Paper explores the intrinsic mechanisms of LLM agent ICL behavior
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Yu Liu, Wenwen Li, Yifan Dou, and Guangnan Ye published a research paper investigating the contextual learning (ICL) behavioral mechanisms of large language model agents in multi-agent incomplete information games. The study constructs a public goods game, manipulates the statistical structure of historical feedback, and uses history-independent rational expectations equilibrium as a benchmark to evaluate decision quality, exploring whether LLM agents' contextual learning behavior stems from recursive reasoning or statistical extrapolation.
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- arXiv q-finRecursive Reasoning or Statistical Extrapolation? In-Context Learning in Multi-Agent Interdependent Decision-Making
To investigate the in-context learning mechanisms of large language model agents in multi-agent incomplete-information games, researchers constructed public goods games and manipulated the statistical structure of historical feedback, using history-independent rational expectations equilibrium as a benchmark to evaluate decision quality.
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