AI RESEARCH
CITE: Anytime-Valid Statistical Inference in LLM Self-Consistency
arXiv CS.LG
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ArXi:2605.05873v1 Announce Type: cross Large language models often improve reasoning by sampling multiple outputs and aggregating their final answers, but precise and efficient control of error levels remains a challenging task. In particular, deciding when to stop sampling remains difficult when the stopping rule is data-dependent and the set of possible answers is not known in advance. We study anytime-valid certification of a prespecified target answer as the unique mode of the model's response distribution, a guarantee distinct from answer correctness.