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%.

翻译：基于大语言模型的多智能体系统在解决复杂任务方面展现出卓越能力，但其有效性高度依赖于协调智能体间交互的底层通信拓扑。在这类系统中，成功的任务求解通常需要针对特定任务的群组结构，以实现子任务的分解与并行处理。然而，现有方法大多以节点为中心生成通信拓扑，使得群组结构隐式地由局部连接决策自发形成，而非显式建模，这往往导致协调次优和不必要的通信开销。为解决这一局限，我们提出GoAgent（群组智能体）——一种将协作群组显式视为多智能体系统构建原子单元的通信拓扑生成方法。具体而言，GoAgent首先通过大语言模型枚举与任务相关的候选群组，再以自回归方式选择这些群组并将其作为原子单元进行连接，构建最终通信图，从而同时捕捉群组内凝聚力和群组间协调性。为缓解拓扑扩展中固有的通信冗余与噪声传播问题，我们进一步引入条件信息瓶颈目标——通过压缩群组间通信，在保留任务相关信号的同时过滤冗余历史噪声。在六个基准测试上的大量实验表明，GoAgent实现了93.84%的平均准确率，同步减少了约17%的令牌消耗，达到当前最优性能。