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Results indicate that a carefully calibrated lightweight fusion stage can strengthen few-shot multimodal anomaly detection while retaining branch-wise localization behavior.
Industrial anomaly detection (IAD) requires reliable identification and precise localization of subtle defects, yet most existing methods depend on manually tuned decision thresholds and large collections of defect-free samples, limiting scalability in real-world production. To address these constraints, we present gro...
A cross-regime diagnostic framework for evaluating symbol consistency in MLLMs is proposed and its multi-stage failure mechanisms are revealed, offering a reusable diagnostic perspective for reliability assessment and symbol-capability improvement in MLLMs.
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