@@ -16,10 +16,10 @@ stable `softmax`, score ranking और विशेष loss-gradient kernels ह
 सामान्य native compiler, GPU tensors और पूरा automatic differentiation
 framework अभी नहीं बने हैं।
 
-## नया विकास: sampled calculus
+## Source में उपलब्ध sampled calculus (अभी रिलीज़ नहीं हुआ)
 
 [Compiler PR #153](https://github.com/scarp-lang/scarp/pull/153)
-में दो नए, typed और pure functions का प्रस्ताव एवं implementation है:
+प्रकाशित alpha.3 के बाद **main में merge हो चुका है**। इसमें दो typed और pure functions जोड़े गए:
 
 | प्रस्तावित फ़ंक्शन | नतीजा | कार्य |
 | --- | --- | --- |
@@ -29,13 +29,19 @@ framework अभी नहीं बने हैं।
 **ये फ़ंक्शन प्रकाशित alpha.3 में उपलब्ध नहीं हैं।**
 किसी नए compatible compiler build के परीक्षण और आधिकारिक प्रकाशन
 के बाद ही इन्हें released APIs मानना चाहिए।
-[असली compiler branch का तकनीकी विवरण](https://github.com/scarp-lang/scarp/pull/153/files)
+[merged compiler के code और tests](https://github.com/scarp-lang/scarp/pull/153/files)
 देख सकते हैं।
 
 दोनों inputs की लंबाई बराबर और कम-से-कम 2 होनी चाहिए। Coordinates
 strictly increasing होने चाहिए। ये numerical estimates हैं, symbolic
 differentiation या बिल्कुल सही गणितीय integration का दावा नहीं करते।
 
+[Compiler PR #155](https://github.com/scarp-lang/scarp/pull/155) में
+`sample_cumulative_area(x, y) -> vector` पर काम हो रहा है। यह हर sample
+तक जमा हुआ integral देगा: पहला परिणाम शून्य और आख़िरी कुल integral होगा
+(जब परिणाम finite और representable हों)। **यह अभी विकास के चरण में है,
+प्रकाशित alpha.3 में उपलब्ध नहीं है।**
+
 ## आगे काम का सही क्रम
 
 1. **सही गणित:** numerical errors, precision, resource budgets, regression tests और benchmarks।
 
@@ -13,26 +13,31 @@ Rust-hosted scalar subset; this is **not** general native executable compilation
 There is no GPU tensor backend, automatic differentiation engine or neural
 network training framework.
 
-## Work in progress: sampled calculus
+## Source-only sampled calculus (unreleased)
 
 [Compiler PR #153](https://github.com/scarp-lang/scarp/pull/153)
-proposes two typed pure kernels:
+was **merged into main** after the published alpha.3 release. It adds two typed pure kernels:
 
 | Proposed call | Result | Purpose |
 | --- | --- | --- |
 | `sample_area(x, y)` | `float` | Integrate measurements using the composite trapezoidal rule |
 | `sample_slopes(x, y)` | `vector` | Estimate a secant derivative for each adjacent interval |
 
-This PR is **not part of alpha.3**. Do not copy its examples into projects
-built against the published compiler until a verified compatible release
-is identified. The [compiler's detailed implementation and tests](https://github.com/scarp-lang/scarp/pull/153/files)
-are reviewable, but the development branch can change.
+These calls are **not part of the published alpha.3 artifacts**. Do not
+copy them into projects built against that published compiler until a newer,
+compatible release is verified. Source code and tests are available in the
+[merged compiler PR](https://github.com/scarp-lang/scarp/pull/153/files).
 
 Both functions require equal-length finite-vector inputs with at least two
 entries and strictly increasing coordinates. They are not symbolic
-differentiation, spline fitting or exact integration. The branch checks
-numerical overflow, enforces bounded work and numerical output allocation,
-and uses finite binary64 arithmetic.
+differentiation, spline fitting or exact integration. The merged code checks numerical overflow, enforces bounded work and
+numerical output allocation, and uses finite binary64 arithmetic.
+
+[Compiler PR #155](https://github.com/scarp-lang/scarp/pull/155) additionally
+proposes `sample_cumulative_area(x, y) -> vector`, a time-series prefix
+integration kernel with one result per input coordinate. Its first value is
+zero and its final value matches the full sampled integral for representable
+results. **PR #155 is development work, not an alpha.3 feature.**
 
 ## Architecture and dependency order