The deterministic proof self-certified: PASS on any loss decrease (incl. 1e-9
noise) and a missing expected hash defaulted to PASS.
- MIN_LOSS_DECREASE=1e-4: a run counts as learning only above float noise; a
noise-only pipeline now FAILS.
- is_pass() requires hash_matches==Some(true); no-hash -> SKIP (exit 2), never
PASS. verify-training fails fast on a sub-margin loss before the hash compare,
so a missing baseline cannot mask a non-learning pipeline.
Documented honestly: the proof certifies reproducibility/determinism on a
synthetic dataset, NOT that real data produced the weights nor that any accuracy
claim is met. Tests: no_committed_hash_is_skip_not_pass,
submargin_loss_change_fails_even_without_hash,
committed_matching_hash_with_real_decrease_passes.
Co-Authored-By: claude-flow <ruv@ruv.net>
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