Eight of nine group-3 tasks were rejected. Each defect was hiding a number,
and the numbers are why the loop was not learning:
- The fixed side was judged against the *vulnerable* side's function
ranges. 191 of 227 pairs with a resolvable labeled span (84%) have
different spans on the two sides, so a leak was recorded as silence,
always in the direction that flatters Youden.
- Round zero measured every family with the generalist. `family:unknown`
earns no credit against any real family, so every family but `unknown`
baselined at a structural zero and every noise floor with it.
- The cross-family sweep counted absolute failures instead of negative
flips. On a corpus where most pairs fail at round zero that rejects
every round for damage it did not do. Unscorable rows voted too.
- K runs was declared and never threaded; `pairs --k-runs` was parsed and
discarded, and its only test grepped `--help`.
- The meta-agent proposer was a stub, so `learning round` could never
propose anything. It now composes MetaAgent.evolve against a scratch
copy of the family directory, and `--proposals FILE` runs a recorded
sequence offline.
- `refused_holdout` was a vocabulary word no code path could produce, and
the covering test accepted either outcome.
- Cost was metered once, after everything had been paid for; a stage that
raised left no record at all.
- The corrected lever number was published beside the earlier one rather
than in place of it, and the test named for that property never checked.
- Refusals were `continue` statements: `Refusal.degradation()` existed and
nothing called it. A train-split identical twin was reported as
`split:train`, which is untrue.
Test repairs that each changed a number: two split tests measured an
`x = 1` fixture that teaches nothing whatever the rule says; two CLI
scoreboard tests asserted against all-zero output produced by a stub that
answers twice and puts its finding outside the labeled function.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Task 3.1. A teacher is a vendored pair, at a review tier a maintainer stands
behind, on the train split, and scorable. Every learning path now asks the same
module that one question, so the split cannot be enforced in one place and
forgotten in another. A refusal is data rather than an exception: the path
records which holdout pairs a candidate touched and moves on, so a leak is
visible in the run instead of silently accepted.
The exporter refuses to seed from a holdout pair, leave-one-out teaches from the
train split while still scoring every pair, and the mechanism lever refuses a
candidate a holdout pair taught, because such a shape can only recover the pair
that produced it.
Measured and committed: on the vibe-py holdout the lever's own number falls from
seven detected and a Youden of +0.059 to five detected and -0.059 once holdout
pairs stop teaching, and the candidate set shrinks from ten shapes to six. That
gap is the inflation this rule removes.
Two existing tests encoded the leak and now encode the truth. The documented
export-to-improve flow had a single holdout pair teaching itself, and now needs
a train pair to teach and a holdout pair to recover. The obligation export
claimed the corpus seeds at least one obligation shape, which was true only
because holdout pairs were teaching: every vibe-py absence pair that yields an
obligation lesson sits on the holdout side, so the honest number today is zero.
The derivation stays covered by fixtures; the corpus gap belongs to the absence
re-harvest and to a maintainer pass over the splits.
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 4.2 (Req 5.3, 5.4, 5.5).
- propose_mechanism_edits: retract admitted records that leak on a
holdout fixed side; admit a candidate only when a trial store holding
it alone recovers a currently missed holdout pair without leaking.
- run_round(mechanism_candidates=, mechanism_store=): validates, applies
after the rule gate passed, re-scores the holdout pairs per profile with
variant search (evaluate_mechanism_profiles), and rejects with a
byte-for-byte revert on mechanism_leak, profile_regression or
mechanism_no_gain; the journal records each mechanism edit with its
pair provenance and the metrics before and after.
- The rule gate and its per-profile clause are unchanged.
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
Task 8.3 (Req 10.1-10.5).
- pairs.py: catalog rows with vendored = false carry their harvest recipe
and no excerpts; evaluate_catalog harvests them into
OPENULTRASAST_PAIR_CACHE (default ~/.cache/openultrasast/pairs) only
when `pairs --pointers` or OPENULTRASAST_PAIRS_NETWORK=1 allows the
network, otherwise records pointer_pair_skipped (a failed fetch is
pointer_pair_fetch_failed with detail); a warm cache never scores
without the switch. select_vendored for tests and gates.
- harvest.py: materialize_pointer harvests into the cache without the
license requirement (nothing is redistributed); redaction applies.
- catalog_gen.py: emits pointer rows with recipe fields; training
manifests skip them.
- pair_gate and improve/evolve.evaluate_profiles score vendored pairs
only; cli `pairs --pointers`; .gitignore covers the cache path.
- tests: loader, skip/degradation, cache hit, fetch failure, warm cache
with network off, profile gate exclusion, harvest and generator paths;
conftest keeps every test offline whatever the shell exports.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Task 8.2 (Req 9.3).
- improve/evolve.py: evaluate_profiles filters holdout pairs to
GATING_TIERS before computing per-profile metrics, so advisory and
title pairs are scored by `pairs` but never gate a round; a regression
on reviewed pairs still rejects with profile_regression:<profile>.
- tests: a regression confined to title pairs is accepted; the same
regression on reviewed pairs is rejected and the title pairs are
excluded from the gated profile rather than outvoted.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Task 6.3.
- improve/evolve.py: evaluate_profiles and profile_regressions score the
holdout pair split per provenance profile every round; a drop in pair
pass rate or Youden beyond --profile-tolerance rejects the round with
reason profile_regression:<profile>; profiles under
--min-holdout-pairs are reported, not gated; journal records both.
- cli improve: --pair-catalog, --no-pair-gate, --profile-tolerance,
--min-holdout-pairs; a missing catalog fails loud instead of skipping
the gate.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Regex inventory stays the proposer. MAP attaches promote/demote/unadjudicated/coverage
from intra-file taint (tree-sitter when a grammar parses, Python ast as last resort).
Deep sandboxes promotions only, ordered by the existing heuristic ranker plus optional
OpenRouter mechanism recall. The SAST pair slice scores overlay outcomes; the stage-1
detection gate and local pair gate stay inventory-only.
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]>
