Maintainer's finding of 2026-10-14 recorded in hunter-learning-loop (a dated section that overrides the shape
annotations as the model's input), its design (amendment pointing to the prover's trace) and the steering
overview's current state.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
rounds.py runs a candidate over compile or held-out folds through runner.run, so a tool-using
program has its tools bound to each change's checkout, and turns paired records into the
FoldResults accept.decide reads (closes the gap noted in bacd7a1). report.py runs an accepted
program once over the held-out pilot: a ledger line reserved before the run refuses a second run
per program id, contaminated or unchecked training runs are refused, the instrument is proved per
change and more than 5% failures makes the run invalid with no numbers quoted, the pilot's own
scorer and metrics give the after numbers, the recorded hunter numbers give the before, baseline
repeats pair with the single run, the proof arm's workload per 100 pushes is counted, and the gate
distance is read off the confidence curve. 124 hunt and stats tests pass. Implemented by Codex
(gpt-6-astra), gated here.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
propose.py: one change per round (instruction, demonstrations or one mask), examples built from
compile folds only with a provenance record of the change ids used, a registry for GEPA, MIPROv2
and BootstrapFewShot (MIPROv2 without the data-aware proposer so labels are never serialised),
compile-fold scoring with Wilson and the repository-paired bootstrap, and a compile-only history
the next round may read. accept.py: the rule as independent clauses (both folds non-negative with
one strictly positive, per-family regression budget on held-out recall, tie tolerance, no
acceptance without an interval, instruction cap, one change only), the comparison recorded in the
candidate artifact's configurations, rejections logged without held-out numbers. 48 fixture tests.
Known gap for live rounds: the optimisers and propose._score call the program directly, so a
tool-using program must be scored through the runner with its tools bound per checkout (6.1).
Implemented by Codex (gpt-6-astra), gated here.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
contamination.py reads the repositories named by the held-out pilot manifest and by every
benchmark report (accepted_repos.json and NOTES.md links under benchmarks/measurements), marks
each run directory clean or contaminated with the report digest and time, and refuses to count
an unchecked run toward a gain. ablation.analyse carries excluded_from_gain when its run is
contaminated. Implemented by Codex (gpt-6-astra), gated here; the benchmark import is lazy so
the package imports without the benchmarks tree.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
ablation.py registers the manifest (table digest, arms as masks, folds, metric, sample, budget,
rule, seed, program id) before any run and refuses a run or an analysis that does not match it.
Arms run over the same changes through runner.run, paired by change; effects on recall and on
suspicions per ordinary push are reported pooled and per family with the repository-clustered
bootstrap and exact McNemar, plus the recall-against-suspicions curve at every reported
confidence (never a tuned cut). The importance table and decisions read held-out effects only;
a neutral annotation survives only when a kept one needs it. 30 fixture tests with a scripted
model. Implemented by Codex (gpt-6-astra), gated here.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
A Semgrep, quick-rule or engine match may enter the feature table as
positive evidence; its absence is never a feature, filter, demotion or
gate on a hunter suspicion, and no acceptance rule counts it. The Semgrep
arm is a comparison only and may at most replace the regex quick tier.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Engine LGPL-2.1, shelling out carries no obligation; the registry rules are
under the Semgrep Rules License since 2024-12-13 and cannot ship, only our
own converted rules can. Measured 4.7 s startup and 5 to 10 ms per file;
converted regex and taint rules fire for Python, PHP and JavaScript; C
propagation and Groovy do not; one sanitizer semantic we refuse on purpose
is accepted by Semgrep. Recommendation: a Semgrep arm on the valid pilot
first. Recorded in the hunter-learning-loop research note and breadth.toml.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
610 pair files, 570 labelled functions. lizard matches the parser engine on
C, Python and Java but loses JavaScript names and TypeScript end lines:
keep ours, take its cyclomatic complexity as a feature candidate.
tree-sitter-language-pack gives identical spans, is faster, replaces six
grammar wheels with one and adds Go, Ruby, PHP and Perl: adopt as grammar
packaging. Also found: .c files should try the c grammar before cpp.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Six groups: table and producers; the hunter as a program with the search
metric and the run artifact; runner integration and a baseline on the 12
pilot positives; training set, folds, paired ablation and the importance
table; proposals and the acceptance rule; the pilot gain report.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Feature engineering is the centre: a feature table with switchable columns
is the contract between producers (source.facts_for, semantic facts, entry
points, diff hunks; lizard and Semgrep only after their evaluations), the
ablation runner and the hunter program. DSPy runs the program (ReAct over a
HuntSignature; GEPA and MIPROv2 propose), our rule accepts on held-out
folds. The pilot stays held out; the hook, its client and the core install
are untouched. Requirements approved by the maintainer ('4 first').
Co-Authored-By: Claude Fable 5.1 <[email protected]>
The hunter gets an annotation model (named, versioned facts about the push)
and statistical ablation decides which annotations stay. Labels from fix
sites and ordinary pushes, training set apart from the held-out pilot,
compiled replayable program, train-and-holdout acceptance rule, two stages
reported separately, no thresholds set in this spec.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
