  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.
