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  "text": "The largest LLM-as-a-Judge reliability audit yet.\n\nResearchers ran 21 judges from nine providers over roughly 541,000 judgments on MT-Bench, JudgeBench, and RewardBench.\n\nFindings:\n\nValidating a judge with exact-match agreement overstates its skill, because exact match does not correct for chance.\n\nSwitching to Cohen's kappa deflates agreement by 33 to 41 points on MT-Bench, and judge rankings move by up to 14 positions across benchmarks.\n\nThere is also a consistency paradox. Two production-deployed judges score above 0.95 test-retest reliability while carrying severe position bias above 0.10, so a judge can agree with itself every time and still be wrong in the same direction every time.\n\nPaper: https://t.co/Jh8U1R2svQ\n\nLearn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c",
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