This paper presents LogiCode, a novel framework that leverages Large Language Models (LLMs) for identifying logical anomalies in industrial settings, moving beyond traditional focus on structural inconsistencies. By harnessing LLMs for logical reasoning, LogiCode autonomously generates Python codes to pinpoint anomalies such as incorrect component quantities or missing elements, marking a significant leap forward in anomaly detection technologies. A custom dataset "LOCO-Annotations" and a benchmark "LogiBench" are introduced to evaluate the LogiCode's performance across various metrics including binary classification accuracy, code generation success rate, and precision in reasoning. Findings demonstrate LogiCode's enhanced interpretability, significantly improving the accuracy of logical anomaly detection and offering detailed explanations for identified anomalies. This represents a notable shift towards more intelligent, LLM-driven approaches in industrial anomaly detection, promising substantial impacts on industry-specific applications.

翻译：本文提出LogiCode，这是一种利用大语言模型（LLMs）识别工业场景中逻辑异常的新型框架，突破了传统方法对结构不一致性的关注局限。通过利用LLMs进行逻辑推理，LogiCode能够自主生成Python代码来定位异常（如组件数量错误或元素缺失），标志着异常检测技术的重大突破。我们引入了定制数据集"LOCO-Annotations"和基准测试"LogiBench"，从二分类准确率、代码生成成功率及推理精度等多个指标评估LogiCode的性能。实验结果表明，LogiCode具备增强的可解释性，显著提升了逻辑异常检测的准确性，并为识别出的异常提供了详细解释。这标志着工业异常检测向更智能化的LLM驱动方法的重要转变，有望对行业特定应用产生深远影响。