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Furthermore, we inject domain knowledge through supervised fine-tuning (SFT) to enhance context understanding and subsequently guide domain reasoning through reinforcement learning (GRPO) with tailored rewar","title":"JUDO: A Juxtaposed Domain-Oriented Multimodal Reasoner for Industrial Anomaly QA","url":"https://arxiv.org/abs/2605.20284","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.20284v1 Announce Type: cross \nAbstract: Industrial anomaly detection has been significantly advanced by Large Multimodal Models (LMMs), enabling diverse human instructions beyond detection, particularly through visually grounded reasoning for better image understanding. However, LMMs lack domain-specific knowledge, which limits their ability to generate accurate responses in complex industrial scenarios. In this work, we present JUDO, Juxtaposed Domain-Oriented Multimodal Reasoner, a framework that efficiently incorporates domain knowledge and context in visual and textual reasoning. 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