Chat-of-Thought：用于生成领域特定信息的协作式多智能体系统
Chat-of-Thought: Collaborative Multi-Agent System for Generating Domain Specific Information
AI总结：
该研究提出协作式多智能体系统Chat-of-Thought，采用多角色LLM智能体、动态任务路由与多角色讨论迭代优化机制，可高效生成工业资产FMEA文档，为工业设备监测领域的相关挑战提供新解决方案。
摘要：
本文提出了一种名为Chat-of-Thought的新型多智能体系统，旨在为工业资产生成失效模式与影响分析（Failure Modes and Effects Analysis, FMEA）文档。Chat-of-Thought采用多个基于大语言模型（Large Language Model, LLM）、具备特定角色的协作智能体，借助先进AI技术与动态任务路由优化FMEA表格的生成与验证。该系统的核心创新是引入“思维聊天”机制，通过动态的多角色驱动讨论实现内容的迭代优化。本研究探索了工业设备监测这一应用领域，点明了关键挑战，并证明Chat-of-Thought通过交互式、模板驱动的工作流与上下文感知的智能体协作，具备应对这些挑战的潜力。
英文摘要：
This paper presents a novel multi-agent system called Chat-of-Thought, designed to facilitate the generation of Failure Modes and Effects Analysis (FMEA) documents for industrial assets. Chat-of-Thought employs multiple collaborative Large Language Model (LLM)-based agents with specific roles, leveraging advanced AI techniques and dynamic task routing to optimize the generation and validation of FMEA tables. A key innovation in this system is the introduction of a Chat of Thought, where dynamic, multi-persona-driven discussions enable iterative refinement of content. This research explores the application domain of industrial equipment monitoring, highlights key challenges, and demonstrates the potential of Chat-of-Thought in addressing these challenges through interactive, template-driven workflows and context-aware agent collaboration.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
