evaluation

The investigators of the first serious agent breakout relied on one of the models involved to read the transcripts, and said so. As human review falls behind, that is the question to ask of every AI finding.

The same model family solved every hidden-test repair through Pi and Grok Build, but one route reported sixteen times the total tokens. Most of the gap was context, not answers.

Forty thousand stars are voting on something. The framework verdict was correct. Closing the file was premature — the value still lives in the dependency tree.

When AI models review each other's work, independence matters more than intelligence. The same principle that makes external audit valuable makes cross-model review sharper than same-family review.

Persona prompting generates the exact type of hallucination that automated LLM judges reward as 'depth.' Two experiments, blind evaluation, and a fact-check that flipped the finding.

Independent parallel reviews produce overlapping findings. The cross-critique round produces resolution. That's where multi-agent value actually emerges.