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Qualitative examples show the PhysBrain 1.5 model's ability to produce end-effector trajectories and predict future scenes through spatially aligned RGB, depth, and robot-mask outputs.
CSM$_{\text{CCTA}}$ outperformed existing metrics, revealing that specialized models can significantly enhance the accuracy of automated CCTA report generation.
Text-to-music models may appear controllable, but a closer look reveals that much of their output is simply a reflection of their training data rather than genuine instruction-following.