The code judge bailed to JUDGE_ERROR on 40% of in-corpus answers: HYBRID
(partial grounding) with low NLI entail, where the entity-grounding
rescue needs ZERO unsourced specifics. A single extra proper noun
('Emperor Honorius', 'Alexander Molossus' — an alias/paraphrase) blocked
rescue even with verbatim quotes verified and the answer correct.
New HYBRID resolution tier: rescue to CORRECT_GROUNDED when the verifier
confirmed >=1 verbatim quote, the subject anchor is in gold (on-topic),
there is NO unsourced NUMERIC specific (wrong dates/counts stay residue),
and NLI isn't strongly contradicting. Unsourced proper nouns are treated
as aliases/paraphrase; unsourced numerics (the real factual-error class)
keep the answer as JUDGE_ERROR. Validated on the 12 real residue cases:
9 -> CORRECT (all genuinely right), 3 stay residue (unsourced numerics).
JUDGE_ERROR 40% -> ~10%. self-test 4/4; 2 new tier tests; suite 2549.
The Arborist arm runs answer_mode='claim_lattice' (per control_sweep.py
:179, control_ab.py:155) so its answers arrive as the JSON envelope
{"claims":[{"text":"...","evidence_ids":["E1"]},...]}.
_descaffold strips the [E1] evidence-pointer markup but the JSON
braces + key syntax remain. The verifier's strategy-2 (span) and
strategy-3 (proper-noun) extractors see brace noise instead of the
inner claim prose — every Arborist record degraded to UNGROUNDED.
The 2026-05-19T17-01-17Z sweep, re-graded with the freshly calibrated
judge (5a17f61), surfaced this: Arborist arm reported 0 CG across all
three variants in the live phase 1 output (the live run was pre-
calibration), and 29/120 CG (24%) under the calibrated rescore — clear
improvement just from theta_contra=0.85, but the JSON envelope was
still hobbling the verifier paths.
Fix: _unwrap_claim_lattice_json runs BEFORE all downstream rules.
Detection is conservative (three independent signals: starts-with-
brace AND "claims" key AND "text" key) so plain-prose answers
pass through unchanged. Multi-claim envelopes concatenate as discrete
sentences (extract_claim_spans treats each as its own span).
Malformed JSON falls back to the original answer — no silent
rewriting on broken input.
Smoke result on the Iceland Arborist case
ans: {"claims":[{"text":"The current president of Iceland is
Ólafur Ragnar Grímsson.","evidence_ids":["E1"]}]}
gold: {{Infobox Political post |post = President |body = Iceland
|incumbent = [[Ólafur Ragnar Grímsson]] ...}}
before: UNGROUNDED → FABRICATED (then WRONG after calibration)
after: short_entity_grounded → CORRECT_GROUNDED
pytest: 27/27 (added 7 unwrap-coverage tests covering single-claim
envelopes, multi-claim concatenation, plain-prose passthrough,
malformed-JSON tolerance, unrelated-JSON passthrough, and the
end-to-end Arborist-envelope CG flow). Self-test 4/4 unchanged.
Re-rescores of 17:01 sweep + phase 1 sweep run after this commit
to measure final Arborist scorecard improvement.
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.
