HyperAgent：利用超圖實現多智能體通信中的拓撲優化

HyperAgent: Leveraging Hypergraphs for Topology Optimization in Multi-Agent Communication

October 12, 2025

作者: Heng Zhang, Yuling Shi, Xiaodong Gu, Zijian Zhang, Haochen You, Lubin Gan, Yilei Yuan, Jin Huang

cs.AI

摘要

近期，基於大型語言模型的多智能體系統在有效溝通方面展現了顯著的集體智能。然而，現有方法面臨兩個主要挑戰：（i）群體協作建模效果不佳，因為它們依賴於圖結構中的成對邊表示，限制了捕捉多智能體之間關係的能力；（ii）通信拓撲設計的任務適應性有限，導致簡單任務的通信成本過高，而複雜場景的協調不足。這些問題限制了自適應協作框架的可擴展性和實際部署。為解決這些挑戰，我們提出了HyperAgent，這是一個基於超圖的框架，通過直接超邊表示優化通信拓撲並有效捕捉群體協作模式。與基於邊的方法不同，HyperAgent使用超邊將同一子任務中的多個智能體連接起來，並利用超圖卷積層實現協作組中的一步信息聚合。此外，它結合了帶有稀疏正則化的變分自編碼器框架，根據任務複雜度動態調整超圖拓撲。實驗結果凸顯了HyperAgent在性能和效率上的優勢。例如，在GSM8K上，HyperAgent達到了95.07%的準確率，同時減少了25.33%的token消耗，展示了基於超圖優化在多智能體通信中的潛力。

English

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.