AI RESEARCH
LLMs for Game Theory: Entropy-Guided In-Context Learning and Adaptive CoT Reasoning
arXiv CS.LG
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ArXi:2601.10775v2 Announce Type: replace-cross We propose a novel LLM-based framework for reasoning in discrete, game-theoretic tasks, illustrated with \emph{Tic-Tac-Toe}. The method integrates in-context learning with entropy-guided chain-of-thought (CoT) reasoning and adaptive context retrieval. The model dynamically adjusts both the number of retrieved examples and reasoning paths according to token-level uncertainty: concise reasoning with minimal context is used when uncertainty is low, whereas higher uncertainty triggers expanded multi-path CoT exploration.