AI 中文总结
该研究针对LLM多智能体协作中伙伴策略变化导致的重规划问题，提出BayesBeliefAgent方法，在Overcooked环境中显著缩小信念-行动差距并减少重规划次数。
AI 中文摘要
多智能体大语言模型（LLM）系统常难以与任务中途策略变化的新队友协作，由于智能体执行多步骤或时间扩展技能，在公共证据显示伙伴已改变技能后，它们仍会继续执行过时计划。现有方法要么将伙伴跟踪视为被动上下文（使智能体知晓变化但行动迟缓），要么无差别重规划。我们提出BayesBeliefAgent，它将分层LLM规划器与贝叶斯跟踪模块结合，智能体仅在伙伴行为与推断技能直接矛盾时才中断当前技能，而非持续重规划。除标准奖励外，我们用重规划效率和信念-行动差距（正确估计伙伴的智能体执行非互补技能的决策占总决策的比例）评估性能。在基准Overcooked环境中，矛盾条件控制大幅缩小了该信念-行动差距，且重规划次数比启发式方法少一个数量级。
英文摘要
Multi-agent Large Language Model (LLM) systems often struggle to collaborate with new teammates whose strategies shift mid-task. Because agents execute multi-step or temporally extended skills, they frequently continue executing outdated plans long after public evidence shows that a partner has changed its skill. Existing methods either treat partner tracking as passive context-leaving the agent aware of the shift but slow to act-or replan indiscriminately. We introduce BayesBeliefAgent, which pairs a hierarchical LLM planner with a Bayesian tracking module. Rather than replanning constantly, our agent interrupts its current skill only when a partner's actions directly contradict the inferred skill. Beyond standard reward, we evaluate performance using replanning efficiency and the belief-action gap: the fraction of total decisions where an agent with a correct partner estimate executes a non-complementary skill. Across benchmark Overcooked environments, contradiction-conditioned control drastically narrows this belief-action gap while requiring an order of magnitude fewer replans than heuristic methods
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