HyperAgent: Leveraging Hypergraphs for Topology Optimization in
  Multi-Agent Communication
Authors: name: Heng Zhang, name: Yuling Shi, name: Xiaodong Gu, name: Zijian Zhang, name: Haochen You, name: Lubin Gan, name: Yilei Yuan, name: Jin Huang

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)
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) 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 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.