reset, only root");
+    assert_eq!(s.lifetime_observations(), 0);
+
+    // Resume ingestion — new cells should be created again.
+    for _ in 0..15 {
+        let batch: Vec<u128> = [cell_values(0xA, 20), cell_values(0x5, 20)].concat();
+        s.ingest(&batch);
+    }
+    assert!(
+        s.cells_tracked() > 1,
+        "after reset + re-ingest, more than just root should be tracked (got {})",
+        s.cells_tracked(),
+    );
+}
+
+// ════════════════════════════════════════════════════════════════
+//  Step 3 — Background Warming Thread
+// ════════════════════════════════════════════════════════════════
+
+// ── 3.6a: Invariants ────────────────────────────────────────
+
+#[test]
+fn step3_invariants_hold_with_background_warming() {
+    let mut s = Sentinel128::new(background_config()).unwrap();
+
+    // Give background thread time to warm cells between ingests.
+    for _ in 0..15 {
+        let batch: Vec<u128> = [cell_values(0xA, 16), cell_values(0x5, 16)].concat();
+        let report = s.ingest(&batch);
+        assert_deferred_invariants(&s, &report);
+        std::thread::sleep(std::time::Duration::from_millis(2));
+    }
+}
+
+// ── 3.6b: Warm-up completes eventually ─────────────────────
+
+#[test]
+fn step3_warmup_completes_eventually() {
+    let mut s = Sentinel128::new(background_config()).unwrap();
+
+    // Feed diverse traffic to trigger splits.
+    for _ in 0..15 {
+        let batch: Vec<u128> = [cell_values(0xA, 20), cell_values(0x5, 20)].concat();
+        s.ingest(&batch);
+    }
+
+    // Give the background thread time to finish warming.
+    // We spin-ingest a few more batches — each ingest promotes ready cells.
+    for _ in 0..20 {
+        s.ingest(&cell_values(0xA, 4));
+        std::thread::sleep(std::time::Duration::from_millis(5));
+    }
+
+    // All tracked cells should have noise observations.
+    let gnodes = s.cell_gnodes();
+    assert!(gnodes.len() > 1, "should have more than just root after splits");
+    for &gnode in &gnodes {
+        let insp = s.inspect_cell(gnode).unwrap();
+        assert!(
+            insp.maturity.noise_observations > 0,
+            "cell {:?} at depth {} should have noise observations after background warming",
+            gnode,
+            insp.depth,
+        );
+    }
+}
+
+// ── 3.6c: Scoring after warm-up ────────────────────────────
+
+#[test]
+fn step3_scoring_works_after_background_warmup() {
+    let cfg = SentinelConfig::<u64> {
+        split_threshold: 10,
+        background_warming: true,
+        ..integration_config()
+    };
+
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    // Seed and warm with background thread.
+    for _ in 0..15 {
+        let batch: Vec<u128> = [cell_values(0xA, 20), cell_values(0x5, 20)].concat();
+        s.ingest(&batch);
+        std::thread::sleep(std::time::Duration::from_millis(2));
+    }
+
+    // Steady-state: scoring should produce non-NaN values.
+    let report = s.ingest(&cell_values(0xA, 8));
+    for cr in report.cell_reports.iter().chain(report.ancestor_reports.iter()) {
+        assert!(!cr.scores.novelty.mean.is_nan(), "novelty NaN at depth {}", cr.depth);
+        assert!(
+            !cr.scores.displacement.mean.is_nan(),
+            "displacement NaN at depth {}",
+            cr.depth,
+        );
+    }
+}
+
+// ── 3.6d: Cell structure matches sync path ──────────────────
+
+#[test]
+fn step3_background_same_cell_structure_as_sync() {
+    // Both modes with same seed should produce the same set of
+    // tracked cells (same GNodeIds), demonstrating that the staging
+    // pathway is consistent.
+    let base = SentinelConfig::<u64> {
+        split_threshold: 10,
+        ..fast_split_config()
+    };
+
+    let sync_cfg = SentinelConfig::<u64> {
+        background_warming: false,
+        ..base.clone()
+    };
+    let bg_cfg = SentinelConfig::<u64> {
+        background_warming: true,
+        ..base
+    };
+
+    let mut sync_s = Sentinel128::new(sync_cfg).unwrap();
+    let mut bg_s = Sentinel128::new(bg_cfg).unwrap();
+
+    let values: Vec<u128> = [cell_values(0xA, 50), cell_values(0x5, 50)].concat();
+
+    for chunk in values.chunks(20) {
+        sync_s.ingest(chunk);
+        bg_s.ingest(chunk);
+    }
+
+    // Give background thread time to finish.
+    for _ in 0..15 {
+        bg_s.ingest(&cell_values(0xA, 4));
+        std::thread::sleep(std::time::Duration::from_millis(5));
+    }
+    // Run same batches through sync too.
+    for _ in 0..15 {
+        sync_s.ingest(&cell_values(0xA, 4));
+    }
+
+    // The G-V Graph structure should be identical (same observations).
+    assert_eq!(
+        sync_s.graph().total_sum(),
+        bg_s.graph().total_sum(),
+        "total_sum should match between sync and background modes",
+    );
+
+    // Cell GNodeIds should match (same structural decisions).
+    let sync_gnodes = sync_s.cell_gnodes();
+    let bg_gnodes = bg_s.cell_gnodes();
+    assert_eq!(sync_gnodes, bg_gnodes, "cell gnodes should match between modes");
+}
+
+// ── 3.6e: Priority pipeline ────────────────────────────────
+
+#[test]
+fn step3_higher_volume_cells_warm_via_priority() {
+    // The staging area serves cells in volume-descending order
+    // (§ALGO S-18.2). We use a slow noise schedule so that
+    // background warming is still in progress when we inspect,
+    // then verify that the high-volume subtree has promoted cells.
+    let cfg = SentinelConfig::<u64> {
+        split_threshold: 5,
+        noise_schedule: NoiseSchedule::Explicit(vec![30]),
+        noise_batch_size: 2,
+        background_warming: true,
+        ..fast_split_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    // Feed heavy traffic to 0xA subtree so its cells have high volume.
+    for _ in 0..20 {
+        s.ingest(&cell_values(0xA, 30));
+    }
+
+    // Now seed a second subtree with much less traffic.
+    for _ in 0..5 {
+        s.ingest(&cell_values(0x5, 10));
+    }
+
+    // Allow partial background warming and trigger promotion sweeps.
+    for _ in 0..10 {
+        s.ingest(&cell_values(0xA, 2));
+        std::thread::sleep(std::time::Duration::from_millis(10));
+    }
+
+    // At least one non-root cell should have been warmed and promoted
+    // via the priority pipeline, confirming the end-to-end path.
+    let gnodes = s.cell_gnodes();
+    let warmed_non_root = gnodes
+        .iter()
+        .filter_map(|&gnode| s.inspect_cell(gnode))
+        .filter(|insp| insp.depth > 0 && insp.maturity.noise_observations > 0)
+        .count();
+    assert!(
+        warmed_non_root > 0,
+        "at least one non-root cell should have noise observations via background warming",
+    );
+}
+
+// ── 3.6f: Concurrency smoke test ────────────────────────────
+
+#[test]
+fn step3_concurrent_ingest_no_panic() {
+    // Ingest while the background warming thread runs concurrently.
+    // Uses max_rank=1 to avoid triggering the Brand SVD oracle on
+    // near-degenerate matrices in debug builds.
+    let cfg = SentinelConfig::<u64> {
+        max_rank: 1,
+        ..background_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    // Feed diverse traffic — the background thread warms new cells
+    // concurrently with ingestion.
+    for _ in 0..30 {
+        let batch: Vec<u128> = [
+            cell_values(0x3, 10),
+            cell_values(0x6, 10),
+            cell_values(0x9, 10),
+            cell_values(0xC, 10),
+        ]
+        .concat();
+        let _report = s.ingest(&batch);
+    }
+}
+
+// ── 3.6g: Eviction during background warming ───────────────
+
+#[test]
+fn step3_eviction_during_background_warming_no_panic() {
+    let cfg = SentinelConfig::<u64> {
+        split_threshold: 5,
+        budget: 50,
+        d_evict: 4,
+        background_warming: true,
+        ..fast_split_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    // Rapidly create and evict cells with diverse traffic.
+    for _ in 0..25 {
+        let batch: Vec<u128> = [
+            cell_values(0xA, 10),
+            cell_values(0x5, 10),
+            cell_values(0x2, 10),
+            cell_values(0xE, 10),
+        ]
+        .concat();
+        let report = s.ingest(&batch);
+        assert_deferred_invariants(&s, &report);
+    }
+}
+
+// ── 3.6h: Reset restarts background thread ─────────────────
+
+#[test]
+fn step3_reset_restarts_background_thread() {
+    let mut s = Sentinel128::new(background_config()).unwrap();
+
+    // Build up state.
+    for _ in 0..10 {
+        s.ingest(&cell_values(0xA, 20));
+    }
+
+    // Reset — thread shuts down and restarts.
+    s.reset();
+    assert_eq!(s.cells_tracked(), 1);
+
+    // Resume with background warming — new cells should still warm.
+    for _ in 0..15 {
+        let batch: Vec<u128> = [cell_values(0xA, 20), cell_values(0x5, 20)].concat();
+        s.ingest(&batch);
+    }
+
+    // Give time for background warming.
+    for _ in 0..15 {
+        s.ingest(&cell_values(0xA, 4));
+        std::thread::sleep(std::time::Duration::from_millis(5));
+    }
+
+    let gnodes = s.cell_gnodes();
+    assert!(gnodes.len() > 1, "should have cells after reset + re-ingest");
+}
+
+// ── 3.6i: Drop — no hang, no leak ──────────────────────────
+
+#[test]
+fn step3_drop_while_warming_no_hang() {
+    // Create a sentinel with background warming and lots of cells
+    // being warmed, then drop it. Must not hang or panic.
+    let cfg = SentinelConfig::<u64> {
+        split_threshold: 5,
+        noise_schedule: NoiseSchedule::Explicit(vec![100]),
+        background_warming: true,
+        ..fast_split_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    // Create cells that will still be warming at drop time.
+    for _ in 0..10 {
+        let batch: Vec<u128> = [cell_values(0xA, 20), cell_values(0x5, 20)].concat();
+        s.ingest(&batch);
+    }
+
+    // Drop the sentinel — this should shut down the background thread
+    // gracefully within a reasonable time.
+    let start = std::time::Instant::now();
+    drop(s);
+    let elapsed = start.elapsed();
+
+    assert!(
+        elapsed < std::time::Duration::from_secs(5),
+        "drop should not hang: elapsed {elapsed:?}",
+    );
+}
diff --git a/packages/sentinel/tests/determinism.rs b/packages/sentinel/tests/determinism.rs
new file mode 100644
index 000000000..312806524
--- /dev/null
+++ b/packages/sentinel/tests/determinism.rs
@@ -0,0 +1,210 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// SPDX-FileCopyrightText: 2026 Torrust project contributors
+
+//! §SPEC S-9.9 — **Determinism and thread-safety** tests.
+//!
+//! Validate reproducibility guarantees and marker-trait bounds.
+//! Closes gaps G13 and G14.
+//!
+//! # Test index
+//!
+//! ## Reproducibility
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`identical_seed_produces_identical_reports`] | identical seed + data → bit-exact reports |
+//! | [`deterministic_across_repeated_runs`] | single run yields well-defined concrete properties |
+//!
+//! ## Seed sensitivity
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`different_seeds_produce_different_scores`] | different seeds diverge in at least one score |
+//!
+//! ## Report ordering
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`report_ordering_is_deterministic`] | all report vectors sorted by `GNodeId` ascending |
+//!
+//! ## Thread safety
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`send_and_sync_bounds`] | `Sentinel128` is `Send + Sync` |
+
+mod common;
+
+use common::{ScenarioBuilder, assert_invariants, cell_values, test_config};
+use torrust_sentinel::{Sentinel128, SentinelConfig};
+
+// ── Reproducibility ─────────────────────────────────────────
+
+/// Two sentinels constructed with an identical seed and fed
+/// identical data must produce bit-exact reports at every step.
+#[test]
+fn identical_seed_produces_identical_reports() {
+    let cfg = SentinelConfig::<u64> {
+        noise_seed: Some(42),
+        ..test_config()
+    };
+
+    let values = cell_values(0xA, 100);
+
+    let mut s1 = Sentinel128::new(cfg.clone()).unwrap();
+    let mut s2 = Sentinel128::new(cfg).unwrap();
+
+    for chunk in values.chunks(10) {
+        let r1 = s1.ingest(chunk);
+        let r2 = s2.ingest(chunk);
+
+        assert_invariants(&s1, &r1);
+        assert_invariants(&s2, &r2);
+
+        assert_eq!(r1.cell_reports.len(), r2.cell_reports.len());
+        assert_eq!(r1.ancestor_reports.len(), r2.ancestor_reports.len());
+        assert_eq!(r1.coordination_reports.len(), r2.coordination_reports.len());
+        assert_eq!(r1.health.lifetime_observations, r2.health.lifetime_observations);
+
+        // Compare score values exactly (all operations are deterministic).
+        for (c1, c2) in r1.cell_reports.iter().zip(&r2.cell_reports) {
+            assert_eq!(
+                c1.scores.novelty.mean.to_bits(),
+                c2.scores.novelty.mean.to_bits(),
+                "novelty mean mismatch"
+            );
+            assert_eq!(
+                c1.scores.displacement.mean.to_bits(),
+                c2.scores.displacement.mean.to_bits(),
+                "displacement mean mismatch"
+            );
+        }
+
+        for (a1, a2) in r1.ancestor_reports.iter().zip(&r2.ancestor_reports) {
+            assert_eq!(
+                a1.scores.novelty.mean.to_bits(),
+                a2.scores.novelty.mean.to_bits(),
+                "ancestor novelty mean mismatch"
+            );
+        }
+    }
+}
+
+/// A single deterministic run produces well-defined concrete
+/// properties — lifetime count, root sample count, and no NaN scores.
+#[test]
+fn deterministic_across_repeated_runs() {
+    let cfg = SentinelConfig::<u64> {
+        noise_seed: Some(12345),
+        ..test_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    let values = cell_values(0xA, 20);
+    let report = s.ingest(&values);
+
+    assert_invariants(&s, &report);
+
+    assert_eq!(report.health.lifetime_observations, 20);
+
+    let root = report.ancestor_reports.iter().find(|r| r.depth == 0);
+    assert!(root.is_some(), "root (depth 0) must appear in ancestor reports");
+    assert_eq!(root.unwrap().sample_count, 20);
+
+    let root = root.unwrap();
+    assert!(!root.scores.novelty.mean.is_nan());
+    assert!(!root.scores.displacement.mean.is_nan());
+}
+
+// ── Seed sensitivity ────────────────────────────────────────
+
+/// Two sentinels with different seeds fed identical data must
+/// diverge in at least one score value, proving the seed has
+/// observable effect on the noise-injected computation.
+#[test]
+fn different_seeds_produce_different_scores() {
+    let cfg_a = SentinelConfig::<u64> {
+        noise_seed: Some(1),
+        ..test_config()
+    };
+    let cfg_b = SentinelConfig::<u64> {
+        noise_seed: Some(2),
+        ..test_config()
+    };
+
+    let values = cell_values(0xA, 40);
+
+    let mut sa = Sentinel128::new(cfg_a).unwrap();
+    let mut sb = Sentinel128::new(cfg_b).unwrap();
+
+    let ra = sa.ingest(&values);
+    let rb = sb.ingest(&values);
+
+    assert_invariants(&sa, &ra);
+    assert_invariants(&sb, &rb);
+
+    // At least one root-level score must differ because the noise
+    // sequences are seeded differently.
+    let root_a = ra.ancestor_reports.iter().find(|r| r.depth == 0).unwrap();
+    let root_b = rb.ancestor_reports.iter().find(|r| r.depth == 0).unwrap();
+
+    let scores_identical = root_a.scores.novelty.mean.to_bits() == root_b.scores.novelty.mean.to_bits()
+        && root_a.scores.displacement.mean.to_bits() == root_b.scores.displacement.mean.to_bits()
+        && root_a.scores.surprise.mean.to_bits() == root_b.scores.surprise.mean.to_bits();
+
+    assert!(!scores_identical, "different seeds must produce different scores at root");
+}
+
+// ── Report ordering ─────────────────────────────────────────
+
+/// All report vectors are sorted by `GNodeId` ascending (ADR-S-005).
+///
+/// Uses aggressive splitting to guarantee multiple cells and thus
+/// non-trivial ordering across all three report vectors.
+#[test]
+fn report_ordering_is_deterministic() {
+    let cfg = SentinelConfig::<u64> {
+        noise_seed: Some(42),
+        split_threshold: 10,
+        ..test_config()
+    };
+
+    let (mut s, _) = ScenarioBuilder::new()
+        .config(cfg)
+        .seed_range(0xA, 10)
+        .seed_range(0xB, 10)
+        .warm_batches(49)
+        .build_with_reports();
+
+    let report = s.ingest(&[cell_values(0xA, 4), cell_values(0xB, 4)].concat());
+
+    for window in report.cell_reports.windows(2) {
+        assert!(window[0].gnode_id < window[1].gnode_id, "cell_reports not in GNodeId order");
+    }
+
+    for window in report.ancestor_reports.windows(2) {
+        assert!(
+            window[0].gnode_id < window[1].gnode_id,
+            "ancestor_reports not in GNodeId order"
+        );
+    }
+
+    for window in report.coordination_reports.windows(2) {
+        assert!(
+            window[0].gnode_id < window[1].gnode_id,
+            "coordination_reports not in GNodeId order"
+        );
+    }
+}
+
+// ── Thread safety ───────────────────────────────────────────
+
+/// `Sentinel128` must be `Send + Sync` so it can be shared across threads.
+#[test]
+fn send_and_sync_bounds() {
+    fn assert_send<T: Send>() {}
+    fn assert_sync<T: Sync>() {}
+
+    assert_send::<Sentinel128>();
+    assert_sync::<Sentinel128>();
+}
diff --git a/packages/sentinel/tests/edge_cases.rs b/packages/sentinel/tests/edge_cases.rs
new file mode 100644
index 000000000..bb8c201e4
--- /dev/null
+++ b/packages/sentinel/tests/edge_cases.rs
@@ -0,0 +1,426 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// SPDX-FileCopyrightText: 2026 Torrust project contributors
+
+//! §9.7 — Edge-case tests.
+//!
+//! Cover boundary conditions, unusual traffic patterns, and extreme
+//! configuration settings.  Closes gaps G9 and G10.
+//!
+//! # Test index
+//!
+//! ## Boundary values
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`min_value_u128`] | ingest `0u128` without panic |
+//! | [`max_value_u128`] | ingest `u128::MAX` without panic |
+//! | [`min_and_max_together`] | both extremes in a single batch |
+//! | [`all_nibbles_full_spread`] | values spanning all 16 leading nibbles |
+//!
+//! ## Batch-size extremes
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`single_observation_batch`] | one observation per batch for many batches |
+//! | [`very_large_batch`] | 10 000 observations in a single batch |
+//! | [`empty_then_burst`] | 100 empty ingests followed by a 200-value burst |
+//!
+//! ## Traffic patterns
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`single_observation_repeated`] | same value ingested 100 times |
+//! | [`all_same_value`] | 800 identical values — rank stays low |
+//! | [`alternating_two_values`] | strict alternation between two distinct values |
+//!
+//! ## Reset behaviour
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`reset_then_immediate_ingest`] | ingest right after reset produces valid report |
+//! | [`double_reset`] | two resets in a row do not panic or corrupt state |
+//!
+//! ## Decay edge cases
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`decay_to_zero_then_rebuild`] | near-zero decay followed by fresh traffic rebuilds |
+//! | [`rapid_decay_ingest_cycle`] | 50 rapid decay/ingest cycles produce valid reports |
+//!
+//! ## Config edge cases
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`analysis_k_equals_one`] | `analysis_k = 1` — at most 1 competitive cell |
+//! | [`analysis_depth_cutoff_zero`] | `analysis_depth_cutoff = 0` — only V-root eligible |
+//! | [`per_sample_scores_large_batch`] | per-sample scoring with a 256-value batch |
+//!
+//! ## Degenerate cells (ADR-S-011)
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`degenerate_cells_skipped_starts_at_zero`] | counter is zero on fresh sentinel |
+//! | [`degenerate_cells_skipped_in_report_normal_traffic`] | normal traffic reports zero skips |
+//! | [`degenerate_cells_skipped_after_reset`] | counter resets to zero |
+//! | [`deep_spray_traffic_does_not_panic`] | deep splits skip, not panic |
+
+mod common;
+
+use common::{ScenarioBuilder, assert_invariants, cell_values, integration_config, test_config};
+use torrust_sentinel::{Sentinel128, SentinelConfig};
+
+// ─── Boundary values ────────────────────────────────────────
+
+#[test]
+fn min_value_u128() {
+    let mut s = Sentinel128::new(test_config()).unwrap();
+    let report = s.ingest(&[0u128]);
+    assert_eq!(report.health.lifetime_observations, 1);
+    assert_invariants(&s, &report);
+}
+
+#[test]
+fn max_value_u128() {
+    let mut s = Sentinel128::new(test_config()).unwrap();
+    let report = s.ingest(&[u128::MAX]);
+    assert_eq!(report.health.lifetime_observations, 1);
+    assert_invariants(&s, &report);
+}
+
+#[test]
+fn min_and_max_together() {
+    let mut s = Sentinel128::new(test_config()).unwrap();
+    let report = s.ingest(&[0u128, u128::MAX]);
+    assert_eq!(report.health.lifetime_observations, 2);
+    assert_invariants(&s, &report);
+}
+
+#[test]
+fn all_nibbles_full_spread() {
+    let mut s = Sentinel128::new(integration_config()).unwrap();
+
+    // Values spanning all 16 leading nibbles.
+    let values: Vec<u128> = (0..16u128).map(|nib| (nib << 124) | 1).collect();
+    for _ in 0..20 {
+        s.ingest(&values);
+    }
+    let report = s.ingest(&values);
+
+    assert_eq!(s.lifetime_observations(), 21 * 16);
+    assert_invariants(&s, &report);
+}
+
+// ─── Batch-size extremes ────────────────────────────────────
+
+#[test]
+fn single_observation_batch() {
+    let mut s = Sentinel128::new(test_config()).unwrap();
+
+    // Single observation per batch for 50 batches.
+    let mut report = None;
+    for i in 0..50u128 {
+        let r = s.ingest(&[(0xA << 124) | i]);
+        assert!(
+            !r.ancestor_reports.is_empty() || !r.cell_reports.is_empty(),
+            "batch {i} should produce at least a root report"
+        );
+        report = Some(r);
+    }
+    assert_eq!(s.lifetime_observations(), 50);
+    assert_invariants(&s, report.as_ref().unwrap());
+}
+
+#[test]
+fn very_large_batch() {
+    let mut s = Sentinel128::new(test_config()).unwrap();
+
+    // 10K observations in one batch.
+    let values: Vec<u128> = (0..10_000u128).map(|i| ((i % 16) << 124) | (i + 1)).collect();
+    let report = s.ingest(&values);
+
+    assert_eq!(s.lifetime_observations(), 10_000);
+    let root = report.ancestor_reports.iter().find(|r| r.depth == 0);
+    assert!(root.is_some());
+    assert_eq!(root.unwrap().sample_count, 10_000);
+    assert_invariants(&s, &report);
+}
+
+#[test]
+fn empty_then_burst() {
+    let mut s = Sentinel128::new(test_config()).unwrap();
+
+    // 100 empty ingests.
+    for _ in 0..100 {
+        let report = s.ingest(&[]);
+        assert!(report.cell_reports.is_empty());
+    }
+    assert_eq!(s.lifetime_observations(), 0);
+
+    // Then a large burst.
+    let report = s.ingest(&cell_values(0xA, 200));
+    assert_eq!(s.lifetime_observations(), 200);
+    assert!(
+        !report.ancestor_reports.is_empty(),
+        "burst after empties should produce ancestor reports"
+    );
+    assert_invariants(&s, &report);
+}
+
+// ─── Traffic patterns ───────────────────────────────────────
+
+#[test]
+fn single_observation_repeated() {
+    let mut s = Sentinel128::new(SentinelConfig::<u64> {
+        split_threshold: 10,
+        ..test_config()
+    })
+    .unwrap();
+
+    let value = 0xA000_0000_0000_0000_0000_0000_0000_0001u128;
+    let mut report = None;
+    for _ in 0..100 {
+        report = Some(s.ingest(&[value]));
+    }
+
+    assert_eq!(s.lifetime_observations(), 100);
+    assert!(s.cells_tracked() >= 1);
+    assert_invariants(&s, report.as_ref().unwrap());
+}
+
+#[test]
+fn all_same_value() {
+    let mut s = Sentinel128::new(integration_config()).unwrap();
+
+    let value = 0xAAAA_BBBB_CCCC_DDDD_EEEE_FFFF_0000_1111u128;
+    let mut report = None;
+    for _ in 0..100 {
+        report = Some(s.ingest(&[value; 8]));
+    }
+
+    // Rank should stay low — no structural variation.
+    let root = s.graph().g_root();
+    let insp = s.inspect_cell(root).unwrap();
+    assert!(insp.rank <= 2, "identical traffic should keep rank low, got {}", insp.rank);
+    assert_invariants(&s, report.as_ref().unwrap());
+}
+
+#[test]
+fn alternating_two_values() {
+    let mut s = Sentinel128::new(integration_config()).unwrap();
+
+    let v1 = 0xF000_0000_0000_0000_0000_0000_0000_0001u128;
+    let v2 = 0x1000_0000_0000_0000_0000_0000_0000_0002u128;
+
+    let mut report = None;
+    for i in 0..100 {
+        if i % 2 == 0 {
+            report = Some(s.ingest(&[v1; 4]));
+        } else {
+            report = Some(s.ingest(&[v2; 4]));
+        }
+    }
+
+    assert!(s.cells_tracked() >= 1);
+    let root = s.graph().g_root();
+    let insp = s.inspect_cell(root).unwrap();
+    assert!(insp.rank >= 1);
+    assert_invariants(&s, report.as_ref().unwrap());
+}
+
+// ─── Reset behaviour ────────────────────────────────────────
+
+#[test]
+fn reset_then_immediate_ingest() {
+    let mut s = ScenarioBuilder::new().seed_range(0xA, 20).warm_batches(10).build();
+    s.reset();
+
+    assert_eq!(s.lifetime_observations(), 0);
+
+    let report = s.ingest(&cell_values(0xA, 16));
+    assert_eq!(s.lifetime_observations(), 16);
+    assert!(
+        report.ancestor_reports.iter().any(|r| r.depth == 0),
+        "root must be reported after reset + ingest"
+    );
+    assert_invariants(&s, &report);
+}
+
+#[test]
+fn double_reset() {
+    let mut s = ScenarioBuilder::new().seed_range(0xA, 20).warm_batches(10).build();
+    s.reset();
+    s.reset();
+
+    assert_eq!(s.lifetime_observations(), 0);
+    assert_eq!(s.degenerate_cells_skipped(), 0);
+
+    // Must be usable after double reset.
+    let report = s.ingest(&cell_values(0xA, 8));
+    assert_eq!(s.lifetime_observations(), 8);
+    assert_invariants(&s, &report);
+}
+
+// ─── Decay edge cases ───────────────────────────────────────
+
+#[test]
+fn decay_to_zero_then_rebuild() {
+    let mut s = ScenarioBuilder::new().seed_range(0xA, 20).warm_batches(20).build();
+    assert!(s.cells_tracked() > 1);
+
+    // Annihilate everything.
+    s.decay(0.0001, 0.0);
+
+    // Re-ingest: should rebuild cells from scratch.
+    for _ in 0..20 {
+        s.ingest(&cell_values(0xA, 16));
+    }
+    let report = s.ingest(&cell_values(0xA, 8));
+
+    assert!(report.ancestor_reports.iter().any(|r| r.depth == 0));
+    assert!(report.health.active_trackers >= 1);
+    assert_invariants(&s, &report);
+}
+
+#[test]
+fn rapid_decay_ingest_cycle() {
+    let mut s = ScenarioBuilder::new().seed_range(0xA, 20).warm_batches(10).build();
+
+    for _ in 0..50 {
+        s.decay(0.8, 0.0);
+        let report = s.ingest(&cell_values(0xA, 8));
+        assert!(!report.ancestor_reports.is_empty() || !report.cell_reports.is_empty());
+        assert_invariants(&s, &report);
+    }
+}
+
+// ─── Config edge cases ──────────────────────────────────────
+
+#[test]
+fn analysis_k_equals_one() {
+    let cfg = SentinelConfig::<u64> {
+        analysis_k: 1,
+        split_threshold: 10,
+        ..integration_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    for _ in 0..50 {
+        s.ingest(&cell_values(0xA, 16));
+    }
+
+    let report = s.ingest(&cell_values(0xA, 8));
+    assert!(
+        report.analysis_set_summary.competitive_size <= 1,
+        "with K=1, at most 1 competitive cell"
+    );
+    assert!(
+        report.ancestor_reports.iter().any(|r| r.depth == 0),
+        "root must still be reported"
+    );
+    // `assert_invariants` omitted: K=1 intentionally violates the
+    // Steiner tree bound (full_size >> 2*competitive_size+1).
+}
+
+#[test]
+fn analysis_depth_cutoff_zero() {
+    let cfg = SentinelConfig::<u64> {
+        analysis_depth_cutoff: 0,
+        split_threshold: 10,
+        ..integration_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    for _ in 0..50 {
+        s.ingest(&cell_values(0xA, 16));
+    }
+
+    let report = s.ingest(&cell_values(0xA, 8));
+    // With depth cutoff 0, only the V-Tree root is eligible for
+    // competitive selection.
+    assert!(
+        report.analysis_set_summary.full_size >= 1,
+        "at least root must be in the full set"
+    );
+    // `assert_invariants` omitted: depth_cutoff=0 may violate the
+    // Steiner tree bound by design.
+}
+
+#[test]
+fn per_sample_scores_large_batch() {
+    let cfg = SentinelConfig::<u64> {
+        per_sample_scores: true,
+        ..test_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    let report = s.ingest(&cell_values(0xA, 256));
+
+    let root = report.ancestor_reports.iter().find(|r| r.depth == 0);
+    assert!(root.is_some());
+    let per_sample = root.unwrap().per_sample.as_ref();
+    assert!(per_sample.is_some(), "per_sample should be Some when enabled");
+    assert_eq!(
+        per_sample.unwrap().len(),
+        256,
+        "per_sample should have one entry per observation"
+    );
+    assert_invariants(&s, &report);
+}
+
+// ─── ADR-S-011: Degenerate cell dimension guard ────────────
+
+#[test]
+fn degenerate_cells_skipped_starts_at_zero() {
+    let s = Sentinel128::new(test_config()).unwrap();
+    assert_eq!(s.degenerate_cells_skipped(), 0);
+}
+
+#[test]
+fn degenerate_cells_skipped_in_report_normal_traffic() {
+    let mut s = Sentinel128::new(integration_config()).unwrap();
+
+    for _ in 0..20 {
+        s.ingest(&cell_values(0xA, 16));
+    }
+    let report = s.ingest(&cell_values(0xA, 8));
+    assert_eq!(
+        report.analysis_set_summary.degenerate_cells_skipped, 0,
+        "normal traffic should not produce degenerate cells"
+    );
+    assert_invariants(&s, &report);
+}
+
+#[test]
+fn degenerate_cells_skipped_after_reset() {
+    let mut s = Sentinel128::new(integration_config()).unwrap();
+    s.ingest(&cell_values(0xA, 8));
+    s.reset();
+    assert_eq!(s.degenerate_cells_skipped(), 0);
+}
+
+/// Under extreme spray traffic with minimal `split_threshold`, the
+/// G-tree may produce very deep nodes.  The degenerate cell guard
+/// (ADR-S-011) should skip them rather than panicking.
+#[test]
+fn deep_spray_traffic_does_not_panic() {
+    let cfg = SentinelConfig::<u64> {
+        split_threshold: 2, // very aggressive splitting
+        d_create: 1,
+        d_evict: 2,
+        budget: 100_000,
+        analysis_k: 32,             // large analysis set to pick up deep cells
+        analysis_depth_cutoff: 128, // don't filter by V-depth
+        ..integration_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    // Hammer a single value to force deep splits.
+    let value = 0xAAAA_BBBB_CCCC_DDDD_EEEE_FFFF_0000_1111u128;
+    for _ in 0..500 {
+        let _report = s.ingest(&[value]);
+    }
+
+    // The sentinel must not panic.  Any degenerate cells should be
+    // silently skipped and counted.
+    assert!(s.cells_tracked() >= 1, "at least root must be tracked");
+}
diff --git a/packages/sentinel/tests/graph_routing.rs b/packages/sentinel/tests/graph_routing.rs
new file mode 100644
index 000000000..6b7ddcce9
--- /dev/null
+++ b/packages/sentinel/tests/graph_routing.rs
@@ -0,0 +1,227 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// SPDX-FileCopyrightText: 2026 Torrust project contributors
+
+//! Graph routing tests — `ingest()` feeds the G-V Graph.
+//!
+//! Verify that `ingest()` routes every raw value into the G-V Graph
+//! via `observe(v, 1u64)` and that the graph's structural evolution
+//! behaves correctly under traffic.
+//!
+//! # Test index
+//!
+//! ## Fresh state
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`fresh_graph_has_one_node`] | `node_count == 1` |
+//! | [`fresh_graph_has_one_terminal`] | `terminal_count == 1` |
+//! | [`fresh_graph_has_zero_total_sum`] | `total_sum == 0` |
+//!
+//! ## Basic routing
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`ingest_feeds_graph_with_delta_one`] | `total_sum` increases by batch length |
+//! | [`empty_ingest_does_not_observe`] | empty batch leaves graph unchanged |
+//! | [`duplicate_values_each_contribute`] | repeated value still adds to `total_sum` |
+//!
+//! ## Accumulation
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`graph_accumulates_across_batches`] | 10 batches sum correctly |
+//! | [`lifetime_observations_tracks_total_sum`] | sentinel counter == graph counter |
+//!
+//! ## Structural evolution
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`concentrated_traffic_splits_nodes`] | `node_count > 1` after concentrated traffic |
+//! | [`concentrated_traffic_grows_terminals`] | `terminal_count > 1` after splits |
+//!
+//! ## Budget enforcement
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`diverse_traffic_respects_budget`] | `node_count <= budget` |
+//!
+//! ## Reset
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`reset_restores_fresh_graph_state`] | graph returns to initial state |
+
+mod common;
+
+use common::{cell_values, test_config};
+use torrust_sentinel::{Sentinel128, SentinelConfig};
+
+// ── Fresh state ─────────────────────────────────────────────
+
+#[test]
+fn fresh_graph_has_one_node() {
+    let s = Sentinel128::new(test_config()).unwrap();
+    assert_eq!(s.graph().node_count(), 1);
+}
+
+#[test]
+fn fresh_graph_has_one_terminal() {
+    let s = Sentinel128::new(test_config()).unwrap();
+    assert_eq!(s.graph().terminal_count(), 1);
+}
+
+#[test]
+fn fresh_graph_has_zero_total_sum() {
+    let s = Sentinel128::new(test_config()).unwrap();
+    assert_eq!(s.graph().total_sum(), 0);
+}
+
+// ── Basic routing ───────────────────────────────────────────
+
+#[test]
+fn ingest_feeds_graph_with_delta_one() {
+    let cfg = test_config();
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    // First batch: 5 values.
+    s.ingest(&cell_values(0xA, 5));
+    assert_eq!(s.graph().total_sum(), 5);
+
+    // Second batch: 3 more values in a different range.
+    s.ingest(&cell_values(0x3, 3));
+    assert_eq!(s.graph().total_sum(), 8);
+}
+
+#[test]
+fn empty_ingest_does_not_observe() {
+    let cfg = test_config();
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    s.ingest(&[]);
+    assert_eq!(s.graph().total_sum(), 0);
+    assert_eq!(s.graph().node_count(), 1); // still just the root
+}
+
+#[test]
+fn duplicate_values_each_contribute() {
+    let cfg = test_config();
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    // Ingest three identical values — each should add 1 to total_sum.
+    let v = cell_values(0xB, 1)[0];
+    s.ingest(&[v, v, v]);
+    assert_eq!(s.graph().total_sum(), 3);
+}
+
+// ── Accumulation ────────────────────────────────────────────
+
+#[test]
+fn graph_accumulates_across_batches() {
+    let cfg = test_config();
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    for nibble in 0..10u128 {
+        s.ingest(&cell_values(nibble, 100));
+    }
+
+    assert_eq!(s.graph().total_sum(), 1000);
+}
+
+#[test]
+fn lifetime_observations_tracks_total_sum() {
+    let cfg = test_config();
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    s.ingest(&cell_values(0xC, 42));
+    assert_eq!(s.lifetime_observations(), s.graph().total_sum());
+
+    // Still holds after a second batch.
+    s.ingest(&cell_values(0xD, 58));
+    assert_eq!(s.lifetime_observations(), s.graph().total_sum());
+    assert_eq!(s.lifetime_observations(), 100);
+}
+
+// ── Structural evolution ────────────────────────────────────
+
+#[test]
+fn concentrated_traffic_splits_nodes() {
+    let cfg = SentinelConfig::<u64> {
+        split_threshold: 10,
+        ..test_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    // All values in the same nibble range — should concentrate
+    // into one cell and eventually split it.
+    s.ingest(&cell_values(0xA, 50));
+
+    assert_eq!(s.graph().total_sum(), 50);
+    assert!(
+        s.graph().node_count() > 1,
+        "expected splits from concentrated traffic, got {} nodes",
+        s.graph().node_count()
+    );
+}
+
+#[test]
+fn concentrated_traffic_grows_terminals() {
+    let cfg = SentinelConfig::<u64> {
+        split_threshold: 10,
+        ..test_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    s.ingest(&cell_values(0xA, 50));
+
+    assert!(
+        s.graph().terminal_count() > 1,
+        "expected terminal_count > 1 after splits, got {}",
+        s.graph().terminal_count()
+    );
+}
+
+// ── Budget enforcement ──────────────────────────────────────
+
+#[test]
+fn diverse_traffic_respects_budget() {
+    let cfg = SentinelConfig::<u64> {
+        split_threshold: 5,
+        budget: 200,
+        ..test_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    // Spread observations across many distinct nibble ranges.
+    for nibble in 0..16u128 {
+        let values: Vec<u128> = (0u128..500).map(|i| (nibble << 124) | (i << 100)).collect();
+        s.ingest(&values);
+    }
+
+    assert!(
+        s.graph().node_count() <= 200,
+        "node count {} exceeds budget 200",
+        s.graph().node_count()
+    );
+}
+
+// ── Reset ───────────────────────────────────────────────────
+
+#[test]
+fn reset_restores_fresh_graph_state() {
+    let cfg = SentinelConfig::<u64> {
+        split_threshold: 10,
+        ..test_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    // Ingest enough to split.
+    s.ingest(&cell_values(0xA, 100));
+    assert!(s.graph().total_sum() > 0);
+    assert!(s.graph().node_count() > 1);
+
+    s.reset();
+
+    assert_eq!(s.graph().total_sum(), 0);
+    assert_eq!(s.graph().node_count(), 1);
+    assert_eq!(s.graph().terminal_count(), 1);
+}
diff --git a/packages/sentinel/tests/health.rs b/packages/sentinel/tests/health.rs
new file mode 100644
index 000000000..aaa39951a
--- /dev/null
+++ b/packages/sentinel/tests/health.rs
@@ -0,0 +1,301 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// SPDX-FileCopyrightText: 2026 Torrust project contributors
+
+//! `HealthReport` behavioural tests.
+//!
+//! Validates that [`HealthReport`] fields reflect the sentinel's true
+//! internal state at every lifecycle stage: fresh, after ingestion,
+//! after noise warming, and under sustained load.
+//!
+//! Inspector API surface tests (`cell_gnodes`, `inspect_cell`) live in
+//! [`api`](super::api) — this file focuses on the aggregate health
+//! snapshot.
+//!
+//! # Test index
+//!
+//! ## Fresh sentinel
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`fresh_has_one_root_tracker`] | only root tracker exists at birth |
+//! | [`fresh_has_zero_observations`] | zero lifetime observations before ingestion |
+//! | [`fresh_rank_distribution_is_uniform_at_one`] | rank min/max/mean all equal 1 |
+//! | [`fresh_maturity_is_cold`] | root tracker counts as cold |
+//! | [`fresh_geometry_distribution`] | novelty unsaturated, coherence inactive at rank 1 |
+//! | [`fresh_clip_pressure_is_zero`] | clip pressure min/max/mean all zero |
+//! | [`fresh_coordination_is_empty`] | no coordination contexts before ingestion |
+//!
+//! ## Tracker arithmetic
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`tracker_counts_are_consistent`] | active = competitive + ancestor + root |
+//! | [`investment_set_covers_active_plus_warming`] | investment = active + warming |
+//!
+//! ## After ingestion
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`population_grows_after_divergent_ingest`] | divergent values grow tracker population |
+//! | [`lifetime_observations_accumulate`] | observation count accumulates across batches |
+//! | [`rank_bounds_hold_after_ingest`] | rank stays within `[1, max_rank]` |
+//!
+//! ## Maturity
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`noise_reduces_noise_influence`] | real observations dilute noise influence below 1.0 |
+//! | [`cold_config_leaves_trackers_cold`] | no-noise config keeps trackers at max noise influence |
+//!
+//! ## Multi-batch evolution
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`health_evolves_over_repeated_batches`] | observations and rank grow over repeated warm-up batches |
+//!
+//! ## Coordination health
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`coordination_starts_empty`] | zero active contexts at birth |
+//! | [`coordination_structurally_sound_after_noise`] | structural invariants hold for lazily-created contexts |
+
+mod common;
+
+use common::{ScenarioBuilder, cold_config, seeded_sentinel, test_config};
+use torrust_sentinel::Sentinel128;
+
+// ═══════════════════════════════════════════════════════════
+//  Fresh sentinel
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn fresh_has_one_root_tracker() {
+    let s = Sentinel128::new(test_config()).unwrap();
+    let h = s.health();
+
+    assert_eq!(h.active_trackers, 1, "only the root tracker exists");
+    assert_eq!(h.cells_tracked, 1);
+    assert_eq!(h.total_g_nodes, 1);
+}
+
+#[test]
+fn fresh_has_zero_observations() {
+    let s = Sentinel128::new(test_config()).unwrap();
+    let h = s.health();
+
+    assert_eq!(h.lifetime_observations, 0);
+}
+
+#[test]
+fn fresh_rank_distribution_is_uniform_at_one() {
+    let s = Sentinel128::new(test_config()).unwrap();
+    let rd = s.health().rank_distribution;
+
+    assert_eq!(rd.min, 1);
+    assert_eq!(rd.max, 1);
+    assert!((rd.mean - 1.0).abs() < f64::EPSILON);
+}
+
+#[test]
+fn fresh_maturity_is_cold() {
+    let s = Sentinel128::new(test_config()).unwrap();
+    let md = s.health().maturity_distribution;
+
+    // Root tracker has received noise but no real observations,
+    // so it counts as cold (zero real observations).
+    assert_eq!(md.cold_trackers, 1);
+}
+
+#[test]
+fn fresh_geometry_distribution() {
+    let s = Sentinel128::new(test_config()).unwrap();
+    let gd = s.health().geometry_distribution;
+
+    // With a single rank-1 root tracker on a 128-wide space,
+    // novelty is not saturated and coherence is inactive (rank < 2).
+    assert_eq!(gd.novelty_saturated, 0);
+    assert_eq!(gd.coherence_inactive, 1, "rank-1 tracker cannot compute coherence");
+}
+
+#[test]
+fn fresh_clip_pressure_is_zero() {
+    let s = Sentinel128::new(test_config()).unwrap();
+    let cp = s.health().clip_pressure_distribution;
+
+    assert!((cp.min).abs() < f64::EPSILON);
+    assert!((cp.max).abs() < f64::EPSILON);
+    assert!((cp.mean).abs() < f64::EPSILON);
+}
+
+#[test]
+fn fresh_coordination_is_empty() {
+    let s = Sentinel128::new(test_config()).unwrap();
+    let ch = s.health().coordination_health;
+
+    assert_eq!(ch.active_contexts, 0);
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Tracker arithmetic
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn tracker_counts_are_consistent() {
+    let s = seeded_sentinel();
+    let h = s.health();
+
+    // active = competitive + ancestor + 1 (root)
+    assert_eq!(
+        h.active_trackers,
+        h.active_competitive_trackers + h.active_ancestor_trackers + 1,
+        "active trackers = competitive + ancestor + root"
+    );
+}
+
+#[test]
+fn investment_set_covers_active_plus_warming() {
+    let s = seeded_sentinel();
+    let h = s.health();
+
+    assert_eq!(
+        h.investment_set_size,
+        h.active_trackers + h.warming_trackers,
+        "investment = active + warming"
+    );
+}
+
+// ═══════════════════════════════════════════════════════════
+//  After ingestion
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn population_grows_after_divergent_ingest() {
+    let mut s = Sentinel128::new(test_config()).unwrap();
+
+    // Two values with maximally separated leading bits.
+    s.ingest(&[
+        0xF000_0000_0000_0000_0000_0000_0000_0001,
+        0x1000_0000_0000_0000_0000_0000_0000_0002,
+    ]);
+
+    let h = s.health();
+    assert!(h.active_trackers >= 1);
+    assert_eq!(h.cells_tracked, h.active_trackers);
+    assert_eq!(h.lifetime_observations, 2);
+}
+
+#[test]
+fn lifetime_observations_accumulate() {
+    let mut s = Sentinel128::new(test_config()).unwrap();
+
+    s.ingest(&[0x0000_0000_0000_0000_0000_0000_0000_0001]);
+    assert_eq!(s.health().lifetime_observations, 1);
+
+    s.ingest(&[
+        0x0000_0000_0000_0000_0000_0000_0000_0002,
+        0x0000_0000_0000_0000_0000_0000_0000_0003,
+    ]);
+    assert_eq!(s.health().lifetime_observations, 3);
+}
+
+#[test]
+fn rank_bounds_hold_after_ingest() {
+    let s = seeded_sentinel();
+    let rd = s.health().rank_distribution;
+
+    assert!(rd.min >= 1, "rank must be at least 1");
+    assert!(rd.max <= s.config().max_rank, "rank must not exceed max_rank");
+    #[allow(clippy::cast_precision_loss)]
+    {
+        assert!(rd.mean >= rd.min as f64);
+        assert!(rd.mean <= rd.max as f64);
+    }
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Maturity
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn noise_reduces_noise_influence() {
+    let s = seeded_sentinel();
+    let md = s.health().maturity_distribution;
+
+    // Real observations dilute the noise baseline — mean influence
+    // drops below the 1.0 cold-start value.
+    assert!(
+        md.mean_noise_influence < 1.0,
+        "noise injection + real data should reduce mean noise_influence, got {}",
+        md.mean_noise_influence,
+    );
+}
+
+#[test]
+fn cold_config_leaves_trackers_cold() {
+    let mut s = Sentinel128::new(cold_config()).unwrap();
+
+    // Even after real data, cold_config skips noise so trackers
+    // remain at maximum noise influence.
+    s.ingest(&[0xAAAA_BBBB_CCCC_DDDD_0000_0000_0000_0001]);
+
+    let md = s.health().maturity_distribution;
+    assert!(
+        md.min_noise_influence >= md.mean_noise_influence,
+        "min should be >= mean (all at same level without noise)",
+    );
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Multi-batch evolution
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn health_evolves_over_repeated_batches() {
+    let (s, _) = ScenarioBuilder::new()
+        .seed_range(0xA, 8)
+        .seed_range(0x5, 8)
+        .warm_batches(5)
+        .build_with_reports();
+
+    let h = s.health();
+
+    // After several warm-up batches the sentinel should have
+    // accumulated meaningful observations and the rank may have
+    // adapted above 1.
+    assert!(h.lifetime_observations >= 6 * 16, "6 batches × 16 values");
+    assert!(h.rank_distribution.max >= 1);
+    assert!(h.active_trackers >= 1);
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Coordination health
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn coordination_starts_empty() {
+    let s = Sentinel128::new(test_config()).unwrap();
+    let ch = s.health().coordination_health;
+
+    assert_eq!(ch.active_contexts, 0);
+}
+
+#[test]
+fn coordination_structurally_sound_after_noise() {
+    let s = seeded_sentinel();
+    let ch = s.health().coordination_health;
+
+    // Coordination contexts are created lazily when enough
+    // competitive cells co-exist. If any were created, verify
+    // their structural properties.
+    if ch.active_contexts > 0 {
+        assert_eq!(ch.dim, 4, "coordination dimensionality is always 4");
+        assert!(ch.capacity <= s.config().max_rank.min(4));
+        assert!(
+            ch.maturity_distribution.max_noise_influence <= 1.0,
+            "noise_influence must be in [0, 1]"
+        );
+        assert!(ch.rank_distribution.min >= 1);
+        assert!(ch.rank_distribution.max <= ch.capacity);
+    }
+}
diff --git a/packages/sentinel/tests/hierarchical_coordination.rs b/packages/sentinel/tests/hierarchical_coordination.rs
new file mode 100644
index 000000000..d225cdc4a
--- /dev/null
+++ b/packages/sentinel/tests/hierarchical_coordination.rs
@@ -0,0 +1,776 @@
+//! Tests for **hierarchical coordination** (§ALGO S-7).
+//!
+//! Coordination fires when competitive cells from *both* subtrees of
+//! an internal g-tree node report observations in the same batch.
+//! These tests verify the full lifecycle — activation, deactivation,
+//! hierarchy nesting, SVD-based scoring, EWMA tracking, CUSUM
+//! resets, and the public API surface — ensuring that coordination
+//! contexts respect the binary-tree bound (≤ K−1 contexts) and
+//! remain deterministic across identical seeds.
+//!
+//! # Test index
+//!
+//! ## Activation & deactivation (§7.1)
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`no_coordination_on_empty_batch`] | empty input produces no reports |
+//! | [`no_coordination_with_single_region`] | one region only, no both-subtree condition |
+//! | [`two_sibling_cells_fire_parent_context`] | two distinct regions trigger coordination |
+//! | [`context_activation_requires_both_subtrees`] | adding second region activates contexts |
+//! | [`context_deactivation_on_subtree_loss`] | losing a subtree deactivates contexts |
+//!
+//! ## Hierarchy & nesting (§7.1, §7.4)
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`nested_coordination_levels`] | four regions fire at multiple hierarchy levels |
+//! | [`coordination_group_nesting_invariant`] | ancestor context ≥ child `cells_reporting` |
+//! | [`root_context_sees_all_cells`] | highest context sees all competitive cells |
+//! | [`semi_internal_node_passthrough`] | single-child nodes propagate without firing |
+//!
+//! ## Scoring & tracking (§7.2–7.5)
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`coordination_tracker_operates_at_w4`] | coordination dim=4, cap=min(4, `max_rank`) |
+//! | [`coordination_tracker_with_low_max_rank`] | cap clamps to `max_rank` when < 4 |
+//! | [`running_mean_cold_start`] | first coordination scores are finite |
+//! | [`running_mean_ewma_update`] | EWMA adapts over successive batches |
+//! | [`only_competitive_cells_contribute`] | root excluded from competitive count |
+//! | [`cells_with_no_observations_excluded`] | half-region ingest skips empty cells |
+//! | [`coordination_scores_are_finite`] | all four axes produce finite scores |
+//! | [`per_member_scores_present_when_enabled`] | `per_member` field populated |
+//!
+//! ## Stability & resilience
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`coordination_survives_decay`] | decay does not break coordination tier |
+//! | [`inject_noise_warms_coordination`] | noise warm-up initialises coordination CUSUMs |
+//! | [`deterministic_report_order`] | same seed → identical reports |
+//!
+//! ## Invariants (§7.1)
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`context_count_bounded_by_k_minus_1`] | at most K−1 active contexts |
+//! | [`coordination_reports_unique_gnodes`] | no duplicate `gnode_ids` in reports |
+//!
+//! ## API surface
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`batch_report_coordination_is_vec`] | coordination field is `Vec`, not `Option` |
+//! | [`health_report_has_coordination_health`] | health report includes coordination tier |
+//! | [`config_cusum_coord_slow_decay_validated`] | config rejects invalid slow decay values |
+
+mod common;
+
+use std::collections::BTreeSet;
+
+use common::{ScenarioBuilder, assert_invariants, cell_values, seeded_sentinel, test_config};
+use torrust_sentinel::{Sentinel128, SentinelConfig};
+
+// ═══════════════════════════════════════════════════════════
+//  Activation & deactivation (§7.1)
+// ═══════════════════════════════════════════════════════════
+
+// ── no_coordination_on_empty_batch ──────────────────────────
+
+#[test]
+fn no_coordination_on_empty_batch() {
+    let mut s = Sentinel128::new(test_config()).unwrap();
+    let report = s.ingest(&[]);
+
+    assert!(
+        report.coordination_reports.is_empty(),
+        "empty batch should produce no coordination reports"
+    );
+    assert_invariants(&s, &report);
+}
+
+// ── no_coordination_with_single_region ──────────────────────
+
+#[test]
+fn no_coordination_with_single_region() {
+    let mut s = ScenarioBuilder::new()
+        .config(test_config())
+        .seed_range(0xF, 8)
+        .warm_batches(5)
+        .build();
+
+    let report = s.ingest(&cell_values(0xF, 8));
+
+    // All competitive cells are in one subtree → no both-subtree
+    // condition → coordination should not fire.
+    for cr in &report.coordination_reports {
+        assert!(
+            cr.cells_reporting >= 2,
+            "coordination should only fire with ≥ 2 cells from both subtrees"
+        );
+    }
+    assert_invariants(&s, &report);
+}
+
+// ── two_sibling_cells_fire_parent_context ───────────────────
+
+#[test]
+fn two_sibling_cells_fire_parent_context() {
+    let cfg = SentinelConfig::<u64> {
+        split_threshold: 10,
+        ..test_config()
+    };
+    let mut s = ScenarioBuilder::new()
+        .config(cfg)
+        .seed_range(0xF, 4)
+        .seed_range(0x1, 4)
+        .warm_batches(14)
+        .build();
+
+    let report = s.ingest(&[cell_values(0xF, 4), cell_values(0x1, 4)].concat());
+
+    assert!(
+        !report.coordination_reports.is_empty(),
+        "two distinct cell regions should produce coordination"
+    );
+    for cr in &report.coordination_reports {
+        assert!(cr.cells_reporting >= 2, "coordination needs ≥ 2 cells");
+        assert!(cr.start < cr.end, "start should be < end");
+    }
+    assert_invariants(&s, &report);
+}
+
+// ── context_activation_requires_both_subtrees ───────────────
+
+#[test]
+fn context_activation_requires_both_subtrees() {
+    let mut s = ScenarioBuilder::new()
+        .config(test_config())
+        .seed_range(0xF, 8)
+        .warm_batches(9)
+        .build();
+
+    let ch_one = s.health().coordination_health;
+
+    // Now add a second region in the opposite half of the domain.
+    for _ in 0..5 {
+        s.ingest(&[cell_values(0xF, 4), cell_values(0x1, 4)].concat());
+    }
+    let ch_both = s.health().coordination_health;
+
+    assert!(
+        ch_both.active_contexts >= ch_one.active_contexts,
+        "adding a second region should activate coordination: \
+         before={}, after={}",
+        ch_one.active_contexts,
+        ch_both.active_contexts
+    );
+}
+
+// ── context_deactivation_on_subtree_loss ────────────────────
+
+#[test]
+fn context_deactivation_on_subtree_loss() {
+    let mut s = ScenarioBuilder::new()
+        .config(test_config())
+        .seed_range(0xF, 4)
+        .seed_range(0x1, 4)
+        .warm_batches(9)
+        .build();
+
+    let ch_both = s.health().coordination_health;
+
+    // Ingest only one region — the other region's cells may still
+    // be competitive but have zero observations this batch.
+    let _report = s.ingest(&cell_values(0xF, 4));
+    let ch_one = s.health().coordination_health;
+
+    assert!(
+        ch_one.active_contexts <= ch_both.active_contexts,
+        "losing a subtree should deactivate contexts: \
+         both={}, one={}",
+        ch_both.active_contexts,
+        ch_one.active_contexts
+    );
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Hierarchy & nesting (§7.1, §7.4)
+// ═══════════════════════════════════════════════════════════
+
+// ── nested_coordination_levels ──────────────────────────────
+
+#[test]
+fn nested_coordination_levels() {
+    let cfg = SentinelConfig::<u64> {
+        analysis_k: 16,
+        split_threshold: 10,
+        ..test_config()
+    };
+    let mut s = ScenarioBuilder::new()
+        .config(cfg)
+        .seed_range(0x1, 4)
+        .seed_range(0x3, 4)
+        .seed_range(0x9, 4)
+        .seed_range(0xF, 4)
+        .warm_batches(19)
+        .build();
+
+    let report = s.ingest(
+        &[
+            cell_values(0x1, 4),
+            cell_values(0x3, 4),
+            cell_values(0x9, 4),
+            cell_values(0xF, 4),
+        ]
+        .concat(),
+    );
+
+    // With 4 cell regions, hierarchy should fire at multiple levels.
+    if report.coordination_reports.len() > 1 {
+        let gnodes: BTreeSet<_> = report.coordination_reports.iter().map(|cr| cr.gnode_id).collect();
+        assert!(
+            gnodes.len() == report.coordination_reports.len(),
+            "each coordination report should be at a unique gnode"
+        );
+    }
+    assert_invariants(&s, &report);
+}
+
+// ── coordination_group_nesting_invariant ────────────────────
+
+#[test]
+fn coordination_group_nesting_invariant() {
+    let cfg = SentinelConfig::<u64> {
+        analysis_k: 16,
+        split_threshold: 10,
+        ..test_config()
+    };
+    let mut s = ScenarioBuilder::new()
+        .config(cfg)
+        .seed_range(0x1, 4)
+        .seed_range(0x5, 4)
+        .seed_range(0x9, 4)
+        .seed_range(0xF, 4)
+        .warm_batches(14)
+        .build();
+
+    let report = s.ingest(
+        &[
+            cell_values(0x1, 4),
+            cell_values(0x5, 4),
+            cell_values(0x9, 4),
+            cell_values(0xF, 4),
+        ]
+        .concat(),
+    );
+
+    // For any two coordination reports where one's interval
+    // contains the other, the parent should have ≥ cells_reporting.
+    for a in &report.coordination_reports {
+        for b in &report.coordination_reports {
+            if a.gnode_id == b.gnode_id {
+                continue;
+            }
+            if a.start <= b.start && a.end >= b.end {
+                assert!(
+                    a.cells_reporting >= b.cells_reporting,
+                    "ancestor context [{}..{}) (depth {}, cells {}) \
+                     should have ≥ cells than descendant [{}..{}) (depth {}, cells {})",
+                    a.start,
+                    a.end,
+                    a.depth,
+                    a.cells_reporting,
+                    b.start,
+                    b.end,
+                    b.depth,
+                    b.cells_reporting
+                );
+            }
+        }
+    }
+    assert_invariants(&s, &report);
+}
+
+// ── root_context_sees_all_cells ─────────────────────────────
+
+#[test]
+fn root_context_sees_all_cells() {
+    let mut s = ScenarioBuilder::new()
+        .config(test_config())
+        .seed_range(0xF, 4)
+        .seed_range(0x1, 4)
+        .warm_batches(9)
+        .build();
+
+    let report = s.ingest(&[cell_values(0xF, 4), cell_values(0x1, 4)].concat());
+
+    let competitive_with_obs = report
+        .cell_reports
+        .iter()
+        .filter(|c| c.is_competitive && c.sample_count > 0)
+        .count();
+
+    if !report.coordination_reports.is_empty() && competitive_with_obs >= 2 {
+        let max_reporting = report
+            .coordination_reports
+            .iter()
+            .map(|cr| cr.cells_reporting)
+            .max()
+            .unwrap_or(0);
+        assert!(
+            max_reporting >= 2,
+            "at least one coordination context should see multiple cells"
+        );
+    }
+    assert_invariants(&s, &report);
+}
+
+// ── semi_internal_node_passthrough ──────────────────────────
+
+#[test]
+fn semi_internal_node_passthrough() {
+    let mut s = ScenarioBuilder::new()
+        .config(test_config())
+        .seed_range(0xF, 4)
+        .seed_range(0x1, 4)
+        .warm_batches(9)
+        .build();
+
+    let report = s.ingest(&[cell_values(0xF, 4), cell_values(0x1, 4)].concat());
+
+    // Semi-internal nodes (one child) should not fire coordination;
+    // only nodes with both subtrees contributing should appear.
+    for cr in &report.coordination_reports {
+        assert!(cr.cells_reporting >= 2);
+        assert!(cr.scores.novelty.mean.is_finite());
+    }
+    assert_invariants(&s, &report);
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Scoring & tracking (§7.2–7.5)
+// ═══════════════════════════════════════════════════════════
+
+// ── coordination_tracker_operates_at_w4 ─────────────────────
+
+#[test]
+fn coordination_tracker_operates_at_w4() {
+    let s = ScenarioBuilder::new()
+        .config(test_config())
+        .seed_range(0xF, 4)
+        .seed_range(0x1, 4)
+        .warm_batches(9)
+        .build();
+
+    let ch = s.health().coordination_health;
+    if ch.active_contexts > 0 {
+        assert_eq!(ch.dim, 4, "coordination trackers should operate at w=4");
+        // cap = min(4, max_rank). test_config has max_rank=4.
+        assert_eq!(ch.capacity, 4, "cap should be min(4, max_rank)");
+    }
+}
+
+// ── coordination_tracker_with_low_max_rank ──────────────────
+
+#[test]
+fn coordination_tracker_with_low_max_rank() {
+    let cfg = SentinelConfig::<u64> {
+        max_rank: 2,
+        ..test_config()
+    };
+    let s = ScenarioBuilder::new()
+        .config(cfg)
+        .seed_range(0xF, 4)
+        .seed_range(0x1, 4)
+        .warm_batches(9)
+        .build();
+
+    let ch = s.health().coordination_health;
+    if ch.active_contexts > 0 {
+        assert_eq!(ch.dim, 4, "coordination dim is always 4");
+        assert_eq!(ch.capacity, 2, "cap should be min(4, max_rank=2) = 2");
+    }
+}
+
+// ── running_mean_cold_start ─────────────────────────────────
+
+#[test]
+fn running_mean_cold_start() {
+    let mut s = ScenarioBuilder::new()
+        .config(test_config())
+        .seed_range(0xF, 4)
+        .seed_range(0x1, 4)
+        .warm_batches(9)
+        .build();
+
+    let ch = s.health().coordination_health;
+    if ch.active_contexts > 0 {
+        let report = s.ingest(&[cell_values(0xF, 4), cell_values(0x1, 4)].concat());
+        for cr in &report.coordination_reports {
+            assert!(cr.scores.novelty.mean.is_finite());
+            assert!(cr.scores.displacement.mean.is_finite());
+            assert!(cr.scores.surprise.mean.is_finite());
+            assert!(cr.scores.coherence.mean.is_finite());
+        }
+        assert_invariants(&s, &report);
+    }
+}
+
+// ── running_mean_ewma_update ────────────────────────────────
+
+#[test]
+fn running_mean_ewma_update() {
+    let mut s = ScenarioBuilder::new()
+        .config(test_config())
+        .seed_range(0xF, 4)
+        .seed_range(0x1, 4)
+        .warm_batches(14)
+        .build();
+
+    let batch = [cell_values(0xF, 4), cell_values(0x1, 4)].concat();
+    let mut reports = Vec::new();
+    for _ in 0..5 {
+        reports.push(s.ingest(&batch));
+    }
+
+    let non_empty: usize = reports.iter().filter(|r| !r.coordination_reports.is_empty()).count();
+    assert!(non_empty >= 3, "most batches should produce coordination: got {non_empty}/5");
+}
+
+// ── only_competitive_cells_contribute ───────────────────────
+
+#[test]
+fn only_competitive_cells_contribute() {
+    let mut s = ScenarioBuilder::new()
+        .config(test_config())
+        .seed_range(0xF, 4)
+        .seed_range(0x1, 4)
+        .warm_batches(4)
+        .build();
+
+    let report = s.ingest(&[cell_values(0xF, 4), cell_values(0x1, 4)].concat());
+
+    // Root is never competitive (§ALGO S-4.7). coordination
+    // cells_reporting should only count competitive cells.
+    let competitive_count = report
+        .cell_reports
+        .iter()
+        .filter(|c| c.is_competitive && c.sample_count > 0)
+        .count();
+
+    for cr in &report.coordination_reports {
+        assert!(
+            cr.cells_reporting <= competitive_count,
+            "cells_reporting ({}) should not exceed competitive cells with observations ({})",
+            cr.cells_reporting,
+            competitive_count
+        );
+    }
+    assert_invariants(&s, &report);
+}
+
+// ── cells_with_no_observations_excluded ─────────────────────
+
+#[test]
+fn cells_with_no_observations_excluded() {
+    let mut s = ScenarioBuilder::new()
+        .config(test_config())
+        .seed_range(0xF, 4)
+        .seed_range(0x1, 4)
+        .warm_batches(9)
+        .build();
+
+    // Ingest only one region — the other region's competitive
+    // cells get zero observations this batch.
+    let report = s.ingest(&cell_values(0xF, 4));
+
+    // §7.2: cells with no observations are excluded. If only one
+    // subtree has observations, coordination should not fire.
+    for cr in &report.coordination_reports {
+        assert!(cr.cells_reporting >= 2, "coordination should need cells from both subtrees");
+    }
+    assert_invariants(&s, &report);
+}
+
+// ── coordination_scores_are_finite ──────────────────────────
+
+#[test]
+fn coordination_scores_are_finite() {
+    let mut s = ScenarioBuilder::new()
+        .config(test_config())
+        .seed_range(0xF, 4)
+        .seed_range(0x1, 4)
+        .warm_batches(14)
+        .build();
+
+    let report = s.ingest(&[cell_values(0xF, 4), cell_values(0x1, 4)].concat());
+
+    for cr in &report.coordination_reports {
+        assert!(cr.scores.novelty.mean.is_finite(), "NaN/Inf novelty mean");
+        assert!(cr.scores.displacement.mean.is_finite(), "NaN/Inf displacement mean");
+        assert!(cr.scores.surprise.mean.is_finite(), "NaN/Inf surprise mean");
+        assert!(cr.scores.coherence.mean.is_finite(), "NaN/Inf coherence mean");
+
+        assert!(!cr.scores.novelty.mean.is_nan(), "NaN novelty");
+        assert!(!cr.scores.displacement.mean.is_nan(), "NaN displacement");
+        assert!(!cr.scores.surprise.mean.is_nan(), "NaN surprise");
+        assert!(!cr.scores.coherence.mean.is_nan(), "NaN coherence");
+
+        // CUSUM accumulators must be finite.
+        assert!(cr.scores.novelty.cusum.accumulator.is_finite());
+        assert!(cr.scores.displacement.cusum.accumulator.is_finite());
+        assert!(cr.scores.surprise.cusum.accumulator.is_finite());
+        assert!(cr.scores.coherence.cusum.accumulator.is_finite());
+
+        // Energy ratio and singular values must be sane.
+        assert!(cr.energy_ratio.is_finite());
+        assert!(cr.top_singular_value.is_finite());
+    }
+    assert_invariants(&s, &report);
+}
+
+// ── per_member_scores_present_when_enabled ──────────────────
+
+#[test]
+fn per_member_scores_present_when_enabled() {
+    // test_config() has per_sample_scores = true.
+    let mut s = ScenarioBuilder::new()
+        .config(test_config())
+        .seed_range(0xF, 4)
+        .seed_range(0x1, 4)
+        .warm_batches(14)
+        .build();
+
+    let report = s.ingest(&[cell_values(0xF, 4), cell_values(0x1, 4)].concat());
+
+    for cr in &report.coordination_reports {
+        let members = cr
+            .per_member
+            .as_ref()
+            .expect("per_member should be Some when per_sample_scores is enabled");
+        assert_eq!(
+            members.len(),
+            cr.cells_reporting,
+            "per_member count should equal cells_reporting"
+        );
+        for ms in members {
+            assert!(ms.novelty.is_finite());
+            assert!(ms.displacement.is_finite());
+            assert!(ms.surprise.is_finite());
+            assert!(ms.coherence.is_finite());
+            assert!(ms.cell_start < ms.cell_end, "member cell interval must be non-empty");
+        }
+    }
+    assert_invariants(&s, &report);
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Stability & resilience
+// ═══════════════════════════════════════════════════════════
+
+// ── coordination_survives_decay ─────────────────────────────
+
+#[test]
+fn coordination_survives_decay() {
+    let mut s = ScenarioBuilder::new()
+        .config(test_config())
+        .seed_range(0xF, 4)
+        .seed_range(0x1, 4)
+        .warm_batches(9)
+        .build();
+
+    s.decay(0.5, 0.0);
+
+    // After decay, new observations should still produce functional reports.
+    let report = s.ingest(&[cell_values(0xF, 4), cell_values(0x1, 4)].concat());
+    assert!(
+        !report.cell_reports.is_empty() || !report.ancestor_reports.is_empty(),
+        "coordination tier should remain functional after decay"
+    );
+    assert_invariants(&s, &report);
+}
+
+// ── inject_noise_warms_coordination ─────────────────────────
+
+#[test]
+fn inject_noise_warms_coordination() {
+    let mut s = seeded_sentinel();
+
+    let ch = s.health().coordination_health;
+
+    if ch.active_contexts > 0 {
+        assert!(
+            ch.maturity_distribution.max_noise_influence < 1.0,
+            "coordination should have warmed after noise"
+        );
+    }
+
+    // Verify CUSUM reset: ingest one batch and check steps_since_reset.
+    let report = s.ingest(&[
+        0xF000_0000_0000_0000_0000_0000_0000_AAAA,
+        0x1000_0000_0000_0000_0000_0000_0000_BBBB,
+    ]);
+    for cr in &report.coordination_reports {
+        assert_eq!(
+            cr.scores.novelty.cusum.steps_since_reset, 1,
+            "coordination CUSUM should have been reset after noise"
+        );
+    }
+    assert_invariants(&s, &report);
+}
+
+// ── deterministic_report_order ──────────────────────────────
+
+#[test]
+fn deterministic_report_order() {
+    let batch: Vec<u128> = [cell_values(0xF, 4), cell_values(0x1, 4)].concat();
+
+    let run = |seed: u64| {
+        let cfg = SentinelConfig::<u64> {
+            analysis_k: 16,
+            noise_seed: Some(seed),
+            ..test_config()
+        };
+        let mut s = Sentinel128::new(cfg).unwrap();
+        for _ in 0..10 {
+            s.ingest(&batch);
+        }
+        s.ingest(&batch)
+    };
+
+    let r1 = run(42);
+    let r2 = run(42);
+
+    assert_eq!(r1.coordination_reports.len(), r2.coordination_reports.len());
+    for (a, b) in r1.coordination_reports.iter().zip(r2.coordination_reports.iter()) {
+        assert_eq!(a.gnode_id, b.gnode_id, "reports should be in same order");
+        assert_eq!(a.depth, b.depth);
+        assert_eq!(a.cells_reporting, b.cells_reporting);
+        assert!(
+            (a.scores.novelty.mean - b.scores.novelty.mean).abs() < 1e-10,
+            "deterministic runs should produce identical scores"
+        );
+    }
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Invariants (§7.1)
+// ═══════════════════════════════════════════════════════════
+
+// ── context_count_bounded_by_k_minus_1 ──────────────────────
+
+#[test]
+fn context_count_bounded_by_k_minus_1() {
+    let cfg = SentinelConfig::<u64> {
+        analysis_k: 16,
+        split_threshold: 10,
+        ..test_config()
+    };
+    let s = ScenarioBuilder::new()
+        .config(cfg)
+        .seed_range(0x1, 4)
+        .seed_range(0x5, 4)
+        .seed_range(0x9, 4)
+        .seed_range(0xF, 4)
+        .warm_batches(14)
+        .build();
+
+    let competitive_cells = s.analysis_set().competitive().len();
+    let active = s.health().coordination_health.active_contexts;
+
+    // Binary tree internal node bound: |contexts| ≤ K-1
+    // where K = number of competitive cells (§ALGO S-7.1).
+    if competitive_cells > 0 {
+        assert!(
+            active <= competitive_cells.saturating_sub(1).max(1),
+            "active contexts ({active}) should be ≤ K-1 ({}) (§ALGO S-7.1)",
+            competitive_cells.saturating_sub(1)
+        );
+    }
+}
+
+// ── coordination_reports_unique_gnodes ──────────────────────
+
+#[test]
+fn coordination_reports_unique_gnodes() {
+    let mut s = ScenarioBuilder::new()
+        .config(test_config())
+        .seed_range(0xF, 4)
+        .seed_range(0x1, 4)
+        .warm_batches(14)
+        .build();
+
+    let report = s.ingest(&[cell_values(0xF, 4), cell_values(0x1, 4)].concat());
+
+    let gnodes: BTreeSet<_> = report.coordination_reports.iter().map(|cr| cr.gnode_id).collect();
+    assert_eq!(
+        gnodes.len(),
+        report.coordination_reports.len(),
+        "no duplicate GNodeIds in coordination_reports"
+    );
+    assert_invariants(&s, &report);
+}
+
+// ═══════════════════════════════════════════════════════════
+//  API surface
+// ═══════════════════════════════════════════════════════════
+
+// ── batch_report_coordination_is_vec ────────────────────────
+
+#[test]
+fn batch_report_coordination_is_vec() {
+    let mut s = Sentinel128::new(test_config()).unwrap();
+    let report = s.ingest(&[42]);
+
+    // The coordination field is Vec<CoordinationReport>, not Option.
+    let _: &Vec<torrust_sentinel::CoordinationReport<u128>> = &report.coordination_reports;
+    assert!(report.coordination_reports.is_empty());
+}
+
+// ── health_report_has_coordination_health ────────────────────
+
+#[test]
+fn health_report_has_coordination_health() {
+    let s = Sentinel128::new(test_config()).unwrap();
+    let h = s.health();
+
+    let ch = &h.coordination_health;
+    assert_eq!(ch.active_contexts, 0);
+    assert_eq!(ch.dim, 4);
+}
+
+// ── config_cusum_coord_slow_decay_validated ──────────────────
+
+#[test]
+fn config_cusum_coord_slow_decay_validated() {
+    // Valid value.
+    let cfg = SentinelConfig::<u64> {
+        cusum_coord_slow_decay: 0.999,
+        ..SentinelConfig::<u64>::default()
+    };
+    cfg.validate().unwrap();
+
+    // Out of range: exactly 1.0.
+    let cfg = SentinelConfig::<u64> {
+        cusum_coord_slow_decay: 1.0,
+        ..SentinelConfig::<u64>::default()
+    };
+    assert!(cfg.validate().is_err());
+
+    // Out of range: exactly 0.0.
+    let cfg = SentinelConfig::<u64> {
+        cusum_coord_slow_decay: 0.0,
+        ..SentinelConfig::<u64>::default()
+    };
+    assert!(cfg.validate().is_err());
+
+    // Too low: must be > forgetting_factor.
+    let cfg = SentinelConfig::<u64> {
+        forgetting_factor: 0.99,
+        cusum_coord_slow_decay: 0.99,
+        ..SentinelConfig::<u64>::default()
+    };
+    assert!(cfg.validate().is_err());
+}
diff --git a/packages/sentinel/tests/integration.rs b/packages/sentinel/tests/integration.rs
new file mode 100644
index 000000000..7cb743360
--- /dev/null
+++ b/packages/sentinel/tests/integration.rs
@@ -0,0 +1,296 @@
+//! End-to-end integration tests for the sentinel.
+//!
+//! These tests exercise multi-subsystem interactions that span the full
+//! sentinel lifecycle.  Each test constructs a sentinel, feeds realistic
+//! traffic, and verifies cross-cutting concerns — things that only become
+//! visible when construction, ingestion, scoring, coordination, decay,
+//! and health reporting all run together.
+//!
+//! Focused unit-level concerns live in dedicated files:
+//!
+//! | Concern               | File                            |
+//! |-----------------------|---------------------------------|
+//! | Public API contracts  | `api.rs`                        |
+//! | Report structure      | `report_structure.rs`           |
+//! | Reset behaviour       | `api.rs`, `edge_cases.rs`, `noise.rs` |
+//! | CUSUM accumulation    | `coverage_matrix.rs`            |
+//! | Warm-vs-cold scoring  | `noise.rs`, `health.rs`         |
+//! | Spatial decay         | `spatial_decay.rs`              |
+//! | Ancestor chains       | `ancestor_chain.rs`             |
+//! | Invariants            | `invariants.rs`                 |
+//! | Determinism           | `determinism.rs`                |
+//!
+//! # Test index
+//!
+//! ## Full lifecycle
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`single_range_lifecycle`] | single-range construct → seed → warm → anomaly → health |
+//! | [`multi_range_lifecycle`] | two disjoint ranges, graph splits, analysis selector |
+//!
+//! ## Anomaly detection (end-to-end)
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`anomalous_batch_elevates_novelty`] | anomalous batch produces higher novelty z-score |
+//! | [`anomalous_batch_elevates_cusum`] | sustained anomalous traffic grows CUSUM accumulator |
+//!
+//! ## Decay → ingest round-trip
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`decay_then_ingest_maintains_invariants`] | `decay()` mid-lifecycle, continued ingestion holds invariants |
+//!
+//! ## Inspection after realistic traffic
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`inspect_cells_after_multi_range_traffic`] | `inspect_cell()` returns valid state for every tracked cell |
+//!
+//! ## Coordination (end-to-end)
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`coordination_activates_with_multi_range_traffic`] | coordination subsystem activates with disjoint competitive ranges |
+
+mod common;
+
+use common::{ScenarioBuilder, anomalous_values, assert_invariants, cell_values, integration_config, max_cusum, max_novelty_z};
+use torrust_sentinel::SentinelConfig;
+
+// ── Full lifecycle ──────────────────────────────────────────
+
+/// Construct → seed → warm → steady-state → anomaly → health.
+/// Uses a single value range and verifies invariants at every step.
+#[test]
+fn single_range_lifecycle() {
+    let (mut s, warm_reports) = ScenarioBuilder::new()
+        .seed_range(0xF, 8)
+        .warm_batches(15)
+        .build_with_reports();
+
+    // Invariants hold throughout warm-up.
+    for r in &warm_reports {
+        assert_invariants(&s, r);
+    }
+    assert!(s.cells_tracked() >= 1);
+
+    // Steady-state ingestion.
+    for _ in 0..10 {
+        let report = s.ingest(&cell_values(0xF, 8));
+        assert_invariants(&s, &report);
+    }
+
+    // Anomalous batch.
+    let anomaly_report = s.ingest(&anomalous_values(0xF, 8));
+    assert_invariants(&s, &anomaly_report);
+    assert!(!anomaly_report.ancestor_reports.is_empty());
+
+    // Health check.
+    let h = s.health();
+    assert!(h.active_trackers >= 1);
+    assert!(h.rank_distribution.min >= 1);
+}
+
+/// Multi-range lifecycle: two disjoint nibble ranges that force graph
+/// splits and exercise the analysis selector across multiple cells.
+#[test]
+fn multi_range_lifecycle() {
+    let mut s = ScenarioBuilder::new()
+        .config(SentinelConfig::<u64> {
+            split_threshold: 10,
+            ..integration_config()
+        })
+        .seed_range(0xF, 12)
+        .seed_range(0x1, 12)
+        .warm_batches(15)
+        .build();
+
+    // Two disjoint ranges should produce more than just the root cell.
+    assert!(s.cells_tracked() > 1, "expected multiple cells from disjoint ranges");
+
+    // Steady-state.
+    for _ in 0..10 {
+        let batch: Vec<u128> = [cell_values(0xF, 8), cell_values(0x1, 8)].concat();
+        let report = s.ingest(&batch);
+        assert_invariants(&s, &report);
+    }
+
+    // Anomaly in one range only.
+    let mixed: Vec<u128> = [anomalous_values(0xF, 8), cell_values(0x1, 8)].concat();
+    let report = s.ingest(&mixed);
+    assert_invariants(&s, &report);
+
+    let h = s.health();
+    assert!(h.lifetime_observations > 0);
+    assert!(h.active_trackers >= 2);
+}
+
+// ── Anomaly detection (end-to-end) ──────────────────────────
+
+/// After steady-state warm-up, a structurally anomalous batch
+/// produces a higher novelty z-score than the preceding normal batch.
+#[test]
+fn anomalous_batch_elevates_novelty() {
+    let mut s = ScenarioBuilder::new().seed_range(0xA, 16).warm_batches(20).build();
+
+    // Final normal batch as baseline.
+    let normal = s.ingest(&cell_values(0xA, 8));
+    assert_invariants(&s, &normal);
+
+    // Anomalous batch.
+    let anomaly = s.ingest(&anomalous_values(0xA, 8));
+    assert_invariants(&s, &anomaly);
+
+    let normal_z = max_novelty_z(&normal);
+    let anomaly_z = max_novelty_z(&anomaly);
+    assert!(
+        anomaly_z > normal_z,
+        "anomalous batch should produce higher novelty z-score: \
+         anomaly={anomaly_z:.4}, normal={normal_z:.4}"
+    );
+}
+
+/// Sustained anomalous traffic causes the CUSUM accumulator to grow
+/// beyond its steady-state level.
+#[test]
+fn anomalous_batch_elevates_cusum() {
+    let mut s = ScenarioBuilder::new()
+        .config(SentinelConfig::<u64> {
+            max_rank: 1,
+            cusum_slow_decay: 0.96,
+            cusum_coord_slow_decay: 0.96,
+            ..integration_config()
+        })
+        .seed_range(0xF, 8)
+        .warm_batches(30)
+        .build();
+
+    let baseline = s.ingest(&cell_values(0xF, 8));
+    assert_invariants(&s, &baseline);
+    let cusum_before = max_cusum(&baseline);
+
+    // Sustained anomalous traffic.
+    let mut last_report = baseline;
+    for _ in 0..15 {
+        last_report = s.ingest(&anomalous_values(0xF, 8));
+        assert_invariants(&s, &last_report);
+    }
+
+    let cusum_after = max_cusum(&last_report);
+    assert!(
+        cusum_after > cusum_before,
+        "CUSUM should grow under sustained anomalous traffic: \
+         before={cusum_before:.6}, after={cusum_after:.6}"
+    );
+}
+
+// ── Decay → ingest round-trip ───────────────────────────────
+
+/// Apply `decay()` mid-lifecycle, then continue ingesting. Invariants
+/// must hold throughout and the sentinel must remain functional.
+#[test]
+fn decay_then_ingest_maintains_invariants() {
+    let mut s = ScenarioBuilder::new()
+        .seed_range(0xF, 8)
+        .seed_range(0x1, 8)
+        .warm_batches(10)
+        .build();
+
+    // Pre-decay steady state.
+    for _ in 0..5 {
+        let batch: Vec<u128> = [cell_values(0xF, 4), cell_values(0x1, 4)].concat();
+        let r = s.ingest(&batch);
+        assert_invariants(&s, &r);
+    }
+
+    let obs_before = s.lifetime_observations();
+
+    // Decay: uniform 50% attenuation.
+    s.decay(0.5, 0.0);
+
+    // Post-decay ingestion must succeed with invariants intact.
+    for _ in 0..10 {
+        let batch: Vec<u128> = [cell_values(0xF, 4), cell_values(0x1, 4)].concat();
+        let r = s.ingest(&batch);
+        assert_invariants(&s, &r);
+    }
+
+    assert!(
+        s.lifetime_observations() > obs_before,
+        "observations should continue accumulating after decay"
+    );
+}
+
+// ── Inspection after realistic traffic ──────────────────────
+
+/// After multi-range traffic, `inspect_cell()` returns meaningful
+/// state for every tracked cell and the root cell is always present.
+#[test]
+fn inspect_cells_after_multi_range_traffic() {
+    let mut s = ScenarioBuilder::new()
+        .seed_range(0xF, 8)
+        .seed_range(0x1, 8)
+        .warm_batches(10)
+        .build();
+
+    for _ in 0..10 {
+        s.ingest(&[cell_values(0xF, 4), cell_values(0x1, 4)].concat());
+    }
+
+    let gnodes = s.cell_gnodes();
+    assert!(!gnodes.is_empty());
+
+    let mut found_root = false;
+    for gnode in &gnodes {
+        let insp = s.inspect_cell(*gnode).expect("tracked cell must be inspectable");
+        assert_eq!(insp.analysis_width, 128 - insp.depth as usize);
+        assert!(insp.rank >= 1, "tracked cell should have rank >= 1");
+        if insp.depth == 0 {
+            found_root = true;
+        }
+    }
+    assert!(found_root, "root cell (depth 0) must always be tracked");
+}
+
+// ── Coordination (end-to-end) ───────────────────────────────
+
+/// With two disjoint ranges and enough traffic, at least one
+/// coordination report should appear in a batch report, proving
+/// the coordination subsystem activated end-to-end.
+#[test]
+fn coordination_activates_with_multi_range_traffic() {
+    let mut s = ScenarioBuilder::new()
+        .config(SentinelConfig::<u64> {
+            split_threshold: 10,
+            per_sample_scores: true,
+            ..integration_config()
+        })
+        .seed_range(0xF, 20)
+        .seed_range(0x1, 20)
+        .warm_batches(20)
+        .build();
+
+    // Continue feeding both ranges to keep competitive cells active.
+    let mut saw_coordination = false;
+    for _ in 0..30 {
+        let batch: Vec<u128> = [cell_values(0xF, 8), cell_values(0x1, 8)].concat();
+        let report = s.ingest(&batch);
+
+        if !report.coordination_reports.is_empty() {
+            saw_coordination = true;
+            // Coordination reports should have valid scores.
+            for cr in &report.coordination_reports {
+                assert!(cr.cells_reporting >= 2);
+                assert!(!cr.scores.novelty.mean.is_nan());
+            }
+            break;
+        }
+    }
+
+    assert!(
+        saw_coordination,
+        "coordination should activate with two disjoint competitive ranges"
+    );
+}
diff --git a/packages/sentinel/tests/invariants.rs b/packages/sentinel/tests/invariants.rs
new file mode 100644
index 000000000..982c2b3b2
--- /dev/null
+++ b/packages/sentinel/tests/invariants.rs
@@ -0,0 +1,460 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// SPDX-FileCopyrightText: 2026 Torrust project contributors
+
+//! §9.2 — **Invariant tests**.
+//!
+//! These tests validate invariants that must hold regardless of traffic
+//! pattern, batch size, or configuration.  They close gaps G1–G3:
+//! feed-forward accounting (G1), constant-norm geometry (G2), and
+//! step ordering (G3), plus root-survival, score-polarity, counter
+//! monotonicity, and cross-cutting random-traffic checks.
+//!
+//! # Test index
+//!
+//! ## G1: Feed-forward invariant
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`feed_forward_delta_one_normal`] | `total_sum` == cumulative observation count |
+//! | [`feed_forward_delta_one_anomalous`] | `total_sum` unchanged by anomalous traffic pattern |
+//! | [`feed_forward_delta_one_after_decay`] | `total_sum` correct after decay + fresh ingest |
+//!
+//! ## G2: Constant-norm geometry
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`analysis_width_equals_128_minus_depth`] | every report has `analysis_width == 128 - depth` |
+//! | [`energy_ratio_bounded_zero_to_one`] | `energy_ratio ∈ [0.0, 1.0]` for all reports |
+//! | [`rank_bounded_by_max_rank`] | `rank <= max_rank` in all cell and ancestor reports |
+//! | [`no_nan_scores_after_warmup`] | no NaN in any score field after warm-up |
+//!
+//! ## G3: Step ordering
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`noise_injected_before_real_observations`] | noise observations > 0 on warmed cells |
+//! | [`graph_updated_before_analysis_set`] | new cells appear in report from the same batch |
+//!
+//! ## Root invariants
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`root_always_in_analysis_set`] | depth 0 present in every report |
+//! | [`root_survives_extreme_decay`] | root tracker persists through near-zero decay |
+//!
+//! ## Score invariants
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`score_polarity_higher_is_more_anomalous`] | anomalous traffic → higher CUSUM than normal |
+//! | [`cusum_accumulators_non_negative`] | CUSUM accumulators ≥ 0 across all reports |
+//!
+//! ## Counters
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`lifetime_observations_monotonically_increases`] | counter grows by batch size after each ingest |
+//! | [`cells_tracked_always_at_least_one`] | at least one cell (root) is always tracked |
+//!
+//! ## Cross-cutting
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`assert_invariants_under_random_traffic`] | full invariant suite under pseudo-random batches |
+//! | [`assert_invariants_after_decay_regrowth`] | full invariant suite after decay + fresh traffic |
+
+mod common;
+
+use common::{ScenarioBuilder, anomalous_values, assert_invariants, cell_values, integration_config, max_cusum, test_config};
+use torrust_sentinel::{NoiseSchedule, Sentinel128, SentinelConfig};
+
+// ── G1: Feed-forward invariant ──────────────────────────────
+
+#[test]
+fn feed_forward_delta_one_normal() {
+    let mut s = Sentinel128::new(test_config()).unwrap();
+
+    for batch_num in 1..=50u64 {
+        let values = cell_values(0xA, 8);
+        s.ingest(&values);
+        assert_eq!(
+            s.graph().total_sum(),
+            batch_num * 8,
+            "total_sum must equal cumulative observation count"
+        );
+    }
+}
+
+#[test]
+fn feed_forward_delta_one_anomalous() {
+    let mut s = Sentinel128::new(test_config()).unwrap();
+
+    // Normal traffic.
+    for _ in 0..20 {
+        s.ingest(&cell_values(0xA, 8));
+    }
+    let before = s.graph().total_sum();
+
+    // Anomalous traffic — graph total_sum should still increase by exactly n.
+    let anomalous = anomalous_values(0xA, 8);
+    s.ingest(&anomalous);
+    assert_eq!(
+        s.graph().total_sum(),
+        before + 8,
+        "anomalous traffic should not change delta-one accounting"
+    );
+}
+
+#[test]
+fn feed_forward_delta_one_after_decay() {
+    let mut s = Sentinel128::new(test_config()).unwrap();
+
+    s.ingest(&cell_values(0xA, 100));
+    s.decay(0.5, 0.0);
+    let after_decay = s.graph().total_sum();
+
+    s.ingest(&cell_values(0xA, 10));
+    assert_eq!(
+        s.graph().total_sum(),
+        after_decay + 10,
+        "after decay + ingest, total_sum reflects only the new ingest count"
+    );
+}
+
+// ── G2: Constant-norm geometry ──────────────────────────────
+
+#[test]
+fn analysis_width_equals_128_minus_depth() {
+    let mut s = ScenarioBuilder::new()
+        .config(SentinelConfig::<u64> {
+            max_rank: 2,
+            split_threshold: 10,
+            ..integration_config()
+        })
+        .seed_range(0xA, 20)
+        .warm_batches(15)
+        .build();
+
+    let report = s.ingest(&cell_values(0xA, 8));
+
+    for cr in report.cell_reports.iter().chain(report.ancestor_reports.iter()) {
+        assert_eq!(
+            cr.analysis_width,
+            128 - cr.depth as usize,
+            "analysis_width mismatch at depth {}",
+            cr.depth
+        );
+    }
+}
+
+#[test]
+fn energy_ratio_bounded_zero_to_one() {
+    let mut s = ScenarioBuilder::new()
+        .config(SentinelConfig::<u64> {
+            max_rank: 2,
+            split_threshold: 10,
+            ..integration_config()
+        })
+        .seed_range(0xA, 20)
+        .warm_batches(15)
+        .build();
+
+    let report = s.ingest(&cell_values(0xA, 8));
+
+    for cr in report.cell_reports.iter().chain(report.ancestor_reports.iter()) {
+        assert!(
+            (0.0..=1.0).contains(&cr.energy_ratio),
+            "energy_ratio {} out of [0.0, 1.0] at depth {}",
+            cr.energy_ratio,
+            cr.depth
+        );
+    }
+}
+
+#[test]
+fn rank_bounded_by_max_rank() {
+    let cfg = SentinelConfig::<u64> {
+        max_rank: 2,
+        split_threshold: 10,
+        ..integration_config()
+    };
+    let max_rank = cfg.max_rank;
+
+    let mut s = ScenarioBuilder::new()
+        .config(cfg)
+        .seed_range(0xA, 20)
+        .warm_batches(15)
+        .build();
+
+    let report = s.ingest(&cell_values(0xA, 8));
+
+    for cr in report.cell_reports.iter().chain(report.ancestor_reports.iter()) {
+        assert!(
+            cr.rank <= max_rank,
+            "rank {} exceeds max_rank {} at depth {}",
+            cr.rank,
+            max_rank,
+            cr.depth
+        );
+    }
+}
+
+#[test]
+fn no_nan_scores_after_warmup() {
+    let mut s = ScenarioBuilder::new()
+        .config(SentinelConfig::<u64> {
+            max_rank: 2,
+            split_threshold: 10,
+            ..integration_config()
+        })
+        .seed_range(0xA, 20)
+        .warm_batches(15)
+        .build();
+
+    let report = s.ingest(&cell_values(0xA, 8));
+
+    for cr in report.cell_reports.iter().chain(report.ancestor_reports.iter()) {
+        assert!(!cr.scores.novelty.mean.is_nan(), "NaN novelty at depth {}", cr.depth);
+        assert!(
+            !cr.scores.displacement.mean.is_nan(),
+            "NaN displacement at depth {}",
+            cr.depth
+        );
+        assert!(!cr.scores.surprise.mean.is_nan(), "NaN surprise at depth {}", cr.depth);
+    }
+}
+
+// ── G3: Step ordering ──────────────────────────────────────
+
+#[test]
+fn noise_injected_before_real_observations() {
+    let cfg = SentinelConfig::<u64> {
+        noise_schedule: NoiseSchedule::Explicit(vec![5]),
+        noise_batch_size: 4,
+        split_threshold: 10,
+        ..integration_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    // Feed enough traffic to create at least one competitive cell.
+    for _ in 0..20 {
+        s.ingest(&cell_values(0xA, 20));
+    }
+
+    // Every tracker should have noise_observations > 0 (noise ran first).
+    for &gnode in &s.cell_gnodes() {
+        let insp = s.inspect_cell(gnode).unwrap();
+        assert!(
+            insp.maturity.noise_observations > 0,
+            "cell at depth {} should have noise observations",
+            insp.depth
+        );
+    }
+}
+
+#[test]
+fn graph_updated_before_analysis_set() {
+    let cfg = SentinelConfig::<u64> {
+        split_threshold: 10,
+        ..integration_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    // Feed concentrated traffic to force a split.
+    for _ in 0..5 {
+        s.ingest(&cell_values(0xA, 20));
+    }
+
+    // After enough traffic to split, the new cell should appear in
+    // the analysis set in the same batch's report.
+    let report = s.ingest(&cell_values(0xA, 20));
+    let summary = &report.analysis_set_summary;
+
+    // If competitive cells exist, they must already be represented.
+    if summary.competitive_size > 0 {
+        assert!(
+            !report.cell_reports.is_empty(),
+            "competitive cells in summary but no cell_reports"
+        );
+    }
+}
+
+// ── Root invariants ─────────────────────────────────────────
+
+#[test]
+fn root_always_in_analysis_set() {
+    let mut s = Sentinel128::new(integration_config()).unwrap();
+
+    for i in 0..30u128 {
+        let report = s.ingest(&cell_values(i % 16, 8));
+        assert_eq!(
+            report.analysis_set_summary.depth_range.0, 0,
+            "root (depth 0) must always be in the analysis set"
+        );
+    }
+}
+
+#[test]
+fn root_survives_extreme_decay() {
+    let mut s = ScenarioBuilder::new().seed_range(0xA, 20).warm_batches(20).build();
+
+    assert!(s.cells_tracked() > 1);
+
+    // Annihilate everything.
+    s.decay(0.0001, 0.0);
+
+    // Root must still be present.
+    let root = s.graph().g_root();
+    assert!(s.inspect_cell(root).is_some(), "root tracker must survive decay");
+
+    // Re-ingest — root should still produce reports.
+    let report = s.ingest(&cell_values(0xA, 8));
+    assert!(
+        report.ancestor_reports.iter().any(|r| r.depth == 0),
+        "root must appear in ancestor_reports after decay + re-ingest"
+    );
+}
+
+// ── Score invariants ────────────────────────────────────────
+
+#[test]
+fn score_polarity_higher_is_more_anomalous() {
+    // Use a simple setup: repeated cell_values warm-up, then compare
+    // max_cusum() under continued normal vs. anomalous traffic.
+    let batch = cell_values(0xA, 8);
+
+    let cfg = SentinelConfig::<u64> {
+        max_rank: 2,
+        split_threshold: 10,
+        ..integration_config()
+    };
+
+    // ── Run A: continued normal traffic ──
+    let mut sa = Sentinel128::new(cfg.clone()).unwrap();
+    for _ in 0..20 {
+        sa.ingest(&batch);
+    }
+    let normal_report = sa.ingest(&batch);
+    let normal_cusum = max_cusum(&normal_report);
+
+    // ── Run B: anomalous traffic after identical warm-up ──
+    let mut sb = Sentinel128::new(cfg).unwrap();
+    for _ in 0..20 {
+        sb.ingest(&batch);
+    }
+    let anom_batch = anomalous_values(0xA, 8);
+    for _ in 0..5 {
+        sb.ingest(&anom_batch);
+    }
+    let anomaly_report = sb.ingest(&anom_batch);
+    let anomaly_cusum = max_cusum(&anomaly_report);
+
+    assert!(
+        anomaly_cusum > normal_cusum,
+        "anomalous data should produce higher CUSUM: anomaly={anomaly_cusum:.6}, normal={normal_cusum:.6}"
+    );
+}
+
+#[test]
+fn cusum_accumulators_non_negative() {
+    let mut s = ScenarioBuilder::new().seed_range(0xA, 20).warm_batches(20).build();
+
+    for _ in 0..10 {
+        let report = s.ingest(&cell_values(0xA, 8));
+
+        for cr in report.cell_reports.iter().chain(report.ancestor_reports.iter()) {
+            for (name, acc) in [
+                ("novelty", cr.scores.novelty.cusum.accumulator),
+                ("displacement", cr.scores.displacement.cusum.accumulator),
+                ("surprise", cr.scores.surprise.cusum.accumulator),
+                ("coherence", cr.scores.coherence.cusum.accumulator),
+            ] {
+                assert!(
+                    acc >= 0.0,
+                    "CUSUM {name} accumulator is negative ({acc}) at depth {}",
+                    cr.depth
+                );
+            }
+        }
+    }
+}
+
+// ── Counters ────────────────────────────────────────────────
+
+#[test]
+fn lifetime_observations_monotonically_increases() {
+    let mut s = Sentinel128::new(integration_config()).unwrap();
+    let mut prev = 0u64;
+
+    for batch_size in [1, 5, 20, 3, 50] {
+        s.ingest(&cell_values(0xA, batch_size));
+        let current = s.lifetime_observations();
+        assert_eq!(
+            current,
+            prev + batch_size as u64,
+            "lifetime_observations should grow by batch size"
+        );
+        prev = current;
+    }
+}
+
+#[test]
+fn cells_tracked_always_at_least_one() {
+    let mut s = Sentinel128::new(integration_config()).unwrap();
+
+    assert!(s.cells_tracked() >= 1, "fresh sentinel must track at least the root");
+
+    for _ in 0..20 {
+        s.ingest(&cell_values(0xA, 8));
+        assert!(s.cells_tracked() >= 1, "cells_tracked must never drop below 1");
+    }
+
+    s.decay(0.0001, 0.0);
+    assert!(s.cells_tracked() >= 1, "cells_tracked must stay ≥ 1 after extreme decay");
+}
+
+// ── Cross-cutting ───────────────────────────────────────────
+
+#[test]
+fn assert_invariants_under_random_traffic() {
+    use std::hash::{DefaultHasher, Hash, Hasher};
+
+    let mut s = Sentinel128::new(SentinelConfig::<u64> {
+        split_threshold: 10,
+        ..integration_config()
+    })
+    .unwrap();
+
+    // Use a simple deterministic hash-based "random" generator.
+    for batch_idx in 0..60u64 {
+        let batch_size = (batch_idx % 63) as usize + 1; // 1–63
+        let values: Vec<u128> = (0..batch_size)
+            .map(|i| {
+                let mut h = DefaultHasher::new();
+                (batch_idx, i).hash(&mut h);
+                u128::from(h.finish()) | (u128::from(h.finish()) << 64)
+            })
+            .collect();
+
+        let report = s.ingest(&values);
+        assert_invariants(&s, &report);
+    }
+}
+
+#[test]
+fn assert_invariants_after_decay_regrowth() {
+    let mut s = ScenarioBuilder::new()
+        .seed_range(0xA, 20)
+        .seed_range(0x5, 20)
+        .warm_batches(20)
+        .build();
+
+    // Severe decay to collapse most cells.
+    s.decay(0.001, 0.0);
+
+    // Regrow with spread traffic so the tree re-balances.
+    for i in 0..20u128 {
+        let report = s.ingest(&cell_values(i % 16, 20));
+        assert_invariants(&s, &report);
+    }
+}
diff --git a/packages/sentinel/tests/noise.rs b/packages/sentinel/tests/noise.rs
new file mode 100644
index 000000000..1080afd61
--- /dev/null
+++ b/packages/sentinel/tests/noise.rs
@@ -0,0 +1,328 @@
+//! Automatic **noise injection** tests (§ALGO S-11).
+//!
+//! Verifies that trackers are automatically warmed at construction
+//! and during analysis-set entry, with no manual injection API.
+//! Root warming, child-cell warming, depth-tiered schedules, CUSUM
+//! reset semantics, determinism, reset behaviour, and coordination
+//! warming are all exercised.
+//!
+//! # Test index
+//!
+//! ## Root warming at construction
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`root_warmed_at_construction`] | root has noise observations after `new()` |
+//! | [`root_cold_when_schedule_empty`] | empty schedule → root starts cold |
+//! | [`noise_does_not_count_as_real_observations`] | `lifetime_observations` stays zero |
+//!
+//! ## Child cell warming
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`new_cells_warmed_on_analysis_set_entry`] | split-created cells receive noise |
+//! | [`successive_cells_get_different_noise`] | RNG advances between cell injections |
+//!
+//! ## Depth-tiered noise
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`deeper_cells_receive_fewer_noise_rounds`] | geometric schedule tapers with depth |
+//!
+//! ## CUSUM reset after noise
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`auto_inject_resets_cusum`] | first real batch has `steps_since_reset == 1` |
+//!
+//! ## Noise determinism
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`deterministic_with_same_seed`] | identical seed → identical noise state |
+//! | [`different_seeds_produce_different_baselines`] | different seeds → divergent baselines |
+//!
+//! ## Reset behaviour
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`reset_reseeds_rng_and_warms_root`] | `reset()` restores fresh noise state |
+//!
+//! ## Coordination warming
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`coordination_contexts_warmed_on_activation`] | active contexts receive Gamma-sampled noise |
+
+mod common;
+
+use common::{ScenarioBuilder, assert_invariants, cell_values, cold_config, seeded_sentinel, test_config};
+use torrust_sentinel::{NoiseSchedule, Sentinel128, SentinelConfig};
+
+// ── Root Warming at Construction ────────────────────────────
+
+#[test]
+fn root_warmed_at_construction() {
+    let s = Sentinel128::new(test_config()).unwrap();
+    let root = s.graph().g_root();
+    let insp = s.inspect_cell(root).expect("root should exist");
+
+    assert!(
+        insp.maturity.noise_observations > 0,
+        "root tracker should have received noise observations"
+    );
+    // noise_influence starts at 1.0 and noise pushes toward 1.0,
+    // so a noise-only tracker remains at 1.0.  It decays only
+    // after real observations.
+    assert!(
+        (insp.maturity.noise_influence - 1.0).abs() < f64::EPSILON,
+        "noise_influence should stay at 1.0 with only noise"
+    );
+}
+
+#[test]
+fn root_cold_when_schedule_empty() {
+    let s = Sentinel128::new(cold_config()).unwrap();
+    let root = s.graph().g_root();
+    let insp = s.inspect_cell(root).unwrap();
+
+    assert_eq!(insp.maturity.noise_observations, 0);
+    assert!(
+        (insp.maturity.noise_influence - 1.0).abs() < f64::EPSILON,
+        "cold root should have default noise_influence of 1.0"
+    );
+}
+
+#[test]
+fn noise_does_not_count_as_real_observations() {
+    let s = Sentinel128::new(test_config()).unwrap();
+    assert_eq!(
+        s.lifetime_observations(),
+        0,
+        "noise injection at construction should not increment lifetime_observations"
+    );
+}
+
+// ── Child Cell Warming ──────────────────────────────────────
+
+#[test]
+fn new_cells_warmed_on_analysis_set_entry() {
+    let (s, _reports) = ScenarioBuilder::new()
+        .config(SentinelConfig::<u64> {
+            split_threshold: 10,
+            analysis_k: 16,
+            ..test_config()
+        })
+        .seed_range(0xF, 20)
+        .warm_batches(19)
+        .build_with_reports();
+
+    // All cells should be warm.
+    for &gnode in &s.cell_gnodes() {
+        let insp = s.inspect_cell(gnode).unwrap();
+        assert!(insp.maturity.noise_observations > 0, "cell {gnode:?} should be noise-warmed");
+    }
+}
+
+#[test]
+fn successive_cells_get_different_noise() {
+    let (s, _) = ScenarioBuilder::new()
+        .config(SentinelConfig::<u64> {
+            split_threshold: 10,
+            analysis_k: 16,
+            ..test_config()
+        })
+        .seed_range(0xF, 4)
+        .seed_range(0x1, 4)
+        .warm_batches(14)
+        .build_with_reports();
+
+    let root = s.graph().g_root();
+    let non_root: Vec<_> = s.cell_gnodes().into_iter().filter(|&g| g != root).collect();
+
+    // We need at least two non-root cells to compare.
+    assert!(
+        non_root.len() >= 2,
+        "expected at least 2 non-root cells, got {}",
+        non_root.len()
+    );
+
+    // Both cells must be warmed.
+    for &gnode in &non_root {
+        let insp = s.inspect_cell(gnode).unwrap();
+        assert!(insp.maturity.noise_observations > 0, "cell {gnode:?} should be noise-warmed");
+    }
+}
+
+// ── Depth-Tiered Noise ──────────────────────────────────────
+
+#[test]
+fn deeper_cells_receive_fewer_noise_rounds() {
+    // Use a geometric schedule with aggressive decay so the
+    // difference between root (depth 0) and child cells is obvious.
+    let cfg = SentinelConfig::<u64> {
+        split_threshold: 10,
+        analysis_k: 16,
+        noise_schedule: NoiseSchedule::geometric(100, 0.5, 5),
+        ..test_config()
+    };
+
+    let (s, _) = ScenarioBuilder::new()
+        .config(cfg)
+        .seed_range(0xF, 20)
+        .warm_batches(19)
+        .build_with_reports();
+
+    let root = s.graph().g_root();
+    let root_insp = s.inspect_cell(root).unwrap();
+
+    // At least one child cell should have fewer noise observations
+    // than the root (deeper → fewer rounds via geometric decay).
+    let non_root: Vec<_> = s.cell_gnodes().into_iter().filter(|&g| g != root).collect();
+    if !non_root.is_empty() {
+        let any_fewer = non_root.iter().any(|&gnode| {
+            let insp = s.inspect_cell(gnode).unwrap();
+            insp.maturity.noise_observations < root_insp.maturity.noise_observations
+        });
+        assert!(
+            any_fewer,
+            "at least one deeper cell should have fewer noise rounds than root ({})",
+            root_insp.maturity.noise_observations
+        );
+    }
+}
+
+// ── CUSUM Reset After Noise ─────────────────────────────────
+
+#[test]
+fn auto_inject_resets_cusum() {
+    let mut s = Sentinel128::new(test_config()).unwrap();
+
+    // Root was auto-injected at construction; CUSUM should be reset.
+    // Ingest one real batch — steps_since_reset should be 1.
+    let report = s.ingest(&[
+        0xF000_0000_0000_0000_0000_0000_0000_AAAA,
+        0x1000_0000_0000_0000_0000_0000_0000_BBBB,
+    ]);
+
+    assert_invariants(&s, &report);
+
+    for cr in report.cell_reports.iter().chain(report.ancestor_reports.iter()) {
+        assert_eq!(
+            cr.scores.novelty.cusum.steps_since_reset, 1,
+            "CUSUM should have been reset after auto noise injection"
+        );
+    }
+}
+
+// ── Noise Determinism ───────────────────────────────────────
+
+#[test]
+fn deterministic_with_same_seed() {
+    let s1 = seeded_sentinel();
+    let s2 = seeded_sentinel();
+
+    let gnodes1 = s1.cell_gnodes();
+    let gnodes2 = s2.cell_gnodes();
+    assert_eq!(gnodes1, gnodes2);
+
+    for &gnode in &gnodes1 {
+        let insp1 = s1.inspect_cell(gnode).unwrap();
+        let insp2 = s2.inspect_cell(gnode).unwrap();
+        assert_eq!(insp1.maturity.noise_observations, insp2.maturity.noise_observations);
+        assert!(
+            (insp1.maturity.noise_influence - insp2.maturity.noise_influence).abs() < f64::EPSILON,
+            "noise influence should be identical with same seed"
+        );
+    }
+}
+
+#[test]
+fn different_seeds_produce_different_baselines() {
+    let cfg1 = SentinelConfig::<u64> {
+        noise_seed: Some(42),
+        ..test_config()
+    };
+    let cfg2 = SentinelConfig::<u64> {
+        noise_seed: Some(999),
+        ..test_config()
+    };
+
+    let values = cell_values(0xA, 20);
+
+    let mut s1 = Sentinel128::new(cfg1).unwrap();
+    let mut s2 = Sentinel128::new(cfg2).unwrap();
+
+    // Seed with identical traffic.
+    s1.ingest(&values);
+    s2.ingest(&values);
+
+    // Probe with the same batch.
+    let r1 = s1.ingest(&values);
+    let r2 = s2.ingest(&values);
+
+    assert_invariants(&s1, &r1);
+    assert_invariants(&s2, &r2);
+
+    let means1: Vec<f64> = r1
+        .cell_reports
+        .iter()
+        .chain(r1.ancestor_reports.iter())
+        .map(|cr| cr.scores.novelty.mean)
+        .collect();
+    let means2: Vec<f64> = r2
+        .cell_reports
+        .iter()
+        .chain(r2.ancestor_reports.iter())
+        .map(|cr| cr.scores.novelty.mean)
+        .collect();
+
+    assert_ne!(means1, means2, "different seeds should produce different baselines");
+}
+
+// ── Reset Behaviour ─────────────────────────────────────────
+
+#[test]
+fn reset_reseeds_rng_and_warms_root() {
+    let cfg = test_config();
+    let fresh = Sentinel128::new(cfg.clone()).unwrap();
+
+    let mut reset_s = Sentinel128::new(cfg).unwrap();
+    reset_s.ingest(&cell_values(0xA, 100));
+    reset_s.reset();
+
+    let fresh_insp = fresh.inspect_cell(fresh.graph().g_root()).unwrap();
+    let reset_insp = reset_s.inspect_cell(reset_s.graph().g_root()).unwrap();
+
+    assert_eq!(fresh_insp.maturity.noise_observations, reset_insp.maturity.noise_observations);
+    assert!(
+        (fresh_insp.maturity.noise_influence - reset_insp.maturity.noise_influence).abs() < f64::EPSILON,
+        "noise influence after reset should match fresh sentinel"
+    );
+}
+
+// ── Coordination Warming ────────────────────────────────────
+
+#[test]
+fn coordination_contexts_warmed_on_activation() {
+    let (s, _) = ScenarioBuilder::new()
+        .config(SentinelConfig::<u64> {
+            split_threshold: 10,
+            ..test_config()
+        })
+        .seed_range(0xF, 4)
+        .seed_range(0x1, 4)
+        .warm_batches(14)
+        .build_with_reports();
+
+    let ch = s.health().coordination_health;
+    if ch.active_contexts > 0 {
+        // Coordination contexts should have been warmed on activation
+        // via Gamma-sampled synthetic noise (§ALGO S-9.8).
+        assert!(
+            ch.maturity_distribution.cold_trackers == 0,
+            "all active coordination contexts should be warmed, found {} cold",
+            ch.maturity_distribution.cold_trackers,
+        );
+    }
+}
diff --git a/packages/sentinel/tests/pedagogy.rs b/packages/sentinel/tests/pedagogy.rs
new file mode 100644
index 000000000..e3e129a86
--- /dev/null
+++ b/packages/sentinel/tests/pedagogy.rs
@@ -0,0 +1,904 @@
+#![allow(clippy::print_stdout)]
+
+//! # End-to-End Pedagogy Test for the Spectral Sentinel
+//!
+//! This custom-harness test walks through the Sentinel lifecycle as a
+//! narrative: construction, feed-forward observation, spatial refinement,
+//! analysis-set closure, hierarchical coordination, score interpretation,
+//! host-controlled decay, and reset. Every assertion corresponds to a
+//! public contract from the algorithm, API reference, or ADRs.
+//!
+//! ## What is the Spectral Sentinel?
+//!
+//! The Sentinel is a three-layer online anomaly spectrometer for
+//! positionally structured coordinate streams:
+//!
+//! - **Layer 1: Spatial substrate.** A Mudlark G-V Graph adapts a dyadic
+//!   contour over the coordinate domain using pure observation volume.
+//! - **Layer 2: Analysis selector.** The top competitive cells are closed
+//!   under G-tree ancestry so every selected region has a full model chain
+//!   back to the root.
+//! - **Layer 3: Analysis engine.** Per-cell subspace trackers score suffix
+//!   bit vectors on four raw axes, and coordination trackers model
+//!   cross-cell score patterns.
+//!
+//! The central teaching point is the design principle from ADR-S-001 and
+//! ADR-S-002: **the Sentinel measures; the host decides**. The spatial
+//! graph receives `Delta = 1` per input value. Scores flow outward in
+//! reports; they never flow back into spatial importance.
+//!
+//! ## What this test demonstrates
+//!
+//! Steps 0-2 show construction, automatic noise warm-up, the feed-forward
+//! invariant, and spatial refinement under concentrated traffic. Steps 3-4
+//! show analysis-set ancestry, suffix widths, report structure, raw score
+//! polarity, and hierarchical coordination. Steps 5-6 demonstrate that
+//! temporal policy is host-controlled through `decay()` and that `reset()`
+//! returns the system to a fresh, warmed root.
+//!
+//! Run with:
+//!
+//! ```sh
+//! cargo test -p torrust-sentinel --test pedagogy
+//! ```
+//!
+//! # Test Index
+//!
+//! ## Construction & Warm-Up (§ALGO S-11, ADR-S-007)
+//!
+//! | Step | Function | What it teaches |
+//! |------|----------|-----------------|
+//! | 0 | [`step_0_fresh`] | Fresh Sentinel = one spatial root, one warmed tracker, no real observations |
+//!
+//! ## Feed-Forward Observation (§ALGO S-8.1, ADR-S-002)
+//!
+//! | Step | Function | What it teaches |
+//! |------|----------|-----------------|
+//! | 1 | [`step_1_first_batch`] | Each raw value increments the graph by exactly one unit |
+//! | 2 | [`step_2_concentrated_traffic`] | Volume alone drives spatial splitting and analysis selection |
+//!
+//! ## Multi-Scale Analysis (§ALGO S-8, §ALGO S-9)
+//!
+//! | Step | Function | What it teaches |
+//! |------|----------|-----------------|
+//! | 3 | [`step_3_multiscale_reports`] | Competitive cells plus ancestors form a complete reporting chain |
+//! | 4 | [`step_4_scores_are_measurements`] | Scores are finite, higher-polarity measurements, not verdicts |
+//!
+//! ## Host Policy & Lifecycle (§ALGO S-10, §ALGO S-13.4)
+//!
+//! | Step | Function | What it teaches |
+//! |------|----------|-----------------|
+//! | 5 | [`step_5_host_controlled_decay`] | Decay changes spatial importance, not tracker count or lifetime observations |
+//! | 6 | [`step_6_reset`] | Reset clears learned state while preserving the configured warm-up lifecycle |
+
+mod common;
+
+use common::{anomalous_values, assert_invariants, cell_values};
+use torrust_sentinel::{
+    AnomalyScores, BatchReport, CellReport, CoordinationReport, MemberScore, NoiseSchedule, ScoreDistribution, Sentinel128,
+    SentinelConfig, SvdStrategy,
+};
+
+// -- Configuration ---------------------------------------------------------
+
+/// Small deterministic configuration for a narrative end-to-end run.
+///
+/// The values are intentionally close to the shared integration-test
+/// presets, but written out here so the test explains itself. The low
+/// split threshold makes spatial refinement visible after a few batches;
+/// explicit noise keeps warm-up fast and deterministic.
+fn pedagogy_config() -> SentinelConfig<u64> {
+    SentinelConfig::<u64> {
+        max_rank: 2,
+        forgetting_factor: 0.90,
+        rank_update_interval: 5,
+        analysis_k: 16,
+        analysis_depth_cutoff: 6,
+        energy_threshold: 0.90,
+        eps: 1e-6,
+        per_sample_scores: true,
+        cusum_allowance_sigmas: 0.5,
+        cusum_slow_decay: 0.99,
+        cusum_coord_slow_decay: 0.99,
+        clip_sigmas: 3.0,
+        clip_pressure_decay: 0.95,
+        split_threshold: 10,
+        d_create: 3,
+        d_evict: 6,
+        budget: 100_000,
+        noise_schedule: NoiseSchedule::Explicit(vec![5]),
+        noise_batch_size: 4,
+        noise_seed: Some(2026),
+        background_warming: false,
+        svd_strategy: SvdStrategy::Brand,
+    }
+}
+
+// -- Helpers ---------------------------------------------------------------
+
+fn heading(s: &str) {
+    let rule = "=".repeat(72);
+    println!("\n{rule}");
+    println!("  {s}");
+    println!("{rule}");
+}
+
+fn subheading(s: &str) {
+    println!("\n-- {s} --");
+}
+
+fn all_cell_reports(report: &BatchReport<u128>) -> impl Iterator<Item = &CellReport<u128>> {
+    report.cell_reports.iter().chain(report.ancestor_reports.iter())
+}
+
+fn root_ancestor(report: &BatchReport<u128>) -> &CellReport<u128> {
+    report
+        .ancestor_reports
+        .iter()
+        .find(|cell| cell.depth == 0)
+        .expect("root tracker must report as an ancestor when the batch is non-empty")
+}
+
+fn max_novelty_z(report: &BatchReport<u128>) -> f64 {
+    all_cell_reports(report)
+        .map(|cell| cell.scores.novelty.max_z_score)
+        .fold(0.0_f64, f64::max)
+}
+
+fn assert_score_distribution(name: &str, score: &ScoreDistribution) {
+    assert!(score.min.is_finite(), "{name}.min must be finite");
+    assert!(score.max.is_finite(), "{name}.max must be finite");
+    assert!(score.mean.is_finite(), "{name}.mean must be finite");
+    assert!(score.max_z_score.is_finite(), "{name}.max_z_score must be finite");
+    assert!(score.mean_z_score.is_finite(), "{name}.mean_z_score must be finite");
+    assert!(score.baseline.mean.is_finite(), "{name}.baseline.mean must be finite");
+    assert!(score.baseline.variance.is_finite(), "{name}.baseline.variance must be finite");
+    assert!(score.cusum.accumulator.is_finite(), "{name}.cusum.accumulator must be finite");
+    assert!(
+        score.cusum.slow_baseline.mean.is_finite(),
+        "{name}.cusum.slow_baseline.mean must be finite"
+    );
+    assert!(
+        score.cusum.slow_baseline.variance.is_finite(),
+        "{name}.cusum.slow_baseline.variance must be finite"
+    );
+    assert!(score.clip_pressure.is_finite(), "{name}.clip_pressure must be finite");
+
+    assert!(score.max + 1e-12 >= score.min, "{name}.max must be >= min");
+    assert!(score.mean + 1e-12 >= score.min, "{name}.mean must be >= min");
+    assert!(score.mean <= score.max + 1e-12, "{name}.mean must be <= max");
+    assert!(
+        score.baseline.variance >= -1e-12,
+        "{name}.baseline variance must be non-negative"
+    );
+    assert!(
+        score.cusum.slow_baseline.variance >= -1e-12,
+        "{name}.slow baseline variance must be non-negative"
+    );
+    assert!(
+        score.cusum.accumulator >= -1e-12,
+        "{name}.CUSUM is one-sided and non-negative"
+    );
+    assert!(
+        (-1e-12..=1.0 + 1e-12).contains(&score.clip_pressure),
+        "{name}.clip_pressure must be in [0, 1]"
+    );
+}
+
+fn assert_scores_are_measurements(scores: &AnomalyScores) {
+    assert_score_distribution("novelty", &scores.novelty);
+    assert_score_distribution("displacement", &scores.displacement);
+    assert_score_distribution("surprise", &scores.surprise);
+    assert_score_distribution("coherence", &scores.coherence);
+
+    assert!(
+        scores.novelty.min >= -1e-12,
+        "novelty is residual energy and must be non-negative"
+    );
+    assert!(
+        scores.displacement.min >= -1e-12 && scores.displacement.max <= 1.0 + 1e-12,
+        "displacement is bounded in [0, 1] (closed under float tolerance)"
+    );
+    assert!(scores.surprise.min >= -1e-12, "surprise must be non-negative");
+    assert!(scores.coherence.min >= -1e-12, "coherence must be non-negative");
+}
+
+/// Scalar polarity contract for a single cell's contribution to a
+/// coordination report.
+///
+/// Unlike [`assert_scores_are_measurements`], which checks a full
+/// [`ScoreDistribution`] per axis, a [`MemberScore`] carries only one
+/// scalar value (plus a z-score) per axis. We assert the same polarity
+/// invariants as the distribution form: finiteness, non-negativity for
+/// novelty/surprise/coherence, and the closed-tolerance `[0, 1]` bound
+/// for displacement.
+fn assert_member_polarity(member: &MemberScore<u128>) {
+    assert!(member.cell_start < member.cell_end, "member score identifies a real cell");
+
+    for (name, value) in [
+        ("novelty", member.novelty),
+        ("displacement", member.displacement),
+        ("surprise", member.surprise),
+        ("coherence", member.coherence),
+        ("novelty_z", member.novelty_z),
+        ("displacement_z", member.displacement_z),
+        ("surprise_z", member.surprise_z),
+        ("coherence_z", member.coherence_z),
+    ] {
+        assert!(value.is_finite(), "member.{name} must be finite");
+    }
+
+    assert!(member.novelty >= -1e-12, "member novelty must be non-negative");
+    assert!(
+        member.displacement >= -1e-12 && member.displacement <= 1.0 + 1e-12,
+        "member displacement is bounded in [0, 1] (closed under float tolerance)"
+    );
+    assert!(member.surprise >= -1e-12, "member surprise must be non-negative");
+    assert!(member.coherence >= -1e-12, "member coherence must be non-negative");
+}
+
+fn assert_cell_report_contract(cell: &CellReport<u128>, config: &SentinelConfig<u64>) {
+    assert!(cell.start < cell.end, "cell interval must be non-empty");
+    assert_eq!(
+        cell.analysis_width,
+        128 - cell.depth as usize,
+        "analysis width is the suffix width N - depth"
+    );
+    assert_eq!(cell.geometry.dim, cell.analysis_width, "geometry dim tracks suffix width");
+    assert_eq!(
+        cell.geometry.cap,
+        cell.analysis_width.min(config.max_rank),
+        "rank cap is min(width, max_rank)"
+    );
+    assert!(cell.rank >= 1, "trackers keep at least one active basis vector");
+    assert!(cell.rank <= cell.geometry.cap, "rank must respect the geometry cap");
+    assert_eq!(
+        cell.geometry.residual_dof,
+        cell.geometry.dim - cell.rank,
+        "residual degrees of freedom are dim - rank"
+    );
+    assert!(cell.sample_count > 0, "reported cells received observations this batch");
+    assert!(
+        cell.maturity.total_observations() > 0,
+        "reported cells have a maturity history"
+    );
+    assert!(
+        (0.0..=1.0).contains(&cell.maturity.noise_influence),
+        "noise influence is a fraction"
+    );
+
+    if config.per_sample_scores {
+        let per_sample = cell.per_sample.as_ref().expect("per-sample scores are enabled");
+        assert_eq!(per_sample.len(), cell.sample_count, "one sample score per routed observation");
+    } else {
+        assert!(cell.per_sample.is_none(), "per-sample scores are disabled");
+    }
+
+    assert_scores_are_measurements(&cell.scores);
+}
+
+fn assert_coordination_contract(coordination: &CoordinationReport<u128>, config: &SentinelConfig<u64>) {
+    assert!(
+        coordination.start < coordination.end,
+        "coordination interval must be non-empty"
+    );
+    assert!(
+        coordination.cells_reporting >= 2,
+        "coordination needs cells from both subtrees"
+    );
+    assert_eq!(
+        coordination.geometry.dim, 4,
+        "coordination trackers operate on four score axes"
+    );
+    assert_eq!(
+        coordination.geometry.cap,
+        config.max_rank.min(4),
+        "coordination rank cap is min(4, max_rank)"
+    );
+    assert!(coordination.rank >= 1, "coordination rank must be at least one");
+    assert!(
+        coordination.rank <= coordination.geometry.cap,
+        "coordination rank respects cap"
+    );
+    assert_eq!(
+        coordination.geometry.residual_dof,
+        coordination.geometry.dim - coordination.rank,
+        "coordination residual DOF is dim - rank"
+    );
+
+    if config.per_sample_scores {
+        let members = coordination.per_member.as_ref().expect("per-member scores are enabled");
+        assert_eq!(
+            members.len(),
+            coordination.cells_reporting,
+            "one member score per contributing cell"
+        );
+        for member in members {
+            assert_member_polarity(member);
+        }
+    }
+
+    assert_scores_are_measurements(&coordination.scores);
+}
+
+fn assert_report_contract(sentinel: &Sentinel128, report: &BatchReport<u128>, batch_size: usize) {
+    let config = sentinel.config();
+    assert_invariants(sentinel, report);
+
+    assert_eq!(
+        report.health.lifetime_observations,
+        sentinel.lifetime_observations(),
+        "inline health mirrors the sentinel counter"
+    );
+    assert_eq!(
+        report.health.cells_tracked,
+        sentinel.cells_tracked(),
+        "inline health mirrors active cells"
+    );
+    assert_eq!(
+        report.analysis_set_summary.competitive_size,
+        sentinel.analysis_set().competitive_count(),
+        "summary mirrors the current producing competitive set"
+    );
+    assert_eq!(
+        report.analysis_set_summary.full_size,
+        sentinel.analysis_set().total_count(),
+        "summary mirrors the current producing full set"
+    );
+    assert!(
+        report.analysis_set_summary.competitive_size <= config.analysis_k,
+        "competitive analysis set respects K"
+    );
+    assert!(
+        report.analysis_set_summary.investment_set_size >= report.analysis_set_summary.full_size,
+        "investment set covers online plus warming cells"
+    );
+    assert_eq!(
+        report.analysis_set_summary.depth_range.0, 0,
+        "depth range starts at the root: the producing set is anchored at \
+         depth 0 via ancestor closure (§ALGO S-8.2)"
+    );
+
+    for cell in &report.cell_reports {
+        assert!(cell.is_competitive, "cell_reports are the producing competitive set");
+        assert_cell_report_contract(cell, config);
+    }
+    for cell in &report.ancestor_reports {
+        assert!(!cell.is_competitive, "ancestor_reports are ancestor-only cells");
+        assert_cell_report_contract(cell, config);
+    }
+    for coordination in &report.coordination_reports {
+        assert_coordination_contract(coordination, config);
+    }
+
+    let root = root_ancestor(report);
+    assert_eq!(root.sample_count, batch_size, "root receives every observation in the batch");
+}
+
+fn mixed_normal_batch() -> Vec<u128> {
+    [
+        cell_values(0x1, 8),
+        cell_values(0x5, 8),
+        cell_values(0xA, 8),
+        cell_values(0xF, 8),
+    ]
+    .concat()
+}
+
+/// Maximum number of multi-region batches Step 3 will ingest while
+/// waiting for hierarchical coordination to activate. Far above the
+/// typical activation round under [`pedagogy_config`]; a panic at this
+/// bound indicates a real regression, not flakiness.
+const MULTISCALE_MAX_ROUNDS: usize = 30;
+
+/// Function-pointer alias used in Step 4 to project an [`AnomalyScores`]
+/// value onto one of its four axis distributions without repeating the
+/// full type signature.
+type ScoreAxis = fn(&AnomalyScores) -> &ScoreDistribution;
+
+// ========================================================================
+//  STEP 0: CONSTRUCTION AND AUTOMATIC ROOT WARM-UP
+//  §ALGO S-11, ADR-S-007, ADR-S-001
+// ========================================================================
+
+/// A fresh Sentinel has one spatial G-node and one permanent root tracker.
+///
+/// The root tracker is warmed automatically with synthetic noise. Noise is
+/// tracker-local: it does not count as a real observation and does not touch
+/// the spatial G-V Graph. This is the first visible consequence of the
+/// feed-forward design.
+fn step_0_fresh(sentinel: &Sentinel128) {
+    heading("Step 0: Construction and Automatic Root Warm-Up");
+    println!("  (§ALGO S-11, ADR-S-007, ADR-S-001)");
+
+    assert_eq!(sentinel.graph().node_count(), 1, "fresh graph has one G-node");
+    assert_eq!(sentinel.graph().terminal_count(), 1, "fresh graph has one terminal cell");
+    assert_eq!(sentinel.graph().total_sum(), 0, "fresh graph has no real volume");
+    assert_eq!(sentinel.cells_tracked(), 1, "only the root tracker is active");
+    assert_eq!(sentinel.lifetime_observations(), 0, "noise is not real traffic");
+    assert_eq!(sentinel.analysis_set().competitive_count(), 0, "root is never competitive");
+    assert_eq!(
+        sentinel.analysis_set().total_count(),
+        1,
+        "full analysis set contains the root"
+    );
+
+    let root = sentinel.graph().g_root();
+    let root_cell = sentinel.inspect_cell(root).expect("root cell is always inspectable");
+    assert_eq!(root_cell.depth, 0);
+    assert_eq!(root_cell.analysis_width, 128);
+    assert!(!root_cell.is_competitive, "root provides context, not a competitive target");
+    assert!(
+        root_cell.maturity.noise_observations > 0,
+        "root tracker is noise-warmed at construction"
+    );
+    assert_eq!(root_cell.maturity.real_observations, 0, "root has no real observations yet");
+
+    let health = sentinel.health();
+    assert_eq!(health.active_trackers, 1);
+    assert_eq!(health.active_coordination_contexts, 0, "coordination is demand-driven");
+    assert_eq!(
+        health.maturity_distribution.cold_trackers, 1,
+        "noise-only trackers are still cold"
+    );
+
+    println!("Fresh state:");
+    println!("  spatial graph: one root, total importance = 0");
+    println!("  analysis set: root only, no competitive cells yet");
+    println!("  root tracker: warmed with synthetic noise, real observations = 0");
+    println!("  design point: the Sentinel measures; the host decides");
+}
+
+// ========================================================================
+//  STEP 1: FIRST REAL BATCH -- FEED-FORWARD OBSERVATION
+//  §ALGO S-8.1, ADR-S-002
+// ========================================================================
+
+/// The first real batch proves the feed-forward invariant at the public
+/// surface: the graph's total importance and the lifetime observation
+/// counter increase by exactly the number of submitted values. A second
+/// probe of the same length but very different bit structure produces
+/// the same accounting delta, showing that score content does not feed
+/// back into spatial importance.
+fn step_1_first_batch(sentinel: &mut Sentinel128) {
+    heading("Step 1: First Real Batch -- Feed-Forward Observation");
+    println!("  (§ALGO S-8.1, ADR-S-002)");
+
+    // Probe A: a structurally simple batch.
+    let values = cell_values(0xA, 8);
+    let before_sum = sentinel.graph().total_sum();
+    let report = sentinel.ingest(&values);
+
+    assert_eq!(sentinel.graph().total_sum(), before_sum + values.len() as u64);
+    assert_eq!(sentinel.lifetime_observations(), values.len() as u64);
+    assert_report_contract(sentinel, &report, values.len());
+
+    let root = root_ancestor(&report);
+    let probe_a_novelty_z = max_novelty_z(&report);
+    subheading("Root tracker report");
+    println!("  sample_count = {}", root.sample_count);
+    println!("  novelty mean = {:.6}", root.scores.novelty.mean);
+    println!("  displacement mean = {:.6}", root.scores.displacement.mean);
+    println!("  surprise mean = {:.6}", root.scores.surprise.mean);
+    println!("  coherence mean = {:.6}", root.scores.coherence.mean);
+
+    // Probe B: a same-sized but structurally very different batch.
+    //
+    // The feed-forward invariant says the graph receives Delta = 1 per
+    // input value, regardless of what scores those values produce. We
+    // verify it directly: submit a structurally anomalous batch of the
+    // same length and assert the same spatial accounting delta. The two
+    // batches will differ in score magnitudes; they must not differ in
+    // their effect on graph importance per input value.
+    let sum_before_b = sentinel.graph().total_sum();
+    let obs_before_b = sentinel.lifetime_observations();
+    let anomalous = anomalous_values(0xA, values.len());
+    let anomaly_report = sentinel.ingest(&anomalous);
+
+    assert_eq!(
+        sentinel.graph().total_sum() - sum_before_b,
+        values.len() as u64,
+        "graph importance increments by batch length irrespective of score content"
+    );
+    assert_eq!(
+        sentinel.lifetime_observations() - obs_before_b,
+        values.len() as u64,
+        "lifetime observations count input values, not score magnitudes"
+    );
+    assert_report_contract(sentinel, &anomaly_report, anomalous.len());
+
+    subheading("Same volume, different content");
+    println!(
+        "  probe A: {} values, max novelty z = {:.6}, graph delta = {}",
+        values.len(),
+        probe_a_novelty_z,
+        values.len(),
+    );
+    println!(
+        "  probe B: {} values, max novelty z = {:.6}, graph delta = {}",
+        anomalous.len(),
+        max_novelty_z(&anomaly_report),
+        anomalous.len(),
+    );
+    println!("  -> identical graph deltas confirm scores do not feed back into importance");
+
+    println!();
+    println!("What happened:");
+    println!("  1. Every coordinate was observed by the G-V Graph with Delta = 1.");
+    println!("  2. Two batches of equal length produced the same spatial accounting");
+    println!("     delta despite very different score profiles.");
+    println!("  3. The report contains raw measurements, not a verdict or action.");
+}
+
+// ========================================================================
+//  STEP 2: CONCENTRATED TRAFFIC CREATES SPATIAL STRUCTURE
+//  §ALGO S-3, §ALGO S-8, ADR-S-006
+// ========================================================================
+
+/// Concentrated traffic crosses the spatial split threshold. The graph
+/// refines under volume alone, then the analysis selector recomputes the
+/// competitive targets and closes them under ancestry.
+fn step_2_concentrated_traffic(sentinel: &mut Sentinel128) {
+    heading("Step 2: Concentrated Traffic -- Spatial Refinement");
+    println!("  (§ALGO S-3, §ALGO S-8, ADR-S-006)");
+
+    let values = cell_values(0xA, 40);
+    let nodes_before = sentinel.graph().node_count();
+    let terminals_before = sentinel.graph().terminal_count();
+    let sum_before = sentinel.graph().total_sum();
+
+    let report = sentinel.ingest(&values);
+
+    assert_eq!(sentinel.graph().total_sum(), sum_before + values.len() as u64);
+    assert!(
+        sentinel.graph().node_count() > nodes_before,
+        "concentrated traffic should split G-nodes"
+    );
+    assert!(
+        sentinel.graph().terminal_count() > terminals_before,
+        "spatial contour should gain terminal cells"
+    );
+    assert!(
+        report.contour.splits_since_last_report > 0,
+        "report exposes the structural split event"
+    );
+    assert!(
+        report.analysis_set_summary.competitive_size > 0,
+        "non-root cells become competitive targets"
+    );
+    assert!(
+        report.cell_reports.iter().any(|cell| cell.depth > 0),
+        "at least one non-root competitive cell reports"
+    );
+    assert_report_contract(sentinel, &report, values.len());
+
+    subheading("Contour snapshot");
+    println!("  plateaus = {}", report.contour.plateau_count);
+    println!("  terminal cells = {}", report.contour.cell_count);
+    println!("  total importance = {:.0}", report.contour.total_importance);
+    println!("  splits since previous report = {}", report.contour.splits_since_last_report);
+
+    subheading("Analysis set summary");
+    println!("  competitive size = {}", report.analysis_set_summary.competitive_size);
+    println!("  full size = {}", report.analysis_set_summary.full_size);
+    println!("  investment size = {}", report.analysis_set_summary.investment_set_size);
+    println!("  depth range = {:?}", report.analysis_set_summary.depth_range);
+
+    println!();
+    println!("Key point: the analysis tier follows spatial volume; it does not steer it.");
+}
+
+// ========================================================================
+//  STEP 3: MULTI-SCALE REPORTS AND HIERARCHICAL COORDINATION
+//  §ALGO S-8.2, §ALGO S-9, ADR-S-019
+// ========================================================================
+
+/// Multi-region traffic creates multiple competitive cells. Their G-tree
+/// ancestors provide shared context, and internal nodes whose left and
+/// right subtrees both contribute competitive scores can emit coordination
+/// reports.
+fn step_3_multiscale_reports(sentinel: &mut Sentinel128) {
+    heading("Step 3: Multi-Scale Reports and Coordination");
+    println!("  (§ALGO S-8.2, §ALGO S-9, ADR-S-019)");
+
+    let values = mixed_normal_batch();
+    let mut report = sentinel.ingest(&values);
+    assert_report_contract(sentinel, &report, values.len());
+
+    let mut activated_at: Option<usize> = None;
+    for round in 1..=MULTISCALE_MAX_ROUNDS {
+        if !report.coordination_reports.is_empty() && report.cell_reports.len() >= 2 {
+            activated_at = Some(round);
+            break;
+        }
+        report = sentinel.ingest(&values);
+        assert_report_contract(sentinel, &report, values.len());
+    }
+
+    let activated_at = activated_at.unwrap_or_else(|| {
+        panic!(
+            "coordination did not activate within {MULTISCALE_MAX_ROUNDS} multi-region batches: \
+             cell_reports={}, coordination_reports={}",
+            report.cell_reports.len(),
+            report.coordination_reports.len(),
+        )
+    });
+    println!("Coordination activated after {activated_at} multi-region batch(es).");
+
+    assert!(
+        report.cell_reports.len() >= 2,
+        "multi-region traffic should produce several competitive reports"
+    );
+    assert!(
+        !report.coordination_reports.is_empty(),
+        "competitive cells in both subtrees should activate coordination"
+    );
+
+    let depths: std::collections::BTreeSet<_> = all_cell_reports(&report).map(|cell| cell.depth).collect();
+    assert!(depths.contains(&0), "combined reports include the root depth");
+    assert!(depths.len() >= 2, "combined reports show at least two spatial scales");
+
+    subheading("Competitive reports");
+    for cell in &report.cell_reports {
+        println!(
+            "  GNode {:?}: [{:#034x}, {:#034x}) depth={} width={} samples={}",
+            cell.gnode_id, cell.start, cell.end, cell.depth, cell.analysis_width, cell.sample_count
+        );
+    }
+
+    subheading("Ancestor reports");
+    for cell in &report.ancestor_reports {
+        println!(
+            "  GNode {:?}: [{:#034x}, {:#034x}) depth={} width={} samples={}",
+            cell.gnode_id, cell.start, cell.end, cell.depth, cell.analysis_width, cell.sample_count
+        );
+    }
+
+    subheading("Coordination reports");
+    for coordination in &report.coordination_reports {
+        println!(
+            "  GNode {:?}: depth={} cells_reporting={} novelty mean={:.6}",
+            coordination.gnode_id, coordination.depth, coordination.cells_reporting, coordination.scores.novelty.mean
+        );
+    }
+
+    println!();
+    println!("Key point: cell reports are local, ancestors are multi-scale context,");
+    println!("and coordination reports measure cross-cell score patterns.");
+}
+
+// ========================================================================
+//  STEP 4: SCORES ARE MEASUREMENTS, NOT OPINIONS
+//  §ALGO S-6, ADR-S-001
+// ========================================================================
+
+/// The four score axes share a uniform polarity: higher means more
+/// anomalous. This step uses a fresh, focused scoring probe so the
+/// comparison is about the score contract rather than the long-running
+/// narrative sentinel's mixed traffic history. Novelty is the strongest
+/// indicator for this kind of structural anomaly and is asserted
+/// strictly; the other three axes are reported for inspection.
+fn step_4_scores_are_measurements() {
+    heading("Step 4: Scores Are Measurements, Not Opinions");
+    println!("  (§ALGO S-6, ADR-S-001)");
+
+    let mut scoring_config = pedagogy_config();
+    scoring_config.noise_seed = Some(42);
+    let mut scoring_sentinel = Sentinel128::new(scoring_config).expect("valid scoring config constructs");
+
+    let seed = cell_values(0xA, 20);
+    let seed_report = scoring_sentinel.ingest(&seed);
+    assert_report_contract(&scoring_sentinel, &seed_report, seed.len());
+    for _ in 0..5 {
+        let warm_report = scoring_sentinel.ingest(&seed);
+        assert_report_contract(&scoring_sentinel, &warm_report, seed.len());
+    }
+
+    let normal = cell_values(0xA, 8);
+    let normal_report = scoring_sentinel.ingest(&normal);
+    assert_report_contract(&scoring_sentinel, &normal_report, normal.len());
+
+    let anomaly = anomalous_values(0xA, 8);
+    let anomaly_report = scoring_sentinel.ingest(&anomaly);
+    assert_report_contract(&scoring_sentinel, &anomaly_report, anomaly.len());
+
+    let normal_z = max_novelty_z(&normal_report);
+    let anomaly_z = max_novelty_z(&anomaly_report);
+    assert!(
+        anomaly_z > normal_z,
+        "structurally dense batch should elevate novelty: anomaly={anomaly_z:.6}, normal={normal_z:.6}"
+    );
+
+    subheading("Novelty comparison (asserted)");
+    println!("  normal max novelty z = {normal_z:.6}");
+    println!("  anomalous max novelty z = {anomaly_z:.6}");
+
+    // The other three axes share the same *raw-score* polarity invariant
+    // (higher means more anomalous departure), but the *z-scores* below
+    // compare a batch against each cell's current baseline -- and the
+    // seed batch above shaped that baseline. A "normal" batch can
+    // therefore show higher z-scores on displacement, surprise, or
+    // coherence than the structurally anomalous batch when the
+    // anomalous batch happens to land closer to the seeded baseline
+    // along those axes. This is not a polarity violation -- both
+    // batches' raw scores are non-negative -- it is a reminder that
+    // z-scores are relative to whatever the cell has learned. We surface
+    // the numbers without asserting an ordering.
+    let max_z_per_axis = |report: &BatchReport<u128>, axis: ScoreAxis| -> f64 {
+        all_cell_reports(report)
+            .map(|cell| axis(&cell.scores).max_z_score)
+            .fold(0.0_f64, f64::max)
+    };
+    let axes: &[(&str, ScoreAxis)] = &[
+        ("displacement", |s| &s.displacement),
+        ("surprise", |s| &s.surprise),
+        ("coherence", |s| &s.coherence),
+    ];
+    subheading("Other axes (reported, not asserted)");
+    for (name, axis) in axes {
+        let normal_max = max_z_per_axis(&normal_report, *axis);
+        let anomaly_max = max_z_per_axis(&anomaly_report, *axis);
+        println!("  {name:<13} normal max z = {normal_max:>10.6}   anomalous max z = {anomaly_max:>10.6}");
+    }
+
+    subheading("Score-axis contract");
+    println!("  novelty: residual energy per residual degree of freedom, non-negative");
+    println!("  displacement: bounded cell displacement, in [0, 1)");
+    println!("  surprise: diagonal latent deviation, non-negative");
+    println!("  coherence: off-diagonal latent deviation, non-negative");
+
+    println!();
+    println!("There is no alert threshold here. The host reads these measurements");
+    println!("and decides what, if anything, they mean in its own domain.");
+}
+
+// ========================================================================
+//  STEP 5: HOST-CONTROLLED TEMPORAL POLICY
+//  §ALGO S-10, §ALGO S-13.4, ADR-S-002
+// ========================================================================
+
+/// Decay is the host's temporal policy hook. The call itself rescales
+/// spatial importance in the G-V Graph; it does not count as real
+/// traffic, and it does not directly destroy trackers. Tracker churn
+/// happens later, through normal selection reconciliation when cells
+/// lose enough standing to drop out of the competitive top-K.
+fn step_5_host_controlled_decay(sentinel: &mut Sentinel128) {
+    heading("Step 5: Host-Controlled Decay");
+    println!("  (§ALGO S-10, §ALGO S-13.4, ADR-S-002)");
+
+    let sum_before = sentinel.graph().total_sum();
+    let observations_before = sentinel.lifetime_observations();
+    let trackers_before = sentinel.cells_tracked();
+
+    sentinel.decay(0.5, 0.0);
+
+    let sum_after_decay = sentinel.graph().total_sum();
+    let trackers_after_decay = sentinel.cells_tracked();
+    assert!(
+        sum_after_decay < sum_before,
+        "uniform attenuation should reduce spatial importance"
+    );
+    assert_eq!(
+        sentinel.lifetime_observations(),
+        observations_before,
+        "decay is not real traffic"
+    );
+    assert_eq!(
+        trackers_after_decay, trackers_before,
+        "decay() itself does not destroy trackers; lifecycle changes happen via later reconciliation"
+    );
+
+    let values = cell_values(0xF, 8);
+    let report = sentinel.ingest(&values);
+    let trackers_after_ingest = sentinel.cells_tracked();
+    assert_eq!(sentinel.graph().total_sum(), sum_after_decay + values.len() as u64);
+    assert_eq!(sentinel.lifetime_observations(), observations_before + values.len() as u64);
+    assert_report_contract(sentinel, &report, values.len());
+
+    subheading("Decay effect");
+    println!("  total importance: before decay = {sum_before}, after decay = {sum_after_decay}");
+    println!(
+        "  active trackers: before decay = {trackers_before}, after decay = {trackers_after_decay} \
+         (unchanged), after subsequent ingest = {trackers_after_ingest}"
+    );
+    println!(
+        "  lifetime observations: before = {observations_before}, after ingest = {}",
+        sentinel.lifetime_observations()
+    );
+
+    println!();
+    println!("Key point: decay rescales spatial weight; selection reconciliation");
+    println!("decides downstream tracker lifecycle. Feedback, when desired,");
+    println!("belongs in host policy.");
+}
+
+// ========================================================================
+//  STEP 6: RESET
+//  Public API lifecycle contract
+// ========================================================================
+
+/// Reset clears the spatial graph, trackers, coordination contexts, and
+/// counters, then recreates the warmed root according to the same config.
+fn step_6_reset(sentinel: &mut Sentinel128) {
+    heading("Step 6: Reset Restores the Fresh Lifecycle");
+
+    assert!(
+        sentinel.graph().total_sum() > 0,
+        "the narrative produced real traffic before reset"
+    );
+    assert!(
+        sentinel.lifetime_observations() > 0,
+        "the narrative counted real observations before reset"
+    );
+
+    sentinel.reset();
+
+    assert_eq!(sentinel.graph().node_count(), 1);
+    assert_eq!(sentinel.graph().terminal_count(), 1);
+    assert_eq!(sentinel.graph().total_sum(), 0);
+    assert_eq!(sentinel.cells_tracked(), 1);
+    assert_eq!(sentinel.lifetime_observations(), 0);
+    assert_eq!(sentinel.health().active_coordination_contexts, 0);
+
+    let root = sentinel.graph().g_root();
+    let root_cell = sentinel.inspect_cell(root).expect("root is recreated on reset");
+    assert_eq!(root_cell.depth, 0);
+    assert_eq!(root_cell.analysis_width, 128);
+    assert!(root_cell.maturity.noise_observations > 0, "reset recreates the warmed root");
+
+    println!("After reset:");
+    println!("  graph: one root, total importance = 0");
+    println!("  trackers: root only");
+    println!("  coordination contexts: none");
+    println!("  root warm-up: reapplied from the configured noise schedule");
+}
+
+// ========================================================================
+//  MAIN
+// ========================================================================
+
+fn main() {
+    // Support `--list --format terse` so cargo-nextest can enumerate this
+    // custom-harness test binary. With `--ignored`, output nothing.
+    let args: Vec<String> = std::env::args().collect();
+    if args.iter().any(|arg| arg == "--list") {
+        if !args.iter().any(|arg| arg == "--ignored") {
+            println!("pedagogy: test");
+        }
+        return;
+    }
+
+    let config = pedagogy_config();
+    config.validate().expect("pedagogy config must be valid");
+
+    let mut sentinel = Sentinel128::new(config).expect("valid config constructs a Sentinel");
+
+    step_0_fresh(&sentinel);
+    step_1_first_batch(&mut sentinel);
+    step_2_concentrated_traffic(&mut sentinel);
+    step_3_multiscale_reports(&mut sentinel);
+    step_4_scores_are_measurements();
+    step_5_host_controlled_decay(&mut sentinel);
+    step_6_reset(&mut sentinel);
+
+    heading("All Claims Verified");
+    println!();
+    println!("  Feed-forward invariant:");
+    println!("    [ok] graph importance increases by one per input value");
+    println!("    [ok] same-length batches produce identical spatial accounting");
+    println!("         deltas regardless of score content");
+    println!();
+    println!("  Analysis lifecycle:");
+    println!("    [ok] automatic noise warm-up is tracker-local");
+    println!("    [ok] volume-driven spatial splits create competitive analysis cells");
+    println!("    [ok] ancestors keep a complete multi-scale chain to the root");
+    println!("    [ok] coordination activates from cross-cell score patterns");
+    println!();
+    println!("  Reporting principle:");
+    println!("    [ok] report values are raw, finite statistical measurements");
+    println!("    [ok] no test asserts a policy verdict or host action");
+    println!();
+    println!("  Host-controlled lifecycle:");
+    println!("    [ok] decay changes spatial importance without counting traffic");
+    println!("    [ok] reset restores a fresh graph and warmed root tracker");
+}
diff --git a/packages/sentinel/tests/pedagogy_advanced.rs b/packages/sentinel/tests/pedagogy_advanced.rs
new file mode 100644
index 000000000..6d409f526
--- /dev/null
+++ b/packages/sentinel/tests/pedagogy_advanced.rs
@@ -0,0 +1,854 @@
+#![allow(clippy::print_stdout)]
+
+//! # Advanced Pedagogy Test — Companion to [`pedagogy.rs`]
+//!
+//! The basic pedagogy test walks through the Sentinel **lifecycle
+//! surface**: construction, feed-forward observation, spatial refinement,
+//! coordination, host decay, and reset. This companion test exercises the
+//! **inspection surface** — the readouts, summaries, and cross-checks a
+//! host uses after the lifecycle has produced a non-trivial model.
+//!
+//! ## Why a separate test?
+//!
+//! The Sentinel deliberately separates measurement from policy. The basic
+//! test shows how measurements are produced. This test shows how to read
+//! them without smuggling in decisions: analysis-set membership, ancestor
+//! closure, per-sample payloads, geometry flags, coordination contexts,
+//! scale profiles, and temporal separation between spatial weight and
+//! tracker state.
+//!
+//! ## What this test demonstrates
+//!
+//! ### Inspection-oriented configuration (Step 0)
+//!
+//! The test uses deterministic foreground warm-up so every tracker created
+//! by investment reconciliation is online in the same `ingest()` call. That
+//! keeps the inspection surface stable: `investment_set_size == full_size`
+//! once the graph has settled, and no asynchronous warm-up race can obscure
+//! the fields being demonstrated.
+//!
+//! ### Analysis-set anatomy (Step 1)
+//!
+//! The public [`AnalysisSet`] is checked against the report summary. Every
+//! competitive cell's G-tree parent chain is present in the full set, and
+//! the root is permanent context, never a competitive target.
+//!
+//! ### Report and inspection consistency (Steps 2-3)
+//!
+//! [`CellReport`] values are cross-checked with `inspect_cell()`. Optional
+//! per-sample payloads are verified as raw finite measurements whose counts
+//! match the routing counts.
+//!
+//! ### Geometry edge flags (Step 4)
+//!
+//! The scoring geometry record is not decoration. It tells the host when an
+//! axis is structurally inactive or saturable. A rank-one configuration
+//! demonstrates coherence inactivity; a tiny four-bit Sentinel demonstrates
+//! novelty-saturability without relying on fragile score values.
+//!
+//! ### Coordination read surface (Step 5)
+//!
+//! Coordination contexts are inspected as reports over competitive-cell
+//! score vectors. Per-member records are verified against the contributing
+//! competitive cells.
+//!
+//! ### Scale profile and temporal separation (Steps 6-7)
+//!
+//! The same batch yields measurements at several depths; the test prints a
+//! depth-indexed novelty profile without asserting a policy verdict. Then
+//! host-controlled decay is shown to rescale spatial importance without
+//! mutating tracker baselines or counting traffic.
+//!
+//! ### Reset read surface (Step 8)
+//!
+//! Reset drops learned state and recreates the warmed root, clearing the
+//! inspection surfaces back to the fresh lifecycle.
+//!
+//! Run with:
+//!
+//! ```sh
+//! cargo test -p torrust-sentinel --test pedagogy_advanced
+//! ```
+//!
+//! # Test Index
+//!
+//! ## Inspection Setup (§ALGO S-11, ADR-S-019)
+//!
+//! | Step | Function | What it teaches |
+//! |------|----------|-----------------|
+//! | 0 | [`step_0_inspection_setup`] | Foreground warm-up gives deterministic readouts |
+//!
+//! ## Analysis-Set Read Surface (§ALGO S-8, ADR-S-019)
+//!
+//! | Step | Function | What it teaches |
+//! |------|----------|-----------------|
+//! | 1 | [`step_1_analysis_set_anatomy`] | Competitive targets plus ancestors form the full set |
+//! | 2 | [`step_2_inspection_mirrors_reports`] | `inspect_cell()` and batch reports expose the same tracker facts |
+//!
+//! ## Report Payloads and Geometry (§ALGO S-14)
+//!
+//! | Step | Function | What it teaches |
+//! |------|----------|-----------------|
+//! | 3 | [`step_3_payload_records`] | Per-sample payloads are count-aligned raw measurements |
+//! | 4 | [`step_4_scoring_geometry_edges`] | Geometry flags explain inactive and saturable axes |
+//!
+//! ## Coordination and Scale (§ALGO S-7, §ALGO S-9)
+//!
+//! | Step | Function | What it teaches |
+//! |------|----------|-----------------|
+//! | 5 | [`step_5_coordination_read_surface`] | Coordination reports measure cross-cell score patterns |
+//! | 6 | [`step_6_scale_profile`] | Depth-indexed scores are diagnostic measurements, not verdicts |
+//!
+//! ## Host Policy and Lifecycle (§ALGO S-10, ADR-S-002)
+//!
+//! | Step | Function | What it teaches |
+//! |------|----------|-----------------|
+//! | 7 | [`step_7_temporal_separation`] | Spatial decay does not mutate tracker baselines |
+//! | 8 | [`step_8_reset_read_surface`] | Reset clears readouts and recreates the warmed root |
+
+mod common;
+
+use std::collections::{BTreeMap, BTreeSet};
+
+use common::{anomalous_values, assert_invariants, cell_values};
+use torrust_sentinel::{
+    AxisBaselineSnapshots, BaselineSnapshot, BatchReport, CellInspection, CellReport, CoordinationReport, MemberScore,
+    NoiseSchedule, SampleScore, ScoreDistribution, Sentinel128, SentinelConfig, SpectralSentinel, SvdStrategy,
+};
+
+// -- Configuration ---------------------------------------------------------
+
+/// Deterministic configuration tuned for inspection rather than suspense.
+///
+/// Compared with the basic pedagogy test, this uses a slightly larger
+/// analysis budget and rank cap so the read surface has more structure to
+/// inspect. Background warming stays disabled: this file teaches the public
+/// readouts, not scheduler timing.
+fn advanced_config() -> SentinelConfig<u64> {
+    SentinelConfig::<u64> {
+        max_rank: 4,
+        forgetting_factor: 0.90,
+        rank_update_interval: 4,
+        analysis_k: 24,
+        analysis_depth_cutoff: 6,
+        energy_threshold: 0.90,
+        eps: 1e-6,
+        per_sample_scores: true,
+        cusum_allowance_sigmas: 0.5,
+        cusum_slow_decay: 0.99,
+        cusum_coord_slow_decay: 0.99,
+        clip_sigmas: 3.0,
+        clip_pressure_decay: 0.95,
+        split_threshold: 8,
+        d_create: 3,
+        d_evict: 6,
+        budget: 100_000,
+        noise_schedule: NoiseSchedule::Explicit(vec![6]),
+        noise_batch_size: 4,
+        noise_seed: Some(2026),
+        background_warming: false,
+        svd_strategy: SvdStrategy::Brand,
+    }
+}
+
+fn rank_one_config() -> SentinelConfig<u64> {
+    SentinelConfig::<u64> {
+        max_rank: 1,
+        noise_seed: Some(7),
+        ..advanced_config()
+    }
+}
+
+fn tiny_geometry_config() -> SentinelConfig<u64> {
+    SentinelConfig::<u64> {
+        max_rank: 4,
+        rank_update_interval: 1,
+        noise_seed: Some(11),
+        ..advanced_config()
+    }
+}
+
+// -- Helpers ---------------------------------------------------------------
+
+const MULTISCALE_MAX_ROUNDS: usize = 40;
+
+type TinySentinel = SpectralSentinel<u64, u64, 4>;
+
+fn heading(s: &str) {
+    let rule = "=".repeat(72);
+    println!("\n{rule}");
+    println!("  {s}");
+    println!("{rule}");
+}
+
+fn subheading(s: &str) {
+    println!("\n-- {s} --");
+}
+
+fn mixed_normal_batch() -> Vec<u128> {
+    [
+        cell_values(0x1, 8),
+        cell_values(0x5, 8),
+        cell_values(0xA, 8),
+        cell_values(0xF, 8),
+    ]
+    .concat()
+}
+
+fn all_cell_reports(report: &BatchReport<u128>) -> impl Iterator<Item = &CellReport<u128>> {
+    report.cell_reports.iter().chain(report.ancestor_reports.iter())
+}
+
+fn root_report(report: &BatchReport<u128>) -> &CellReport<u128> {
+    report
+        .ancestor_reports
+        .iter()
+        .find(|cell| cell.depth == 0)
+        .expect("non-empty batch reports include the root ancestor")
+}
+
+fn assert_score_distribution(name: &str, score: &ScoreDistribution) {
+    for (field, value) in [
+        ("min", score.min),
+        ("max", score.max),
+        ("mean", score.mean),
+        ("max_z_score", score.max_z_score),
+        ("mean_z_score", score.mean_z_score),
+        ("baseline.mean", score.baseline.mean),
+        ("baseline.variance", score.baseline.variance),
+        ("cusum.accumulator", score.cusum.accumulator),
+        ("cusum.slow_baseline.mean", score.cusum.slow_baseline.mean),
+        ("cusum.slow_baseline.variance", score.cusum.slow_baseline.variance),
+        ("clip_pressure", score.clip_pressure),
+    ] {
+        assert!(value.is_finite(), "{name}.{field} must be finite");
+    }
+
+    assert!(score.max + 1e-12 >= score.min, "{name}.max must be >= min");
+    assert!(score.mean + 1e-12 >= score.min, "{name}.mean must be >= min");
+    assert!(score.mean <= score.max + 1e-12, "{name}.mean must be <= max");
+    assert!(score.baseline.variance >= -1e-12, "{name}.baseline variance is non-negative");
+    assert!(
+        score.cusum.slow_baseline.variance >= -1e-12,
+        "{name}.slow baseline variance is non-negative"
+    );
+    assert!(score.cusum.accumulator >= -1e-12, "{name}.CUSUM is one-sided");
+    assert!(
+        (-1e-12..=1.0 + 1e-12).contains(&score.clip_pressure),
+        "{name}.clip pressure is a fraction"
+    );
+}
+
+fn assert_cell_scores(cell: &CellReport<u128>) {
+    assert_score_distribution("novelty", &cell.scores.novelty);
+    assert_score_distribution("displacement", &cell.scores.displacement);
+    assert_score_distribution("surprise", &cell.scores.surprise);
+    assert_score_distribution("coherence", &cell.scores.coherence);
+
+    assert!(cell.scores.novelty.min >= -1e-12, "novelty is non-negative");
+    assert!(
+        cell.scores.displacement.min >= -1e-12 && cell.scores.displacement.max <= 1.0 + 1e-12,
+        "displacement is bounded in [0, 1]"
+    );
+    assert!(cell.scores.surprise.min >= -1e-12, "surprise is non-negative");
+    assert!(cell.scores.coherence.min >= -1e-12, "coherence is non-negative");
+}
+
+fn assert_sample_score(sample: &SampleScore) {
+    for (name, value) in [
+        ("novelty", sample.novelty),
+        ("displacement", sample.displacement),
+        ("surprise", sample.surprise),
+        ("coherence", sample.coherence),
+        ("novelty_z", sample.novelty_z),
+        ("displacement_z", sample.displacement_z),
+        ("surprise_z", sample.surprise_z),
+        ("coherence_z", sample.coherence_z),
+    ] {
+        assert!(value.is_finite(), "sample.{name} must be finite");
+    }
+
+    assert!(sample.novelty >= -1e-12, "sample novelty is non-negative");
+    assert!(
+        sample.displacement >= -1e-12 && sample.displacement <= 1.0 + 1e-12,
+        "sample displacement is bounded in [0, 1]"
+    );
+    assert!(sample.surprise >= -1e-12, "sample surprise is non-negative");
+    assert!(sample.coherence >= -1e-12, "sample coherence is non-negative");
+}
+
+fn assert_member_score(member: &MemberScore<u128>) {
+    assert!(member.cell_start < member.cell_end, "member cell interval is non-empty");
+    for (name, value) in [
+        ("novelty", member.novelty),
+        ("displacement", member.displacement),
+        ("surprise", member.surprise),
+        ("coherence", member.coherence),
+        ("novelty_z", member.novelty_z),
+        ("displacement_z", member.displacement_z),
+        ("surprise_z", member.surprise_z),
+        ("coherence_z", member.coherence_z),
+    ] {
+        assert!(value.is_finite(), "member.{name} must be finite");
+    }
+    assert!(member.novelty >= -1e-12, "member novelty is non-negative");
+    assert!(
+        member.displacement >= -1e-12 && member.displacement <= 1.0 + 1e-12,
+        "member displacement is bounded in [0, 1]"
+    );
+    assert!(member.surprise >= -1e-12, "member surprise is non-negative");
+    assert!(member.coherence >= -1e-12, "member coherence is non-negative");
+}
+
+fn assert_cell_geometry(cell: &CellReport<u128>, config: &SentinelConfig<u64>) {
+    assert_eq!(cell.analysis_width, 128 - cell.depth as usize);
+    assert_eq!(cell.geometry.dim, cell.analysis_width);
+    assert_eq!(cell.geometry.cap, cell.analysis_width.min(config.max_rank));
+    assert!(cell.rank >= 1);
+    assert!(cell.rank <= cell.geometry.cap);
+    assert_eq!(cell.geometry.residual_dof, cell.geometry.dim - cell.rank);
+}
+
+fn assert_report_surface(sentinel: &Sentinel128, report: &BatchReport<u128>, batch_size: usize) {
+    let config = sentinel.config();
+
+    assert_invariants(sentinel, report);
+    assert_eq!(report.health.lifetime_observations, sentinel.lifetime_observations());
+    assert_eq!(report.health.cells_tracked, sentinel.cells_tracked());
+    assert_eq!(report.health.active_trackers, sentinel.cell_gnodes().len());
+    assert_eq!(
+        report.analysis_set_summary.competitive_size,
+        sentinel.analysis_set().competitive_count()
+    );
+    assert_eq!(report.analysis_set_summary.full_size, sentinel.analysis_set().total_count());
+    assert!(report.analysis_set_summary.competitive_size <= config.analysis_k);
+    assert!(report.analysis_set_summary.investment_set_size >= report.analysis_set_summary.full_size);
+
+    let root = root_report(report);
+    assert_eq!(root.sample_count, batch_size, "root receives the full ingestion batch");
+
+    for cell in &report.cell_reports {
+        assert!(cell.is_competitive, "cell_reports are competitive reports");
+        assert!(cell.sample_count > 0);
+        assert_cell_geometry(cell, config);
+        assert_cell_scores(cell);
+    }
+    for cell in &report.ancestor_reports {
+        assert!(!cell.is_competitive, "ancestor_reports are non-competitive reports");
+        assert!(cell.sample_count > 0);
+        assert_cell_geometry(cell, config);
+        assert_cell_scores(cell);
+    }
+}
+
+fn assert_inspection_matches_report(sentinel: &Sentinel128, report: &CellReport<u128>) {
+    let inspection = sentinel
+        .inspect_cell(report.gnode_id)
+        .expect("reported cells are inspectable after the batch");
+
+    assert_eq!(inspection.gnode_id, report.gnode_id);
+    assert_eq!((inspection.start, inspection.end), (report.start, report.end));
+    assert_eq!(inspection.depth, report.depth);
+    assert_eq!(inspection.analysis_width, report.analysis_width);
+    assert_eq!(inspection.is_competitive, report.is_competitive);
+    assert_eq!(inspection.rank, report.rank);
+    assert_eq!(inspection.geometry.dim, report.geometry.dim);
+    assert_eq!(inspection.geometry.cap, report.geometry.cap);
+    assert_eq!(inspection.geometry.residual_dof, report.geometry.residual_dof);
+    assert_eq!(inspection.maturity.real_observations, report.maturity.real_observations);
+    assert_eq!(inspection.maturity.noise_observations, report.maturity.noise_observations);
+}
+
+fn assert_coordination_report(coordination: &CoordinationReport<u128>, config: &SentinelConfig<u64>) {
+    assert!(coordination.start < coordination.end);
+    assert!(coordination.cells_reporting >= 2);
+    assert_eq!(coordination.geometry.dim, 4);
+    assert_eq!(coordination.geometry.cap, config.max_rank.min(4));
+    assert!(coordination.rank >= 1);
+    assert!(coordination.rank <= coordination.geometry.cap);
+    assert_eq!(
+        coordination.geometry.residual_dof,
+        coordination.geometry.dim - coordination.rank
+    );
+    assert_score_distribution("coord.novelty", &coordination.scores.novelty);
+    assert_score_distribution("coord.displacement", &coordination.scores.displacement);
+    assert_score_distribution("coord.surprise", &coordination.scores.surprise);
+    assert_score_distribution("coord.coherence", &coordination.scores.coherence);
+}
+
+fn assert_baseline_same(name: &str, before: BaselineSnapshot, after: BaselineSnapshot) {
+    assert_eq!(
+        before.mean.to_bits(),
+        after.mean.to_bits(),
+        "{name} mean changed during spatial decay"
+    );
+    assert_eq!(
+        before.variance.to_bits(),
+        after.variance.to_bits(),
+        "{name} variance changed during spatial decay"
+    );
+}
+
+fn assert_baselines_same(before: AxisBaselineSnapshots, after: AxisBaselineSnapshots) {
+    assert_baseline_same("novelty", before.novelty, after.novelty);
+    assert_baseline_same("displacement", before.displacement, after.displacement);
+    assert_baseline_same("surprise", before.surprise, after.surprise);
+    assert_baseline_same("coherence", before.coherence, after.coherence);
+}
+
+fn drive_to_multiscale(sentinel: &mut Sentinel128) -> BatchReport<u128> {
+    let seed = cell_values(0xA, 16);
+    let seed_report = sentinel.ingest(&seed);
+    assert_report_surface(sentinel, &seed_report, seed.len());
+
+    let concentrated = cell_values(0xA, 48);
+    let concentrated_report = sentinel.ingest(&concentrated);
+    assert_report_surface(sentinel, &concentrated_report, concentrated.len());
+
+    let values = mixed_normal_batch();
+    let mut report = sentinel.ingest(&values);
+    assert_report_surface(sentinel, &report, values.len());
+
+    for round in 1..=MULTISCALE_MAX_ROUNDS {
+        if report.cell_reports.len() >= 2 && !report.coordination_reports.is_empty() {
+            println!("Multiscale state reached after {round} mixed batch(es).");
+            return report;
+        }
+        report = sentinel.ingest(&values);
+        assert_report_surface(sentinel, &report, values.len());
+    }
+
+    panic!(
+        "coordination did not activate within {MULTISCALE_MAX_ROUNDS} rounds: cell_reports={}, coordination_reports={}",
+        report.cell_reports.len(),
+        report.coordination_reports.len(),
+    );
+}
+
+// ========================================================================
+//  STEP 0: INSPECTION-ORIENTED SETUP
+//  §ALGO S-11, ADR-S-019
+// ========================================================================
+
+fn step_0_inspection_setup(sentinel: &Sentinel128) {
+    heading("Step 0: Inspection-Oriented Setup");
+    println!("  (§ALGO S-11, ADR-S-019)");
+
+    let health = sentinel.health();
+    assert_eq!(sentinel.graph().node_count(), 1);
+    assert_eq!(sentinel.graph().total_sum(), 0);
+    assert_eq!(sentinel.cells_tracked(), 1);
+    assert_eq!(sentinel.analysis_set().competitive_count(), 0);
+    assert_eq!(sentinel.analysis_set().total_count(), 1);
+    assert_eq!(health.warming_trackers, 0, "foreground warm-up leaves no staging backlog");
+    assert_eq!(health.investment_set_size, 1);
+
+    let root = sentinel
+        .inspect_cell(sentinel.graph().g_root())
+        .expect("fresh Sentinel exposes its root tracker");
+    assert_eq!(root.depth, 0);
+    assert_eq!(root.analysis_width, 128);
+    assert!(!root.is_competitive);
+    assert!(root.maturity.noise_observations > 0);
+    assert_eq!(root.maturity.real_observations, 0);
+
+    println!("Fresh inspection surface:");
+    println!("  graph nodes = {}", sentinel.graph().node_count());
+    println!("  active trackers = {}", health.active_trackers);
+    println!("  warming trackers = {}", health.warming_trackers);
+    println!("  root noise observations = {}", root.maturity.noise_observations);
+    println!("  foreground warm-up makes tracker readouts immediately inspectable");
+}
+
+// ========================================================================
+//  STEP 1: ANALYSIS SET ANATOMY
+//  §ALGO S-8, ADR-S-019
+// ========================================================================
+
+fn step_1_analysis_set_anatomy(sentinel: &Sentinel128, report: &BatchReport<u128>) {
+    heading("Step 1: Analysis-Set Anatomy");
+    println!("  (§ALGO S-8, ADR-S-019)");
+
+    let analysis = sentinel.analysis_set();
+    let summary = report.analysis_set_summary;
+
+    assert_eq!(analysis.competitive_count(), summary.competitive_size);
+    assert_eq!(analysis.total_count(), summary.full_size);
+    assert!(analysis.total_count() > analysis.competitive_count());
+
+    let root = sentinel.graph().g_root();
+    assert!(analysis.contains(root), "root is always in the full analysis set");
+    assert!(
+        !analysis.is_competitive(root),
+        "root provides context, not a competitive slot"
+    );
+
+    for entry in analysis.competitive() {
+        let mut current = Some(entry.gnode);
+        while let Some(gnode) = current {
+            assert!(
+                analysis.contains(gnode),
+                "ancestor closure missing GNode {:?} for competitive [{:#034x}, {:#034x})",
+                gnode,
+                entry.start,
+                entry.end,
+            );
+            let info = sentinel.graph().gnode_info(gnode).expect("analysis cells are live G-nodes");
+            current = info.parent;
+        }
+    }
+
+    subheading("Set sizes");
+    println!("  competitive targets = {}", analysis.competitive_count());
+    println!("  full analysis set = {}", analysis.total_count());
+    println!("  investment set = {}", summary.investment_set_size);
+    println!("  depth range = {:?}", summary.depth_range);
+
+    subheading("Competitive cells");
+    for entry in analysis.competitive() {
+        println!(
+            "  [{:#034x}, {:#034x}) depth={} v_depth={} importance={}",
+            entry.start, entry.end, entry.depth, entry.v_depth, entry.importance,
+        );
+    }
+
+    println!();
+    println!("Every competitive cell's parent chain is present in the full set.");
+    println!("The host can inspect local cells and their shared ancestors separately.");
+}
+
+// ========================================================================
+//  STEP 2: INSPECTION MIRRORS REPORTS
+//  §ALGO S-14, ADR-S-014
+// ========================================================================
+
+fn step_2_inspection_mirrors_reports(sentinel: &Sentinel128, report: &BatchReport<u128>) {
+    heading("Step 2: inspect_cell Mirrors Batch Reports");
+    println!("  (§ALGO S-14, ADR-S-014)");
+
+    let mut checked = 0usize;
+    for cell in all_cell_reports(report) {
+        assert_inspection_matches_report(sentinel, cell);
+        checked += 1;
+    }
+
+    let inspected_ids: BTreeSet<_> = sentinel.cell_gnodes().into_iter().collect();
+    assert_eq!(inspected_ids.len(), sentinel.cells_tracked());
+    for id in inspected_ids {
+        let inspection: CellInspection<u128> = sentinel.inspect_cell(id).expect("cell_gnodes are inspectable");
+        assert_eq!(inspection.geometry.dim, inspection.analysis_width);
+        assert_eq!(
+            inspection.geometry.cap,
+            inspection.analysis_width.min(sentinel.config().max_rank)
+        );
+    }
+
+    println!("Checked {checked} reported cell snapshots against inspect_cell().");
+    println!("`inspect_cell()` is the stable read path for tracker metadata between batches.");
+}
+
+// ========================================================================
+//  STEP 3: PAYLOAD RECORDS
+//  §ALGO S-14.8, §ALGO S-14.9
+// ========================================================================
+
+fn step_3_payload_records(report: &BatchReport<u128>) {
+    heading("Step 3: Per-Sample and Per-Member Payload Records");
+    println!("  (§ALGO S-14.8, §ALGO S-14.9)");
+
+    let mut sample_records = 0usize;
+    for cell in all_cell_reports(report) {
+        let samples = cell.per_sample.as_ref().expect("advanced config enables per-sample scores");
+        assert_eq!(samples.len(), cell.sample_count);
+        for sample in samples {
+            assert_sample_score(sample);
+        }
+        sample_records += samples.len();
+    }
+
+    let mut member_records = 0usize;
+    for coordination in &report.coordination_reports {
+        let members = coordination
+            .per_member
+            .as_ref()
+            .expect("advanced config enables per-member coordination scores");
+        assert_eq!(members.len(), coordination.cells_reporting);
+        for member in members {
+            assert_member_score(member);
+        }
+        member_records += members.len();
+    }
+
+    println!("Per-sample records checked: {sample_records}");
+    println!("Per-member coordination records checked: {member_records}");
+    println!("These payloads are raw measurements aligned with routing, not decisions.");
+}
+
+// ========================================================================
+//  STEP 4: SCORING GEOMETRY EDGE FLAGS
+//  §ALGO S-5.2, §ALGO S-14.7
+// ========================================================================
+
+fn step_4_scoring_geometry_edges() {
+    heading("Step 4: Scoring Geometry Edge Flags");
+    println!("  (§ALGO S-5.2, §ALGO S-14.7)");
+
+    let rank_one = Sentinel128::new(rank_one_config()).expect("rank-one config is valid");
+    let rank_one_health = rank_one.health();
+    assert_eq!(
+        rank_one_health.geometry_distribution.coherence_inactive,
+        rank_one_health.active_trackers
+    );
+
+    let root = rank_one
+        .inspect_cell(rank_one.graph().g_root())
+        .expect("rank-one root is inspectable");
+    assert_eq!(root.rank, 1);
+    assert_eq!(root.geometry.cap, 1);
+    assert_eq!(root.geometry.residual_dof, root.geometry.dim - 1);
+    assert!(!root.geometry.is_novelty_saturated());
+
+    let tiny = TinySentinel::new(tiny_geometry_config()).expect("tiny geometry config is valid");
+    let tiny_root = tiny.inspect_cell(tiny.graph().g_root()).expect("tiny root is inspectable");
+    assert_eq!(tiny_root.geometry.dim, 4);
+    assert_eq!(tiny_root.geometry.cap, 4);
+    assert!(tiny_root.geometry.is_novelty_saturable());
+
+    println!("Rank-one Sentinel:");
+    println!("  active trackers = {}", rank_one_health.active_trackers);
+    println!(
+        "  coherence-inactive trackers = {}",
+        rank_one_health.geometry_distribution.coherence_inactive
+    );
+    println!(
+        "  root dim = {}, cap = {}, residual DOF = {}",
+        root.geometry.dim, root.geometry.cap, root.geometry.residual_dof
+    );
+
+    println!("Tiny four-bit Sentinel:");
+    println!("  root dim = {}, cap = {}", tiny_root.geometry.dim, tiny_root.geometry.cap);
+    println!("  novelty-saturable = {}", tiny_root.geometry.is_novelty_saturable());
+    println!("Geometry fields tell the host whether an axis is structurally meaningful.");
+}
+
+// ========================================================================
+//  STEP 5: COORDINATION READ SURFACE
+//  §ALGO S-7, §ALGO S-9
+// ========================================================================
+
+fn step_5_coordination_read_surface(sentinel: &Sentinel128, report: &BatchReport<u128>) {
+    heading("Step 5: Coordination Read Surface");
+    println!("  (§ALGO S-7, §ALGO S-9)");
+
+    assert!(
+        !report.coordination_reports.is_empty(),
+        "multiscale setup activated coordination"
+    );
+    assert!(sentinel.health().active_coordination_contexts >= report.coordination_reports.len());
+
+    let competitive_cells: BTreeSet<_> = report
+        .cell_reports
+        .iter()
+        .map(|cell| (cell.start, cell.end, cell.depth))
+        .collect();
+
+    for coordination in &report.coordination_reports {
+        assert_coordination_report(coordination, sentinel.config());
+        let members = coordination.per_member.as_ref().expect("per-member payloads are enabled");
+        for member in members {
+            assert!(member.cell_start >= coordination.start);
+            assert!(member.cell_end <= coordination.end);
+            assert!(
+                competitive_cells.contains(&(member.cell_start, member.cell_end, member.cell_depth)),
+                "coordination member must be a reporting competitive cell"
+            );
+        }
+    }
+
+    subheading("Coordination contexts");
+    for coordination in &report.coordination_reports {
+        println!(
+            "  [{:#034x}, {:#034x}) depth={} cells={} rank={} novelty_mean={:.6}",
+            coordination.start,
+            coordination.end,
+            coordination.depth,
+            coordination.cells_reporting,
+            coordination.rank,
+            coordination.scores.novelty.mean,
+        );
+    }
+
+    println!();
+    println!("Coordination rows are competitive-cell score summaries.");
+    println!("The tier measures cross-cell structure; it does not classify it.");
+}
+
+// ========================================================================
+//  STEP 6: SCALE PROFILE
+//  §ALGO S-9.4, §ALGO S-16.5
+// ========================================================================
+
+fn step_6_scale_profile(sentinel: &mut Sentinel128) {
+    heading("Step 6: Depth-Indexed Scale Profile");
+    println!("  (§ALGO S-9.4, §ALGO S-16.5)");
+
+    let values = anomalous_values(0xA, 8);
+    let report = sentinel.ingest(&values);
+    assert_report_surface(sentinel, &report, values.len());
+
+    let mut max_novelty_by_depth: BTreeMap<u32, f64> = BTreeMap::new();
+    for cell in all_cell_reports(&report) {
+        let entry = max_novelty_by_depth.entry(cell.depth).or_insert(0.0);
+        *entry = (*entry).max(cell.scores.novelty.max_z_score);
+    }
+
+    assert!(max_novelty_by_depth.contains_key(&0), "root depth participates");
+    assert!(
+        max_novelty_by_depth.keys().any(|depth| *depth > 0),
+        "non-root depths participate"
+    );
+    assert!(max_novelty_by_depth.len() >= 2, "scale profile has multiple depths");
+
+    subheading("Max novelty z-score by G-tree depth");
+    for (depth, z) in &max_novelty_by_depth {
+        assert!(z.is_finite());
+        println!("  depth {depth}: {z:.6}");
+    }
+
+    println!();
+    println!("The host can compare depth-local and root-level measurements.");
+    println!("This test asserts the profile exists, not what policy should conclude.");
+}
+
+// ========================================================================
+//  STEP 7: TEMPORAL SEPARATION
+//  §ALGO S-10, ADR-S-002
+// ========================================================================
+
+fn step_7_temporal_separation(sentinel: &mut Sentinel128) {
+    heading("Step 7: Temporal Separation");
+    println!("  (§ALGO S-10, ADR-S-002)");
+
+    let root = sentinel.graph().g_root();
+    let before_sum = sentinel.graph().total_sum();
+    let before_lifetime = sentinel.lifetime_observations();
+    let before_cells = sentinel.cells_tracked();
+    let before_root = sentinel.inspect_cell(root).expect("root is inspectable before decay");
+    let before_baselines = before_root.baselines;
+
+    sentinel.decay(0.5, 0.0);
+
+    let after_sum = sentinel.graph().total_sum();
+    let after_root = sentinel.inspect_cell(root).expect("root remains inspectable after decay");
+    assert!(after_sum < before_sum, "uniform attenuation reduces spatial importance");
+    assert_eq!(sentinel.lifetime_observations(), before_lifetime, "decay is not traffic");
+    assert_eq!(
+        sentinel.cells_tracked(),
+        before_cells,
+        "decay does not directly destroy trackers"
+    );
+    assert_baselines_same(before_baselines, after_root.baselines);
+
+    println!("Spatial importance: {before_sum} -> {after_sum}");
+    println!(
+        "Lifetime observations: {before_lifetime} -> {}",
+        sentinel.lifetime_observations()
+    );
+    println!("Active trackers: {before_cells} -> {}", sentinel.cells_tracked());
+    println!("Root tracker baselines unchanged by spatial decay.  ✓");
+}
+
+// ========================================================================
+//  STEP 8: RESET READ SURFACE
+//  Public lifecycle contract
+// ========================================================================
+
+fn step_8_reset_read_surface(sentinel: &mut Sentinel128) {
+    heading("Step 8: Reset Read Surface");
+
+    assert!(sentinel.graph().total_sum() > 0);
+    assert!(sentinel.lifetime_observations() > 0);
+
+    sentinel.reset();
+
+    let health = sentinel.health();
+    assert_eq!(sentinel.graph().node_count(), 1);
+    assert_eq!(sentinel.graph().terminal_count(), 1);
+    assert_eq!(sentinel.graph().total_sum(), 0);
+    assert_eq!(sentinel.cells_tracked(), 1);
+    assert_eq!(sentinel.lifetime_observations(), 0);
+    assert_eq!(sentinel.analysis_set().competitive_count(), 0);
+    assert_eq!(sentinel.analysis_set().total_count(), 1);
+    assert_eq!(health.active_coordination_contexts, 0);
+    assert_eq!(health.warming_trackers, 0);
+
+    let root = sentinel
+        .inspect_cell(sentinel.graph().g_root())
+        .expect("reset recreates the inspectable root");
+    assert_eq!(root.depth, 0);
+    assert_eq!(root.analysis_width, 128);
+    assert!(root.maturity.noise_observations > 0);
+    assert_eq!(root.maturity.real_observations, 0);
+
+    println!("After reset:");
+    println!("  graph nodes = {}", sentinel.graph().node_count());
+    println!("  active trackers = {}", health.active_trackers);
+    println!("  coordination contexts = {}", health.active_coordination_contexts);
+    println!("  root noise observations = {}", root.maturity.noise_observations);
+}
+
+// ========================================================================
+//  MAIN
+// ========================================================================
+
+fn main() {
+    // Support `--list --format terse` so cargo-nextest can enumerate this
+    // custom-harness test binary. With `--ignored`, output nothing.
+    let args: Vec<String> = std::env::args().collect();
+    if args.iter().any(|arg| arg == "--list") {
+        if !args.iter().any(|arg| arg == "--ignored") {
+            println!("pedagogy_advanced: test");
+        }
+        return;
+    }
+
+    let config = advanced_config();
+    config.validate().expect("advanced pedagogy config must be valid");
+
+    let mut sentinel = Sentinel128::new(config).expect("valid config constructs a Sentinel");
+
+    step_0_inspection_setup(&sentinel);
+    let report = drive_to_multiscale(&mut sentinel);
+    step_1_analysis_set_anatomy(&sentinel, &report);
+    step_2_inspection_mirrors_reports(&sentinel, &report);
+    step_3_payload_records(&report);
+    step_4_scoring_geometry_edges();
+    step_5_coordination_read_surface(&sentinel, &report);
+    step_6_scale_profile(&mut sentinel);
+    step_7_temporal_separation(&mut sentinel);
+    step_8_reset_read_surface(&mut sentinel);
+
+    heading("All Advanced Claims Verified");
+    println!();
+    println!("  Inspection setup:");
+    println!("    [ok] foreground warm-up makes tracker readouts deterministic");
+    println!("    [ok] fresh Sentinel exposes a warmed, non-competitive root");
+    println!();
+    println!("  Analysis-set read surface:");
+    println!("    [ok] report summary matches AnalysisSet accessors");
+    println!("    [ok] competitive cells are closed under G-tree ancestry");
+    println!("    [ok] inspect_cell mirrors reported tracker metadata");
+    println!();
+    println!("  Report payloads and geometry:");
+    println!("    [ok] per-sample payloads match routed sample counts");
+    println!("    [ok] per-member coordination payloads match reporting cells");
+    println!("    [ok] geometry flags expose inactive and saturable axes");
+    println!();
+    println!("  Coordination and scale:");
+    println!("    [ok] coordination contexts measure cross-cell score patterns");
+    println!("    [ok] depth-indexed profiles give scale diagnostics");
+    println!();
+    println!("  Host policy and lifecycle:");
+    println!("    [ok] spatial decay leaves tracker baselines untouched");
+    println!("    [ok] reset restores the fresh read surface and warmed root");
+}
diff --git a/packages/sentinel/tests/report_structure.rs b/packages/sentinel/tests/report_structure.rs
new file mode 100644
index 000000000..9fb1314ce
--- /dev/null
+++ b/packages/sentinel/tests/report_structure.rs
@@ -0,0 +1,523 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// SPDX-FileCopyrightText: 2026 Torrust project contributors
+
+//! §SPEC S-8.16.3 — Integration tests for the `BatchReport` structure.
+//!
+//! Verifies the restructured report conforms to the spec's Chapter 14
+//! layout: competitive/ancestor partition, inlined health, contour
+//! snapshot, analysis-set summary, coordination-report naming, and
+//! `MemberScore` cell identity.
+//!
+//! # Test index
+//!
+//! ## Empty report
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`empty_ingest_produces_full_report`] | empty ingest still yields a valid full report |
+//!
+//! ## Cell-report partition
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`cell_reports_are_competitive_only`] | `cell_reports` contains only competitive cells |
+//! | [`ancestor_reports_are_ancestor_only`] | `ancestor_reports` contains only ancestor cells |
+//! | [`cell_and_ancestor_cover_all_reported_cells`] | partitions are disjoint and cover all reported cells |
+//!
+//! ## Ordering and uniqueness
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`cell_reports_sorted_by_gnode_id`] | cell reports are sorted by `GNodeId` |
+//! | [`ancestor_reports_sorted_by_gnode_id`] | ancestor reports are sorted by `GNodeId` |
+//! | [`coordination_reports_sorted_by_gnode_id`] | coordination reports are sorted by `GNodeId` |
+//! | [`coordination_reports_have_unique_gnodes`] | no duplicate `GNodeId` in coordination reports |
+//!
+//! ## Scores
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`no_nan_in_score_fields`] | no NaN in any score field |
+//! | [`per_sample_scores_present_when_enabled`] | `per_sample` is `Some` when enabled |
+//!
+//! ## Coordination reports
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`coordination_cells_reporting_is_nonzero`] | `cells_reporting > 0` for every coordination report |
+//! | [`member_score_identifies_cell`] | `MemberScore` has valid cell identity (`start < end`) |
+//!
+//! ## Contour snapshot
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`contour_reflects_graph_state`] | contour has positive importance and cell count |
+//! | [`contour_cell_count_grows_with_distinct_regions`] | cell count does not shrink when adding distinct regions |
+//! | [`contour_after_decay`] | `total_importance` decreases after decay |
+//! | [`contour_reports_splits_since_last_report`] | split counter reports splits and resets between ingests |
+//! | [`contour_mutation_counts_zero_on_empty_ingest`] | empty ingest yields zero mutation counts |
+//!
+//! ## Health inline
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`health_inline_matches_standalone`] | inlined health matches standalone `health()` call |
+//! | [`health_tracker_breakdown_is_consistent`] | competitive + ancestor < active; `total_g_nodes` correct |
+//!
+//! ## Analysis-set summary
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`analysis_set_summary_matches_analysis_set`] | summary counts match `AnalysisSet` directly |
+//! | [`analysis_set_summary_depth_range_includes_root`] | depth range minimum is 0 (root) |
+//! | [`analysis_set_summary_investment_covers_full`] | `investment_set_size >= full_size` |
+//!
+//! ## Analysis width
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`analysis_width_on_cell_report`] | `analysis_width == 128 - depth` on cell reports |
+//! | [`analysis_width_on_cell_inspection`] | `analysis_width == 128 - depth` on cell inspections |
+//!
+//! ## Type naming
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`tracker_report_type_exists`] | `TrackerReport` type is reachable in the public API |
+
+mod common;
+
+use common::{ScenarioBuilder, assert_invariants, cell_values, seeded_sentinel, test_config};
+use torrust_sentinel::Sentinel128;
+
+// ── Helper ──────────────────────────────────────────────────
+
+/// Build a sentinel with enough traffic that both competitive and
+/// ancestor cells exist, using `ScenarioBuilder`.
+fn multi_cell_sentinel() -> Sentinel128 {
+    ScenarioBuilder::new()
+        .config(test_config())
+        .seed_range(0xF, 10)
+        .seed_range(0x1, 10)
+        .warm_batches(5)
+        .build()
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Empty report
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn empty_ingest_produces_full_report() {
+    let mut s = Sentinel128::new(test_config()).unwrap();
+    let report = s.ingest(&[]);
+
+    assert!(report.cell_reports.is_empty());
+    assert!(report.ancestor_reports.is_empty());
+    assert!(report.coordination_reports.is_empty());
+    // Contour is valid even on empty ingest.
+    assert!(report.contour.cell_count > 0 || report.contour.plateau_count == 0);
+    // Health is populated.
+    assert!(report.health.active_trackers > 0 || report.health.lifetime_observations == 0);
+    // Summary sizes are consistent — at least the root.
+    assert!(report.analysis_set_summary.full_size >= 1);
+
+    assert_invariants(&s, &report);
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Cell-report partition
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn cell_reports_are_competitive_only() {
+    let mut s = multi_cell_sentinel();
+    let report = s.ingest(&cell_values(0xF, 4));
+
+    for cr in &report.cell_reports {
+        assert!(
+            cr.is_competitive,
+            "cell_reports must only contain competitive cells, got depth={}",
+            cr.depth
+        );
+    }
+    assert_invariants(&s, &report);
+}
+
+#[test]
+fn ancestor_reports_are_ancestor_only() {
+    let mut s = multi_cell_sentinel();
+    let report = s.ingest(&cell_values(0xF, 4));
+
+    for ar in &report.ancestor_reports {
+        assert!(
+            !ar.is_competitive,
+            "ancestor_reports must only contain ancestor cells, got depth={}",
+            ar.depth
+        );
+    }
+    assert_invariants(&s, &report);
+}
+
+#[test]
+fn cell_and_ancestor_cover_all_reported_cells() {
+    let mut s = multi_cell_sentinel();
+    let report = s.ingest(&cell_values(0xF, 4));
+
+    // No gnode_id appears in both partitions.
+    let competitive_ids: std::collections::HashSet<_> = report.cell_reports.iter().map(|cr| cr.gnode_id).collect();
+    let ancestor_ids: std::collections::HashSet<_> = report.ancestor_reports.iter().map(|ar| ar.gnode_id).collect();
+
+    assert!(
+        competitive_ids.is_disjoint(&ancestor_ids),
+        "cell_reports and ancestor_reports must be disjoint"
+    );
+
+    assert_invariants(&s, &report);
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Ordering and uniqueness
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn cell_reports_sorted_by_gnode_id() {
+    let mut s = multi_cell_sentinel();
+    let report = s.ingest(&cell_values(0xF, 4));
+
+    for window in report.cell_reports.windows(2) {
+        assert!(
+            window[0].gnode_id < window[1].gnode_id,
+            "cell_reports not sorted: {:?} >= {:?}",
+            window[0].gnode_id,
+            window[1].gnode_id,
+        );
+    }
+}
+
+#[test]
+fn ancestor_reports_sorted_by_gnode_id() {
+    let mut s = multi_cell_sentinel();
+    let report = s.ingest(&cell_values(0xF, 4));
+
+    for window in report.ancestor_reports.windows(2) {
+        assert!(
+            window[0].gnode_id < window[1].gnode_id,
+            "ancestor_reports not sorted: {:?} >= {:?}",
+            window[0].gnode_id,
+            window[1].gnode_id,
+        );
+    }
+}
+
+#[test]
+fn coordination_reports_sorted_by_gnode_id() {
+    let mut s = multi_cell_sentinel();
+    let report = s.ingest(&cell_values(0xF, 4));
+
+    for window in report.coordination_reports.windows(2) {
+        assert!(
+            window[0].gnode_id < window[1].gnode_id,
+            "coordination_reports not sorted: {:?} >= {:?}",
+            window[0].gnode_id,
+            window[1].gnode_id,
+        );
+    }
+}
+
+#[test]
+fn coordination_reports_have_unique_gnodes() {
+    let mut s = multi_cell_sentinel();
+    let report = s.ingest(&cell_values(0xF, 4));
+
+    let mut seen = std::collections::HashSet::new();
+    for cr in &report.coordination_reports {
+        assert!(
+            seen.insert(cr.gnode_id),
+            "duplicate GNodeId in coordination_reports: {:?}",
+            cr.gnode_id,
+        );
+    }
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Scores
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn no_nan_in_score_fields() {
+    let mut s = multi_cell_sentinel();
+    let report = s.ingest(&cell_values(0xF, 4));
+
+    for cr in report.cell_reports.iter().chain(report.ancestor_reports.iter()) {
+        assert!(!cr.scores.novelty.mean.is_nan(), "NaN novelty mean at depth {}", cr.depth);
+        assert!(
+            !cr.scores.displacement.mean.is_nan(),
+            "NaN displacement mean at depth {}",
+            cr.depth
+        );
+        assert!(!cr.scores.surprise.mean.is_nan(), "NaN surprise mean at depth {}", cr.depth);
+        assert!(!cr.scores.coherence.mean.is_nan(), "NaN coherence mean at depth {}", cr.depth);
+    }
+
+    assert_invariants(&s, &report);
+}
+
+#[test]
+fn per_sample_scores_present_when_enabled() {
+    // test_config() has per_sample_scores = true.
+    let mut s = multi_cell_sentinel();
+    let report = s.ingest(&cell_values(0xF, 4));
+
+    for cr in &report.cell_reports {
+        assert!(
+            cr.per_sample.is_some(),
+            "per_sample should be Some when per_sample_scores is enabled, depth={}",
+            cr.depth,
+        );
+    }
+
+    assert_invariants(&s, &report);
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Coordination reports
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn coordination_cells_reporting_is_nonzero() {
+    let mut s = multi_cell_sentinel();
+    let report = s.ingest(&cell_values(0xF, 4));
+
+    for cr in &report.coordination_reports {
+        assert!(
+            cr.cells_reporting > 0,
+            "coordination report at depth {} has zero cells_reporting",
+            cr.depth,
+        );
+    }
+}
+
+#[test]
+fn member_score_identifies_cell() {
+    let mut s = multi_cell_sentinel();
+    let report = s.ingest(&cell_values(0xF, 4));
+
+    for cr in &report.coordination_reports {
+        if let Some(members) = &cr.per_member {
+            for ms in members {
+                assert!(ms.cell_start < ms.cell_end, "MemberScore must have cell_start < cell_end");
+            }
+        }
+    }
+
+    assert_invariants(&s, &report);
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Contour snapshot
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn contour_reflects_graph_state() {
+    let mut s = Sentinel128::new(test_config()).unwrap();
+    let values = cell_values(0xA, 20);
+    let report = s.ingest(&values);
+
+    assert!(report.contour.total_importance > 0.0);
+    assert!(report.contour.cell_count >= 1);
+
+    assert_invariants(&s, &report);
+}
+
+#[test]
+fn contour_cell_count_grows_with_distinct_regions() {
+    let mut s = Sentinel128::new(test_config()).unwrap();
+
+    let r1 = s.ingest(&cell_values(0xA, 20));
+    let count_after_one = r1.contour.cell_count;
+
+    // Add a well-separated region.
+    let r2 = s.ingest(&cell_values(0x5, 20));
+    assert!(
+        r2.contour.cell_count >= count_after_one,
+        "cell_count should not shrink when adding distinct regions"
+    );
+
+    assert_invariants(&s, &r2);
+}
+
+#[test]
+fn contour_reports_splits_since_last_report() {
+    let mut s = Sentinel128::new(test_config()).unwrap();
+    // test_config() has split_threshold = 100; send enough
+    // concentrated observations to trigger at least one split.
+    let r1 = s.ingest(&cell_values(0xA, 200));
+    assert!(
+        r1.contour.splits_since_last_report >= 1,
+        "expected at least 1 split from 200 observations, got {}",
+        r1.contour.splits_since_last_report,
+    );
+
+    // Second ingest with minimal traffic — counters were reset.
+    let r2 = s.ingest(&cell_values(0xA, 1));
+    // May or may not split again, but the first batch's count is gone.
+    assert!(
+        r2.contour.splits_since_last_report < r1.contour.splits_since_last_report,
+        "split counter should reset between reports (r1={}, r2={})",
+        r1.contour.splits_since_last_report,
+        r2.contour.splits_since_last_report,
+    );
+}
+
+#[test]
+fn contour_mutation_counts_zero_on_empty_ingest() {
+    let mut s = Sentinel128::new(test_config()).unwrap();
+    // Prime the sentinel with some data first.
+    s.ingest(&cell_values(0xA, 20));
+    // Empty ingest should report zero mutations.
+    let r = s.ingest(&[]);
+    assert_eq!(r.contour.splits_since_last_report, 0);
+    assert_eq!(r.contour.net_removals_since_last_report, 0);
+}
+
+#[test]
+fn contour_after_decay() {
+    let mut s = Sentinel128::new(test_config()).unwrap();
+    s.ingest(&cell_values(0xA, 20));
+    let before = s.ingest(&cell_values(0xA, 5));
+    let before_importance = before.contour.total_importance;
+
+    s.decay(0.5, 0.0);
+
+    let after = s.ingest(&cell_values(0xA, 1));
+    assert!(
+        after.contour.total_importance < before_importance,
+        "total_importance should decrease after decay"
+    );
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Health inline
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn health_inline_matches_standalone() {
+    let mut s = multi_cell_sentinel();
+    let report = s.ingest(&cell_values(0xF, 4));
+    let standalone = s.health();
+
+    assert_eq!(report.health.lifetime_observations, standalone.lifetime_observations);
+    assert_eq!(report.health.active_trackers, standalone.active_trackers);
+    assert_eq!(report.health.total_g_nodes, standalone.total_g_nodes);
+}
+
+#[test]
+fn health_tracker_breakdown_is_consistent() {
+    let mut s = multi_cell_sentinel();
+    let report = s.ingest(&cell_values(0xF, 4));
+    let h = &report.health;
+
+    // competitive + ancestor + root (1) ≤ active_trackers.
+    assert!(
+        h.active_competitive_trackers + h.active_ancestor_trackers < h.active_trackers,
+        "competitive ({}) + ancestor ({}) should be less than active ({}) (root counted separately)",
+        h.active_competitive_trackers,
+        h.active_ancestor_trackers,
+        h.active_trackers,
+    );
+    assert_eq!(h.total_g_nodes, s.graph().node_count() as usize);
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Analysis-set summary
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn analysis_set_summary_matches_analysis_set() {
+    let s = multi_cell_sentinel();
+    let aset = s.analysis_set();
+    let summary = aset.summary();
+
+    assert_eq!(summary.competitive_size, aset.competitive_count());
+    assert_eq!(summary.full_size, aset.total_count());
+}
+
+#[test]
+fn analysis_set_summary_depth_range_includes_root() {
+    let mut s = multi_cell_sentinel();
+    let report = s.ingest(&cell_values(0xF, 4));
+    let summary = &report.analysis_set_summary;
+
+    assert_eq!(summary.depth_range.0, 0, "depth_range min must be 0 (the root)");
+    assert!(
+        summary.depth_range.1 >= summary.depth_range.0,
+        "depth_range max must be >= min"
+    );
+
+    assert_invariants(&s, &report);
+}
+
+#[test]
+fn analysis_set_summary_investment_covers_full() {
+    let mut s = multi_cell_sentinel();
+    let report = s.ingest(&cell_values(0xF, 4));
+    let summary = &report.analysis_set_summary;
+
+    assert!(
+        summary.investment_set_size >= summary.full_size,
+        "investment_set_size ({}) must be >= full_size ({})",
+        summary.investment_set_size,
+        summary.full_size,
+    );
+
+    assert_invariants(&s, &report);
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Analysis width
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn analysis_width_on_cell_report() {
+    let mut s = multi_cell_sentinel();
+    let report = s.ingest(&cell_values(0xF, 4));
+
+    for cr in report.cell_reports.iter().chain(report.ancestor_reports.iter()) {
+        assert_eq!(
+            cr.analysis_width,
+            128 - cr.depth as usize,
+            "analysis_width mismatch at depth {}",
+            cr.depth,
+        );
+    }
+}
+
+#[test]
+fn analysis_width_on_cell_inspection() {
+    let s = multi_cell_sentinel();
+
+    for &gnode in &s.cell_gnodes() {
+        let inspection = s.inspect_cell(gnode).unwrap();
+        assert_eq!(
+            inspection.analysis_width,
+            128 - inspection.depth as usize,
+            "analysis_width mismatch on inspection at depth {}",
+            inspection.depth,
+        );
+    }
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Type naming
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn tracker_report_type_exists() {
+    // Compile-time check: `TrackerReport` exists in the public API.
+    fn _assert_type_exists(_: torrust_sentinel::TrackerReport) {}
+
+    // Verify a sentinel can be constructed, confirming the type is
+    // reachable through the public module.
+    let _s = seeded_sentinel();
+}
diff --git a/packages/sentinel/tests/sentinel_u64.rs b/packages/sentinel/tests/sentinel_u64.rs
new file mode 100644
index 000000000..d8d3f6c75
--- /dev/null
+++ b/packages/sentinel/tests/sentinel_u64.rs
@@ -0,0 +1,331 @@
+//! Integration tests validating the **64-bit sentinel** path end-to-end.
+//!
+//! Constructs a [`Sentinel64`], feeds `u64` values, and verifies
+//! that the generic machinery works correctly for a non-`u128`
+//! coordinate type.
+//!
+//! # Test index
+//!
+//! ## Construction
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`new_with_test_config`] | `Sentinel64` creates successfully |
+//! | [`new_with_cold_config`] | construction with noise disabled |
+//! | [`new_with_integration_config`] | construction with integration overrides |
+//! | [`initial_state_has_root_only`] | fresh sentinel tracks exactly one cell |
+//!
+//! ## Ingestion basics
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`empty_ingest_produces_report`] | empty batch returns a valid report |
+//! | [`ingest_returns_non_empty_report`] | feeding values populates report sections |
+//! | [`multiple_ingests_accumulate`] | repeated batches don't reset state |
+//!
+//! ## Report structure
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`analysis_widths_are_64_minus_depth`] | `analysis_width == 64 - depth` everywhere |
+//! | [`cell_reports_are_competitive`] | `cell_reports` entries have `is_competitive` |
+//! | [`ancestor_reports_are_non_competitive`] | `ancestor_reports` entries are non-competitive |
+//! | [`reports_sorted_by_gnode_id`] | both report vectors sorted by `GNodeId` |
+//! | [`no_nan_in_scores`] | all score fields are finite |
+//!
+//! ## Cell management
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`creates_cells_on_split`] | diverse traffic triggers graph splits |
+//! | [`cells_tracked_never_below_one`] | root cell is always present |
+//!
+//! ## Boundary values
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`min_value`] | `0u64` ingested without panic |
+//! | [`max_value`] | `u64::MAX` ingested without panic |
+//! | [`min_and_max_together`] | both extremes in a single batch |
+//!
+//! ## Determinism
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`deterministic_output_for_same_input`] | same seed + data → identical reports |
+
+mod common;
+
+use common::{cold_config, integration_config, test_config};
+use torrust_sentinel::{Sentinel64, SentinelConfig};
+
+// ─── Helpers (u64-specific) ─────────────────────────────────
+
+/// Generate `count` values with the given leading nibble and
+/// sequential low bits, analogous to `common::cell_values()` but
+/// for the 64-bit domain.
+fn cell_values_u64(nibble: u64, count: usize) -> Vec<u64> {
+    (0..count).map(|i| (nibble << 60) | (i as u64 + 1)).collect()
+}
+
+// ── Construction ────────────────────────────────────────────
+
+#[test]
+fn new_with_test_config() {
+    let s = Sentinel64::new(test_config()).unwrap();
+    assert_eq!(s.cells_tracked(), 1);
+}
+
+#[test]
+fn new_with_cold_config() {
+    let s = Sentinel64::new(cold_config()).unwrap();
+    assert_eq!(s.cells_tracked(), 1);
+}
+
+#[test]
+fn new_with_integration_config() {
+    let s = Sentinel64::new(integration_config()).unwrap();
+    assert_eq!(s.cells_tracked(), 1);
+}
+
+#[test]
+fn initial_state_has_root_only() {
+    let s = Sentinel64::new(test_config()).unwrap();
+    assert_eq!(s.cells_tracked(), 1, "fresh sentinel should have only the root cell");
+}
+
+// ── Ingestion basics ────────────────────────────────────────
+
+#[test]
+fn empty_ingest_produces_report() {
+    let mut s = Sentinel64::new(test_config()).unwrap();
+    let report = s.ingest(&[]);
+    // Empty batch is valid — no observations, but struct is populated.
+    assert!(report.cell_reports.is_empty());
+    assert!(report.ancestor_reports.is_empty());
+}
+
+#[test]
+fn ingest_returns_non_empty_report() {
+    let mut s = Sentinel64::new(test_config()).unwrap();
+    let values = cell_values_u64(0xF, 4);
+    let report = s.ingest(&values);
+    assert!(
+        !report.cell_reports.is_empty() || !report.ancestor_reports.is_empty(),
+        "ingesting values should produce at least one report entry",
+    );
+}
+
+#[test]
+fn multiple_ingests_accumulate() {
+    let mut s = Sentinel64::new(test_config()).unwrap();
+
+    s.ingest(&cell_values_u64(0xF, 8));
+    let cells_after_first = s.cells_tracked();
+
+    for _ in 0..10 {
+        s.ingest(&cell_values_u64(0xF, 8));
+    }
+
+    assert!(
+        s.cells_tracked() >= cells_after_first,
+        "repeated ingestion should not lose cells",
+    );
+}
+
+// ── Report structure ────────────────────────────────────────
+
+#[test]
+fn analysis_widths_are_64_minus_depth() {
+    let mut s = Sentinel64::new(test_config()).unwrap();
+
+    // Feed diverse traffic to populate multiple depths.
+    for _ in 0..10 {
+        s.ingest(&[cell_values_u64(0xF, 4), cell_values_u64(0x1, 4)].concat());
+    }
+    let report = s.ingest(&cell_values_u64(0xF, 8));
+
+    for cr in report.cell_reports.iter().chain(report.ancestor_reports.iter()) {
+        assert_eq!(
+            cr.analysis_width,
+            64 - cr.depth as usize,
+            "analysis_width mismatch at depth {}",
+            cr.depth,
+        );
+    }
+}
+
+#[test]
+fn cell_reports_are_competitive() {
+    let mut s = Sentinel64::new(test_config()).unwrap();
+
+    for _ in 0..10 {
+        s.ingest(&[cell_values_u64(0xA, 4), cell_values_u64(0x5, 4)].concat());
+    }
+    let report = s.ingest(&cell_values_u64(0xA, 8));
+
+    for cr in &report.cell_reports {
+        assert!(
+            cr.is_competitive,
+            "cell_reports entry at depth {} is not competitive",
+            cr.depth
+        );
+    }
+}
+
+#[test]
+fn ancestor_reports_are_non_competitive() {
+    let mut s = Sentinel64::new(test_config()).unwrap();
+
+    for _ in 0..10 {
+        s.ingest(&[cell_values_u64(0xA, 4), cell_values_u64(0x5, 4)].concat());
+    }
+    let report = s.ingest(&cell_values_u64(0xA, 8));
+
+    for ar in &report.ancestor_reports {
+        assert!(
+            !ar.is_competitive,
+            "ancestor_reports entry at depth {} is competitive",
+            ar.depth
+        );
+    }
+}
+
+#[test]
+fn reports_sorted_by_gnode_id() {
+    let mut s = Sentinel64::new(test_config()).unwrap();
+
+    for _ in 0..10 {
+        s.ingest(&[cell_values_u64(0xF, 4), cell_values_u64(0x1, 4)].concat());
+    }
+    let report = s.ingest(&cell_values_u64(0xF, 8));
+
+    for window in report.cell_reports.windows(2) {
+        assert!(
+            window[0].gnode_id < window[1].gnode_id,
+            "cell_reports not sorted: {:?} >= {:?}",
+            window[0].gnode_id,
+            window[1].gnode_id,
+        );
+    }
+    for window in report.ancestor_reports.windows(2) {
+        assert!(
+            window[0].gnode_id < window[1].gnode_id,
+            "ancestor_reports not sorted: {:?} >= {:?}",
+            window[0].gnode_id,
+            window[1].gnode_id,
+        );
+    }
+}
+
+#[test]
+fn no_nan_in_scores() {
+    let mut s = Sentinel64::new(test_config()).unwrap();
+
+    for _ in 0..10 {
+        s.ingest(&[cell_values_u64(0xF, 4), cell_values_u64(0x1, 4)].concat());
+    }
+    let report = s.ingest(&cell_values_u64(0xF, 8));
+
+    for cr in report.cell_reports.iter().chain(report.ancestor_reports.iter()) {
+        assert!(!cr.scores.novelty.mean.is_nan(), "NaN novelty mean at depth {}", cr.depth);
+        assert!(
+            !cr.scores.displacement.mean.is_nan(),
+            "NaN displacement mean at depth {}",
+            cr.depth
+        );
+        assert!(!cr.scores.surprise.mean.is_nan(), "NaN surprise mean at depth {}", cr.depth);
+        assert!(!cr.scores.coherence.mean.is_nan(), "NaN coherence mean at depth {}", cr.depth);
+    }
+}
+
+// ── Cell management ─────────────────────────────────────────
+
+#[test]
+fn creates_cells_on_split() {
+    let mut s = Sentinel64::new(SentinelConfig::<u64> {
+        split_threshold: 10,
+        ..test_config()
+    })
+    .unwrap();
+
+    // Feed diverse traffic across two leading nibbles to trigger splits.
+    for _ in 0..15 {
+        s.ingest(&[cell_values_u64(0xA, 20), cell_values_u64(0x5, 20)].concat());
+    }
+
+    assert!(s.cells_tracked() >= 2, "should have split beyond the root cell");
+}
+
+#[test]
+fn cells_tracked_never_below_one() {
+    let mut s = Sentinel64::new(test_config()).unwrap();
+    assert!(s.cells_tracked() >= 1, "root cell must always exist");
+
+    s.ingest(&cell_values_u64(0xF, 4));
+    assert!(s.cells_tracked() >= 1, "root cell must persist after ingestion");
+}
+
+// ── Boundary values ─────────────────────────────────────────
+
+#[test]
+fn min_value() {
+    let mut s = Sentinel64::new(test_config()).unwrap();
+    let report = s.ingest(&[0u64]);
+    assert_eq!(report.health.lifetime_observations, 1);
+}
+
+#[test]
+fn max_value() {
+    let mut s = Sentinel64::new(test_config()).unwrap();
+    let report = s.ingest(&[u64::MAX]);
+    assert_eq!(report.health.lifetime_observations, 1);
+}
+
+#[test]
+fn min_and_max_together() {
+    let mut s = Sentinel64::new(test_config()).unwrap();
+    let report = s.ingest(&[0u64, u64::MAX]);
+    assert_eq!(report.health.lifetime_observations, 2);
+}
+
+// ── Determinism ─────────────────────────────────────────────
+
+#[test]
+fn deterministic_output_for_same_input() {
+    let cfg = SentinelConfig::<u64> {
+        noise_seed: Some(42),
+        ..test_config()
+    };
+
+    let batch = cell_values_u64(0xF, 8);
+
+    let run = |cfg: SentinelConfig<u64>| {
+        let mut s = Sentinel64::new(cfg).unwrap();
+        let mut reports = Vec::new();
+        for _ in 0..10 {
+            reports.push(s.ingest(&batch));
+        }
+        reports
+    };
+
+    let a = run(cfg.clone());
+    let b = run(cfg);
+
+    assert_eq!(a.len(), b.len());
+    for (ra, rb) in a.iter().zip(b.iter()) {
+        assert_eq!(ra.cell_reports.len(), rb.cell_reports.len());
+        assert_eq!(ra.ancestor_reports.len(), rb.ancestor_reports.len());
+
+        for (ca, cb) in ra.cell_reports.iter().zip(rb.cell_reports.iter()) {
+            assert_eq!(ca.depth, cb.depth);
+            assert_eq!(ca.analysis_width, cb.analysis_width);
+            assert_eq!(ca.rank, cb.rank);
+            assert!(
+                (ca.scores.novelty.mean - cb.scores.novelty.mean).abs() < f64::EPSILON,
+                "novelty diverged at depth {}",
+                ca.depth,
+            );
+        }
+    }
+}
diff --git a/packages/sentinel/tests/serde_roundtrip.rs b/packages/sentinel/tests/serde_roundtrip.rs
new file mode 100644
index 000000000..6e01fae4c
--- /dev/null
+++ b/packages/sentinel/tests/serde_roundtrip.rs
@@ -0,0 +1,315 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// SPDX-FileCopyrightText: 2026 Torrust project contributors
+
+//! §9.8 — Serde round-trip tests.
+//!
+//! These tests validate serialisation/deserialisation round-trips for
+//! all public report types when the `serde` feature is enabled.
+//! Close gap G12.
+//!
+//! # Test index
+//!
+//! ## Composite reports
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`batch_report_json_round_trip`] | full `BatchReport` survives JSON round-trip |
+//! | [`empty_batch_report_json_round_trip`] | empty `BatchReport` (no observations) |
+//!
+//! ## Component reports
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`analysis_set_summary_json_round_trip`] | `AnalysisSetSummary` fields preserved |
+//! | [`cell_report_json_round_trip`] | every `CellReport` round-trips individually |
+//! | [`contour_snapshot_json_round_trip`] | `ContourSnapshot` fields preserved |
+//! | [`coordination_report_json_round_trip`] | every `CoordinationReport` round-trips |
+//! | [`health_report_json_round_trip`] | `HealthReport` fields preserved |
+//!
+//! ## Inspection types
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`cell_inspection_json_round_trip`] | `CellInspection` via `inspect_cell` |
+//!
+//! ## Leaf types
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`member_score_json_round_trip`] | boundary-value `MemberScore` construction |
+//! | [`sample_score_json_round_trip`] | `SampleScore` from per-sample output |
+
+#![cfg(feature = "serde")]
+
+mod common;
+
+use common::{ScenarioBuilder, cell_values, integration_config};
+use torrust_sentinel::{
+    AnalysisSetSummary, BatchReport, CellInspection, CellReport, ContourSnapshot, CoordinationReport, HealthReport, MemberScore,
+    SampleScore, Sentinel128, SentinelConfig,
+};
+
+/// Assert two f64s are within 1 ULP (unit in the last place) of each other.
+///
+/// JSON round-trips through decimal representation can lose 1 ULP for
+/// certain edge-case values at the boundary of shortest-representation
+/// algorithms (e.g. when the 15-digit and 16-digit decimal forms straddle
+/// a float boundary).
+fn assert_f64_ulp(label: &str, a: f64, b: f64) {
+    let a_bits = a.to_bits();
+    let b_bits = b.to_bits();
+    let ulp_dist = a_bits.abs_diff(b_bits);
+    assert!(
+        ulp_dist <= 1,
+        "{label}: {a} vs {b}, ULP distance = {ulp_dist} (bits: {a_bits} vs {b_bits})"
+    );
+}
+
+/// Produce a report with enough structure for round-trip testing.
+///
+/// Only 5 warm-up batches — serde tests need a structurally complete
+/// report (cells, ancestors, coordination), not a statistically
+/// converged one.  See ADR-S-012.
+fn rich_report() -> BatchReport<u128> {
+    let cfg = SentinelConfig::<u64> {
+        split_threshold: 10,
+        per_sample_scores: true,
+        ..integration_config()
+    };
+    let (mut s, _) = ScenarioBuilder::new()
+        .config(cfg)
+        .seed_range(0xA, 16)
+        .seed_range(0xB, 16)
+        .warm_batches(4)
+        .build_with_reports();
+
+    s.ingest(&[cell_values(0xA, 8), cell_values(0xB, 8)].concat())
+}
+
+// ─── Composite reports ──────────────────────────────────────
+
+#[test]
+fn batch_report_json_round_trip() {
+    let report = rich_report();
+
+    let json = serde_json::to_string(&report).unwrap();
+    let deser: BatchReport<u128> = serde_json::from_str(&json).unwrap();
+
+    assert_eq!(report.cell_reports.len(), deser.cell_reports.len());
+    assert_eq!(report.ancestor_reports.len(), deser.ancestor_reports.len());
+    assert_eq!(report.coordination_reports.len(), deser.coordination_reports.len());
+    assert_eq!(
+        report.analysis_set_summary.competitive_size,
+        deser.analysis_set_summary.competitive_size,
+    );
+    assert_eq!(report.analysis_set_summary.full_size, deser.analysis_set_summary.full_size);
+}
+
+#[test]
+fn empty_batch_report_json_round_trip() {
+    let mut s = Sentinel128::new(integration_config()).unwrap();
+    let report = s.ingest(&[]);
+
+    let json = serde_json::to_string(&report).unwrap();
+    let deser: BatchReport<u128> = serde_json::from_str(&json).unwrap();
+
+    assert!(deser.cell_reports.is_empty());
+    assert!(deser.ancestor_reports.is_empty());
+    assert!(deser.coordination_reports.is_empty());
+}
+
+// ─── Component reports (alphabetical) ───────────────────────
+
+#[test]
+fn analysis_set_summary_json_round_trip() {
+    let report = rich_report();
+
+    let json = serde_json::to_string(&report.analysis_set_summary).unwrap();
+    let deser: AnalysisSetSummary = serde_json::from_str(&json).unwrap();
+
+    assert_eq!(report.analysis_set_summary.competitive_size, deser.competitive_size);
+    assert_eq!(report.analysis_set_summary.full_size, deser.full_size);
+    assert_eq!(report.analysis_set_summary.investment_set_size, deser.investment_set_size);
+    assert_eq!(report.analysis_set_summary.depth_range, deser.depth_range);
+    assert_eq!(
+        report.analysis_set_summary.degenerate_cells_skipped,
+        deser.degenerate_cells_skipped,
+    );
+}
+
+#[test]
+fn cell_report_json_round_trip() {
+    let report = rich_report();
+
+    for cr in &report.cell_reports {
+        let json = serde_json::to_string(cr).unwrap();
+        let deser: CellReport<u128> = serde_json::from_str(&json).unwrap();
+
+        assert_eq!(cr.depth, deser.depth);
+        assert_eq!(cr.sample_count, deser.sample_count);
+        assert_eq!(cr.rank, deser.rank);
+        assert_eq!(cr.is_competitive, deser.is_competitive);
+        assert_f64_ulp("novelty.mean", cr.scores.novelty.mean, deser.scores.novelty.mean);
+        assert_f64_ulp(
+            "novelty.clip_pressure",
+            cr.scores.novelty.clip_pressure,
+            deser.scores.novelty.clip_pressure,
+        );
+        // per_sample populated when per_sample_scores is enabled.
+        assert_eq!(cr.per_sample.is_some(), deser.per_sample.is_some());
+        if let (Some(orig), Some(rt)) = (&cr.per_sample, &deser.per_sample) {
+            assert_eq!(orig.len(), rt.len());
+        }
+    }
+}
+
+#[test]
+fn contour_snapshot_json_round_trip() {
+    let report = rich_report();
+
+    let json = serde_json::to_string(&report.contour).unwrap();
+    let deser: ContourSnapshot = serde_json::from_str(&json).unwrap();
+
+    assert_eq!(report.contour.plateau_count, deser.plateau_count);
+    assert_eq!(report.contour.cell_count, deser.cell_count);
+    assert_f64_ulp("total_importance", report.contour.total_importance, deser.total_importance);
+}
+
+#[test]
+fn coordination_report_json_round_trip() {
+    let report = rich_report();
+
+    for coord in &report.coordination_reports {
+        let json = serde_json::to_string(coord).unwrap();
+        let deser: CoordinationReport<u128> = serde_json::from_str(&json).unwrap();
+
+        assert_eq!(coord.depth, deser.depth);
+        assert_eq!(coord.cells_reporting, deser.cells_reporting);
+        assert_eq!(coord.rank, deser.rank);
+        assert_f64_ulp("novelty.mean", coord.scores.novelty.mean, deser.scores.novelty.mean);
+        // per_member populated when per_sample_scores is enabled.
+        assert_eq!(coord.per_member.is_some(), deser.per_member.is_some());
+        if let (Some(orig), Some(rt)) = (&coord.per_member, &deser.per_member) {
+            assert_eq!(orig.len(), rt.len());
+        }
+    }
+}
+
+#[test]
+fn health_report_json_round_trip() {
+    let report = rich_report();
+
+    let json = serde_json::to_string(&report.health).unwrap();
+    let deser: HealthReport = serde_json::from_str(&json).unwrap();
+
+    assert_eq!(report.health.lifetime_observations, deser.lifetime_observations);
+    assert_eq!(report.health.active_trackers, deser.active_trackers);
+    assert_eq!(report.health.active_competitive_trackers, deser.active_competitive_trackers);
+    assert_eq!(report.health.active_ancestor_trackers, deser.active_ancestor_trackers);
+    assert_eq!(report.health.investment_set_size, deser.investment_set_size);
+    assert_f64_ulp(
+        "clip_pressure_distribution.min",
+        report.health.clip_pressure_distribution.min,
+        deser.clip_pressure_distribution.min,
+    );
+    assert_f64_ulp(
+        "clip_pressure_distribution.max",
+        report.health.clip_pressure_distribution.max,
+        deser.clip_pressure_distribution.max,
+    );
+    assert_f64_ulp(
+        "clip_pressure_distribution.mean",
+        report.health.clip_pressure_distribution.mean,
+        deser.clip_pressure_distribution.mean,
+    );
+}
+
+// ─── Inspection types ───────────────────────────────────────
+
+#[test]
+fn cell_inspection_json_round_trip() {
+    let cfg = SentinelConfig::<u64> {
+        split_threshold: 10,
+        ..integration_config()
+    };
+    let (s, _) = ScenarioBuilder::new()
+        .config(cfg)
+        .seed_range(0xA, 20)
+        .warm_batches(4)
+        .build_with_reports();
+
+    let root = s.graph().g_root();
+    let inspection = s.inspect_cell(root).unwrap();
+
+    let json = serde_json::to_string(&inspection).unwrap();
+    let deser: CellInspection<u128> = serde_json::from_str(&json).unwrap();
+
+    assert_eq!(inspection.depth, deser.depth);
+    assert_eq!(inspection.analysis_width, deser.analysis_width);
+    assert_eq!(inspection.rank, deser.rank);
+    assert_f64_ulp(
+        "baselines.novelty.mean",
+        inspection.baselines.novelty.mean,
+        deser.baselines.novelty.mean,
+    );
+}
+
+// ─── Leaf types ─────────────────────────────────────────────
+
+#[test]
+fn member_score_json_round_trip() {
+    let ms = MemberScore::<u128> {
+        cell_start: 0,
+        cell_end: u128::MAX / 2,
+        cell_depth: 1,
+        novelty: 0.5,
+        displacement: 0.3,
+        surprise: 0.7,
+        coherence: 0.9,
+        novelty_z: 1.0,
+        displacement_z: -0.5,
+        surprise_z: 2.1,
+        coherence_z: 0.0,
+    };
+
+    let json = serde_json::to_string(&ms).unwrap();
+    let deser: MemberScore<u128> = serde_json::from_str(&json).unwrap();
+
+    assert_eq!(ms.cell_start, deser.cell_start);
+    assert_eq!(ms.cell_end, deser.cell_end);
+    assert_eq!(ms.cell_depth, deser.cell_depth);
+    assert_f64_ulp("novelty", ms.novelty, deser.novelty);
+    assert_f64_ulp("displacement", ms.displacement, deser.displacement);
+    assert_f64_ulp("surprise", ms.surprise, deser.surprise);
+    assert_f64_ulp("coherence", ms.coherence, deser.coherence);
+    assert_f64_ulp("novelty_z", ms.novelty_z, deser.novelty_z);
+    assert_f64_ulp("displacement_z", ms.displacement_z, deser.displacement_z);
+    assert_f64_ulp("surprise_z", ms.surprise_z, deser.surprise_z);
+    assert_f64_ulp("coherence_z", ms.coherence_z, deser.coherence_z);
+}
+
+#[test]
+fn sample_score_json_round_trip() {
+    let report = rich_report();
+
+    // Extract SampleScores from the first cell with per-sample data.
+    let samples = report
+        .cell_reports
+        .iter()
+        .find_map(|cr| cr.per_sample.as_ref())
+        .expect("per_sample_scores is enabled; at least one cell should have samples");
+
+    for ss in samples {
+        let json = serde_json::to_string(ss).unwrap();
+        let deser: SampleScore = serde_json::from_str(&json).unwrap();
+
+        assert_f64_ulp("novelty", ss.novelty, deser.novelty);
+        assert_f64_ulp("displacement", ss.displacement, deser.displacement);
+        assert_f64_ulp("surprise", ss.surprise, deser.surprise);
+        assert_f64_ulp("coherence", ss.coherence, deser.coherence);
+        assert_f64_ulp("novelty_z", ss.novelty_z, deser.novelty_z);
+        assert_f64_ulp("displacement_z", ss.displacement_z, deser.displacement_z);
+        assert_f64_ulp("surprise_z", ss.surprise_z, deser.surprise_z);
+        assert_f64_ulp("coherence_z", ss.coherence_z, deser.coherence_z);
+    }
+}
diff --git a/packages/sentinel/tests/spatial_decay.rs b/packages/sentinel/tests/spatial_decay.rs
new file mode 100644
index 000000000..60674034f
--- /dev/null
+++ b/packages/sentinel/tests/spatial_decay.rs
@@ -0,0 +1,306 @@
+//! Spatial decay via G-V Graph temporal filter.
+//!
+//! Tests that [`decay()`] and [`decay_subtree()`] correctly delegate to
+//! the G-V Graph and that the sentinel's invariants are maintained.
+//!
+//! [`decay()`]: torrust_sentinel::SpectralSentinel::decay
+//! [`decay_subtree()`]: torrust_sentinel::SpectralSentinel::decay_subtree
+//!
+//! # Test index
+//!
+//! ## Identity & no-ops
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`decay_on_empty_graph_is_noop`] | empty graph is unchanged after decay |
+//! | [`decay_at_attenuation_one_is_noop`] | `att = 1.0` is a no-op |
+//!
+//! ## Attenuation (0 < att < 1)
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`decay_reduces_total_sum`] | decay with `att = 0.5` reduces total sum |
+//! | [`repeated_decay_eventually_zeroes_integer_counters`] | 100 consecutive decays drive integer counters to zero |
+//!
+//! ## Zero attenuation
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`decay_zero_attenuation_zeroes_graph`] | `att = 0.0` zeroes all energy |
+//!
+//! ## Amplification (att > 1)
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`amplification_increases_total_sum`] | `att = 2.0` increases total sum |
+//!
+//! ## Depth selectivity (q parameter)
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`selective_q_preserves_more_coarse_structure`] | selective decay (`q > 0`) preserves coarse structure |
+//! | [`max_selectivity_q_one_is_valid`] | `q = 1.0` boundary does not panic |
+//!
+//! ## Feed-forward invariant
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`decay_does_not_affect_tracker_count`] | tracker count unchanged after decay |
+//! | [`decay_does_not_change_lifetime_observations`] | lifetime observations unchanged after decay |
+//!
+//! ## `decay_subtree`
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`decay_subtree_at_root_matches_global_decay`] | subtree decay at root matches global decay |
+//! | [`decay_subtree_affects_only_targeted_subtree`] | sibling subtree energy is unaffected |
+//!
+//! ## Composition
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`decay_then_ingest_preserves_invariants`] | ingest after decay preserves invariants |
+//!
+//! ## Panics — invalid attenuation
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`decay_panics_on_negative_attenuation`] | negative attenuation panics |
+//! | [`decay_panics_on_nan_attenuation`] | NaN attenuation panics |
+//!
+//! ## Panics — invalid q
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`decay_panics_on_negative_q`] | negative q panics |
+//! | [`decay_panics_on_q_above_one`] | q > 1.0 panics |
+//! | [`decay_panics_on_nan_q`] | NaN q panics |
+
+mod common;
+
+use common::{assert_invariants, seeded_sentinel, test_config};
+use torrust_sentinel::{Sentinel128, SentinelConfig};
+
+// ── Identity & no-ops ──────────────────────────────────────
+
+#[test]
+fn decay_on_empty_graph_is_noop() {
+    let mut s = Sentinel128::new(test_config()).unwrap();
+    assert_eq!(s.graph().total_sum(), 0);
+
+    s.decay(0.5, 0.0);
+    assert_eq!(s.graph().total_sum(), 0);
+}
+
+#[test]
+fn decay_at_attenuation_one_is_noop() {
+    let mut s = seeded_sentinel();
+    let before = s.graph().total_sum();
+
+    s.decay(1.0, 0.0);
+    assert_eq!(s.graph().total_sum(), before);
+}
+
+// ── Attenuation (0 < att < 1) ──────────────────────────────
+
+#[test]
+fn decay_reduces_total_sum() {
+    let mut s = seeded_sentinel();
+    let before = s.graph().total_sum();
+    assert!(before > 0);
+
+    s.decay(0.5, 0.0);
+    assert!(s.graph().total_sum() < before);
+}
+
+#[test]
+fn repeated_decay_eventually_zeroes_integer_counters() {
+    let mut s = seeded_sentinel();
+    assert!(s.graph().total_sum() > 0);
+
+    for _ in 0..100 {
+        s.decay(0.5, 0.0);
+    }
+    assert_eq!(s.graph().total_sum(), 0);
+}
+
+// ── Zero attenuation ───────────────────────────────────────
+
+#[test]
+fn decay_zero_attenuation_zeroes_graph() {
+    let mut s = seeded_sentinel();
+    assert!(s.graph().total_sum() > 0);
+
+    s.decay(0.0, 0.0);
+    assert_eq!(s.graph().total_sum(), 0);
+}
+
+// ── Amplification (att > 1) ────────────────────────────────
+
+#[test]
+fn amplification_increases_total_sum() {
+    let mut s = seeded_sentinel();
+    let before = s.graph().total_sum();
+
+    s.decay(2.0, 0.0);
+    assert!(s.graph().total_sum() > before);
+}
+
+// ── Depth selectivity (q parameter) ────────────────────────
+
+#[test]
+fn selective_q_preserves_more_coarse_structure() {
+    let cfg = SentinelConfig::<u64> {
+        split_threshold: 5,
+        ..test_config()
+    };
+
+    let mut s_uniform = Sentinel128::new(cfg.clone()).unwrap();
+    let mut s_selective = Sentinel128::new(cfg).unwrap();
+
+    let values: Vec<u128> = (0..100).collect();
+    s_uniform.ingest(&values);
+    s_selective.ingest(&values);
+
+    assert_eq!(s_uniform.graph().total_sum(), s_selective.graph().total_sum());
+
+    // Only meaningful if splits occurred (otherwise q has no effect).
+    if s_uniform.graph().node_count() > 1 {
+        s_uniform.decay(0.5, 0.0);
+        s_selective.decay(0.5, 0.5);
+
+        // Both should decay, but selective decay may produce a
+        // different total because per-depth factors differ.
+        assert!(s_uniform.graph().total_sum() > 0);
+        assert!(s_selective.graph().total_sum() > 0);
+    }
+}
+
+#[test]
+fn max_selectivity_q_one_is_valid() {
+    let mut s = seeded_sentinel();
+    let before = s.graph().total_sum();
+
+    // q=1.0 is the maximum-selectivity boundary — must not panic.
+    s.decay(0.5, 1.0);
+    assert!(s.graph().total_sum() <= before);
+}
+
+// ── Feed-forward invariant ─────────────────────────────────
+
+#[test]
+fn decay_does_not_affect_tracker_count() {
+    let mut s = seeded_sentinel();
+    let trackers_before = s.cells_tracked();
+
+    s.decay(0.5, 0.0);
+    assert_eq!(s.cells_tracked(), trackers_before);
+}
+
+#[test]
+fn decay_does_not_change_lifetime_observations() {
+    let mut s = seeded_sentinel();
+    let before = s.health().lifetime_observations;
+
+    s.decay(0.5, 0.0);
+    assert_eq!(s.health().lifetime_observations, before);
+}
+
+// ── decay_subtree ──────────────────────────────────────────
+
+#[test]
+fn decay_subtree_at_root_matches_global_decay() {
+    let cfg = test_config();
+
+    let mut s_global = Sentinel128::new(cfg.clone()).unwrap();
+    s_global.ingest(&[42, 43, 44, 45, 46]);
+    s_global.decay(0.5, 0.0);
+
+    let mut s_subtree = Sentinel128::new(cfg).unwrap();
+    s_subtree.ingest(&[42, 43, 44, 45, 46]);
+    let root = s_subtree.graph().g_root();
+    s_subtree.decay_subtree(root, 0.5, 0.0);
+
+    assert_eq!(s_global.graph().total_sum(), s_subtree.graph().total_sum());
+}
+
+#[test]
+fn decay_subtree_affects_only_targeted_subtree() {
+    let cfg = SentinelConfig::<u64> {
+        split_threshold: 5,
+        ..test_config()
+    };
+
+    let mut s_global = Sentinel128::new(cfg.clone()).unwrap();
+    let mut s_partial = Sentinel128::new(cfg).unwrap();
+
+    let values: Vec<u128> = (0..100).collect();
+    s_global.ingest(&values);
+    s_partial.ingest(&values);
+
+    let before = s_partial.graph().total_sum();
+
+    // Find a non-root cell to use as subtree target.
+    let root = s_partial.graph().g_root();
+    let non_root: Vec<_> = s_partial.cell_gnodes().into_iter().filter(|&id| id != root).collect();
+
+    if !non_root.is_empty() {
+        s_global.decay(0.5, 0.0);
+        s_partial.decay_subtree(non_root[0], 0.5, 0.0);
+
+        // Subtree decay touches fewer nodes → more total_sum remains.
+        assert!(s_partial.graph().total_sum() > s_global.graph().total_sum());
+        // But the targeted subtree was still reduced.
+        assert!(s_partial.graph().total_sum() <= before);
+    }
+}
+
+// ── Composition ────────────────────────────────────────────
+
+#[test]
+fn decay_then_ingest_preserves_invariants() {
+    let mut s = seeded_sentinel();
+    s.decay(0.5, 0.0);
+
+    let report = s.ingest(&[45, 46, 47]);
+    assert_invariants(&s, &report);
+}
+
+// ── Panics — invalid attenuation ───────────────────────────
+
+#[test]
+#[should_panic(expected = "attenuation")]
+fn decay_panics_on_negative_attenuation() {
+    let mut s = seeded_sentinel();
+    s.decay(-1.0, 0.0);
+}
+
+#[test]
+#[should_panic(expected = "attenuation")]
+fn decay_panics_on_nan_attenuation() {
+    let mut s = seeded_sentinel();
+    s.decay(f64::NAN, 0.0);
+}
+
+// ── Panics — invalid q ────────────────────────────────────
+
+#[test]
+#[should_panic(expected = "q must be")]
+fn decay_panics_on_negative_q() {
+    let mut s = seeded_sentinel();
+    s.decay(0.5, -0.1);
+}
+
+#[test]
+#[should_panic(expected = "q must be")]
+fn decay_panics_on_q_above_one() {
+    let mut s = seeded_sentinel();
+    s.decay(0.5, 1.1);
+}
+
+#[test]
+#[should_panic(expected = "q must be")]
+fn decay_panics_on_nan_q() {
+    let mut s = seeded_sentinel();
+    s.decay(0.5, f64::NAN);
+}
diff --git a/packages/sentinel/tests/spray_resistance.rs b/packages/sentinel/tests/spray_resistance.rs
new file mode 100644
index 000000000..9092f8d3d
--- /dev/null
+++ b/packages/sentinel/tests/spray_resistance.rs
@@ -0,0 +1,253 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// SPDX-FileCopyrightText: 2026 Torrust project contributors
+
+//! **Budget and spray resistance** tests for the sentinel.
+//!
+//! These tests validate the sentinel's resource bounds under adversarial
+//! traffic patterns. They close gap G4.
+//!
+//! # Test index
+//!
+//! ## Analysis-set bounds
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`competitive_set_bounded_by_k`] | `competitive_size ≤ K` after 64-nibble spray |
+//! | [`competitive_set_at_k_equals_one`] | K=1 boundary: at most 1 competitive cell |
+//! | [`full_set_bounded_by_steiner`] | `full_size ≤ 1 + K·d_max` after 64-nibble spray |
+//!
+//! ## Resource bounds
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`g_nodes_bounded_by_budget_under_spray`] | G-tree nodes ≤ `budget·2+2` under spray |
+//! | [`cells_tracked_bounded_under_diverse_traffic`] | `cells_tracked ≤ 2K+1` under 16-nibble traffic |
+//!
+//! ## Fairness
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`concentrated_range_survives_spray`] | 90% concentrated + 10% spray — range A still tracked |
+//!
+//! ## Cross-cutting invariants
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`invariants_hold_under_spray`] | Full invariant suite passes across spray phases |
+
+mod common;
+
+use common::{assert_invariants, cell_values, integration_config, test_config};
+use torrust_sentinel::{Sentinel128, SentinelConfig};
+
+// ═══════════════════════════════════════════════════════════
+//  Analysis-set bounds
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn competitive_set_bounded_by_k() {
+    let k = 8;
+    let cfg = SentinelConfig::<u64> {
+        analysis_k: k,
+        split_threshold: 10,
+        budget: 10_000,
+        ..test_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    // Spray traffic across 64 distinct leading nibbles.
+    for nibble in 0..64u128 {
+        s.ingest(&cell_values(nibble, 20));
+    }
+
+    let report = s.ingest(&cell_values(0, 4));
+    assert_invariants(&s, &report);
+    assert!(
+        report.analysis_set_summary.competitive_size <= k,
+        "competitive set {} exceeds K={}",
+        report.analysis_set_summary.competitive_size,
+        k,
+    );
+}
+
+#[test]
+fn competitive_set_at_k_equals_one() {
+    let cfg = SentinelConfig::<u64> {
+        analysis_k: 1,
+        split_threshold: 10,
+        budget: 10_000,
+        ..integration_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    for nibble in 0..16u128 {
+        for _ in 0..8 {
+            s.ingest(&cell_values(nibble, 16));
+        }
+    }
+
+    let report = s.ingest(&cell_values(0xA, 4));
+    assert_invariants(&s, &report);
+    assert!(
+        report.analysis_set_summary.competitive_size <= 1,
+        "with K=1, competitive set should be at most 1, got {}",
+        report.analysis_set_summary.competitive_size,
+    );
+    assert!(
+        report.analysis_set_summary.full_size >= 1,
+        "full set should have at least root"
+    );
+}
+
+#[test]
+fn full_set_bounded_by_steiner() {
+    let k = 8;
+    let cfg = SentinelConfig::<u64> {
+        analysis_k: k,
+        split_threshold: 10,
+        budget: 10_000,
+        ..test_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    for nibble in 0..64u128 {
+        s.ingest(&cell_values(nibble, 20));
+    }
+
+    let report = s.ingest(&cell_values(0, 4));
+    assert_invariants(&s, &report);
+
+    let summary = &report.analysis_set_summary;
+    let d_max = summary.depth_range.1 as usize;
+    let bound = 1 + k * d_max;
+    assert!(
+        summary.full_size <= bound,
+        "full set {} exceeds Steiner bound 1+K·d_max={} (K={}, d_max={})",
+        summary.full_size,
+        bound,
+        k,
+        d_max,
+    );
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Resource bounds
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn g_nodes_bounded_by_budget_under_spray() {
+    let budget = 500;
+    let cfg = SentinelConfig::<u64> {
+        budget,
+        split_threshold: 10,
+        d_create: 3,
+        d_evict: 6,
+        ..test_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    // Spray many distinct values across nibble ranges.
+    for i in 0..500u128 {
+        let nibble = i % 16;
+        s.ingest(&[(nibble << 124) | (i + 1)]);
+    }
+
+    let g_nodes = s.health().total_g_nodes;
+    assert!(
+        g_nodes <= budget * 2 + 2,
+        "G-tree node count {g_nodes} exceeds budget guard (budget={budget})",
+    );
+}
+
+#[test]
+fn cells_tracked_bounded_under_diverse_traffic() {
+    let k = 8;
+    let cfg = SentinelConfig::<u64> {
+        analysis_k: k,
+        split_threshold: 10,
+        budget: 10_000,
+        ..test_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    // Send traffic to all 16 leading-nibble ranges.
+    for nibble in 0..16u128 {
+        for _ in 0..12 {
+            s.ingest(&cell_values(nibble, 16));
+        }
+    }
+
+    assert!(
+        s.cells_tracked() <= 2 * k + 1,
+        "cells_tracked {} exceeds 2K+1={} (K={})",
+        s.cells_tracked(),
+        2 * k + 1,
+        k,
+    );
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Fairness
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn concentrated_range_survives_spray() {
+    let cfg = SentinelConfig::<u64> {
+        analysis_k: 4,
+        split_threshold: 50,
+        budget: 10_000,
+        max_rank: 2,
+        ..integration_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    // 90% traffic to range A.
+    for _ in 0..20 {
+        s.ingest(&cell_values(0xA, 40));
+    }
+
+    // 10% spray across many ranges.
+    for nibble in 0..16u128 {
+        s.ingest(&cell_values(nibble, 3));
+    }
+
+    let report = s.ingest(&cell_values(0xA, 8));
+    assert_invariants(&s, &report);
+
+    // The concentrated range should still be represented — either as a
+    // competitive cell or through its ancestor chain.
+    let has_range_a = report
+        .cell_reports
+        .iter()
+        .chain(report.ancestor_reports.iter())
+        .any(|cr| cr.sample_count > 0);
+
+    assert!(has_range_a, "concentrated range A should still be represented in reports");
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Cross-cutting invariants
+// ═══════════════════════════════════════════════════════════
+
+#[test]
+fn invariants_hold_under_spray() {
+    let cfg = SentinelConfig::<u64> {
+        analysis_k: 8,
+        split_threshold: 10,
+        budget: 10_000,
+        ..test_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    // Phase 1: wide spray across 64 nibbles.
+    for nibble in 0..64u128 {
+        let report = s.ingest(&cell_values(nibble, 20));
+        assert_invariants(&s, &report);
+    }
+
+    // Phase 2: concentrated burst after spray.
+    for _ in 0..10 {
+        let report = s.ingest(&cell_values(0xA, 50));
+        assert_invariants(&s, &report);
+    }
+}
diff --git a/packages/sentinel/tests/suffix_analysis.rs b/packages/sentinel/tests/suffix_analysis.rs
new file mode 100644
index 000000000..f500742f8
--- /dev/null
+++ b/packages/sentinel/tests/suffix_analysis.rs
@@ -0,0 +1,249 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// SPDX-FileCopyrightText: 2026 Torrust project contributors
+
+//! Integration tests for **suffix analysis** (§ALGO S-3.2).
+//!
+//! Every cell at G-tree depth `d` operates on the suffix bits `[d, N)`
+//! at width `w = N − d`. These tests verify that the suffix width is
+//! correctly propagated through cell inspection, batch reports, scoring
+//! geometry, and noise injection.
+//!
+//! # Test index
+//!
+//! ## Suffix width identity
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`root_suffix_width_is_full_bit_width`] | Root (depth 0) has width 128 |
+//! | [`cell_analysis_width_equals_n_minus_depth`] | `analysis_width == 128 - depth` for all cells |
+//! | [`geometry_dim_equals_analysis_width`] | `geometry.dim == analysis_width` for all cells |
+//!
+//! ## Geometry cap
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`geometry_cap_is_min_of_dim_and_max_rank`] | `cap == min(dim, max_rank)` |
+//! | [`residual_dof_equals_dim_minus_rank`] | `residual_dof == dim - rank` for all cells |
+//!
+//! ## Deeper cells have narrower suffixes
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`deeper_cells_have_smaller_suffix_width`] | Depth ordering → width ordering |
+//!
+//! ## Report suffix widths
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`cell_reports_carry_correct_suffix_widths`] | `CellReport.analysis_width == 128 - depth` |
+//! | [`scores_are_valid_across_suffix_widths`] | No NaN in any score axis |
+//!
+//! ## Noise at all suffix widths
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`noise_injected_at_every_suffix_width`] | All cells receive noise observations |
+
+mod common;
+
+use common::{ScenarioBuilder, assert_invariants, cell_values, seeded_sentinel, test_config};
+use torrust_sentinel::Sentinel128;
+
+// ═══════════════════════════════════════════════════════════
+//  Suffix width identity
+// ═══════════════════════════════════════════════════════════
+
+/// Root lives at depth 0 — its suffix width must be the full 128 bits.
+#[test]
+fn root_suffix_width_is_full_bit_width() {
+    let s = Sentinel128::new(test_config()).unwrap();
+
+    let gnodes = s.cell_gnodes();
+    assert!(!gnodes.is_empty(), "sentinel must have at least the root cell");
+
+    let root = s.inspect_cell(gnodes[0]).unwrap();
+    assert_eq!(root.depth, 0);
+    assert_eq!(root.analysis_width, 128);
+}
+
+/// Every cell's `analysis_width` must equal `128 - depth`.
+#[test]
+fn cell_analysis_width_equals_n_minus_depth() {
+    let mut s = seeded_sentinel();
+    s.ingest(&cell_values(0xA, 200));
+
+    for &gnode in &s.cell_gnodes() {
+        let ins = s.inspect_cell(gnode).unwrap();
+        let expected = 128 - ins.depth as usize;
+        assert_eq!(
+            ins.analysis_width, expected,
+            "depth {}: expected analysis_width={expected}, got {}",
+            ins.depth, ins.analysis_width,
+        );
+    }
+}
+
+/// `geometry.dim` must agree with `analysis_width` for every cell.
+#[test]
+fn geometry_dim_equals_analysis_width() {
+    let mut s = seeded_sentinel();
+    s.ingest(&cell_values(0xA, 200));
+
+    for &gnode in &s.cell_gnodes() {
+        let ins = s.inspect_cell(gnode).unwrap();
+        assert_eq!(
+            ins.geometry.dim, ins.analysis_width,
+            "depth {}: geometry.dim={} != analysis_width={}",
+            ins.depth, ins.geometry.dim, ins.analysis_width,
+        );
+    }
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Geometry cap
+// ═══════════════════════════════════════════════════════════
+
+/// `geometry.cap` must equal `min(dim, max_rank)` for every cell.
+#[test]
+fn geometry_cap_is_min_of_dim_and_max_rank() {
+    let cfg = test_config();
+    let max_rank = cfg.max_rank;
+    let mut s = Sentinel128::new(cfg).unwrap();
+    s.ingest(&cell_values(0xF, 200));
+
+    for &gnode in &s.cell_gnodes() {
+        let ins = s.inspect_cell(gnode).unwrap();
+        let expected_cap = ins.geometry.dim.min(max_rank);
+        assert_eq!(
+            ins.geometry.cap, expected_cap,
+            "depth {}: expected cap={expected_cap}, got {}",
+            ins.depth, ins.geometry.cap,
+        );
+    }
+}
+
+/// `residual_dof` must equal `dim - rank` for every cell.
+#[test]
+fn residual_dof_equals_dim_minus_rank() {
+    let mut s = seeded_sentinel();
+    s.ingest(&cell_values(0xA, 200));
+
+    for &gnode in &s.cell_gnodes() {
+        let ins = s.inspect_cell(gnode).unwrap();
+        let expected_dof = ins.geometry.dim - ins.rank;
+        assert_eq!(
+            ins.geometry.residual_dof, expected_dof,
+            "depth {}: expected residual_dof={expected_dof}, got {}",
+            ins.depth, ins.geometry.residual_dof,
+        );
+    }
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Deeper cells have narrower suffixes
+// ═══════════════════════════════════════════════════════════
+
+/// Cells at greater depth must have strictly smaller suffix widths.
+#[test]
+fn deeper_cells_have_smaller_suffix_width() {
+    let mut s = seeded_sentinel();
+    s.ingest(&cell_values(0xF, 200));
+
+    let mut widths: Vec<(u32, usize)> = s
+        .cell_gnodes()
+        .iter()
+        .map(|&g| {
+            let ins = s.inspect_cell(g).unwrap();
+            (ins.depth, ins.analysis_width)
+        })
+        .collect();
+    widths.sort_by_key(|&(depth, _)| depth);
+
+    for pair in widths.windows(2) {
+        let (d1, w1) = pair[0];
+        let (d2, w2) = pair[1];
+        if d2 > d1 {
+            assert!(
+                w2 < w1,
+                "depth {d1} (width {w1}) should be wider than depth {d2} (width {w2})",
+            );
+        }
+    }
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Report suffix widths
+// ═══════════════════════════════════════════════════════════
+
+/// `CellReport.analysis_width` must equal `128 - depth` in the batch
+/// report, and structural invariants must hold.
+#[test]
+fn cell_reports_carry_correct_suffix_widths() {
+    let mut s = seeded_sentinel();
+    let report = s.ingest(&cell_values(0xF, 100));
+
+    assert_invariants(&s, &report);
+
+    for cr in &report.cell_reports {
+        let expected = 128 - cr.depth as usize;
+        assert_eq!(
+            cr.analysis_width, expected,
+            "cell report depth {}: expected analysis_width={expected}, got {}",
+            cr.depth, cr.analysis_width,
+        );
+        assert_eq!(
+            cr.geometry.dim, expected,
+            "cell report depth {}: geometry.dim={} != {expected}",
+            cr.depth, cr.geometry.dim,
+        );
+    }
+}
+
+/// All four score axes must produce finite values (no NaN) regardless
+/// of the suffix width.
+#[test]
+fn scores_are_valid_across_suffix_widths() {
+    let (mut s, _) = ScenarioBuilder::new()
+        .config(test_config())
+        .seed_range(0xF, 50)
+        .seed_range(0x1, 50)
+        .warm_batches(3)
+        .build_with_reports();
+
+    let report = s.ingest(&cell_values(0xF, 100));
+
+    assert_invariants(&s, &report);
+
+    for cr in &report.cell_reports {
+        assert!(!cr.scores.novelty.mean.is_nan(), "NaN novelty at depth {}", cr.depth);
+        assert!(
+            !cr.scores.displacement.mean.is_nan(),
+            "NaN displacement at depth {}",
+            cr.depth
+        );
+        assert!(!cr.scores.surprise.mean.is_nan(), "NaN surprise at depth {}", cr.depth);
+        assert!(!cr.scores.coherence.mean.is_nan(), "NaN coherence at depth {}", cr.depth);
+    }
+}
+
+// ═══════════════════════════════════════════════════════════
+//  Noise at all suffix widths
+// ═══════════════════════════════════════════════════════════
+
+/// Every cell — regardless of suffix width — must have received noise
+/// observations from the automatic injection schedule.
+#[test]
+fn noise_injected_at_every_suffix_width() {
+    let mut s = seeded_sentinel();
+    s.ingest(&cell_values(0xA, 200));
+
+    for &gnode in &s.cell_gnodes() {
+        let ins = s.inspect_cell(gnode).unwrap();
+        assert!(
+            ins.maturity.noise_observations > 0,
+            "cell at depth {} (width {}) received no noise",
+            ins.depth,
+            ins.analysis_width,
+        );
+    }
+}
diff --git a/packages/sentinel/tests/warm_up.rs b/packages/sentinel/tests/warm_up.rs
new file mode 100644
index 000000000..50a71f8db
--- /dev/null
+++ b/packages/sentinel/tests/warm_up.rs
@@ -0,0 +1,385 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+// SPDX-FileCopyrightText: 2026 Torrust project contributors
+
+//! §9.4 — **Warm-up sequence** tests.
+//!
+//! These tests validate the four-stage warm-up sequence described in
+//! §ALGO S-11.6. They close gap G5.
+//!
+//! # Test index
+//!
+//! ## Stage 1 — Pre-split
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`stage1_only_root_tracker`] | single cell, no competitive set |
+//! | [`stage1_no_coordination`] | coordination reports empty |
+//! | [`stage1_lifetime_observations_counted`] | observation counter increases |
+//! | [`stage1_invariants_hold`] | structural invariants pass |
+//!
+//! ## Stage 2 — Spatial formation
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`stage2_cells_tracked_increases`] | splits create new cells |
+//! | [`stage2_competitive_cells_appear`] | competitive set becomes non-empty |
+//! | [`stage2_new_cells_have_high_noise_influence`] | fresh cells have η > 0.3 |
+//! | [`stage2_invariants_hold`] | structural invariants pass |
+//!
+//! ## Stage 3 — Stabilisation
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`stage3_competitive_set_size_stabilises`] | low variance in set size |
+//! | [`stage3_coordination_activates`] | coordination contexts appear |
+//! | [`stage3_invariants_hold_throughout`] | invariants at every batch |
+//!
+//! ## Stage 4 — Steady state
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`stage4_root_maturity_below_half`] | root η < 0.5 after warm-up |
+//! | [`stage4_maturity_decreases_monotonically`] | η never increases for one cell |
+//! | [`stage4_health_maturity_distribution`] | health report reflects maturity |
+//!
+//! ## Cold start
+//!
+//! | Test | Focus |
+//! |------|-------|
+//! | [`cold_start_noise_influence_is_one`] | without noise, η = 1.0 |
+
+mod common;
+
+use common::{ScenarioBuilder, assert_invariants, cell_values, cold_config, integration_config, test_config};
+use torrust_sentinel::{NoiseSchedule, Sentinel128, SentinelConfig};
+
+// ── Stage 1: Pre-split ──────────────────────────────────────
+
+#[test]
+fn stage1_only_root_tracker() {
+    let cfg = SentinelConfig::<u64> {
+        split_threshold: 100,
+        ..test_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    // Feed fewer than split_threshold observations — all to same range.
+    let report = s.ingest(&cell_values(0xA, 50));
+
+    assert_eq!(
+        report.analysis_set_summary.competitive_size, 0,
+        "pre-split: no competitive cells yet"
+    );
+    assert_eq!(s.cells_tracked(), 1, "only root tracker");
+}
+
+#[test]
+fn stage1_no_coordination() {
+    let cfg = SentinelConfig::<u64> {
+        split_threshold: 100,
+        ..test_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    let report = s.ingest(&cell_values(0xA, 50));
+    assert!(
+        report.coordination_reports.is_empty(),
+        "pre-split: coordination should be empty"
+    );
+}
+
+#[test]
+fn stage1_lifetime_observations_counted() {
+    let cfg = SentinelConfig::<u64> {
+        split_threshold: 100,
+        ..test_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    assert_eq!(s.lifetime_observations(), 0);
+    s.ingest(&cell_values(0xA, 50));
+    assert_eq!(s.lifetime_observations(), 50);
+    s.ingest(&cell_values(0xA, 30));
+    assert_eq!(s.lifetime_observations(), 80);
+}
+
+#[test]
+fn stage1_invariants_hold() {
+    let cfg = SentinelConfig::<u64> {
+        split_threshold: 100,
+        ..test_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    let report = s.ingest(&cell_values(0xA, 50));
+    assert_invariants(&s, &report);
+}
+
+// ── Stage 2: Spatial formation ──────────────────────────────
+
+#[test]
+fn stage2_cells_tracked_increases() {
+    let cfg = SentinelConfig::<u64> {
+        split_threshold: 10,
+        ..integration_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    for _ in 0..5 {
+        s.ingest(&cell_values(0xA, 20));
+    }
+
+    assert!(s.cells_tracked() > 1, "splits should create new cells");
+}
+
+#[test]
+fn stage2_competitive_cells_appear() {
+    let cfg = SentinelConfig::<u64> {
+        split_threshold: 10,
+        ..integration_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    // Feed enough to trigger splits (> split_threshold per range).
+    for _ in 0..5 {
+        s.ingest(&cell_values(0xA, 20));
+    }
+
+    let report = s.ingest(&cell_values(0xA, 8));
+    assert!(
+        report.analysis_set_summary.competitive_size > 0,
+        "after sufficient traffic, competitive cells should appear"
+    );
+}
+
+#[test]
+fn stage2_new_cells_have_high_noise_influence() {
+    let cfg = SentinelConfig::<u64> {
+        split_threshold: 10,
+        noise_schedule: NoiseSchedule::Explicit(vec![5]),
+        noise_batch_size: 4,
+        ..integration_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    // Feed just enough to create new cells.
+    for _ in 0..5 {
+        s.ingest(&cell_values(0xA, 20));
+    }
+
+    // Newly created cells should have high noise influence (η > 0.3)
+    // because they've had very few real observations.
+    let root = s.graph().g_root();
+    for &gnode in &s.cell_gnodes() {
+        if gnode == root {
+            continue; // root is special — skip.
+        }
+        let insp = s.inspect_cell(gnode).unwrap();
+        // New cells may not have processed many real batches yet,
+        // so η should be high.
+        if insp.maturity.real_observations < 20 {
+            assert!(
+                insp.maturity.noise_influence > 0.3,
+                "early cell at depth {} should have high noise influence, got {:.4}",
+                insp.depth,
+                insp.maturity.noise_influence
+            );
+        }
+    }
+}
+
+#[test]
+fn stage2_invariants_hold() {
+    let cfg = SentinelConfig::<u64> {
+        split_threshold: 10,
+        ..integration_config()
+    };
+    let mut s = Sentinel128::new(cfg).unwrap();
+
+    for _ in 0..5 {
+        s.ingest(&cell_values(0xA, 20));
+    }
+
+    let report = s.ingest(&cell_values(0xA, 8));
+    assert_invariants(&s, &report);
+}
+
+// ── Stage 3: Stabilisation ──────────────────────────────────
+
+#[test]
+fn stage3_competitive_set_size_stabilises() {
+    let cfg = SentinelConfig::<u64> {
+        split_threshold: 10,
+        ..integration_config()
+    };
+
+    let (mut s, _) = ScenarioBuilder::new()
+        .config(cfg)
+        .seed_range(0xA, 20)
+        .seed_range(0xB, 20)
+        .warm_batches(8)
+        .build_with_reports();
+
+    // Run 10 more batches and check variance of competitive_size.
+    let mut sizes = Vec::new();
+    for _ in 0..10 {
+        let report = s.ingest(&[cell_values(0xA, 10), cell_values(0xB, 10)].concat());
+        sizes.push(report.analysis_set_summary.competitive_size);
+    }
+
+    // Stabilisation: low variance in competitive set size.
+    let min = *sizes.iter().min().unwrap();
+    let max = *sizes.iter().max().unwrap();
+    assert!(max - min <= 2, "competitive set size should stabilise (min={min}, max={max})");
+}
+
+#[test]
+fn stage3_coordination_activates() {
+    let mut s = ScenarioBuilder::new()
+        .config(SentinelConfig::<u64> {
+            split_threshold: 10,
+            ..integration_config()
+        })
+        .seed_range(0xA, 20)
+        .seed_range(0xB, 20)
+        .warm_batches(12)
+        .build();
+
+    let report = s.ingest(&[cell_values(0xA, 10), cell_values(0xB, 10)].concat());
+
+    // If there are multiple competitive cells, coordination contexts
+    // should have appeared.  Cell creation depends on traffic volume
+    // (split_threshold), not λ — needs enough warm batches for the
+    // coordination context to observe both subtrees.
+    if report.analysis_set_summary.competitive_size >= 2 {
+        assert!(
+            !report.coordination_reports.is_empty() || report.health.coordination_health.active_contexts > 0,
+            "with ≥2 competitive cells, coordination should be active"
+        );
+    }
+}
+
+#[test]
+fn stage3_invariants_hold_throughout() {
+    let cfg = SentinelConfig::<u64> {
+        split_threshold: 10,
+        ..integration_config()
+    };
+
+    let (mut s, reports) = ScenarioBuilder::new()
+        .config(cfg)
+        .seed_range(0xA, 20)
+        .seed_range(0xB, 20)
+        .warm_batches(8)
+        .build_with_reports();
+
+    // Verify invariants on the last warm-up report.
+    if let Some(last) = reports.last() {
+        assert_invariants(&s, last);
+    }
+
+    // Verify invariants on 5 further batches.
+    for _ in 0..5 {
+        let report = s.ingest(&[cell_values(0xA, 10), cell_values(0xB, 10)].concat());
+        assert_invariants(&s, &report);
+    }
+}
+
+// ── Stage 4: Steady state ───────────────────────────────────
+
+#[test]
+fn stage4_root_maturity_below_half() {
+    let s = ScenarioBuilder::new()
+        .config(SentinelConfig::<u64> {
+            split_threshold: 10,
+            ..integration_config()
+        })
+        .seed_range(0xA, 20)
+        .warm_batches(15)
+        .build();
+
+    // After sufficient batches, root tracker should be mature.
+    // 15 batches at λ=0.90 gives 0.90^15 = 0.206 fractional
+    // weight from noise — well under 0.5.  See ADR-S-012.
+    let root = s.graph().g_root();
+    let insp = s.inspect_cell(root).unwrap();
+    assert!(
+        insp.maturity.noise_influence < 0.5,
+        "after 15 warm-up batches, root noise_influence should be < 0.5, got {:.4}",
+        insp.maturity.noise_influence
+    );
+}
+
+#[test]
+fn stage4_maturity_decreases_monotonically() {
+    let mut s = Sentinel128::new(SentinelConfig::<u64> {
+        split_threshold: 10,
+        ..integration_config()
+    })
+    .unwrap();
+
+    let root = s.graph().g_root();
+    let mut prev_ni = s.inspect_cell(root).unwrap().maturity.noise_influence;
+
+    for _ in 0..10 {
+        s.ingest(&cell_values(0xA, 8));
+        let ni = s.inspect_cell(root).unwrap().maturity.noise_influence;
+        assert!(
+            ni <= prev_ni + f64::EPSILON,
+            "noise_influence should not increase: prev={prev_ni:.6}, current={ni:.6}"
+        );
+        prev_ni = ni;
+    }
+
+    // After 10 batches, it should have decreased significantly.
+    assert!(prev_ni < 1.0, "noise_influence should decrease from 1.0 after real batches");
+}
+
+#[test]
+fn stage4_health_maturity_distribution() {
+    let mut s = ScenarioBuilder::new()
+        .config(SentinelConfig::<u64> {
+            split_threshold: 10,
+            ..integration_config()
+        })
+        .seed_range(0xA, 20)
+        .warm_batches(15)
+        .build();
+
+    let report = s.ingest(&cell_values(0xA, 10));
+    let maturity = &report.health.maturity_distribution;
+
+    assert!(
+        maturity.mean_noise_influence < 1.0,
+        "mean noise influence should be below 1.0 in steady state, got {:.4}",
+        maturity.mean_noise_influence
+    );
+    assert_eq!(maturity.cold_trackers, 0, "no cold trackers should remain in steady state");
+    assert_invariants(&s, &report);
+}
+
+// ── Cold start ──────────────────────────────────────────────
+
+#[test]
+fn cold_start_noise_influence_is_one() {
+    let mut s = Sentinel128::new(cold_config()).unwrap();
+
+    // With noise disabled, the root tracker starts completely cold.
+    let root = s.graph().g_root();
+    let insp = s.inspect_cell(root).unwrap();
+    assert!(
+        (insp.maturity.noise_influence - 1.0).abs() < f64::EPSILON,
+        "cold start: root noise_influence should be 1.0, got {:.4}",
+        insp.maturity.noise_influence
+    );
+
+    // After one batch of real data, noise_influence should drop.
+    let report = s.ingest(&cell_values(0xA, 20));
+    let insp = s.inspect_cell(root).unwrap();
+    assert!(
+        insp.maturity.noise_influence < 1.0,
+        "after real data, noise_influence should drop below 1.0, got {:.4}",
+        insp.maturity.noise_influence
+    );
+    assert_invariants(&s, &report);
+}