动态调整多智能体通信方式，让系统自适应任务变化。
TodyComm: Task-Oriented Dynamic Communication for Multi-Round LLM-based Multi-Agent System
- 根据任务需求实时调整智能体间通信结构
- 在对抗环境与带宽受限下仍保持高效表现
- 适合需要灵活协作的复杂多轮任务场景
多轮基于大语言模型的多智能体系统依赖有效的通信结构以支持跨轮次协作。然而，现有方法在推理中采用固定通信拓扑，在动态对手、任务推进或通信带宽等时变约束下表现不足。本文提出TodyComm——一种任务导向的动态通信算法，通过策略梯度优化生成行为驱动的协作拓扑，使其随每轮动态变化。五个基准测试结果表明，在动态对抗环境与通信预算约束下，TodyComm在任务性能、令牌效率、可扩展性及对不同对抗条件的泛化能力上均优于现有方法。
原文摘要 · Abstract (English)
Multi-round LLM-based multi-agent systems rely on effective communication structures to support collaboration across rounds. However, most existing methods employ a fixed communication topology during inference, which falls short in many realistic applications where the agents' roles may change \textit{across rounds} due to dynamic adversary, task progression, or time-varying constraints such as communication bandwidth. In this paper, we propose addressing this issue through TodyComm, a \textbf{t}ask-\textbf{o}riented \textbf{dy}namic \textbf{comm}unication algorithm. It produces behavior-driven collaboration topologies that adapt to the dynamics at each round, optimizing the utility for the task through policy gradient. Experiments on five benchmarks demonstrate that, under both dynamic adversarial settings and communication budget constraints, TodyComm achieves superior task performance while maintaining token efficiency, scalability, and strong generalizability across varying adversarial conditions.
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