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 adversary and communications budgets, TodyComm delivers superior task effectiveness while retaining token efficiency and scalability.

翻译：基于大型语言模型（LLM）的多轮多智能体系统依赖于有效的通信结构来支持跨轮次的协作。然而，现有方法大多在推理过程中采用固定的通信拓扑，这在许多现实应用中存在不足，因为智能体的角色可能因动态对抗、任务进展或时变约束（如通信带宽）而随轮次发生变化。本文提出通过TodyComm算法来解决这一问题，这是一种面向任务的动态通信算法。它生成行为驱动的协作拓扑，能够适应每轮动态，并通过策略梯度优化任务效用。在五个基准测试上的实验表明，在动态对抗和通信预算约束下，TodyComm在保持令牌效率和可扩展性的同时，实现了更优的任务效能。