K-Level Reasoning: Establishing Higher Order Beliefs in Large Language Models for Strategic Reasoning
Yadong Zhang, Shaoguang Mao, Tao Ge, Xun Wang, Yan Xia, Man Lan, Furu, Wei

TL;DR
This paper introduces K-R, a recursive framework enabling large language models to perform higher order strategic reasoning by modeling beliefs about others' beliefs, validated across game theory and social intelligence tasks.
Contribution
It presents the first recursive implementation of strategic depth in LLMs, extending their reasoning capabilities to include higher order beliefs.
Findings
K-R improves strategic reasoning performance in testbeds
Enables LLMs to form beliefs about others' beliefs
Demonstrates advantages over baseline models
Abstract
Strategic reasoning is a complex yet essential capability for intelligent agents. It requires Large Language Model (LLM) agents to adapt their strategies dynamically in multi-agent environments. Unlike static reasoning tasks, success in these contexts depends on anticipating other agents' beliefs and actions while continuously adjusting strategies to achieve individual goals. LLMs and LLM agents often struggle with strategic reasoning due to the absence of a reasoning framework that enables them to dynamically infer others' perspectives and adapt to changing environments. Inspired by the Level-K framework from game theory and behavioral economics, which extends reasoning from simple reactions to structured strategic depth, we propose a novel framework: "K-Level Reasoning with Large Language Models (K-R)." This framework employs recursive mechanisms to enable LLMs to achieve varying…
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Taxonomy
TopicsNatural Language Processing Techniques
