AD-VRPD: ANOMALY DETECTION VIA VERIFIABLE ROLLOUT-GUIDED SELF-DISTILLATION

Zero-shot visual anomaly detection requires identifying and localizing defects without reference images at inference, yet generic anomaly queries leave the expected normal state and relevant defects unspecified. We address this ambiguity through diagnostic-text-conditioned inspection and adapt MMR-AD by pairing images with reusable descriptions of normality and possible defects. Explicit criteria clarify the task, but accurate localization remains challenging, particularly for small defects. We propose AD-VRPD, a verifiable rollout-guided on-policy self-distillation method that transfers spatial guidance from a frozen teacher viewing GT-marked images to a student viewing unmarked images. A task verifier regulates this supervision by reconstructing distillation targets when the teacher's guidance conflicts with verified rollout outcomes. Across four datasets held out from detector training, AD-VRPD improves localization over its pretrained backbone and vanilla self-distillation, enabling a compact multimodal detector to achieve competitive performance against larger and proprietary models.