让多智能体通信拓扑同时在空间和时间上自适应演化，提升协作效率。
ST-EVO: Towards Generative Spatio-Temporal Evolution of Multi-Agent Communication Topologies
- 提出时空联合演化的通信调度框架，基于流匹配实现对话级精准控制。
- 在9个基准上实现5%–25%的准确率提升，显著优于现有方法。
- 支持不确定性感知与自我反馈，适合复杂协作任务中的动态系统设计。
基于大语言模型的多智能体系统（MAS）已成为实现协同智能的有效途径，受到广泛关注。其中，“自演化”多智能体系统因其灵活性与强大能力，可构建任务自适应的工作流程或通信拓扑，而非依赖预定义的静态结构模板。当前自演化多智能体系统主要集中在空间演化或时间演化范式，仅考虑单一维度的演化，未能充分激发大语言模型的协同潜力。本文从全新的时空视角出发，提出ST-EVO，支持对话级通信调度，采用基于流匹配的紧凑而强大的调度器。为实现精确的时空调度，ST-EVO具备感知多智能体系统不确定性并进行自我反馈学习的能力。在九个基准上的大量实验表明，ST-EVO达到最先进性能，准确率提升约5%–25%。
原文摘要 · Abstract (English)
LLM-powered Multi-Agent Systems (MAS) have emerged as an effective approach towards collaborative intelligence, and have attracted wide research interests. Among them, ``self-evolving'' MAS, treated as a more flexible and powerful technical route, can construct task-adaptive workflows or communication topologies, instead of relying on a predefined static structue template. Current self-evolving MAS mainly focus on Spatial Evolving or Temporal Evolving paradigm, which only considers the single dimension of evolution and does not fully incentivize LLMs' collaborative capability. In this work, we start from a novel Spatio-Temporal perspective by proposing ST-EVO, which supports dialogue-wise communication scheduling with a compact yet powerful flow-matching based Scheduler. To make precise Spatio-Temporal scheduling, ST-EVO can also perceive the uncertainty of MAS, and possesses self-feedback ability to learn from accumulated experience. Extensive experiments on nine benchmarks demonstrate the state-of-the-art performance of ST-EVO, achieving about 5%--25% accuracy improvement.
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