Commit Graph
3 Commits
Author SHA1 Message Date
Marius CiepluchandClaude Opus 5.5 7b17eacd44 bench(learn): injection first slice -- compiled AI classifier, out-of-repo evaluation (decision-engine 6.2, 6.6)
6.2: the 724 static examples' 616 distinct excerpts embedded with openai/text-embedding-3-small via
OpenRouter, 198,819 tokens, $0.0040 metered (ceiling $1).

6.6, family injection only, memory = every family (at most 2 contrast examples per prompt), input
profile v1: smoke fixed k = 5 (repeat prefix-cache hit share 0.96); one compile (program c1237a49,
proposed instruction 5, balanced Brier 0.128 on 13 C_val candidates vs 0.199 baseline); outer grouped
5-fold, 110 out-of-repository candidates in 29 groups: ADVISORY recall 0.90 [0.82, 0.97], precision
0.54 [0.44, 0.65], AUC 0.74; BLOCK not offered (ECE 0.064 > 0.05; no fold reached a Wilson lower bound
of 0.95); canary re-ask agreement 0.83. $0.91 metered at list price; DeepSeek balance $18.20 -> $17.90.

Instrument fixes found on the way, each with a test:
- memory build: pair sides join the record stored under their harvest unit pin (--units), and only the
  built profile's records are read (static and plane shared a key; 85 -> 724 examples);
- retrieval: stage 1 per side (family / contrast) and at most 2 contrast examples, never padded;
- program: the JSON answer contract is part of the signature, appended to proposed instructions
  (DeepSeek JSON mode refused a prompt without "json");
- compile: candidate_families for a one-family compile; compile.toml k = 5 from the smoke rule.

Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
2026-10-01 20:40:41 +02:00
Marius CiepluchandClaude Opus 5.5 9e3c83cfdc fix(learn): withhold label-tracking signals from the v1 classifier; leak audit
Maintainer decision "Neutralise both for v1".

1. Phantom candidates. A pair side's candidates are now only the functions that
   side declares (plane/harvest.py `declares`, the excerpt builder's
   function_bounds); a labelled function absent from a side is listed under
   `absent_on_side` and never asked, and a side declaring none of its verify
   family's functions is no verify unit. The recorded harvest units held 58
   phantom verify candidates (53 fixed, 5 vulnerable sides). build_features.py
   skips label rows whose side does not declare the function and drops their
   earlier rows (MemoryStore.drop_rows); rebuilt the pair records from the
   recorded signals, no model call: 126 label rows absent_on_side (117 fixed,
   9 vulnerable) -- access_control 13, config_secrets 4, deserialization 20,
   injection 28, memory 4, output_encoding 16, path 15, untrusted_destination
   26 -- so 252 feature rows dropped (1712 -> 1460); no other row changed.

2. Engine withheld. Input profiles in learn/schema.py (`v1` withholds the
   engine instrument, `full` withholds nothing); ProgramSpec.inputs defaults to
   v1 and compile.toml records it. Neither the engine's state nor its values
   reach the candidate, examples, demonstrations or the instruction proposer's
   error summary, and the Gower retrieval distance leaves the engine block out.
   Stored records are unchanged. design.md Risks records the decision and that
   the engine returns only through a pre-registered A/B on assumed-benign and
   benign-control negatives.

3. `ousast learn audit-leaks` (learn/leaks.py): per feature and per instrument
   state, separation = (pairs the signal alone puts on the right side - on the
   wrong side) / pairs, over all pairs and per family; flagged at >= 0.20 over
   >= 10 pairs. Counts under
   benchmarks/measurements/2026-10-01-decision-engine-leak-audit/ (before and
   after the rebuild). After: facts.function_lines (0.80 untrusted_destination,
   0.75 config_secrets, 0.26 injection; higher on fixed), verify.turns_mean
   0.67 config_secrets, eng.* 0.35-0.40 output_encoding, verify.flag_a/votes/
   site_offset and agree.final 0.20-0.30 on 10-12 pair families. Before the
   rebuild the phantom fixed sides drove state.source/state.roles/ms.flagged
   to 1.0 and state.engine to 0.95 on deserialization.

Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
2026-10-01 19:40:58 +02:00
Marius CiepluchandClaude Opus 5.5 ea61dc1bb3 feat(learn): the classifier program and its response cache (decision-engine 6.4)
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
2026-10-01 10:13:31 +02:00