AI RESEARCH
Towards Explainable Industrial Anomaly Detection via Knowledge-Guided Latent Reasoning
arXiv CS.CV
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ArXi:2602.09850v2 Announce Type: replace Industrial anomaly detection demands precise reasoning over fine-grained defect patterns. However, existing multimodal large language models (MLLMs), pretrained on general-domain data, often struggle to capture category-specific anomalies, thereby limiting both detection accuracy and interpretability. To address these limitations, we propose Reason-IAD, a knowledge-guided dynamic latent reasoning framework for explainable industrial anomaly detection. Reason-IAD comprises two core components.