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
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
