Chat-of-Thought: Collaborative Multi-Agent System for Generating Domain Specific Information

2025-06-11

AI Summary

To address the low efficiency, high domain expertise requirements, and error-proneness of manual FMEA documentation for industrial assets, this paper proposes a multi-role LLM agent collaboration framework. Methodologically, we introduce a “Chat of Thought” mechanism that integrates dynamic task routing, context-aware collaboration, structured template guidance, and domain-specific FMEA knowledge injection to enable iterative, persona-driven generation and cross-validation. Our key contribution is the first application of an interpretable multi-agent negotiation mechanism to industrial reliability documentation generation, ensuring both accuracy and process traceability. Experimental results in an equipment monitoring scenario demonstrate a 42% reduction in FMEA table generation error rate and a 91% expert review pass rate—significantly outperforming baseline approaches.

Technology Category

• Multiagent Systems: Mechanism Design
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Agentic search

Abstract

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.

Problem

• Generates FMEA documents for industrial assets
• Uses multi-agent LLM collaboration for content refinement
• Addresses industrial equipment monitoring challenges

Innovation

• Multi-agent LLM system for FMEA generation
• Dynamic task routing optimizes validation
• Chat of Thought enables iterative refinement