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
