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Anytime-Valid LLM Leaderboards via Benchmark-weighted and Block-Factorized e-Processes
Large language model (LLM) leaderboards compare model capabilities by ranking models according to their mean performance on fixed benchmarks. However, variability in evaluation outcomes across runs may produce unsupported claims of model superiority on the benchmark, a risk compounded by leaderboard
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