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Models trained with VBVR-Pro not only excel in a controlled task space but also show significant transferability to external benchmarks, revealing critical insights into visual reasoning mechanisms.
Achieving over 10% improvement in manipulation success rates, Lift3D-VLA redefines the integration of 3D geometry and action generation in robotic systems.
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.
