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UCA-Flow achieves a remarkable 9.3% increase in action success rates while being up to 45.6 times faster than previous approaches.
Achieving state-of-the-art performance in scene understanding and controllable 4D generation, GaussianDWM++ redefines how we integrate language and 3D scene editing.
RynnValue shows that using temporal distance as a supervision target can outperform traditional preference-based methods in robotic learning, achieving higher accuracy and broader generalization.
