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LLMs can generate significantly more novel and technically rigorous scientific ideas by explicitly learning to reason from motivations to methodologies.
Achieve 9x lower trajectory error and 3x better FID in motion generation by using a diffusion-based discrete motion tokenizer that elegantly handles both semantic and kinematic constraints.
