Comparative Evidence Policy Optimization for Multimodal Industrial Anomaly Detection

Industrial anomaly detection (IAD) is a critical task for automated quality inspection and intelligent manufacturing. Recent advances in multimodal large language models (MLLMs) have extended IAD beyond conventional anomaly classification and localization toward unified visual reasoning and natural-language explanation. However, our analysis reveals a fundamental evidence-grounding gap in reference-guided anomaly reasoning: MLLMs often fail to reliably ground their predictions in anomaly-relevant regions and underutilize the normal reference. More importantly, weak anomaly grounding and insufficient Query–Reference comparison substantially degrade anomaly reasoning performance. This observation motivates a simple principle: reliable anomaly reasoning should be grounded in defect-relevant visual evidence and explicitly supported by comparison against the corresponding normal reference. To this end, we first introduce Comparative Attention Supervision (CAS), which establishes anomaly-aware visual grounding by encouraging the generated reasoning to attend to defect-relevant Query regions and their corresponding normal Reference context. We further propose Comparative Evidence Policy Optimization (CEPO), which moves beyond outcome-only optimization by explicitly evaluating whether the generated reasoning is grounded in anomaly-relevant Query–Reference comparative evidence. Experiments on MMAD and MMR-AD demonstrate the effectiveness of our approach across diverse anomaly understanding and localization tasks, as well as its generalization to unseen datasets. Further ablations and qualitative analyses highlight the complementary benefits of anomaly-aware grounding and comparative evidence in improving IAD performance.