结构化交流，分层行动：LLM多智能体系统的协作框架

Talk Structurally, Act Hierarchically: A Collaborative Framework for LLM Multi-Agent Systems

February 16, 2025

作者: Zhao Wang, Sota Moriyama, Wei-Yao Wang, Briti Gangopadhyay, Shingo Takamatsu

cs.AI

摘要

最近基于LLM的多智能体（LLM-MA）系统取得了一些进展，展现出潜力，但在智能体协作处理复杂任务时，仍然存在重大挑战，特别是在管理沟通和改进方面。本文提出了“结构化对话，分层行动”（TalkHier）的新框架，引入了结构化通信协议以进行富有上下文的交流，并采用分层改进系统来解决输出错误、虚假和偏见等问题。TalkHier在各种任务上超越了各种类型的最先进技术，包括推理扩展模型（OpenAI-o1）、开源多智能体模型（例如AgentVerse）以及当前LLM和单智能体基线（例如ReAct、GPT4o）上的多数投票策略，包括开放领域问答、特定领域选择性提问和实用广告文本生成。这些结果突显了其为LLM-MA系统设立新标准的潜力，为更有效、适应性强和协作性更强的多智能体框架铺平了道路。代码可在https://github.com/sony/talkhier找到。

English

Recent advancements in LLM-based multi-agent (LLM-MA) systems have shown promise, yet significant challenges remain in managing communication and refinement when agents collaborate on complex tasks. In this paper, we propose Talk Structurally, Act Hierarchically (TalkHier), a novel framework that introduces a structured communication protocol for context-rich exchanges and a hierarchical refinement system to address issues such as incorrect outputs, falsehoods, and biases. TalkHier surpasses various types of SoTA, including inference scaling model (OpenAI-o1), open-source multi-agent models (e.g., AgentVerse), and majority voting strategies on current LLM and single-agent baselines (e.g., ReAct, GPT4o), across diverse tasks, including open-domain question answering, domain-specific selective questioning, and practical advertisement text generation. These results highlight its potential to set a new standard for LLM-MA systems, paving the way for more effective, adaptable, and collaborative multi-agent frameworks. The code is available https://github.com/sony/talkhier.