docs(spec): SHIP-TWO-001 §65 — SHIP-005 NOT-DISCHARGE (gx10 164-run pass@1 = 34.15%, 50pp below floor)#1626
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docs(spec): SHIP-TWO-001 §65 — SHIP-005 NOT-DISCHARGE (gx10 164-run pass@1 = 34.15%, 50pp below floor)#1626noahgift wants to merge 1 commit into
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…64-run pass@1 = 34.15%) (PMAT-CODE-SHIP-TWO-SECTION-65) Records the empirical RED outcome from the gx10 164-problem HumanEval run. SHIP-005 does NOT LIVE-discharge; 50pp gap from the 84.80% effective floor. Critical signal that the model IS capable: - pass@1 = 34.15% (FAIL) - pass@10 = 98.68% - pass@100 = 100.00% The bug is in greedy-temperature-0 sampling/decoding, not in model knowledge. Three falsifiable hypotheses: H1 (50%): gx10 teacher sha256 differs from lambda-vector - gx10: 0a854098d05b15921c173b7c8deb87c1cbecdffc66e918825c11a02775c73666 - lambda-vector: a394dd286732a5f32dfb983fd2ea0eeba4d6239ac4c47e44bcfe62f590ddeb28 - Sync canonical artifact + rerun → resolves H1 in ~5h. H2 (30%): align_continuation_indent (PR #1617) too aggressive H3 (20%): BPE tokenization artifacts on complex prompts Methodology lesson #12 NEW: A directional empirical sample (10-problem 80%) can lie about full-distribution performance (164-problem 34%). Spec movement: v3.10.0 → v3.11.0. MODEL-1 ship %: stays at 94%. Closes task #39 PMAT-CODE-SHIP-TWO-SECTION-65. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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Closing as superseded — the §65→§71 cascade narrative is complete on main via PRs #1629/#1631/#1633/#1634/#1636/#1642 (and the in-tree §67/§68/§69/§70/§71 sections). SHIP-005 LIVE-DISCHARGED at 86.59% pass@1 (§71); see contracts/apr-eval-humaneval-harness-invariant-v1.yaml v1.1.0 for the empirical evidence and root cause. |
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Summary
Records the empirical RED outcome from the gx10 164-problem HumanEval run. SHIP-005 does NOT LIVE-discharge.
Verdict
Critical signal — model IS capable
The bug is in greedy-temperature-0 sampling/decoding, not in model knowledge. The model can solve every problem given enough samples.
Three falsifiable hypotheses
H1 (priority 50%): gx10 teacher sha256 differs from lambda-vector
0a854098d05b...a394dd2867...H2 (priority 30%):
align_continuation_indent(PR #1617) too aggressiveH3 (priority 20%): BPE tokenization artifacts on complex prompts
Methodology Lesson #12 (NEW)
A directional empirical sample can lie about full-distribution performance. The 10-problem lambda-vector sample (80% pass@1) was within 95% CI [44%, 97%] of the 86% nominal floor — appeared a strong directional signal. The 164-run revealed 34.15% — well outside that CI. The first 10 problems happen to be the easiest; harder problems concentrate later (HumanEval/100+).
Ship-% movement
Changes
docs/specifications/aprender-train/ship-two-models-spec.md:evidence/section-65-ship-005-not-discharge-2026-05-11/(NEW):humaneval-164-gx10.json(raw apr eval --json, 1174 lines)per-problem-summary.json(passed/failed task IDs)findings.json(structured H1/H2/H3 analysis + verdict)🤖 Generated with Claude Code