HyperAgent: Leveraging Hypergraphs for Topology Optimization in
  Multi-Agent Communication
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.
68ef4bbb486b78128f0e35e1
Heng Zhang
68ef4bbb486b78128f0e35e2
Yuling Shi
claimed_verified
2025-10-15T15:24:40.752Z
645b0c3ec35da9c7afd95421
https://cdn-avatars.huggingface.co/v1/production/uploads/645b0c3ec35da9c7afd95421/vYBrCDagHsXAo6J2p-uG0.jpeg
Yuling
user
YerbaPage
68ef4bbb486b78128f0e35e3
Xiaodong Gu
68ef4bbb486b78128f0e35e4
Zijian Zhang
68ef4bbb486b78128f0e35e5
Haochen You
68ef4bbb486b78128f0e35e6
Lubin Gan
68ef4bbb486b78128f0e35e7
Yilei Yuan
68ef4bbb486b78128f0e35e8
Jin Huang
HyperAgent: Leveraging Hypergraphs for Topology Optimization in
  Multi-Agent Communication
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.
HyperAgent, a hypergraph-based framework, optimizes communication topologies and captures group collaboration patterns, improving performance and efficiency in multi-agent systems.
hypergraph-based framework hyperedges hypergraph convolutional layers variational autoencoder sparsity regularization GSM8K token consumption