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Bypassing synthetic data, MLLMCLIP achieves state-of-the-art compositional accuracy in vision-language tasks through innovative feature-level distillation.
DynaVieW reveals that a schema-guided approach can drastically improve the modeling of visual dynamics, leading to superior performance in narrative generation and simulation tasks.
Achieving state-of-the-art performance with just 8 billion parameters, Embodied-R1.5 redefines the capabilities of embodied models in complex physical tasks.
