Chat-of-Thought: Collaborative Multi-Agent System for Generating Domain Specific Information
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913889550073856 |
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| author | Constantinides, Christodoulos Lin, Shuxin Zhou, Nianjun Patel, Dhaval |
| author_facet | Constantinides, Christodoulos Lin, Shuxin Zhou, Nianjun Patel, Dhaval |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_10086 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Chat-of-Thought: Collaborative Multi-Agent System for Generating Domain Specific Information Constantinides, Christodoulos Lin, Shuxin Zhou, Nianjun Patel, Dhaval Computation and Language 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. |
| title | Chat-of-Thought: Collaborative Multi-Agent System for Generating Domain Specific Information |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2506.10086 |