test(fact-extraction): stop judging phrasing/attribution the system already captures#2769
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…lready captures
Two hs_llm_core quality tests failed frequently on the core-LLM job, not
because the judge flaked (it is already temp-0 primary + majority-vote
confirmations) but because they judged model output that is genuinely
variable and already checked deterministically elsewhere.
test_date_field_calculation_yesterday: the resolved date lives in the
structured `occurred_start` field, which the test already asserts is
Nov 12/13. The judge additionally required the absolute date to appear in
the free-text fact prose ("...state the absolute date in the fact text"),
so a correct extraction that wrote "Yesterday" in prose but Nov 12 in
occurred_start still failed. That tested phrasing, not capability. Make the
occurred_start assertion mandatory (require a dated fact — calculating the
date is the point of the test) and drop the date clause from the judged
criteria; the judge now only checks the fuzzy activity-content claim.
test_cognitive_epistemic_dimension: the judge penalised entity/speaker
attribution ("Involving: She/He") that is not what this test is about — it
asserts cognitive/epistemic *states* survive extraction. Scope the criteria
to that dimension and instruct the judge to ignore attribution and wording,
so a state counts as preserved even if attributed to the wrong person.
Both still catch real regressions (missing/incorrect dates, dropped
cognitive states); they just no longer flake on aspects the system either
captures structurally or does not claim to get right. Verified locally: both
pass (extraction gpt-4o-mini, judge gpt-4.1-mini).
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Two
hs_llm_corefact-extraction-quality tests failed frequently on the Core LLM job (seen on #2727 and elsewhere). The judge was not the flaky part —llm_judge.pyalready does a temp-0 primary plus majority-vote higher-temperature confirmations. The tests were failing because they judged model output that is genuinely variable and already checked deterministically elsewhere.test_date_field_calculation_yesterdayThe resolved date lives in the structured
occurred_startfield, which the test already asserts is Nov 12/13. But the judged criteria additionally required the absolute date to appear in the free-text fact prose ("…state the absolute date in the fact text"). So an extraction that correctly setoccurred_start = 2024-11-12but wrote "Yesterday" in the prose still failed — that tests phrasing, not capability.Fix: make the
occurred_startassertion mandatory (require a dated fact — calculating the date is the test's point) and drop the date clause from the judged criteria. The judge now only checks the fuzzy activity-content claim; the date is verified structurally.test_cognitive_epistemic_dimensionThe judge penalised entity/speaker attribution ("Involving: She/He") — e.g. "2/3 judges agree not met … 'The meeting might not happen | Involving: She' … input states 'uncertainty about a meeting'". But this test asserts that cognitive/epistemic states survive extraction, not who they're attributed to.
Fix: scope the criteria to the dimension and instruct the judge to ignore attribution and exact wording — a state counts as preserved even if attributed to the wrong person.
Why this doesn't weaken the tests
Both still catch real regressions: a dropped/incorrect date fails the deterministic
occurred_startassertion, and dropped cognitive states fail the (still-judged) dimension criteria. They just no longer flake on aspects the system either captures structurally (occurred_start) or doesn't claim to get right (entity attribution in a dimension-preservation test).This follows the repo's own testing guidance (CLAUDE.md): assert deterministic structure directly; judge only the irreducibly-fuzzy semantic claim.
Verification
Both pass locally (extraction gpt-4o-mini, judge gpt-4.1-mini). This branch touches the API tests, so CI runs the Core LLM job against the real Gemini judge.
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