Topological Structure Learning Should Be A Research Priority for LLM-Based Multi-Agent Systems
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912653342932992 |
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| author | Yang, Jiaxi Zhang, Mengqi Jin, Yiqiao Chen, Hao Wen, Qingsong Lin, Lu He, Yi Kumar, Srijan Xu, Weijie Evans, James Wang, Jindong |
| author_facet | Yang, Jiaxi Zhang, Mengqi Jin, Yiqiao Chen, Hao Wen, Qingsong Lin, Lu He, Yi Kumar, Srijan Xu, Weijie Evans, James Wang, Jindong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_22467 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Topological Structure Learning Should Be A Research Priority for LLM-Based Multi-Agent Systems Yang, Jiaxi Zhang, Mengqi Jin, Yiqiao Chen, Hao Wen, Qingsong Lin, Lu He, Yi Kumar, Srijan Xu, Weijie Evans, James Wang, Jindong Multiagent Systems Artificial Intelligence Machine Learning 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. |
| title | Topological Structure Learning Should Be A Research Priority for LLM-Based Multi-Agent Systems |
| topic | Multiagent Systems Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2505.22467 |