Optimal Bayesian Stopping for Efficient Inference of Consistent LLM Answers

ArXi:2602.05395v2 Announce Type: replace-cross A simple strategy for improving LLM accuracy, especially in math and reasoning problems, is to sample multiple responses and submit the answer most consistently reached. In this paper we leverage Bayesian prior information to save on sampling costs, stopping once sufficient consistency is reached. Although the exact posterior is computationally intractable, we further