label_rows honoured only a change's primary site, so 43 of the 48 accepted access-control
locations were unlabelled; every entry of targets is now a positive with its own label_ref and
span. The training clones were emptied with sparse-checkout to save disk and a new worktree
inherits it: every file read failed and the source and semantic producers reported an unread tree
on all 599 rows. The runner now disables sparse-checkout in the worktree it adds.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
hunter-learning-loop groups 1-3 (first increment). The feature table is the
contract: one row per touched function with diff, source, semantic and
taxonomy columns, provenance per producer, labels by the benchmark's rule.
Producers read the repository at head only (a planted advisory id and
commit text never reach a feature). The hunt program is a DSPy ReAct loop
over a signature whose inputs are the unmasked columns in a fixed order;
answers outside the closed family list or citing unseen lines score as
unknown. The runner writes records the benchmark scorer reads, resumable
by change, program id and mask, and refuses the held-out pilot unless the
run is a report run. Research extra [hunt] = dspy, pandas; the core
install is unchanged. 43 tests on a scripted model, no network.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
