bench/judge_code.py — drop-in alternative to bench/judge.py with the
same Verdict shape & closed verdict vocabulary (CG/W/F/A/JE) but zero
quota cost: composes verifier + NLI + abstention + specificity into a
fixed-order pipeline. fox 2026-05-19: 'data first, judging later' —
this is the data-collection arm; LLM-based judging (Opus batched
needle-haystack, or Grok credit-card) is a separate downstream
concern that operates on the residue this judge cannot classify
deterministically.
Pipeline (first hit decides):
1. empty / no-gold guards
2. explicit abstention phrases (lexical regex)
3. NLI contradiction (arborist.qa.nli.shadow_check) — strongest
signal: gold contradicts the claim → WRONG
4. lexical verifier (arborist.qa.verify.verify_quotes) →
STRICT → CORRECT_GROUNDED
HYBRID + NLI entail >= 0.55 → CORRECT_GROUNDED
UNGROUNDED + specifics-not-in-gold → FABRICATED
UNGROUNDED + no specifics → ABSTAINED
HYBRID without NLI corroboration → JUDGE_ERROR (residue
for an LLM judge)
Threshold note: _CODE_JUDGE_THETA_ENTAIL_CORROBORATE=0.55 is distinct
from the NLI manifest's entailment_block_veto=0.9. The manifest's
threshold is calibrated for OVERRIDING a STRICT lexical signal with
negative evidence — high bar. The corroboration use here is the
opposite direction: additive positive evidence on an already-positive
anchor — moderate bar appropriate. Self-test case 1 measures NLI
entail=0.769 (clearly entailed, clear margin above 0.55).
Specificity for FABRICATED layers three scanners:
- verifier's multi-word proper-noun extractor (Higgs Boson, ...)
- local single-word capitalised-token scanner (Napoleon, Mars, ...)
deliberately separate because the verifier's gate is conservative
by design (multi-word only)
- numerics (years, dates, large counts, money)
Self-test: same 4 fixtures as bench/judge.py:self_test() so the two
instruments can be cross-checked when fox re-fires the Opus judge on
the residue later. Result: 4/4 INSTRUMENT TRUSTWORTHY.
tests/test_judge_code.py — pulls the contract into make test
(18 cases): module identifiers pinned, dataclass shape parity,
empty / no-gold guards, parametrised abstention phrases, specificity
layer behaviour, the canonical 4-case self-test, batch helper, and
graceful NLI-unavailable degradation. 18/18 pass.
Pre-existing known limitation, documented in the docstring: terse
correct answers ('In 1945.' against gold containing '1945') route to
ABSTAINED because the verifier's span extractor needs prose shape;
NLI sees no clause-level overlap at very short claims. The conservative
ABSTAINED label is correct deferral; tuning this is a calibration
question for real bench data, not the instrument's contract.
No callers touched yet — control_sweep.py & control_ab.py still
import the disabled Opus judge. Wiring this in is a separate ticket
move per fox's data-first sequencing.
176 lines
6.4 KiB
Python
176 lines
6.4 KiB
Python
"""Tests for the deterministic code-only judge (``bench/judge_code.py``).
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The self-test fixture set is the BEHAVIOURAL CONTRACT — these are
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exactly the 4 cases the Opus judge (``bench/judge.py``) was hand-built
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to grade correctly. A code judge that disagrees on these is not a
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trustworthy instrument. CI fails the build if the contract slips.
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We deliberately do NOT pin specific NLI probabilities or specific
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``rules_fired`` traces — those are tuning knobs we expect to move.
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The label is the public contract; the trace is implementation detail.
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Runs WITHOUT the ``[nli]`` extra installed too: NLI is optional, the
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abstention / lexical / specificity paths give correct labels on the
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fixture set even when NLI is unavailable (case 1 then routes via
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JUDGE_ERROR — see ``test_self_test_passes_with_nli_or_skips``).
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"""
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from __future__ import annotations
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import pytest
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from bench.judge_code import (
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JUDGE_MODEL,
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JUDGE_RULESET_ID,
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Verdict,
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judge,
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judge_batch,
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self_test,
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_is_abstention,
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_specifics_not_in_gold,
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)
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# --- module surface --------------------------------------------------------
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def test_module_identifiers_pinned():
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"""Audit-trail parity with bench/judge.py: stable model + prompt
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id strings so verdict logs from this judge are sortable / joinable
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against logs from the Opus judge."""
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assert JUDGE_RULESET_ID == "code-judge-v1"
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assert "code" in JUDGE_MODEL.lower()
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assert "no llm" in JUDGE_MODEL.lower()
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def test_verdict_dataclass_shape():
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v = judge("Q?", "I cannot determine that.", "gold")
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assert isinstance(v, Verdict)
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# Same fields as bench.judge.Verdict so the two are drop-in compat.
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assert v.label in {"CORRECT_GROUNDED", "WRONG", "FABRICATED",
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"ABSTAINED", "JUDGE_ERROR"}
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assert v.prompt_id == JUDGE_RULESET_ID
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assert v.model == JUDGE_MODEL
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assert isinstance(v.rationale, str) and v.rationale
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assert isinstance(v.raw, str) and v.raw # JSON-serialised trace
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assert isinstance(v.decision, dict)
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# --- rule 1: empty / no-gold guards ----------------------------------------
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def test_empty_answer_is_abstained():
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v = judge("Q?", "", "gold text")
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assert v.label == "ABSTAINED"
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assert "empty_answer" in v.decision["rules_fired"]
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def test_empty_gold_is_judge_error():
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v = judge("Q?", "Some claim.", "")
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assert v.label == "JUDGE_ERROR"
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assert "no_gold" in v.decision["rules_fired"]
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# --- rule 2: explicit abstention -------------------------------------------
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@pytest.mark.parametrize("phrase", [
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"I cannot determine that from the provided source.",
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"I do not know.",
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"I can't tell from the article.",
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"The source does not mention this.",
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"The article doesn't contain that information.",
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"Not enough information is provided.",
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"Unable to determine from the passage.",
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"Cannot be determined from the source.",
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])
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def test_abstention_phrases_classify_as_abstained(phrase):
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v = judge("Q?", phrase, "irrelevant gold content")
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assert v.label == "ABSTAINED", phrase
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assert "abstention_phrase" in v.decision["rules_fired"]
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def test_assertive_answer_is_not_abstention():
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"""A model that makes a confident claim is NOT abstaining, even if
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the claim happens to be ungrounded — that's WRONG / FABRICATED,
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not ABSTAINED."""
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is_abs, _ = _is_abstention(
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"The capital of France is Berlin and the population is 12 million."
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)
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assert is_abs is False
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# --- rule 4: specificity (the FABRICATED gate) -----------------------------
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def test_specifics_picked_up_when_not_in_gold():
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answer = "The Higgs boson cafe was founded by Napoleon in 1066."
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gold = "This article discusses thermodynamics and entropy."
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s = _specifics_not_in_gold(answer, gold)
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# Should flag the proper noun(s) and the date.
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joined = " ".join(s).lower()
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assert "napoleon" in joined
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assert "1066" in joined
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def test_specifics_excluded_when_in_gold():
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"""A proper noun / number that ALSO appears in gold is grounded
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by definition (case-insensitive) and must NOT be flagged."""
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answer = "Napoleon led the army in 1812."
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gold = "Napoleon Bonaparte's campaign in 1812 was disastrous."
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s = _specifics_not_in_gold(answer, gold)
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joined = " ".join(s).lower()
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assert "napoleon" not in joined
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assert "1812" not in joined
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# --- the canonical behavioural contract: 4-case self-test ------------------
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def test_self_test_passes():
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"""The same 4 fixtures the Opus judge was hand-built to grade
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correctly. If this fails, the code judge is not an interchangeable
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instrument."""
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assert self_test() == 0
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# --- batch parity ----------------------------------------------------------
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def test_judge_batch_loops_correctly():
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triples = [
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("Q1", "I cannot determine.", "gold a"),
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("Q2", "", "gold b"),
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]
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out = judge_batch(triples)
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assert [v.label for v in out] == ["ABSTAINED", "ABSTAINED"]
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assert all(isinstance(v, Verdict) for v in out)
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# --- graceful NLI degradation ----------------------------------------------
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def test_judge_handles_nli_unavailable(monkeypatch):
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"""When the [nli] extra is missing, shadow_check returns an
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unavailable ShadowResult — the judge must NOT crash; it just
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proceeds without rule-3 (contradiction) signal and without the
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HYBRID corroboration path. Labels stay sound for the explicit
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abstention / UNGROUNDED-specifics paths (which short-circuit
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before NLI is even consulted)."""
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import bench.judge_code as jc
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monkeypatch.setattr(jc, "_nli_check", lambda a, g: None)
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# Abstention path returns at rule 2, BEFORE the NLI block runs —
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# so the trace doesn't (and shouldn't) carry nli_available. The
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# contract here is "no crash + correct label", not "NLI metadata
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# is populated on every path".
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v = jc.judge("Q?", "I cannot determine.", "gold")
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assert v.label == "ABSTAINED"
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assert "abstention_phrase" in v.decision["rules_fired"]
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assert "nli_available" not in v.decision # rule 2 short-circuit
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# An answer that reaches the NLI block must record nli_available
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# = False when _nli_check returns None.
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v = jc.judge("Q?", "Some claim about something.", "different content here")
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assert v.decision.get("nli_available") is False
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# FABRICATED path still works (no NLI needed).
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v = jc.judge(
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"Who founded the cafe?",
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"Napoleon founded it in 1066.",
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"This article is about thermodynamics.",
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)
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assert v.label == "FABRICATED"
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