Task 2.4. Per family, and per slice when asked: outcome counts, recall, silence,
Youden, a directional-bias index and recall at a fixed false-positive ceiling.
Rows that cannot be scored are listed by reason and leave every denominator, and
a family with nothing scorable reports no rate rather than a perfect one.
Two numbers exist because Youden cannot tell apart a detector that flags
everything from one that says everything is safe: the directional-bias index
separates them, and recall at a fixed ceiling is forfeited entirely once a
family leaks more than the budget allows.
Change against a baseline is not a difference of means. Flips are counted per
pair in both directions and a two-sided exact sign test decides whether the
change is bigger than the noise, so one flip in ten reads as noise and six reads
as a real regression.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Foundation: taxonomy, DeepSeek endpoint, corpus fields, TypeScript grammar,
curated hunter tools. Core: classifier tiers and measurement, per-pair family
scoring and aggregation, detector configurations and runner, verifier registry
with oracle callback, canary templates and second judge, journal and archive.
Integration: one teacher rule, family scoring on the pairs hunter path, round
zero with per-model noise floors, scan-path tags and manifest block, proposers,
acceptance and revert, round orchestration, publish, subcommands. Corpus
repairs and validation last. Both sanity reviews' findings recorded in the
plan; K-run evaluation honors requirement 3.5 at every stage.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
A learning package layered families -> classify -> scoring -> split ->
detectors and verifiers -> rounds -> publish, composing the existing seams:
the tool hunter gains prompt, context and tool parameters; the hunter path of
the pair scorer takes an injected family matcher so pairs never imports
learning; every learning path selects teachers through one train-only rule;
the regress verdict takes an optional canary oracle; the chat endpoint resolves
DeepSeek before OpenRouter with thinking disabled and reasoning replayed, while
embeddings keep their own endpoint; HarnessX proposes behind the seam with a
scripted proposer for offline runs. Fifty-nine acceptance criteria traced;
boundary sections, file structure plan and migration phases populated.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Scope: catch the ten taxonomy families reliably on real-world and generated
application code, including forgotten authorization checks; not browser
engines, interpreters or memory-safety fuzzing. Decisions: verifier-aligned
families, a measured per-family noise floor as the regression budget, a cheap
model on an independent chat endpoint with embeddings elsewhere, canary-oracle
verifiers for the four most dangerous families first, the obligations lever
folded into the access-control family's first round, web families before the
C slice. Eleven requirements in EARS form; awaiting approval.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
AIxCC cyber reasoning systems, self-improving harness methods (GEPA, DGM,
Self-Harness, SkillOpt, AHE), vulnerability-dataset pitfalls and CWE taxonomies,
and routing/regression-control literature. The design changes they force:
coarse verifier-aligned families instead of fine CWE, multi-label routing with
an unknown class, pair-wise outcomes with a directional-bias index, K-run
reliable-change tests, a per-class seed-noise floor as the regression budget,
a no-regression acceptance rule with ties rejected, structural edits over prose
edits, a proposer blind to the holdout, and verifier code and gold labels
outside every editable surface.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Measured 2026-09-06 on the vibe-py holdout: the existing tool hunter, with no
learning, scores 12/14 pair-correct (Youden +0.857, no leaks) once detection
and silence are keyed on the labeled class and function and the byte-identical
twins are excluded; the overlay scores 5/14 on the same rows. The published
hunter number (0/17, Youden -0.588) was a scoring artifact: the scorer wanted
the literal CWE id in the finding text and the prompt never asked for it.
The brief proposes an auto-classifier as the routing key, one evolved hunter
configuration per class, a class-aware scorer with unscorable denominators,
the split enforced by construction, and the HarnessX evolve loop with per-class
attribution as the round primitive. Raw measurements are committed under
reports/measurements.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
