[2603.19677] GoAgent: Group-of-Agents Communication Topology Generation for LLM-based Multi-Agent Systems

Computer Science > Machine Learning arXiv:2603.19677 (cs) [Submitted on 20 Mar 2026] Title:GoAgent: Group-of-Agents Communication Topology Generation for LLM-based Multi-Agent Systems Authors:Hongjiang Chen, Xin Zheng, Yixin Liu, Pengfei Jiao, Shiyuan Li, Huan Liu, Zhidong Zhao, Ziqi Xu, Ibrahim Khalil, Shirui Pan View a PDF of the paper titled GoAgent: Group-of-Agents Communication Topology Generation for LLM-based Multi-Agent Systems, by Hongjiang Chen and 9 other authors View PDF HTML (experimental) Abstract:Large language model (LLM)-based multi-agent systems (MAS) have demonstrated exceptional capabilities in solving complex tasks, yet their effectiveness depends heavily on the underlying communication topology that coordinates agent interactions. Within these systems, successful problem-solving often necessitates task-specific group structures to divide and conquer subtasks. However, most existing approaches generate communication topologies in a node-centric manner, leaving group structures to emerge implicitly from local connectivity decisions rather than modeling them explicitly, often leading to suboptimal coordination and unnecessary communication overhead. To address this limitation, we propose GoAgent (Group-of-Agents), a communication topology generation method that explicitly treats collaborative groups as the atomic units of MAS construction. Specifically, GoAgent first enumerates task-relevant candidate groups through an LLM and then autoregressively selec...