Claude f9742e6b0e refine(ablation): risk_score policy, normalized penalty, witness log ... PolicyKernel refinements: - Fixed policy (Mode A): risk_score = R + k*D, k=30, T=140 Fixed constants (not learned) — Mode A is the control arm. One distractor raises perceived risk by ~30 range-days. Weekday only when range is large AND distractor-free. - Normalized EarlyCommitPenalty: (remaining/initial) * scale Committing at 5% scan = cheap (0.05), at 90% = expensive (0.90). Only charged on wrong commits. - Hybrid minimum evidence: stop_after_first disabled in Hybrid mode so solver checks all matching weekdays before committing. Witness log: - SolutionAttempt now carries skip_mode and context_bucket strings - record_attempt_witnessed() for full policy audit trail - Every trajectory records which skip mode was chosen and why Observability: - Puzzle tags now include distractor_count and has_dow (deterministic) - count_distractors() made public for generator to tag puzzles Ablation assertions (two new): - a_skip_nonzero: Mode A uses skip at least sometimes (proves not hobbled) - c_multi_mode: Mode C uses different skip modes across contexts (proves learning) - Skip-mode distribution table printed per context bucket for Mode C posterior_target monotonicity verified: 2→4→8→12→18→25→35→50→70→100 (never shrinks with difficulty) 81 tests passing (61 lib + 20 integration). https://claude.ai/code/session_01RnwD4x5cbpB7FPvoyYQz8G		2026-02-15 23:08:02 +00:00
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src	refine(ablation): risk_score policy, normalized penalty, witness log	2026-02-15 23:08:02 +00:00
tests	feat(generator): posterior-targeting puzzle generation, weekday skipping PolicyKernel	2026-02-15 22:31:12 +00:00
Cargo.toml	feat(agi-contract): multi-dimensional IQ with cost, robustness, and AGI contract	2026-02-15 20:43:31 +00:00