Multi-agent systems (MAS) based on large language models (LLMs) have demonstrated significant potential in collaborative problem-solving. However, they still face substantial challenges of low communication efficiency and suboptimal task performance, making the careful design of the agents' communication topologies particularly important. Inspired by the management theory that roles in an efficient team are often dynamically adjusted, we propose AgentDropout, which identifies redundant agents and communication across different communication rounds by optimizing the adjacency matrices of the communication graphs and eliminates them to enhance both token efficiency and task performance. Compared to state-of-the-art methods, AgentDropout achieves an average reduction of 21.6% in prompt token consumption and 18.4% in completion token consumption, along with a performance improvement of 1.14 on the tasks. Furthermore, the extended experiments demonstrate that AgentDropout achieves notable domain transferability and structure robustness, revealing its reliability and effectiveness. We release our code at https://github.com/wangzx1219/AgentDropout.

翻译：基于大语言模型（LLM）的多智能体系统（MAS）在协同问题解决方面已展现出巨大潜力。然而，它们仍面临通信效率低下和任务性能欠佳的重大挑战，这使得对智能体通信拓扑结构的精心设计尤为重要。受高效团队中角色常动态调整的管理理论启发，我们提出了AgentDropout。该方法通过优化通信图的邻接矩阵，识别不同通信轮次中的冗余智能体与冗余通信，并将其淘汰，以同时提升令牌效率和任务性能。与现有最先进方法相比，AgentDropout在提示令牌消耗上平均降低了21.6%，在补全令牌消耗上平均降低了18.4%，同时在任务性能上提升了1.14。此外，扩展实验表明，AgentDropout具有显著的领域可迁移性和结构鲁棒性，揭示了其可靠性与有效性。我们的代码发布于 https://github.com/wangzx1219/AgentDropout。