新闻
Agentic Root Cause Analysis through Evidence-Grounded Reasoning
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- AgentRCA, a zero-shot framework combining digital twins with language models, diagnoses industrial equipment faults without labeled training data.
- 为何重要
- Operations engineers managing complex systems like chemical plants or multiphase-flow facilities need transparent, explainable root cause analysis.
- 注意
- Paper demonstrates results on two specific facilities; generalization to other industrial domains and real-time performance constraints remain unvalidated.
收听本摘要
文章节选
-->
Computer Science > Artificial Intelligence
arXiv:2607.22385v1 (cs)
[Submitted on 24 Jul 2026]
Title: Agentic Root Cause Analysis through Evidence-Grounded Reasoning
Authors: Amaury Wei , Olga Fink
View a PDF of the paper titled Agentic Root Cause Analysis through Evidence-Grounded Reasoning, by Amaury Wei and Olga Fink
View PDF HTML (experimental)
Abstract: Diagnosing the root cause of anomalies is essential for safe industrial operation. Despite extensive sensor instrumentation, formulating hypotheses and gathering evidence remains a manual process, creating a major operational bottleneck. While existing data-driven approaches aim to automate this, two critical limitations restrict their deployment: their operate as black boxes unable to justify their diagnosis, and they require scarce labeled examples of faulty operation. To address this gap, we introduce AgentRCA, a zero-shot agentic framework for evidence-grounded root cause analysis. Rather than learning fault-specific mappings, AgentRCA performs inference-time reasoning by combining a data-driven digital twin (modeling normal system dynamics) with a tool-augmented large language model. The agent iteratively gathers statistical evidence, evaluates competing hypotheses, and identifies the physical fault that best explains the observed behavior. Evaluated on a real-world multiphase-flow facility and a large-scale
节选自原文。请前往来源阅读全文。
- agent
- agentic
- reasoning
The Agent Architect
每周一个模式、一个权衡、一个生产事故案例。为构建智能体系统的人准备的每周简报。
每周一封邮件,一键退订。您的地址仅用于发送简报。