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

TL;DR

GoAgent introduces a novel method for explicitly generating group-based communication topologies in LLM multi-agent systems, improving coordination efficiency and reducing communication costs.

Contribution

This paper presents a new approach that explicitly models collaborative groups as atomic units in topology generation, enhancing multi-agent system performance.

Findings

Achieves 93.84% average accuracy on six benchmarks.

Reduces token consumption by approximately 17%.

Outperforms existing methods in communication efficiency.

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 selects and connects these groups as atomic units to construct the final communication graph, jointly capturing intra-group cohesion and inter-group coordination. To mitigate communication redundancy and noise propagation inherent in expanding topologies, we further introduce a conditional information bottleneck (CIB) objective that compresses inter-group communication, preserving task-relevant signals while filtering out redundant historical noise. Extensive experiments on six benchmarks demonstrate the state-of-the-art performance of GoAgent with 93.84% average accuracy while reducing token consumption by about 17%.