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Reconfiguring attention in frozen Vision Foundation Models can dramatically enhance their ability to detect anomalies in industrial settings, achieving superior localization without retraining.
Current MLLMs excel at visual reproduction but falter in generating the necessary data semantics and interaction logic for coordinated multi-view interfaces.
General-purpose self-supervised audio representations can outperform specialized supervised models, reshaping the landscape of audio understanding in ALLMs.
