用动态选人机制让多智能体协作更灵活高效
AnyMAC: Cascading Flexible Multi-Agent Collaboration via Next-Agent Prediction
- 通过预测下一参与智能体实现角色动态切换
- 在多个基准上性能领先且通信开销更低
- 适合需要灵活协作的复杂任务场景
基于大语言模型的多智能体协作近年来展现出集体智能的强大潜力，但现有方法多依赖静态或图结构的智能体拓扑，缺乏通信灵活性。本文提出一种新框架，以序列结构替代图结构，大幅扩展智能体通信拓扑空间。核心包含两个方向：(1) 下一智能体预测（Next-Agent Prediction），每步动态选择最合适的智能体角色；(2) 下一上下文选择（NCS），使每个智能体可有选择地访问任意先前步骤的相关信息。两者协同构建任务自适应的通信路径，支持角色灵活切换与全局信息流动。在多个基准上的广泛评估表明，该方法在性能上显著优于现有方案，同时大幅降低通信开销。
原文摘要 · Abstract (English)
Recent progress in large language model (LLM)-based multi-agent collaboration highlights the power of structured communication in enabling collective intelligence. However, existing methods largely rely on static or graph-based inter-agent topologies, lacking the potential adaptability and flexibility in communication. In this work, we propose a new framework that rethinks multi-agent coordination through a sequential structure rather than a graph structure, offering a significantly larger topology space for multi-agent communication. Our method focuses on two key directions: (1) Next-Agent Prediction, which selects the most suitable agent role at each step, and (2) Next-Context Selection (NCS), which enables each agent to selectively access relevant information from any previous step. Together, these components construct task-adaptive communication pipelines that support both role flexibility and global information flow. Extensive evaluations across multiple benchmarks demonstrate that our approach achieves superior performance while substantially reducing communication overhead.
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