learn/program.py: the declared signature (code, signals, roles, family in;
verdict, family, confidence, rationale with cited lines out), the
baseline instruction, rendering as [system: instruction + demonstrations]
-- byte-identical for every candidate, so the provider's prefix cache can
hit -- then [user: retrieved examples with their labels, the candidate
without one]. Signals pass validate_x first (no EXCLUDED_FIELDS, missing
instruments in words). Answers are one JSON object; one that does not
parse or cites no line inside the excerpt is asked once more at
temperature 0 and otherwise becomes unsure/parse_failed, never
not_vulnerable. Classify(k): sample 1 at 0.0, the rest at 0.7; score s =
mean p_j; majority verdict, ties unsure; a majority unsure cannot BLOCK.
Caller caches each response as responses/<sha>.json keyed by (model,
Model parameters digest, messages, temperature, sample, json_object) and
replays at $0; spend is priced from the recorded usage once per distinct
request, so a replay reports the same usd. Program.decide asserts the
evaluation boundary on the rendered ids before any call.
MeteredClient.complete passes temperature/logprobs through (only when
set); DeepSeekChatClient sends temperature beside thinking=disabled.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
learn/retrieve.py: stage 1 is a per-instrument Gower distance over the
profile's allow-listed features with explicit missing (both ran: mean
feature distance; differing states 1; same non-ran state 0; null in one 1;
priors dropped with priors=off), n1 = 40 with seeded tie-breaks; stage 2
re-ranks by r = lam(1 - D) + (1 - lam)cos, signals_only without the
target's vector; the balance rule takes k_ret = 6 (<= k/2 per label, <= 2
per group, <= 2 other families; `short` recorded). eligible() is the only
way into a prompt: not the target's group, not an evaluated group, not the
held-out source or framework, and outside deployment not a near-duplicate
(cos >= 0.98). assert_boundary repeats the check on rendered ids;
demonstrations of a held-out framework swap to their alternates;
deployment_fold excludes a scanned repository that is a corpus group;
seed_memory never hands a case its own group.
learn/folds.py: compile split (25% of groups per primary family, 60/40
boot/val), outer grouped 5-fold stratified by family and label mix,
leave-one-source-out, leave-one-framework-out (insufficient data below 10
groups), assert_disjoint and a folds digest; all seeded.
Tests: test_learn_retrieve.py (hand-computed Gower, lam 0/0.5, balance,
near-duplicates, test_boundary over outer/LOSO/LOFO folds with
demonstrations), test_learn_folds.py.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
