A third kind of decision-maker between deterministic code and a full LLM
call: Choice / Score / Noul questions answered as probability
distributions with a confidence, behind one Decider trait, with the
composition patterns (confidence gating, composite scoring, rerank) as
code. Two backends: TypeSafe's Jev over HTTP, and a DeBERTa-v3 MNLI
cross-encoder run in-process with candle (feature nli; metal/cuda).
decide-eval measures a backend on labelled cases the way judge-eval
measures the judge. eval/skill-triage.json: 20 mission tasks × 53 skills,
75 positives, hand-labelled. Measured 2026-09-21:
lexical overlap AUROC 0.851 [email protected] 0.47 top-k 48/75 ECE 0.095
jev (named wording) AUROC 0.989 [email protected] 0.84 top-k 63/75 ECE 0.064 213 ms
jev (plain wording) AUROC 0.970 [email protected] 0.66 top-k 52/75
nli mnli-base AUROC 0.790 [email protected] 0.28 top-k 38/75 ECE 0.263 1.5 s
nli zeroshot-v2 AUROC 0.782 [email protected] 0.43 top-k 39/75 ECE 0.054 1.2 s
The vendor's calibration claim survives our data; the local cross-encoder
ranks below keyword overlap on either checkpoint or wording and is kept
as the measured negative, not shipped. A local backend would need the
logit-readout route over the fleet's 9B model — a separate spike.
Shadow: one Jev call per phase launch (spawned, 10 s cap, silent without
TYPESAFE_API_KEY) records a skill.triage event; the Skill-Use report
carries triage_p beside each skill's Trigger verdict. It selects nothing.
Co-Authored-By: Claude Opus 5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01WZb5A2kfVfjpdwSochkuHz
