A game-theoretic reformulation of sequential detection shows the LIL as the minimax boundary, with the optimal mixing prior being the Jeffreys prior on the scale-of-scales selected by the Erdős-Kolmogorov test, yielding a 3/2 coefficient for the first iterated-log correction.
Position: Don’t Use the CLT in LLM Evals With Fewer Than a Few Hundred Datapoints
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The multiply iterated law of the iterated logarithm: game-theoretic foundations of sequential detection boundaries
A game-theoretic reformulation of sequential detection shows the LIL as the minimax boundary, with the optimal mixing prior being the Jeffreys prior on the scale-of-scales selected by the Erdős-Kolmogorov test, yielding a 3/2 coefficient for the first iterated-log correction.
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Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation
A Dirichlet-prior Bayesian estimator for model success probability replaces Pass@k, delivering faster-converging and more stable rankings with credible intervals on math benchmarks.
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