Towards Explainable Industrial Anomaly Detection via Knowledge-Guided Latent Reasoning

TL;DR

This paper introduces Reason-IAD, a knowledge-guided latent reasoning framework that enhances explainability and accuracy in industrial anomaly detection by integrating domain-specific knowledge and dynamic visual cues.

Contribution

It proposes a novel dynamic latent reasoning approach with knowledge retrieval and visual injection strategies for improved industrial anomaly detection.

Findings

Outperforms state-of-the-art methods across multiple tasks.

Improves interpretability through knowledge-guided reasoning.

Achieves higher detection accuracy with dynamic visual cues.

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.