摘要
Multimodal large language models have recently shown promise for industrial anomaly understanding, where a model is expected not only to decide whether an object is defective, but also to localize the supporting visual evidence and explain its decision. However, existing models often rely on language priors or shortcut reasoning when facing fine-grained industrial defects. This leads to inconsistent behaviors, such as correct answers with invalid locations, hallucinated defect boxes on normal samples, or rationales that contradict the final prediction.
We propose EGVLR, an evidence-grounded vision–language reinforcement framework for industrial anomaly reasoning. EGVLR enforces a unified diagnostic response format that explicitly separates visual evidence, diagnostic logic, spatial location, and the final answer. The framework progressively teaches the model to attend to localized defect evidence, incorporate industrial-domain reasoning, and optimize answer–location–rationale consistency through geometry- and semantics-aware preference optimization. For visualization, predicted boxes can be further converted into dense masks using an off-the-shelf segmentation backend.
Experiments on MMAD show that EGVLR improves evidence-grounded anomaly reasoning, with consistent gains in defect localization, reasoning-dependent question answering, and spatial false-positive control. These results suggest that explicitly coupling answers, locations, and rationales is important for reliable industrial anomaly understanding. Project page: https://github.com/leolin65/EGVLR-project.