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Phase-consistent expert allocation in PAMAE boosts task success rates by over 9%, revolutionizing action generation in multi-stage robotic manipulation.
Embedding physical consistency checks and self-reflection into VLA policies boosts robotic manipulation success rates by over 5%.
PhySciBench reveals that top LLMs struggle with scientific reasoning, achieving only 33.5% accuracy, while DelveAgent demonstrates a promising 7.5% improvement in performance.
Autoregressive video generation gets a 6x speed boost without sacrificing quality, thanks to a motion-aware caching strategy that finally respects the fact that not all pixels are created equal.
