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Evaluation runs against synthetic APIs whose specs match the code exactly to begin with. Drift is then injected, so the correct answer for every case is known in advance.

Go suite, full pipeline

15 known drifts across 12 cases, plus 4 decoys that change code without changing the contract. Targets were committed in writing before the first run: recall ≥ 0.80, precision ≥ 0.85, all critical drifts caught, all decoys clean. All four were met.

What the gate contributes

The agent’s own raw precision was 0.23. Ten of its thirteen claims were false. The verification gate refuted all ten and confirmed all three true ones.
That is the difference between the two columns above. Without the gate, the agent would have made the report worse than the deterministic layer on its own.

Without any AI at all

The deterministic layer needs no account, no key and no network, and finishes in under a second. Everything it reported was real, in every language, with no false alarms, and it never raised a complaint about the sample applications we deliberately left correct.
prints that table straight from the code.

Reproduce it

Only the last line needs a key. make check feeds ground truth to the scorer as a perfect run and should return precision 1.0, recall 1.0, decoys 4/4. Anything else means the scorer is broken, not the auditor.
These numbers come from synthetic fixtures with injected drift, which is why anyone can check them. A real repository has no known list of correct answers, so auditing one gives you a ranked report instead of a score.