The second access-control screen spun for 2h20m on one ordinary mage-ai change: a Next.js build commit whose 3 MB
one-line bundles hold 12,355 functions each. `source.ranges_from_ir` ran two regex scans of the whole line once per
function, and the screen kept its 422 finished changes in memory only, so stopping it lost them.
- `source.ranges_from_ir`: a line over MINIFIED_LINE_CHARS (2000) is not checked per function; JavaScript names on it
stay anonymous, other languages keep the parser's name.
- `source.generated_reason`: why a changed file is build output (a directory the product walk leaves out, a minified
name, over MAX_FILE_BYTES, a line over the cap). `hunt.producers._candidates` leaves such files out and records the
reason on the context; the fix site of a vulnerability is always kept. The DSPy arm shares the enumeration.
- The screen writes rows.jsonl and log.jsonl per change (rows first, entry last) and resumes by skipping logged ids.
Tests: the minified line never reaches the per-function scan; generated_reason on the four shapes and two source files;
build output in an ordinary change yields no rows while a vulnerability path in a one-line file is kept.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Java parsed already, but the producer failed every Java row because no obligation facts existed for the
language and that failure took roles and def_use down with it. PHP had no grammar. Now: tree-sitter-php in the
semantic extra with PHP node kinds in the CST reader; obligation facts for Jenkins/Stapler, Spring Security and
Servlet code (java.toml) and for Laravel, WordPress and Symfony (php.toml), each header naming the training
clones and references the names were checked against; a language with an IR but no obligation facts keeps
roles and def_use and records obligations as unchecked (None), which the engineered features treat as missing,
never as zero. Proven on the committed pool: 6 of 6 Java rows and 33 of 33 PHP rows of one fix change each now
carry semantic features. Implemented by Codex; gated here with ruff, mypy and the full suite.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
The 150 unread rows were not a checkout failure: the entry-point scan read every product file and one empty
file raised empty_source, which escaped the cached property and failed the source producer on every row of
the change (and re-read the whole tree per row). Empty product files are skipped; the row's own file still
has to be non-empty.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Two instrument findings from the first access-control feature table (952 rows, 74 positive rows):
1. A fix that adds a guard is, replayed base-to-head, a push that removes it. The diff producer only kept
hunks with added lines at head, so 38 of 74 positive rows had no hunk in their file at all and 69 of 74
were "not on a changed line", while 60% of ordinary rows were. The diff annotation separated the labels
by construction and was blind to exactly the access-control shape. The producer (version 2) now records
per file the head lines after which the push removed code; distance and on_changed_line count them, the
model's hunks input renders them as removed_after_line, and diff.deletion_count is an engineered feature.
2. 150 rows (16%, every positive of one repository) read empty files from the worktree although git holds
the blobs; the build ran while another process checked out from the same clone. _full_checkout now
proves the checkout: git status must be clean, a dirty tree is restored once, then InstrumentFailure
checkout_incomplete. The table script cross-checks every unread row against the blob size and refuses
to screen a partly unread tree, and records the diff-scoped recall ceiling (positive rows a label-blind
enumeration would reach).
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]>
