Recent advances in large language model-powered multi-agent systems have demonstrated remarkable collective intelligence through effective communication. However, existing approaches face two primary challenges: (i) \textit{Ineffective group collaboration modeling}, as they rely on pairwise edge representations in graph structures, limiting their ability to capture relationships among multiple agents; and (ii) \textit{Limited task-adaptiveness in communication topology design}, leading to excessive communication cost for simple tasks and insufficient coordination for complex scenarios. These issues restrict the scalability and practical deployment of adaptive collaboration frameworks. To address these challenges, we propose \textbf{HyperAgent}, a hypergraph-based framework that optimizes communication topologies and effectively captures group collaboration patterns using direct hyperedge representations. Unlike edge-based approaches, HyperAgent uses hyperedges to link multiple agents within the same subtask and employs hypergraph convolutional layers to achieve one-step information aggregation in collaboration groups. Additionally, it incorporates a variational autoencoder framework with sparsity regularization to dynamically adjust hypergraph topologies based on task complexity. Experiments highlight the superiority of HyperAgent in both performance and efficiency. For instance, on GSM8K, HyperAgent achieves 95.07\% accuracy while reducing token consumption by 25.33\%, demonstrating the potential of hypergraph-based optimization for multi-agent communication.

翻译：近年来，基于大语言模型的多智能体系统通过高效通信展现出卓越的集体智能。然而，现有方法面临两大核心挑战：（一）**群体协作建模效能不足**：现有方法依赖图结构中的成对边表示，限制了其捕捉多智能体间复杂关系的能力；（二）**通信拓扑设计的任务适应性有限**：导致简单任务中通信开销过大，而复杂场景下协调能力不足。这些问题制约了自适应协作框架的可扩展性与实际部署。为应对这些挑战，我们提出**HyperAgent**——一种基于超图的框架，该框架通过直接超边表示优化通信拓扑并有效捕捉群体协作模式。与基于边的传统方法不同，HyperAgent使用超边连接同一子任务中的多个智能体，并采用超图卷积层实现协作组内的一步式信息聚合。此外，该框架结合了稀疏正则化的变分自编码器，可根据任务复杂度动态调整超图拓扑结构。实验结果表明HyperAgent在性能与效率方面均具有显著优势。例如在GSM8K数据集上，HyperAgent在准确率达到95.07%的同时，将令牌消耗降低了25.33%，这证明了基于超图的优化方法在多智能体通信领域的巨大潜力。