Global Logic and Local Search: Dual-Stream Multimodal In-Context Learning for Verifiable Industrial Anomaly Detection

Large Multimodal Models (LMMs) show strong few-shotgeneralization, but industrial anomaly detection remains difficult becausedefects are small, input resolution is limited, and textual standardsare not always grounded in visual evidence. Recent optimization-basedmethods improve alignment through fine-tuning, but they often requiremany defective samples, which are unavailable in early deployment. Wepresent Global Logic and Local Search (GLLS), a training-freeframework for reference-guided multimodal in-context verification. GLLSuses a Part-Aware Visual-Logical Atlas to organize normal references andstructured specifications in the inference context. It combines a Global& Logic Stream, where SAM 3 extracts partially checkable visual facts,with a Fine-Grained & Actions Stream, where MCTS selects localevidence crops under a fixed budget. Experiments on MMAD-QA andadditional anomaly detection datasets show consistent gains over matchedand general-purpose baselines, while keeping the final diagnostic decisiontraceable to explicit visual evidence throughout the inspection trace.