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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.