AI RESEARCH
AgentIAD: Agentic Industrial Anomaly Detection via Adaptive Memory Augmentation
arXiv CS.CV
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ArXi:2512.13671v2 Announce Type: replace Industrial anomaly detection (IAD) is challenging due to the subtle and highly localized nature of many defects, which single-pass vision--language models (VLMs) often fail to capture. Moreover, existing approaches lack mechanisms to actively acquire complementary evidence during inference. We propose AgentIAD, an agentic vision--language framework that enables iterative industrial inspection through a unified action space.