99 source modules -> 81. 832 tests -> 646. The whole learning/ package (16
modules, ~4,100 lines) and six semantic/ modules removed, with the three gates
byte-identical to 2026-09-06-gate-baseline.txt at every step.
The sequence is what made it safe: vocabularies ported first (1.1), importers
detached second (1.2-1.3), deletion third (1.4). Every import break during the
deletion was therefore a detachment miss in the importer and was fixed there --
no module was restored to satisfy one. Both misses were the same shape and were
fixed as a class: operations.py and shapes.py each imported trailing_name from
the deleted variants.py, so it moved to semantic/ir.py, its natural home, and
both importers changed in one edit.
Ported before deleting: the closed guard vocabulary (verbatim, nothing else
holds it) into model/specs.py, which derives TaintSpec and DominanceSpec from
the retained ruleset facts rather than copying them. Re-homed: families ->
model/taxonomy, candidates -> model/candidates, endpoint -> model/endpoint,
plus Region, labeled_family and labeled_spans. labeled_family no longer falls
back to the LLM classifier -- an unlabeled pair is honestly unknown.
Four deviations, each recorded in the spec notes with its reason. improve/
evolve.py and validator.py were stripped, not deleted: each mixes the mechanism
lever (gone) with the deterministic rule-status loop `ousast improve` still
runs, which has no LLM in it; a test now asserts the lever's absence by symbol
so it cannot creep back. The learning CLI group became `model candidates`,
because deleting it outright would have orphaned the retained enumerator by
removing its only entry point. thinking moved from [learning] to [models].
A gap named rather than papered over: no retained sink fact routes by CWE to
path, output_encoding, untrusted_destination or prototype -- the same hole the
entailment ceiling diagnosed. The test asserts the seeded set and the gap set
exactly, so task 4.2 must update it when it authors those sinks.
The audit found exactly one genuine orphan: vectorstore.py, a bakeoff harness
with no production caller and no CLI, alive only through its own test. The five
named subsystems each carry an explicit decision. Manifest committed, with a
test asserting the tree still matches it.
Group 2 (the Joern go/no-go) has not started.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Group 1 complete. Tasks 1.2 and 1.3: `candidates.ceiling`, `ousast learning
candidates`, and the stop-or-go reading. No model is called anywhere in this
group, so the condition that could have ended the feature was reachable for
free.
Verdict: go.
agent-vfc (deciding) 89.7% ceiling against 44.4% round-zero detection
vfc (deciding) 84.7%, baseline still running, non-gating
vfc-js (deciding) 52.9% -- see below
github (deciding) 100.0%
vibe-py (development)100.0% against 83.3%
The margin is largest on agent-vfc, which is the slice the tool is actually
for.
The measurement forced a shape correction before the detector existed.
config_secrets first read 63.6% with a bind-and-literal shape, because a
permissive default is as often an argument to a call -- cors({origin: '*'}),
app.use(session({secure: false})) -- as it is a binding. Adding call sites to
that family's shape took it to 100% and recovered four agent-vfc rows.
Carry-forward, and not this feature's to fix: vfc-js sits at 52.9% because
eight of seventeen rows label a function the JavaScript CST names `<anon>` --
`module.exports.publish = function () {}` and its relatives. That is a
function-naming gap, it caps the existing hunter on that slice exactly as
much, and it is the same class as the `_declarator_start` defects found in
learning-harness task 4.1.
The ruleset arm of `ceiling` is kept so the 12.1% that rejected it stays a
measurement rather than a remembered number.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Task 1.1. The detector stops searching: something deterministic decides what
it looks at, ordered by (path, line, name) and bounded by a constant, so the
batches a later task forms are determined by the input alone.
A call candidate carries what the model used to grep for -- the arguments,
and the binding chain the IR resolved for them, nearest first. An access
control candidate is the operation with the guards in scope, because an
absence bug has no sink.
Two things the fixture taught while writing it. The IR emits a chained call
twice at one line, once bare and once carrying the keyword arguments, so
deduplication keeps the richest: a bare `filter_by` and
`filter_by(user_id=owner)` are the same site and only one of them says what
the constraint was. And `leaky` and `constrained` in the shared obligations
fixture both discharge through `filter_by` -- what separates them is that one
chain ends at `request.args.get('owner')` and the other at `request.user.id`.
That contrast is now the test, and it is exactly the judgement the model is
being narrowed down to make.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
