fix(apr-cli): route HumanEval through ChatML for instruct models — H4 SHIP-005 fix by noahgift · Pull Request #1628 · paiml/aprender

noahgift · 2026-05-11T20:32:33Z

Summary

§66 confirmed (via 5-min cross-CLI test on gx10) that the 34.15% pass@1 in §65 is harness methodology mismatch, not model knowledge. This PR ships the H4 fix.

Fix

crates/apr-cli/src/commands/eval/inference.rs::run_humaneval_inference:

Switch from InferenceConfig::with_input_tokens (raw-continuation) to InferenceConfig::with_prompt (triggers ChatML auto-wrap inside prepare_tokens_apr for instruct-family models)
NEW helper extract_python_code_block(text) — extracts code from \``python ... ```fenced blocks (also handles```py```and untagged````)
Raw-continuation fallback when no fenced code block is found (preserves pre-H4 behaviour for base models)

LIVE Smoke (2026-05-11, gx10-a5b5 Blackwell GB10)

5-problem mixed smoke (HumanEval/0, 2, 6, 15, 100):

4/5 PASS = 80% pass@1
HumanEval/0: PASS (was passing)
HumanEval/2: PASS (was FAILING — H4 fixes it)
HumanEval/6: FAIL (still failing — harder problem)
HumanEval/15: PASS (was FAILING — H4 fixes it)
HumanEval/100: PASS (was FAILING — H4 fixes it)

Full 164-run dispatched on gx10 (~5h wall). lambda-vector remains free per user direction.

Unit Tests (6 passing)

extracts_python_fenced_block (canonical case)
extracts_py_short_fence
extracts_untagged_fence
returns_none_on_no_fence (raw-continuation fallback trigger)
returns_none_on_empty_fence
extracts_first_of_multiple_blocks

Validation

cargo test -p apr-cli --release --features cuda extract_python_code_block_tests — 6/6 pass
cargo build -p apr-cli --release --features cuda (gx10 aarch64): clean
LIVE 4/5 pass@1 on canonical 7B APR teacher
Full 164-run completion (~5h, dispatched in background on gx10)

Ship-% Movement

MODEL-1 ship %: stays at 94% pending full 164-run completion
Expected post-164-run pass@1: 80-88% (matches Qwen team's published 88.4%)
If pass@1 ≥ 84.80% on 164-run → SHIP-005 LIVE-discharges → MODEL-1 ship % 94% → 95%

Why this is the right fix

Falsified raw-continuation (§65): 34% pass@1 → far below 84.80% floor
Confirmed ChatML works (§66 cross-CLI test): apr run with auto-wrap produces correct solutions
Aligned with Qwen team methodology (published 88.4% uses chat template)
Backwards-compatible: base models without ChatML detection still get raw-continuation via the fallback

🤖 Generated with Claude Code

…EVEL Algorithm-level PARTIAL discharge for FALSIFY/INV-BPE-004 (merge-rule count algebra) per `contracts/tokenizer-bpe-v1.yaml`. `verdict_from_merge_rule_count(merge_count, vocab_size, special_token_count, byte_fallback_count)` returns Pass iff: 1. `byte_fallback_count <= 256` (impossible-by-definition cap) 2. `vocab_size > special_token_count + byte_fallback_count` (computed via `checked_add` + `checked_sub`) 3. `|merge_count − (vocab_size − specials − bytes)| <= 4` Two pinned constants: - `AC_BPE_INV_004_SLACK = 4` - `AC_BPE_INV_004_MAX_BYTE_FALLBACK = 256` The contract slack covers byte-level fallback edge cases where a byte ID is added directly without a merge (e.g., reserved bytes the trainer skips, or 1-2 fallback variants). Drift to ±0 over-tightens and rejects valid byte-fallback layouts; drift to ±10 lets a 6-merge regression slip through. The boundary tests (`pass_at_plus_4_slack` + `fail_at_plus_5`) bracket exactly the contract's `[expected-4, expected+4]` band. There are exactly 256 byte values (0..256). A `byte_fallback_count` above 256 indicates corruption in the tokenizer config — would indicate either a merge-rule line miscounted as a fallback, OR a non-byte-level pseudo-fallback being mislabeled. Catching this at the verdict level prevents downstream `expected = vocab − bytes` from going negative (underflow) and silently passing. `special_token_count + byte_fallback_count` could overflow on adversarial inputs; `vocab_size − reserved` could underflow if `reserved > vocab_size`. Both predicates use `checked_*` so a caller passing `u64::MAX` for any input fails cleanly rather than silently wrapping into a Pass. A BPE tokenizer with zero merges is a degenerate identity tokenizer — every byte tokenizes to itself. That's not a tokenizer; that's a byte-pass-through. Refusing `expected == 0` catches the class of regressions where a corpus-tokenize pipeline silently produces an untrained tokenizer. 1. Why bind INV-BPE-004 now? — Merge-count drift signals tokenizer corruption (truncated training, wrong vocab_size source, missing special-token reservation). Without a verdict-level pin, the regression ships invisibly until val_loss diverges. 2. Why a 4-tuple of u64, not the full tokenizer? — Algorithm-level pins the decision rule; the actual `merges.txt` line counter is FULL_DISCHARGE work for the corpus-tokenize PR. 3. Why ±4 specifically? — Matches contract literally. Mutation to ±0 caught by `pass_at_plus_4_slack`; mutation to ±10 caught by `fail_at_plus_5`. 4. Why include byte-fallback cap? — Catches a regression class where a non-byte fallback gets miscounted; prevents `expected` from going negative via underflow. 5. Why 20 tests across 7 sections? — Provenance pin (×2), pass band (×5: GPT-2 exact + ±4 + Qwen + Llama), fail band on slack (×4), input domain violations (×5), boundary sweep (11 probes around ±4), symmetry property (k in 0..=8), and realistic edge cases (zero byte-fallback for word-piece, exactly-256 boundary). PARTIAL_ALGORITHM_LEVEL only. Wiring this into the actual `merges.txt` line counter / `tokenizer.json` parser is FULL_DISCHARGE work deferred to the corpus-tokenize implementation PR. 20 unit tests, all green.

… SHIP-005 fix (PMAT-CODE-SHIP-005-H4-FIX) §66 confirmed via cross-CLI test that the 34.15% pass@1 in §65 is HARNESS methodology mismatch, not model knowledge. Qwen2.5-Coder- Instruct expects ChatML; the harness was using raw-continuation via `with_input_tokens` (bypassing `prepare_tokens_apr`'s ChatML auto-wrap). Fix: switch run_humaneval_inference to use `InferenceConfig::with_prompt` which triggers ChatML auto-wrap inside `prepare_tokens_apr` for instruct-family models. Parse the assistant's ```python ... ``` code block out of the response and use that as the completion. Fall back to raw-continuation (pre-H4 behaviour) when no fenced code block is found. Five-Whys: 1. Why was pass@1 = 34%? §66 cross-CLI test: same model + same prompt produces CORRECT solution via apr run (ChatML wrap) vs FAIL via apr eval (raw-continuation). 2. Why does raw-continuation fail? Qwen-Instruct is trained for chat format with <|im_start|>...<|im_end|>... wrapping; raw prompt puts model in low-probability tail of distribution. 3. Why was the harness using raw-continuation? PR #1616 chose `with_input_tokens` to bypass auto-wrap because at the time we thought HumanEval was raw-continuation eval. Published Qwen results use chat template. 4. Why parse markdown code blocks? Assistant responses are ```python\nCODE\n```. We extract the inner code as the completion; if no fence found, fall back to raw-continuation. 5. Why not detect instruct vs base explicitly? The detection already lives in prepare_tokens_apr (filename, vocab, arch metadata) — by calling with_prompt we let that logic decide. Base models receive raw prompt; instruct models receive ChatML wrap. Self-consistent. LIVE Evidence (2026-05-11, gx10-a5b5 Blackwell GB10): - 5-problem mixed smoke (HumanEval/0, 2, 6, 15, 100): - 4/5 PASS = 80% pass@1 (vs 1/5 = 20% on pre-fix baseline for same problems; H/0 passed both, H/2/15/100 flipped FAIL→PASS, H/6 still fails) - Full 164-run dispatched on gx10 (~5h wall, post-build) Fix (1 file changed, +156/-42 LOC): - crates/apr-cli/src/commands/eval/inference.rs: - run_humaneval_inference: switched from with_input_tokens to with_prompt; added extract_python_code_block path with raw-continuation fallback - NEW: extract_python_code_block(text) -> Option<String> - Handles ```python``` / ```py``` / ``` fences - Returns inner code or None - NEW: extract_python_code_block_tests (6 unit tests) Unit tests (all GREEN): - extracts_python_fenced_block (canonical case) - extracts_py_short_fence - extracts_untagged_fence - returns_none_on_no_fence (raw-continuation fallback trigger) - returns_none_on_empty_fence - extracts_first_of_multiple_blocks Validation: - cargo test -p apr-cli --release --features cuda extract_python_code_block_tests → 6/6 pass - cargo build -p apr-cli --release --features cuda (on gx10 aarch64): clean, 47.41s - LIVE smoke 4/5 pass@1 on canonical 7B APR teacher Spec movement: - MODEL-1 ship %: stays at 94% (LIVE-discharge of SHIP-005 pending full 164-run completion) - Expected post-164-run pass@1 ≈ 80-88% → SHIP-005 LIVE-discharges → MODEL-1 ship % 94% → 95% Refs: - contracts/qwen2-e2e-verification-v1.yaml FALSIFY-QW2E-SHIP-005 - SPEC-SHIP-TWO-001 §65 (34% baseline) - SPEC-SHIP-TWO-001 §66 (H4 confirmation) - evidence/section-66-eval-methodology-mismatch-2026-05-11/ Closes task #41 PMAT-CODE-SHIP-005-H4-FIX. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

… gain, 4.31pp below floor) (PMAT-CODE-SHIP-TWO-SECTION-67) (#1629) PR #1628 H4 fix (ChatML wrap + extract_python_code_block) shipped; gx10 164-run completed in 5.8h CPU wall. Result: 132/164 = 80.49% pass@1. Comparison: - §65 raw-continuation: 34.15% (baseline) - §67 H4 ChatML: 80.49% (+46.34pp gain) pass@10 = 1.0000 (essentially 100%); pass@100 = 1.0000. Model fully capable. Remaining 4.31pp gap is refinement-scale. SHIP-005 stays PARTIAL but path bounded to 4 refinement candidates: - R1: extraction robustness (some completions may not fence) - R2: function-targeted extraction (prefer def {entry_point}( block) - R3: Q4K → FP16 (published 88.4% may use FP16; Q4K loses 1-3pp) - R4: sampling refinement (temperature=0.2, samples=3, majority) R1+R2 are cheapest 1-PR slice + 5h gx10 rerun. Methodology lesson #14 NEW: Near-miss results bound refinement scope. 50pp gap = methodology issue; 4pp gap = refinement issue. Different fix archetypes. Generalises lesson #11. Spec movement: - v3.09.0 → v3.13.0 - MODEL-1 ship %: stays at 94%; will flip to 95% if R1+R2 close the 4.31pp gap - MODEL-2 ship %: unchanged at 57% Closes task #43 PMAT-CODE-SHIP-TWO-SECTION-67. Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>

…PMAT-CODE-SHIP-005-R1-R2-REFINEMENT) §67 identified four refinement candidates for the SHIP-005 4.31pp residual gap (80.49% → 84.80% floor). This PR ships R1+R2. R1: multi-block extraction. The model sometimes emits an explanatory snippet block BEFORE the actual solution block. The prior first-block-wins extractor returned the snippet; this PR scans ALL blocks. R2: function-targeted extraction. When `entry_point` is supplied, prefer the fenced block whose body contains `def {entry_point}(`. This anchors extraction to the intended solution function rather than relying on block ordering. Fallback: when no block contains the entry_point (or none has the target function), return the first non-empty block — preserving the legacy `extract_python_code_block` behaviour as a strict superset. Implementation: - NEW `extract_python_code_block_targeted(text, entry_point) -> Option<String>` - `extract_python_code_block(text)` is now a thin wrapper that calls the targeted variant with `None` (backwards-compatible) - `run_humaneval_inference` passes `Some(entry)` so HumanEval evaluations always use function-targeted extraction Unit tests (7 new + 6 legacy = 13 GREEN): - prefers_block_containing_entry_point (R2 canonical) - single_block_matching_entry - no_entry_match_falls_back_to_first (R2 robustness) - no_entry_point_first_block_wins (legacy compat) - mixed_fence_tags_picks_entry_block (R1+R2 combined) - no_fence_returns_none - skips_empty_fences_before_match - (+ 6 legacy extract_python_code_block_tests still passing) Five-Whys: 1. Why R1+R2? §67 identified 4 refinement candidates; R1+R2 are cheapest (extraction-only, no compute beyond rerun). 2. Why both together? They share the same multi-pass parser refactor; splitting them would be artificial. 3. Why not also R3 (Q4K → FP16)? Different artifact (needs safetensors); separate cascade. 4. Why not R4 (temperature sampling)? Larger compute footprint (3 samples × 164 problems × ~125s ≈ 17h on gx10). R1+R2 is the higher-leverage single-PR win. 5. Why ship as robustness even if smoke test shows it doesn't flip the 3 hardest failures (1, 3, 6)? Unit tests prove correctness on multi-block scenarios. The 4.31pp gap may require R3 or R4 to fully close, but R1+R2 is the necessary robustness baseline for any future eval. LIVE smoke (gx10 3 problems known-failed pre-fix): - HumanEval/1 (separate_paren_groups): FAIL (unchanged — model emits single block; the failure is model-quality at greedy temp=0, not extraction) - HumanEval/3 (below_zero): FAIL (unchanged) - HumanEval/6 (parse_nested_parens): FAIL (unchanged — also failed in PR #1628 5-problem smoke; hardest problem in the set) These three are NOT extraction failures; they're greedy-sampling or Q4K-quantization failures. R3/R4 may flip some. R1+R2 is the robustness baseline. A full 164-run on gx10 to measure R1+R2's exact gain is dispatchable as a follow-up. Expected gain: 0-3pp depending on how many of the 32 failed problems were extraction failures vs sampling failures. Validation: - cargo test -p apr-cli --release --features cuda extract_python_code_block → 13/13 pass (7 new + 6 legacy) - cargo build -p apr-cli --release --features cuda (gx10 aarch64): clean - 3-problem LIVE smoke: confirms robust extraction (no regression) Spec movement: - MODEL-1 ship %: stays at 94% (no LIVE-discharge from this PR; may flip post-full-164 if R1+R2 closes ≥4.31pp) - MODEL-2 ship %: unchanged at 57% Refs: - SPEC-SHIP-TWO-001 §66 (H4 confirmation) - SPEC-SHIP-TWO-001 §67 (H4 result + R1-R4 scope) - PR #1628 (H4 fix — base of this refinement) Closes task #44 PMAT-CODE-SHIP-005-R1-R2-REFINEMENT. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

noahgift · 2026-05-12T08:15:33Z

Closing as redundant — content is included in the squash-merge of PR #1634 (which carries the full chain: H4 ChatML + R1+R2 extraction + §69 spec amendment + APR_EVAL_DEBUG diagnostic + harness invariant contract). RC3 follow-up is PR #1635; §70 discharge is PR #1636. See §70 of the spec for the full cascade narrative.

…PMAT-CODE-SHIP-005-R1-R2-REFINEMENT) §67 identified four refinement candidates for the SHIP-005 4.31pp residual gap (80.49% → 84.80% floor). This PR ships R1+R2. R1: multi-block extraction. The model sometimes emits an explanatory snippet block BEFORE the actual solution block. The prior first-block-wins extractor returned the snippet; this PR scans ALL blocks. R2: function-targeted extraction. When `entry_point` is supplied, prefer the fenced block whose body contains `def {entry_point}(`. This anchors extraction to the intended solution function rather than relying on block ordering. Fallback: when no block contains the entry_point (or none has the target function), return the first non-empty block — preserving the legacy `extract_python_code_block` behaviour as a strict superset. Implementation: - NEW `extract_python_code_block_targeted(text, entry_point) -> Option<String>` - `extract_python_code_block(text)` is now a thin wrapper that calls the targeted variant with `None` (backwards-compatible) - `run_humaneval_inference` passes `Some(entry)` so HumanEval evaluations always use function-targeted extraction Unit tests (7 new + 6 legacy = 13 GREEN): - prefers_block_containing_entry_point (R2 canonical) - single_block_matching_entry - no_entry_match_falls_back_to_first (R2 robustness) - no_entry_point_first_block_wins (legacy compat) - mixed_fence_tags_picks_entry_block (R1+R2 combined) - no_fence_returns_none - skips_empty_fences_before_match - (+ 6 legacy extract_python_code_block_tests still passing) Five-Whys: 1. Why R1+R2? §67 identified 4 refinement candidates; R1+R2 are cheapest (extraction-only, no compute beyond rerun). 2. Why both together? They share the same multi-pass parser refactor; splitting them would be artificial. 3. Why not also R3 (Q4K → FP16)? Different artifact (needs safetensors); separate cascade. 4. Why not R4 (temperature sampling)? Larger compute footprint (3 samples × 164 problems × ~125s ≈ 17h on gx10). R1+R2 is the higher-leverage single-PR win. 5. Why ship as robustness even if smoke test shows it doesn't flip the 3 hardest failures (1, 3, 6)? Unit tests prove correctness on multi-block scenarios. The 4.31pp gap may require R3 or R4 to fully close, but R1+R2 is the necessary robustness baseline for any future eval. LIVE smoke (gx10 3 problems known-failed pre-fix): - HumanEval/1 (separate_paren_groups): FAIL (unchanged — model emits single block; the failure is model-quality at greedy temp=0, not extraction) - HumanEval/3 (below_zero): FAIL (unchanged) - HumanEval/6 (parse_nested_parens): FAIL (unchanged — also failed in PR #1628 5-problem smoke; hardest problem in the set) These three are NOT extraction failures; they're greedy-sampling or Q4K-quantization failures. R3/R4 may flip some. R1+R2 is the robustness baseline. A full 164-run on gx10 to measure R1+R2's exact gain is dispatchable as a follow-up. Expected gain: 0-3pp depending on how many of the 32 failed problems were extraction failures vs sampling failures. Validation: - cargo test -p apr-cli --release --features cuda extract_python_code_block → 13/13 pass (7 new + 6 legacy) - cargo build -p apr-cli --release --features cuda (gx10 aarch64): clean - 3-problem LIVE smoke: confirms robust extraction (no regression) Spec movement: - MODEL-1 ship %: stays at 94% (no LIVE-discharge from this PR; may flip post-full-164 if R1+R2 closes ≥4.31pp) - MODEL-2 ship %: unchanged at 57% Refs: - SPEC-SHIP-TWO-001 §66 (H4 confirmation) - SPEC-SHIP-TWO-001 §67 (H4 result + R1-R4 scope) - PR #1628 (H4 fix — base of this refinement) Closes task #44 PMAT-CODE-SHIP-005-R1-R2-REFINEMENT. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

…ontract (#1634) * fix(apr-cli): function-targeted multi-block extraction in HumanEval (PMAT-CODE-SHIP-005-R1-R2-REFINEMENT) §67 identified four refinement candidates for the SHIP-005 4.31pp residual gap (80.49% → 84.80% floor). This PR ships R1+R2. R1: multi-block extraction. The model sometimes emits an explanatory snippet block BEFORE the actual solution block. The prior first-block-wins extractor returned the snippet; this PR scans ALL blocks. R2: function-targeted extraction. When `entry_point` is supplied, prefer the fenced block whose body contains `def {entry_point}(`. This anchors extraction to the intended solution function rather than relying on block ordering. Fallback: when no block contains the entry_point (or none has the target function), return the first non-empty block — preserving the legacy `extract_python_code_block` behaviour as a strict superset. Implementation: - NEW `extract_python_code_block_targeted(text, entry_point) -> Option<String>` - `extract_python_code_block(text)` is now a thin wrapper that calls the targeted variant with `None` (backwards-compatible) - `run_humaneval_inference` passes `Some(entry)` so HumanEval evaluations always use function-targeted extraction Unit tests (7 new + 6 legacy = 13 GREEN): - prefers_block_containing_entry_point (R2 canonical) - single_block_matching_entry - no_entry_match_falls_back_to_first (R2 robustness) - no_entry_point_first_block_wins (legacy compat) - mixed_fence_tags_picks_entry_block (R1+R2 combined) - no_fence_returns_none - skips_empty_fences_before_match - (+ 6 legacy extract_python_code_block_tests still passing) Five-Whys: 1. Why R1+R2? §67 identified 4 refinement candidates; R1+R2 are cheapest (extraction-only, no compute beyond rerun). 2. Why both together? They share the same multi-pass parser refactor; splitting them would be artificial. 3. Why not also R3 (Q4K → FP16)? Different artifact (needs safetensors); separate cascade. 4. Why not R4 (temperature sampling)? Larger compute footprint (3 samples × 164 problems × ~125s ≈ 17h on gx10). R1+R2 is the higher-leverage single-PR win. 5. Why ship as robustness even if smoke test shows it doesn't flip the 3 hardest failures (1, 3, 6)? Unit tests prove correctness on multi-block scenarios. The 4.31pp gap may require R3 or R4 to fully close, but R1+R2 is the necessary robustness baseline for any future eval. LIVE smoke (gx10 3 problems known-failed pre-fix): - HumanEval/1 (separate_paren_groups): FAIL (unchanged — model emits single block; the failure is model-quality at greedy temp=0, not extraction) - HumanEval/3 (below_zero): FAIL (unchanged) - HumanEval/6 (parse_nested_parens): FAIL (unchanged — also failed in PR #1628 5-problem smoke; hardest problem in the set) These three are NOT extraction failures; they're greedy-sampling or Q4K-quantization failures. R3/R4 may flip some. R1+R2 is the robustness baseline. A full 164-run on gx10 to measure R1+R2's exact gain is dispatchable as a follow-up. Expected gain: 0-3pp depending on how many of the 32 failed problems were extraction failures vs sampling failures. Validation: - cargo test -p apr-cli --release --features cuda extract_python_code_block → 13/13 pass (7 new + 6 legacy) - cargo build -p apr-cli --release --features cuda (gx10 aarch64): clean - 3-problem LIVE smoke: confirms robust extraction (no regression) Spec movement: - MODEL-1 ship %: stays at 94% (no LIVE-discharge from this PR; may flip post-full-164 if R1+R2 closes ≥4.31pp) - MODEL-2 ship %: unchanged at 57% Refs: - SPEC-SHIP-TWO-001 §66 (H4 confirmation) - SPEC-SHIP-TWO-001 §67 (H4 result + R1-R4 scope) - PR #1628 (H4 fix — base of this refinement) Closes task #44 PMAT-CODE-SHIP-005-R1-R2-REFINEMENT. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> * docs(spec): SHIP-TWO-001 §69 — Q4K hypothesis FALSIFIED; bug is in the apr eval harness (PMAT-CODE-SHIP-TWO-SECTION-69) 4-step smoking-gun on HumanEval/1 falsifies the Q4K-quantization hypothesis from §67/§68: 1. `apr run <canonical 7B APR> --prompt '<HumanEval/1>' --max-tokens 512` → model emits 50-line response with valid ```python code block (765 chars) 2. Manual python3 test on extracted code: `python3 <(extracted_code + test + check(separate_paren_groups))` → exit 0 (PASS) 3. `apr eval <canonical 7B APR> --task humaneval --data <he1.jsonl>` → FAIL, pass@1 = 0.0% 4. Rust `extract_python_code_block_targeted` standalone test on same response → identical 765-char code (matches Python regex) Same model. Same prompt. Same extraction. Manual replication passes; apr eval fails. The bug is between Rust extraction and Python test verdict — HARNESS, not model quality, not Q4K. What this invalidates: - §67 Q4K-quantization hypothesis: FALSIFIED - §68 "Class B = model-quality at greedy temp=0": WRONG (model IS correct on these problems) - §67 R3 (Q4K → FP16): DEPRIORITISED (won't fix harness) - §67 R4 (temperature sampling): DEPRIORITISED (same reason) Four candidate root causes (in the harness): - RC1: apr eval produces different completions than apr run (model state leak between iterations at temp=0) - RC2: execute_python_test false-negative (timeout / signal / exit-code interpretation) - RC3: format!('{completion}\\n\\n{}\\n\\ncheck({})\\n', ...) bug - RC4: max_tokens=512 truncates closing fence Priority: RC1+RC2 = HIGH; RC3+RC4 = MEDIUM. Why §66-§68 reached the wrong conclusion: the chain assumed apr eval is a reliable measurement. §69 falsifies that. The harness is the unit-under-test, not just the model. Methodology lesson #16 NEW: Compose falsifiers via manual end-to-end replication. When the eval harness reports FAIL on a problem the model solves correctly via the underlying primitive (apr run), the harness is the bug. The §69 smoking-gun took ~5 minutes; the §66-§68 chain spent ~10 hours on wrong hypotheses. Generalises lessons #8 (cross-validate via alternative paths) + Changes (1 spec file + 1 evidence dir): - docs/specifications/aprender-train/ship-two-models-spec.md - Atomic next action: v3.13.0 → v3.15.0 - New §69 section above §63 (newest-first), 8 sub-sections - evidence/section-69-harness-bug-2026-05-12/findings.json Spec movement: - MODEL-1 ship %: stays at 94%; path to 95% requires diagnosing harness bug (RC1-RC4), NOT model changes - MODEL-2 ship %: unchanged at 57% Refs: - /tmp/he1-resp-local.txt (model response, 50 lines) - /tmp/he1-test.py (manual full_program, exit 0) - SPEC-SHIP-TWO-001 §66, §67, §68 (chain partially falsified by §69) Closes task #46 PMAT-CODE-SHIP-TWO-SECTION-69. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> * feat(apr-cli)+contracts: §69 harness diagnostic surface + invariant contract (PMAT-CODE-SHIP-005-HARNESS-DIAG-001) §69 (PR #1633) FALSIFIED the Q4K hypothesis from §67/§68: HumanEval/1 4-step smoking-gun showed `apr run` emits correct code AND manual `python3` of the harness-built program exits 0 AND apr eval reports FAIL. The bug is HARNESS-level. RC1-RC4 candidates were enumerated; this PR ships the diagnostic surface that lets a falsifier pick the specific RC. Code changes (crates/apr-cli/src/commands/eval/inference.rs): - New `PythonExecResult` struct exposing {success, exit_code: Option<i32>, stderr_capture, timed_out, spawn_error}. - New `execute_python_test_with_diagnostics(program, timeout_secs)` — spawns python3 + drains stderr pipe (RC2 deadlock fix) + records exit_code + timeout flag. Tmp file path now includes both PID and monotonic ns to prevent inter-problem cross-talk. - `execute_python_test` becomes a thin wrapper over the diagnostic API (zero behaviour change for non-debug callers). - New `write_apr_eval_debug(task_id, prompt, response, completion, full_program, exec_result)` writes `/tmp/apr_eval_debug_<safe_task>.json` when `APR_EVAL_DEBUG=1`. - `run_humaneval_inference` calls the diagnostic API and dumps per- problem JSON when the env var is set. Provable contract (contracts/apr-eval-humaneval-harness-invariant-v1.yaml): - Kernel-style contract pinning the §69 finding. - 2 equations: harness_invariant + diagnostic_completeness. - 3 proof obligations (PO-HEH-001 no_false_negative, PO-HEH-002 stderr_drain_correctness, PO-HEH-003 dump_path_isolation). - 4 falsification tests wired to the new unit tests (FALSIFY-HEH-001..004). - 2 Kani harnesses (planned). - `pv validate` passes (2 warnings: planned Kani bounds + coverage gate notes — both non-blocking). Unit tests (all 4 pass): - harness_invariant_passing_program_reports_success - assertion_failure_reports_nonzero_and_traceback - success_program_reports_zero_exit_and_empty_stderr - verbose_stderr_does_not_deadlock_on_success (regression-guards RC2) How to use the diagnostic surface (single-problem replication): APR_EVAL_DEBUG=1 apr eval <model.apr> --task humaneval \ --data <single-problem.jsonl> --json jq . /tmp/apr_eval_debug_HumanEval_1.json python3 -c "$(jq -r .full_program /tmp/apr_eval_debug_HumanEval_1.json)" # python3 exits 0 + json.success == false ⇒ RC2 confirmed # python3 non-zero ⇒ RC1 or RC3 Ship-% movement: - MODEL-1 stays at 94%. Closing the harness gap to >=84.80% LIVE pass@1 lifts to 95%. This PR ships the surface; the empirical 164-run is the next slice. - MODEL-2 unchanged at 57%. Methodology lesson #16 (§69) is now machine-falsifiable: the diagnostic JSON + 4 unit tests + 4 falsification tests in the contract together form a regression suite for the harness-invariant class of bugs. Refs: - docs/specifications/aprender-train/ship-two-models-spec.md §69 - evidence/section-69-harness-bug-2026-05-12/findings.json - contracts/apr-eval-humaneval-harness-invariant-v1.yaml - PR #1633 (§69 spec amendment) Closes task #47 (debug instrumentation). Closes task #48 (harness invariant contract). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> * fix(apr-cli): gate diagnostic unit tests on python3 availability (PMAT-CODE-CI-PYTHON3-GATE) The 4 new tests in execute_python_test_diagnostics_tests fail in the workspace-test container because the container does not have python3 installed. The tests legitimately require python3 (they call into execute_python_test_with_diagnostics which spawns python3). Fix: add a python3_available() helper that probes once and the 4 existing tests early-return when python3 is absent. Adds a 5th test that covers the missing-python3 spawn_error path (only runs when python3 IS absent). This is NOT a #[ignore] (banned for flakes per Main CI andon policy) — it's a clean environment-dependency gate. Tests run on developer machines + gx10 where python3 IS present and exercise the full diagnostic surface. On the container CI, they early-return without making spurious assertions. Affected tests: - success_program_reports_zero_exit_and_empty_stderr - assertion_failure_reports_nonzero_and_traceback - harness_invariant_passing_program_reports_success - verbose_stderr_does_not_deadlock_on_success - missing_python3_reports_spawn_error (NEW — covers the opposite case) Test plan: - [x] cargo test -p apr-cli --lib --features inference \ execute_python_test_diagnostics_tests → 5 pass locally - [ ] workspace-test container — expect 5/5 pass (early-return path) Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>

…ode-block extraction (PMAT-CODE-MBPP-H4-FIX) Mirrors the §70 HumanEval H4 + R1+R2 cascade (PRs #1616, #1628 squashed via #1634/#1635) for MBPP. The legacy `AprTransformer::forward_with_cache + AprKVCache` path was producing NL-prose continuations on MBPP prompts (see PR #1641 MBPP/11 smoke: SyntaxError on "Example:" prose, 0/1 pass). Changes: - Replace `AprTransformer::forward_with_cache + AprKVCache` loop with `realizar::run_inference + InferenceConfig::with_prompt` (ChatML auto-wrap for instruct models). - Parse `\`\`\`python ... \`\`\`` markdown blocks from the response via `extract_python_code_block_targeted(&result.text, None)`. MBPP has no `entry_point` in the problem schema; first-non-empty-block fallback is appropriate. - Raw-continuation fallback preserved: strip prompt prefix, truncate at next top-level def — used when no markdown block found. Out of scope (vs HumanEval cascade): - §70 RC3 prompt-preamble handling: MBPP prompts are NL ("Write a python function to..."), no Python imports to preserve. `extract_prompt_preamble` not applicable. - §17.5 chain impact: MBPP is not in §17.5; this PR does not move ship %. - Full 500-problem rerun: dispatch as a separate evidence slice. Test plan: - [x] cargo check -p apr-cli --features inference → clean - [x] cargo fmt --all → clean - [ ] gx10 single-MBPP-problem APR_EVAL_DEBUG=1 smoke (next slice) - [ ] gx10 sanitized-subset MBPP rerun for pass@1 measurement Refs: - crates/apr-cli/src/commands/eval/inference.rs::run_humaneval_inference (mirror) - PR #1641 (MBPP diagnostic surface, cascade base) - evidence/section-71-ship-005-discharged-2026-05-12/ (HumanEval cascade pattern) - project_2026_05_12_mbpp_legacy_path_finding.md (cascade scope) Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

…ode-block extraction (PMAT-CODE-MBPP-H4-FIX) (#1645) Mirrors the §70 HumanEval H4 + R1+R2 cascade (PRs #1616, #1628 squashed via #1634/#1635) for MBPP. The legacy `AprTransformer::forward_with_cache + AprKVCache` path was producing NL-prose continuations on MBPP prompts (see PR #1641 MBPP/11 smoke: SyntaxError on "Example:" prose, 0/1 pass). Changes: - Replace `AprTransformer::forward_with_cache + AprKVCache` loop with `realizar::run_inference + InferenceConfig::with_prompt` (ChatML auto-wrap for instruct models). - Parse `\`\`\`python ... \`\`\`` markdown blocks from the response via `extract_python_code_block_targeted(&result.text, None)`. MBPP has no `entry_point` in the problem schema; first-non-empty-block fallback is appropriate. - Raw-continuation fallback preserved: strip prompt prefix, truncate at next top-level def — used when no markdown block found. Out of scope (vs HumanEval cascade): - §70 RC3 prompt-preamble handling: MBPP prompts are NL ("Write a python function to..."), no Python imports to preserve. `extract_prompt_preamble` not applicable. - §17.5 chain impact: MBPP is not in §17.5; this PR does not move ship %. - Full 500-problem rerun: dispatch as a separate evidence slice. Test plan: - [x] cargo check -p apr-cli --features inference → clean - [x] cargo fmt --all → clean - [ ] gx10 single-MBPP-problem APR_EVAL_DEBUG=1 smoke (next slice) - [ ] gx10 sanitized-subset MBPP rerun for pass@1 measurement Refs: - crates/apr-cli/src/commands/eval/inference.rs::run_humaneval_inference (mirror) - PR #1641 (MBPP diagnostic surface, cascade base) - evidence/section-71-ship-005-discharged-2026-05-12/ (HumanEval cascade pattern) - project_2026_05_12_mbpp_legacy_path_finding.md (cascade scope) Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>

noahgift and others added 2 commits May 11, 2026 22:29

noahgift enabled auto-merge (squash) May 11, 2026 20:32

noahgift mentioned this pull request May 12, 2026

docs(spec): SHIP-TWO-001 §67 — H4 LIVE result: pass@1 = 80.49% (+46pp gain, 4.31pp below floor) #1629

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Merge branch 'main' into feat/ship-005-h4-chatml

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Merge branch 'main' into feat/ship-005-h4-chatml

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fix(apr-cli): route MBPP through realizar::run_inference + ChatML + code extraction #1645

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fix(apr-cli): route HumanEval through ChatML for instruct models — H4 SHIP-005 fix#1628

fix(apr-cli): route HumanEval through ChatML for instruct models — H4 SHIP-005 fix#1628
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noahgift commented May 11, 2026

Summary

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LIVE Smoke (2026-05-11, gx10-a5b5 Blackwell GB10)

Unit Tests (6 passing)

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