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UNIFUSION achieves unprecedented performance in generative tasks by seamlessly adapting autoregressive models to uniform-noise diffusion, outperforming all evaluated models on key metrics.
Projecting raw scores onto the bridge polytope eliminates negative weights and boosts generative performance, leading to a remarkable reduction in perplexity.
OLEDLM can generate novel OLED candidates that meet stringent optoelectronic criteria, revolutionizing the search in a vast chemical space.
Achieving an 88.75% success rate in dexterous manipulation tasks, RealDexUMI bridges the gap between human demonstrations and robot execution without losing critical dexterity.
VLMs can now drive embodied agents to navigate complex environments with unprecedented efficiency, thanks to a novel framework that bridges the gap between 2D semantic understanding and 3D spatial reasoning.
