# Towards Explainable Industrial Anomaly Detection via Knowledge-Guided Latent Reasoning Source: [Towards Explainable Industrial Anomaly Detection via Knowledge-Guided Latent Reasoning]() Collection index: [llms.txt]() Data updated: 2026-05-12T05:00:04.826Z Authors: Peng Chen, Chao Huang, Yunkang Cao, Chengliang Liu, Wei Wang, Wenqiang Wang, Mingbo Yang, Li Shen, Wenqi Ren, Xiaochun Cao Published: 2026-02-10T14:54:17.000Z ## Abstract 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. First, a retrieval-augmented knowledge module incorporates category-specific textual descriptions into the model input, enabling context-aware reasoning over domain-specific defects. Second, an entropy-driven latent reasoning mechanism conducts iterative exploration within a compact latent space using optimizable latent think tokens, guided by an entropy-based reward that encourages confident and stable predictions. Furthermore, a dynamic visual injection strategy selectively incorporates the most informative image patches into the latent sequence, directing the reasoning process toward regions critical for anomaly detection. Extensive experimental results demonstrate that Reason-IAD consistently outperforms state-of-the-art methods across multiple tasks. The code will be publicly available at https://github.com/chenpeng052/Reason-IAD. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Industrial Anomaly Detection on MMAD](): Industrial Anomaly Detection; MMAD; Anomaly Discrimination: 67.73. - [Anomaly Discrimination on Anomaly Discrimination Tasks](): Anomaly Discrimination; Anomaly Discrimination Tasks; Accuracy: 79.42. - [Industrial Anomaly Detection on MMAD one-shot 1.0 (test)](): Industrial Anomaly Detection; MMAD one-shot 1.0 (test); Anomaly Discrimination Score: 75.24. ## Original sources and implementation [Original paper]() [PDF]()