Cache work that depends only on the candidate minute, drop unread response fields, and reject windows above 241 candidates before any chart is built. Eleven canonical scenario hashes match 77057a819. Sealed and reported-offset replays match the 2026-10-08 records (changed_trial_count 8 and 581) and are stored as scoring_perf_2026_10_13.
Co-Authored-By: Grok (agent loop)
Product decision 2026-10-02 (TASK-upstream-sync5 R1): a time range is offered
only with at least 4 dated, primary-scoreable events covering 3 domains,
counted on all of them (training + reserved holdout). Was 3 training events /
2 domains in three TS copies and the Python acceptance gate while the policy
file already said 4/3.
- One definition: references/rectification_policy.v1.json
(minConfirmationEvents / minConfirmationDomains). TS core/types MIN_DATED_*,
rectification-decision MIN_STANDALONE_*, evidence-model MIN_ACCEPTANCE_*,
the convergence evaluator and the post-inference trainingGateOpen all read
it; Python decision_policy MIN_ACCEPTANCE_* alias MIN_CONFIRMATION_*.
- Python receipt counts all scoreable events / domains for event_quality and
domain_diversity; decision policy identity v3 -> v4 (candidate UUIDs carry
it). Candidate scores unchanged (77 v5 cases A/B identical), so the
algorithm stays rectification-v5-matrix-scoring-10.
- Memoization golden v3 written by write_golden; v2 frozen by sha256 with a
test that its scores equal v3 and only the receipt policy moved.
- Collect gap copy names the exact gap ("再来两件……其中至少一件不是……")
instead of always "再来一件"; VOICE.md updated. Legacy life-events form copy
4/3 as well.
- 30 frontend test files, 4 Python tests: fixtures extended to the same
scenario at 4/3, or assertions changed with 原值/新值/原因 notes.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01N4f2nya58RoRu4yEmJgRGE
Functional roles feed the *_functional_*_auxiliary rules, so 57782aea changes
candidate scores for identical input (memoization fixture 12:00: 8.6274 ->
8.4977). Per the "scoring semantics change => bump ALGORITHM_VERSION"
precedent (scoring-7 -> 8 -> 9), the identity moves to scoring-10; policy v3,
input contract v5 and Skill versions are unchanged, history is not relabeled.
Five frontend sites and one SQL guard tested `=== "...scoring-9"` for the
dated candidate-window contract; they now use isDatedScoringAlgorithmVersion /
a generation regex (>= 9). Migration 20261002010000 only recreates
validate_dated_rectification_candidate (one-line guard change).
Memoization golden v2 written by the test's own write_golden; v1 (scoring-8)
frozen by sha256. Real-engine scoring-10 cross-midnight golden added. Research
records re-frozen per ERR-110 (label functional_v2_2026_10_02) and
scripts/functional_benefics.py added to the frozen production identity
(ERR-114: 57782aea changed scores without tripping it).
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01N4f2nya58RoRu4yEmJgRGE
Carry explicit local date intervals instead of inferring the day from clock
order. Cluster width, delivery, adoption, and reports keep the actual civil
date; adopted date is stored separately from the reported birth_date.
Algorithm identity is scoring-9 / spec-v5. Scoring weights, confirmation
thresholds, and Skill version are unchanged. Isolated Linux final-3 gates
passed; four pre-existing Python failures remain. This is not a production
release.
Add date-isolated caches and regression coverage, align scoring identity, and freeze full research reruns while preserving historical artifacts. Record unresolved cache/receipt identity and end-to-end acceptance gaps for branch review only.
Co-Authored-By: Claude Code <noreply@anthropic.com>
Replace whole-structure golden == with same-process cached vs None fallback equality, plus discrete-strict / measured-tolerance golden comparison. Do not rebuild the golden JSON.
