标题： 多智能体环境中的动态策略适应与大型语言模型 显示英文标题

标题： Dynamic Strategy Adaptation in Multi-Agent Environments with Large Language Models

摘要： 大型语言模型（LLMs）在数学、策略和语言任务中表现出强大的推理能力，然而关于它们在动态、实时、多智能体场景中的推理能力了解甚少，例如协作环境中，智能体需要持续适应彼此的行为，如合作游戏设置中的情况。 在本文中，我们通过将基于LLM的智能体与博弈论原则（如信念一致性与纳什均衡）相结合的战略推理和实时适应能力，弥补了这一差距。 所提出的框架广泛适用于动态场景，其中智能体在不断变化的条件下进行协调、沟通和决策。 我们提供了实时策略优化和自适应反馈机制，使智能体能够根据即时的上下文交互动态调整策略，这与之前在静态或回合制环境中评估LLM能力的努力形成对比。 实证结果表明，在高噪声环境中，我们的方法在回报方面比PPO基线提高了高达26%，同时保持实时延迟低于1.05毫秒。 我们的方法提高了协作效率、任务完成率和灵活性，说明与实时反馈结合的博弈论指导增强了LLM性能，最终促进了更具弹性和灵活性的战略多智能体系统。 显示英文摘要

摘要： Large language models (LLMs) demonstrate strong reasoning abilities across mathematical, strategic, and linguistic tasks, yet little is known about how well they reason in dynamic, real-time, multi-agent scenarios, such as collaborative environments in which agents continuously adapt to each other's behavior, as in cooperative gameplay settings. In this paper, we bridge this gap by combining LLM-driven agents with strategic reasoning and real-time adaptation in cooperative, multi-agent environments grounded in game-theoretic principles such as belief consistency and Nash equilibrium. The proposed framework applies broadly to dynamic scenarios in which agents coordinate, communicate, and make decisions in response to continuously changing conditions. We provide real-time strategy refinement and adaptive feedback mechanisms that enable agents to dynamically adjust policies based on immediate contextual interactions, in contrast to previous efforts that evaluate LLM capabilities in static or turn-based settings. Empirical results show that our method achieves up to a 26\% improvement in return over PPO baselines in high-noise environments, while maintaining real-time latency under 1.05 milliseconds. Our approach improves collaboration efficiency, task completion rates, and flexibility, illustrating that game-theoretic guidance integrated with real-time feedback enhances LLM performance, ultimately fostering more resilient and flexible strategic multi-agent systems.