99 source modules -> 81. 832 tests -> 646. The whole learning/ package (16
modules, ~4,100 lines) and six semantic/ modules removed, with the three gates
byte-identical to 2026-09-06-gate-baseline.txt at every step.
The sequence is what made it safe: vocabularies ported first (1.1), importers
detached second (1.2-1.3), deletion third (1.4). Every import break during the
deletion was therefore a detachment miss in the importer and was fixed there --
no module was restored to satisfy one. Both misses were the same shape and were
fixed as a class: operations.py and shapes.py each imported trailing_name from
the deleted variants.py, so it moved to semantic/ir.py, its natural home, and
both importers changed in one edit.
Ported before deleting: the closed guard vocabulary (verbatim, nothing else
holds it) into model/specs.py, which derives TaintSpec and DominanceSpec from
the retained ruleset facts rather than copying them. Re-homed: families ->
model/taxonomy, candidates -> model/candidates, endpoint -> model/endpoint,
plus Region, labeled_family and labeled_spans. labeled_family no longer falls
back to the LLM classifier -- an unlabeled pair is honestly unknown.
Four deviations, each recorded in the spec notes with its reason. improve/
evolve.py and validator.py were stripped, not deleted: each mixes the mechanism
lever (gone) with the deterministic rule-status loop `ousast improve` still
runs, which has no LLM in it; a test now asserts the lever's absence by symbol
so it cannot creep back. The learning CLI group became `model candidates`,
because deleting it outright would have orphaned the retained enumerator by
removing its only entry point. thinking moved from [learning] to [models].
A gap named rather than papered over: no retained sink fact routes by CWE to
path, output_encoding, untrusted_destination or prototype -- the same hole the
entailment ceiling diagnosed. The test asserts the seeded set and the gap set
exactly, so task 4.2 must update it when it authors those sinks.
The audit found exactly one genuine orphan: vectorstore.py, a bakeoff harness
with no production caller and no CLI, alive only through its own test. The five
named subsystems each carry an explicit decision. Manifest committed, with a
test asserting the tree still matches it.
Group 2 (the Joern go/no-go) has not started.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Review rounds 3-5 remediations (Req 5, 7).
- ousast improve resolves candidates from the target's calibration dir,
then the working directory's (where `mechanisms export` writes), and
admits into <target>/.openultrasast/calibration/mechanisms.jsonl,
where a scan of that target reads; --mechanism-candidates,
--mechanism-store, --no-mechanisms; --dry-run redirects the store to
scratch; accepted rounds print admit/retract <id>.
- run_improvement threads the store paths; RoundOutcome carries the
mechanism edits; reverted mechanism keys are blocked outright; a
no-gain admission is rejected as mechanism_no_gain; a retraction may
name an admitted record that left the candidate set.
- leave-one-out forwards variant-search degradations and pins the
containment and label rules; mechanisms export defaults to the
candidates file so it never re-admits a tombstone.
- design §Lever rewritten to the implemented gate; README and docs show
export -> improve -> scan.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Task 4.1 (Req 5.1, 5.2, 5.5).
- validator: MechanismEdit (admit | retract by record id) under the
`mechanisms` lever; validate_mechanism accepts only records from the
exporter candidate set with origin corpus, a closed guard and source
kinds, and identifier-only shape text; free-form shapes are rejected.
- apply_mechanism_edits appends the record (admit) or a retracted
tombstone (retract) so the log stays append-only; StoreSnapshot.restore
reverts byte for byte. MechanismStore.load folds tombstones away.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Stand up the deterministic self-improvement loop over the ruleset/policy
governance data. Zero-dependency and fully tested offline; detection gate intact.
- 7.1/7.2 bounded levers + validator (improve/validator.py): two levers (rule,
policy); typed edits make pattern-text and 0-5 severity changes structurally
impossible; EvolveValidator rejects bad status, unknown rule_ids (loop cannot add
rules), enabled->disabled jumps (shadow staging mandatory), unresolved CWEs, and
out-of-bounds K/MIN_SCORE, raising StrictValidationError.
- 7.3 novelty + replay + journal (improve/journal.py): a JSON round journal records
every edit/lever/outcome; reverted edits are blocked from re-proposal (novelty);
a replay smoke gate boots the candidate ruleset before re-benchmarking.
- 7.4 benchmark deltas -> rule_signals.json (recall side: miss; precision side: fp).
- 7.5 hard acceptance gate + byte-for-byte revert (improve/evolve.py): a round is
accepted only if recall >= 90% AND FP < 10% AND project score did not regress AND
no matched-finding regression; otherwise the ledger is never written (byte-for-byte
revert). run_round/run_improvement drive rounds to convergence.
- 7.6 auto-shadow precision-draggers (enabled rules with FPs -> shadow, never
delete), nominate coverage gaps via rule_signals, and keep the project score as the
reward separate from the recall/FP feasibility gate (the loop cannot raise the
score by shadowing rules that breach the gate — proven by the revert test).
Design note: the loop's safety machinery is deterministic and benchmark-driven
(the proposer is the per-rule signal); the LLM MetaAgent.evolve is the optional
richer proposer that plugs into this same validated/gated machinery when the
HarnessX extra is present (offline-untestable). This mirrors how DocuHarnessX
realizes self-improvement.
9 new tests (validator, proposer/signals, accept, byte-for-byte revert, convergence);
118 pass total, 2 construction smokes skip without the extra; ruff + mypy clean;
detection gate 93.6%/0% intact. Tasks 7.1-7.6 checked off.
Co-Authored-By: Claude Opus 4.8 <[email protected]>
