让大模型多智能体系统主动优化连接结构，提升协作效率。
Topological Structure Learning Should Be A Research Priority for LLM-Based Multi-Agent Systems
- 提出三阶段框架：选智能体、分析结构、合成拓扑
- 强调结构设计直接影响系统适应性与公平性
- 适合研究智能体协作与复杂任务的AI学者
基于大语言模型的多智能体系统（MAS）通过协同智能处理复杂任务，展现出强大潜力。然而，系统中智能体的配置、连接与协调方式——即拓扑结构——仍缺乏系统研究。本文呼吁将拓扑感知作为核心研究方向，主张显式建模并动态优化智能体间交互结构。我们识别出三个关键组件：智能体本身、通信链路以及整体拓扑，共同决定系统的适应性、效率、鲁棒性与公平性。为实现这一愿景，提出一个三阶段系统框架：1）智能体选择，2）结构表征，3）拓扑生成。该框架不仅为多智能体系统设计提供理论基础，更在语言建模、强化学习、图学习与生成建模等领域开辟新研究前沿，有望充分释放其在复杂现实应用中的潜力。最后，本文指出评估多智能体系统面临的关键挑战与机遇，希望所提框架与观点能为代理型AI时代提供关键洞见。
原文摘要 · Abstract (English)
Large Language Model-based Multi-Agent Systems (MASs) have emerged as a powerful paradigm for tackling complex tasks through collaborative intelligence. However, the topology of these systems--how agents in MASs should be configured, connected, and coordinated--remains largely unexplored. In this position paper, we call for a paradigm shift toward \emph{topology-aware MASs} that explicitly model and dynamically optimize the structure of inter-agent interactions. We identify three fundamental components--agents, communication links, and overall topology--that collectively determine the system's adaptability, efficiency, robustness, and fairness. To operationalize this vision, we introduce a systematic three-stage framework: 1) agent selection, 2) structure profiling, and 3) topology synthesis. This framework not only provides a principled foundation for designing MASs but also opens new research frontiers across language modeling, reinforcement learning, graph learning, and generative modeling to ultimately unleash their full potential in complex real-world applications. We conclude by outlining key challenges and opportunities in MASs evaluation. We hope our framework and perspectives offer critical new insights in the era of agentic AI.
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