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.

LogiCode 是一个利用大型语言模型，针对工业环境中的逻辑异常进行识别的新型框架，通过利用大型语言模型进行逻辑推理，LogiCode 自动生成 Python 代码来定位诸如组件数量错误或缺失元素等异常，这在异常检测技术上取得了重大突破。引入了自定义数据集 “LOCO-Annotations” 和基准 “LogiBench” 来评估 LogiCode 在二元分类准确度、代码生成成功率和推理准确性等各种指标上的性能。研究结果表明，LogiCode 在逻辑异常检测方面具有提高解释能力，显著提高了逻辑异常检测的准确性，并为已识别的异常提供了详细解释。这代表了工业异常检测中更智能、基于大型语言模型的方法的重要转变，对特定于行业的应用具有重大影响。