Beyond Common Sense: Grounding Logical Anomaly Detection in Inspection Criteria

Multimodal large language models have significantly advancedzero-shot industrial anomaly detection, yet they remain highly constrainedwhen identifying logical anomalies. Fundamentally, logical anomalies arisefrom discrepancies between inspected products and their predefined con-straints. Current detection paradigms typically operate without accessto these explicit standards, forcing models to rely on pretrained com-mon sense rather than strict logical rules. To address this limitation,we introduce SCAN, a Systematic Criteria-driven ANomaly inspec-tion framework optimized through a two-stage post-training pipeline.We first employ supervised finetuning based on rule-by-rule Chain-of-Thought (CoT) reasoning to systematically verify explicit inspection cri-teria. We then apply reinforcement learning equipped with a rule-levelcredit assignment mechanism to enforce precise anomaly attribution andovercome severe errors caused by sparse feedback. However, such criteria-driven training demands large-scale logical anomaly data paired with ex-plicit inspection rules, yet existing datasets remain largely skewed towardstructural defects. To bridge this gap, we propose FLAW, a Fine-grainedLogical Anomaly dataset With criteria. This dataset leverages imagesynthesis and logical data retrieval to endow images with rich object-levelmetadata, enabling the construction of diverse inspection standards andlogical violations. Extensive experiments on MVTec LOCO demonstratethat our 8B-parameter SCAN model improves upon its base model by7.8% and establishes a new state of the art, outperforming GPT5.2-Chatand Gemini-3.1-Pro by 4.8% and 1.0%, respectively. Source code anddata will be publicly available at https://github.com/tzjtatata/SCAN.