用结构化流程提升工业异常检测的可靠性，先规划后评估。
Plan First, Judge Later, Run Better: A DMAIC-Inspired Agentic System for Industrial Anomaly Detection
- 基于DMAIC框架构建多智能体系统，先制定标准流程再评估策略。
- 在四种模态上平均检测性能比基线提升37.76%。
- 无需实际执行即可预判策略效果，节省计算成本，适合工业部署。
大型语言模型（LLM）代理在自动化复杂数据分析流程方面展现出潜力，但在高风险工业场景中的可靠部署仍具挑战性。工业异常检测（IAD）对制造质量、安全与效率至关重要，但现有基于LLM的IAD代理主要聚焦于执行，忽视了策略制定。因此，难以以统一且低成本的方式处理异构模态数据。受DMAIC质量管理框架启发，我们提出DMAIC-IAD（DMAIC-inspired Agentic Industrial Anomaly Detection），一种“先规划、后评估”的多智能体系统，使LLM代理与结构化工业问题解决流程对齐。DMAIC-IAD在生成策略前，将异构参考信息提炼为标准化操作流程（SOPs），并引入一个预训练的无执行裁判模型，无需昂贵的运行时试验即可对候选策略进行排序。在四种模态上的大量实验表明，相较于可比较的代理基线，DMAIC-IAD平均检测性能提升37.76%。
原文摘要 · Abstract (English)
Large language model (LLM) agents have shown promise in automating complex data-analysis workflows, but their reliable deployment remains challenging in high-stakes industrial scenarios. Industrial anomaly detection (IAD) is essential for manufacturing quality, safety, and efficiency, yet existing LLM-based IAD agents mainly focus on execution while under-exploiting strategy formulation. Consequently, they struggle to handle heterogeneous modalities in a unified and cost-effective manner. Inspired by the DMAIC quality-management framework, we propose DMAIC-IAD (DMAIC-inspired Agentic Industrial Anomaly Detection), a "Plan First, Judge Later" multi-agent system that aligns LLM agents with structured industrial problem-solving. DMAIC-IAD distills heterogeneous references into standardized operating procedures (SOPs) before strategy generation, and introduces a pre-trained execution-free judge model to rank candidate strategies without costly runtime trials. Extensive experiments across four modalities show that DMAIC-IAD improves average detection performance over applicable agentic baselines by 37.76%.
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