hunter (K=5) pipeline (K=1)
pair-correct 76.7% 80.0%
leak 12.3% 6.7%
cost ~$0.69 $0.0076
determinism none entailed band deterministic
Higher recall, half the leak, a fifth of the model calls, ~90x cheaper.
Three runs, and the middle one earned its keep by changing nothing. Run 2 fixed
family routing and returned numbers IDENTICAL to run 1 -- which was the clue
that the dominance arbiter was being reached and its verdict thrown away. Every
corroboration was put to the judge as a taint question ("a sanitizer appears on
the path -- does it suffice?"), meaningless for a missing guard, so the judge
said no and the finding was dropped. Run 3 phrases the residual question for
the arbiter that raised it and converts 2 of 3 access_control pairs from silent
to pair-correct, arriving at model_corroborated -- a rung that existed but had
never reached a finding.
This settles a question open since the start of the session, and reverses an
earlier answer of mine. "Detection has regressed" was measured with the
proposer disconnected: an arbiter compared against a detector, before four
wiring bugs were found.
Division of labour: 15 suspicion, 9 model_entailed, 2 model_corroborated. The
model arbitrates 11 of 26 findings, and every conversion from suspicion to a
model rung removes a leak and a model call at once -- the payoff curve for sink
coverage and interprocedural resolution, now measured rather than assumed.
Still silent: 3 config_secrets (the settings table is Flask/FastAPI-shaped
while the pairs are aiohttp) and 1 access_control still to diagnose.
Three full runs cost about $0.03 in total.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
The first like-for-like comparison with the LLM hunter, on the same 30 vibe-py
pairs and the same metric.
hunter (K=5) pipeline (K=1)
pair-correct 76.7% 73.3%
leak 12.3% 6.7%
cost ~$0.69 $0.0126
determinism none entailed band deterministic
Half the leak, 55x cheaper, a fifth of the model calls, one pair short of the
hunter's recall (22 against 23 of 30). And understated: all six both_silent
pairs are access_control and config_secrets, which had no pipeline path to
their arbiters when this ran. A corrected run supersedes those rows.
The rung split says what is doing the work: 15 suspicion to 9 model_entailed on
the vulnerable side. The LLM supplies most of the recall; the model supplies
the precision, halving the leak by dropping claims it could contradict. That is
the division of labour the architecture was designed around and the first
evidence it pays.
It also revises an earlier conclusion of mine. "Detection has regressed" was
measured with the proposer disconnected -- an arbiter compared against a
detector. With the enumerator wired in, the comparison reverses on precision
and cost while holding recall.
And it makes the strategy concrete: the model arbitrates about a third of
findings today, and every suspicion converted to model_entailed is leak removed
for free and one fewer model call. Sink coverage and interprocedural resolution
now have a measured payoff rather than a hoped-for one.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
