Why does the model I use for evaluation prefer the model it came from?
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LLM-as-judge has become the default way to run quality evaluations at scale, but judges from the same model family rate their own outputs 10 to 25 percent higher than equivalent outputs from a different family. Multiple 2025 and 2026 papers confirm the bias holds across position, verbosity, and rubric-constrained formats, and simple mitigations like panel voting reduce but do not eliminate it. Existing measurement frameworks cannot separate genuine quality differences from stylistic self-matching, so the effect cannot be corrected without first being cleanly isolated. Any team using the incumbent model to evaluate a replacement is running an evaluation with a structural thumb on the scale.
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The entire AI improvement loop depends on honest quality signals, and a biased evaluator corrupts that signal at the source without raising any alert.
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