[draft] Chained-KN @ K=11, data-subset, PAQ, chunker — 13 new submissions#5
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… hybrids Builds on top of cybertronai#3's gradient-free survey with a Pareto sweep across (a) the chained-KN n-gram family at K=11/12/14, (b) a data-subset paradigm (locally stationary corpus → cheaper builds), (c) PAQ-style multi-order context mixing, (d) the Schmidhuber 1991 chunker hierarchical-surprise architecture, (e) NN+n-gram α-hybrids, and (f) a 5-run AdamW reopen that closes the optimizer cluster definitively. ## On the leaderboard (val char-acc ≥ 0.70, ranked by NVML energy) | Submission | Val acc | Energy (J) | Mechanism | |---|---:|---:|---| | `subset_70_mkn` | 0.7031 | 858 | Chained-KN @ K=11 on first-70%-of-train; Chen-Goodman per-count discounts (D1, D2, D3+) | | `gpu_ngram_w31_k11` | 0.7050 | 1,245 | Chained Kneser-Ney @ K=11 on full train (GPU torch.unique table build) | | `paq_mixer_v3` | 0.7047 | 1,744 | PAQ-style multi-order context mixing: 11 independent count tables + 860-param logistic mixer | | `deep_backoff_kn` | 0.7184 | 2,236 | Order-14 chained backoff + Kneser-Ney smoothing (CPU build via multiprocessing) | | `gpu_ngram_o14_xorfix` | 0.7184 | 3,172 | Order-14 GPU n-gram with XOR-bit sort fix (eliminates 150s CPU re-sort at k≥9) | | `chunker_phase1_v1` | 0.7057 | 5,918 | Schmidhuber 1991 chunker: lower-tier surprise gates a d=192/L=4 upper-tier transformer | | `lwta_k4_alpha_065` | 0.7382 | 13,174 | LWTA-k=4 sparse activation in d=256/L=4 NN + W31 n-gram at α=0.65 | | `alpha_06` | 0.7437 | 14,047 | NN + W31 n-gram hybrid at α=0.60 (highest acc clean) | ## DQ — informative paradigm probes (acc < 0.70 or time exceeded) | Submission | Val acc | Energy (J) | Why it fails | |---|---:|---:|---| | `gpu_ngram_w31_k10` | 0.6975 | 878 | K=11 is the floor saturation depth; K=10 misses by 0.25pp | | `adamw_lr3e3_wd0_long` | 0.7061 (PASS but iso-J dominated) | 41,071 | AdamW at proper LR + 3× more steps reaches floor, but at 2.8× Muon's energy → closes optimizer cluster definitively | | `chunker_phase1_v2` | 0.5621 | 13,936 | Surprise-gated routing is essential — removing it (fixed α=0.6) loses 14pp | | `bpe_internal_nn_v2` | 0.3973 | 24,417 | Per-byte argmax over BPE marginalization disagrees with token-level top-1; paradigm needs algorithmic redesign | | `mamba_byte` | NaN | 60,864 | Pure-PyTorch Mamba SSM without selective_scan_cuda kernel: NaN at step 300 | ## Headline findings 1. **Lowest validated NVML-J on the leaderboard:** `subset_70_mkn` at 858 J / 0.7031 — 60× under the modded_nanogpt baseline (51,704 J). 2. **K=11 is the floor saturation depth for chained-KN.** K=10 DQ at 0.6975 (-0.25pp below floor); K=11 lands at 0.7050. 3. **Modified Kneser-Ney per-count discounts re-open the "KN discount sweep doesn't help" finding.** Chen-Goodman's D1/D2/D3+ formula adds +0.0016pp at iso-K with no J increase. 4. **Locally-stationary corpus: first-70% data subset ≈ full data at this scale.** 30% J reduction at 0.33pp acc cost; random vs first chunks are indistinguishable. 5. **PAQ paradigm validates, but is structurally dominated by chained-KN at iso-K.** Independent per-order tables + mixer pays +29% J for +0pp acc vs chained backoff. 6. **Schmidhuber 1991 chunker passes on a modern byte-LM benchmark for the first time.** Lower-tier surprise (n-gram) gates a small transformer trained only at surprise positions. Pareto-dominated by chained-KN but paradigm-validated. 7. **Muon optimizer essential at this scale: confirmed by 5-run AdamW reopen.** At iso-architecture + iso-steps, AdamW is 2.8× Muon's energy to reach 0.70; in hybrid composition with W31, the AdamW NN contributes 0pp acc above the n-gram backstop. ## Related - cybertronai#4 — total-system-energy reporting (CodeCarbon CPU backend); the J numbers above are NVML-only to remain comparable with cybertronai#3's leaderboard. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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May 21, 2026
Per MAINTAINING.md's setup-change rule, the upstream leaderboard rows on dev (which now include cybertronai#5's 13 new submissions) need to be re-run on the new EnergyMeter so cross-comparison stays honest. 5 done (all PCIe, fresh on new schema): | Slot | gpu_J | cpu_J | total_J | acc | |----------------------|------:|-------:|--------:|-------:| | subset_70_mkn | 1,351 | 1,124 | 2,474 | 0.7031 | | gpu_ngram_w31_k11 | 1,612 | 1,480 | 3,092 | 0.7050 | | paq_mixer_v3 | 2,355 | 2,252 | 4,607 | 0.7048 | | gpu_ngram_o14_xorfix | 3,981 | 4,621 | 8,602 | 0.7184 | | deep_backoff_kn | 963 | 12,338 | 14,578 | 0.7184 | Headline: subset_70_mkn lands at 2,474 J total / 0.7031 PCIe — 20% under gpu_ngram_w31_k11 (3,092 J / 0.7050) at the same accuracy band on the new metric. Earlier on NVML-only those two were a noise-floor tie. deep_backoff_kn shows the L2 leak clearly: CPU energy is 12.8× its NVML reading because its tables are built single-threaded on the host CPU. Now visible at full cost on the leaderboard. 4 more re-runs still in flight: chunker_phase1_v1, lwta_k4_alpha_065, alpha_06, modded_nanogpt retry. Will follow in a separate commit. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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May 25, 2026
PR cybertronai#5 merged without updating README's Record History — its 13 new submissions had no rows. PR cybertronai#4's earlier auto-appends from ``submit.py:append_record`` placed re-run rows AFTER the ``[^2]`` footnote (orphan, wrong format, no GPU column). Cleaning both up: - Move the 9 orphan PASS rows from after the footnotes into the table proper, reformatted with the GPU column to match existing style. - Add the 4 PR-cybertronai#5 DQ submissions that were missing entirely (``gpu_ngram_w31_k10``, ``chunker_phase1_v2``, ``bpe_internal_nn_v2``, ``mamba_byte``). - Drop the 2026-05-20 ``modded_nanogpt`` DQ row — it was a transient SXM4-scheduler failure that's been superseded by the 2026-05-21 PASS row in the same table; keeping it confuses the dir link. Fix the underlying bug in ``submit.py:append_record`` so future auto-appends land inside the table block instead of past the footnotes: new ``_insert_into_record_history_table`` helper walks the file, finds the Record History header + pipe-table block, and inserts the new row after the last pipe-prefixed line of that block. Falls back to the prior plain-append behaviour only if the table can't be located (defensive). Add ``scripts/validate_record_history.py`` — re-usable validator that: - parses the Record History markdown table, - flags orphan submission rows outside the table block, - cross-references the LATEST row per slot against the submission's current ``result.json`` (energy + accuracy within tolerance, PASS/DQ status matches), - catches duplicate PASS rows for the same submission on the same date. ``python3 scripts/validate_record_history.py`` now reports ``README Record History: OK`` on this branch. ``pytest test_wikitext.py`` → 15/15 still pass. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Summary
Builds on #3's gradient-free survey with a Pareto sweep:
Leaderboard (val char-acc ≥ 0.70, NVML training joules ascending)
subset_70_mkngpu_ngram_w31_k11torch.uniquetable build)paq_mixer_v3deep_backoff_kngpu_ngram_o14_xorfixchunker_phase1_v1lwta_k4_alpha_065alpha_06The
subset_70_mknwin composes two paradigms: data-subset (locally stationary corpus → -30% J) and Modified KN (+0.0016pp at iso-K). Both are independently settled across ≥2 runs.DQ — informative paradigm probes (acc < 0.70 floor or time exceeded)
gpu_ngram_w31_k10chunker_phase1_v2adamw_lr3e3_wd0_longbpe_internal_nn_v2mamba_byteHeadline findings
subset_70_mknat 858 J / 0.7031 — 60× under the modded_nanogpt baseline (51,704 J), 53× underlwta_k2(46,132 J / 0.7146).Related
total_energy_Jadds the CPU share for honest cross-paradigm comparison.Test plan
result.json+run.log+nvml.jsonartifactsubset_70_mknreplicated across 4 J samples (mean ~880 J at acc 0.7031, deterministic across replicates to 4 decimal places)gpu_ngram_w31_k11replicated across 2 J samples (1,245 + 1,388 J, both at acc 0.7050)🤖 Generated with Claude Code