AutoIAD：面向自动化工业异常检测的管理者驱动多智能体协作框架
AutoIAD: Manager-Driven Multi-Agent Collaboration for Automated Industrial Anomaly Detection
AI总结：
AutoIAD提出管理者驱动的多智能体协作框架，利用中央智能体编排数据准备、加载、模型设计与训练子智能体，结合领域知识库，在MVTec AD基准上显著提升工业异常检测的任务完成率与AUROC性能。
摘要：
工业异常检测（IAD）对于制造质量控制至关重要，但传统上针对各种应用场景需要大量人工投入。本文介绍了AutoIAD，一个多智能体协作框架，专门设计用于端到端自动化开发工业视觉异常检测。AutoIAD利用一个管理者驱动的中央智能体来编排专门的子智能体（包括数据准备、数据加载器、模型设计器、训练器），并集成一个领域特定的知识库，该框架智能地处理整个流水线，使用原始工业图像数据来开发训练好的异常检测模型。我们使用MVTec AD数据集构建了一个全面的基准，以评估AutoIAD在各种LLM后端上的表现。大量实验表明，AutoIAD在任务完成率和模型性能（AUROC）方面显著优于现有的通用智能体协作框架和传统AutoML框架，同时通过迭代细化有效缓解了幻觉等问题。消融研究进一步证实了管理者中央智能体和领域知识库模块在生成稳健且高质量的IAD解决方案中的关键作用。
英文摘要：
Industrial anomaly detection (IAD) is critical for manufacturing quality control, but conventionally requires significant manual effort for various application scenarios. This paper introduces AutoIAD, a multi-agent collaboration framework, specifically designed for end-to-end automated development of industrial visual anomaly detection. AutoIAD leverages a Manager-Driven central agent to orchestrate specialized sub-agents (including Data Preparation, Data Loader, Model Designer, Trainer) and integrates a domain-specific knowledge base, which intelligently handles the entire pipeline using raw industrial image data to develop a trained anomaly detection model. We construct a comprehensive benchmark using MVTec AD datasets to evaluate AutoIAD across various LLM backends. Extensive experiments demonstrate that AutoIAD significantly outperforms existing general-purpose agentic collaboration frameworks and traditional AutoML frameworks in task completion rate and model performance (AUROC), while effectively mitigating issues like hallucination through iterative refinement. Ablation studies further confirm the crucial roles of the Manager central agent and the domain knowledge base module in producing robust and high-quality IAD solutions.
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。
