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[2602.14681] ST-EVO: Towards Generative Spatio-Temporal Evolution of Multi-Agent Communication Topologies
Summary
The paper presents ST-EVO, a novel framework for generative spatio-temporal evolution of multi-agent communication topologies, enhancing collaborative intelligence through adaptive workflows.
Why It Matters
This research is significant as it addresses the limitations of existing self-evolving multi-agent systems by introducing a dual spatio-temporal approach, which could lead to more efficient and flexible communication strategies in AI systems. The findings could impact various applications in robotics and AI collaboration.
Key Takeaways
- ST-EVO enhances multi-agent systems by integrating spatio-temporal evolution.
- The framework improves communication scheduling and adapts to uncertainties.
- Experimental results show a 5%-25% accuracy improvement over existing methods.
Computer Science > Multiagent Systems arXiv:2602.14681 (cs) [Submitted on 16 Feb 2026] Title:ST-EVO: Towards Generative Spatio-Temporal Evolution of Multi-Agent Communication Topologies Authors:Xingjian Wu, Xvyuan Liu, Junkai Lu, Siyuan Wang, Yang Shu, Jilin Hu, Chenjuan Guo, Bin Yang View a PDF of the paper titled ST-EVO: Towards Generative Spatio-Temporal Evolution of Multi-Agent Communication Topologies, by Xingjian Wu and 7 other authors View PDF Abstract: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 performanc...
