Large Language Model-based Multi-Agent Systems (MASs) have emerged as a powerful paradigm for tackling complex tasks through collaborative intelligence. Nevertheless, the question of how agents should be structurally organized for optimal cooperation remains largely unexplored. In this position paper, we aim to gently redirect the focus of the MAS research community toward this critical dimension: develop topology-aware MASs for specific tasks. Specifically, the system consists of three core components - agents, communication links, and communication patterns - that collectively shape its coordination performance and efficiency. To this end, we introduce a systematic, three-stage framework: agent selection, structure profiling, and topology synthesis. Each stage would trigger new research opportunities in areas such as language models, reinforcement learning, graph learning, and generative modeling; together, they could unleash the full potential of MASs in complicated real-world applications. Then, we discuss the potential challenges and opportunities in the evaluation of multiple systems. We hope our perspective and framework can offer critical new insights in the era of agentic AI.
View on arXiv@article{yang2025_2505.22467,
title={ Topological Structure Learning Should Be A Research Priority for LLM-Based Multi-Agent Systems },
author={ Jiaxi Yang and Mengqi Zhang and Yiqiao Jin and Hao Chen and Qingsong Wen and Lu Lin and Yi He and Weijie Xu and James Evans and Jindong Wang },
journal={arXiv preprint arXiv:2505.22467},
year={ 2025 }
}