Judging the Judges: When AI Evaluation Becomes a Fingerprint
Opening — Why this matters now LLM-as-judge has quietly become infrastructure. It ranks models, filters outputs, trains reward models, and increasingly decides what ships. The industry treats these judges as interchangeable instruments—different thermometers measuring the same temperature. This paper suggests that assumption is not just wrong, but dangerously so. Across thousands of evaluations, LLM judges show near-zero agreement with each other, yet striking consistency with themselves. They are not noisy sensors of a shared truth. They are stable, opinionated evaluators—each enforcing its own private theory of quality. ...