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Achieving superior zero-shot generalization, the World Action Planner outperforms leading policy models by leveraging the reasoning power of Vision-Language Models.
Fixed-position models struggle with any-order inference, but new masked diffusion techniques unlock flexible generation capabilities that enhance performance in coding and reasoning tasks.
Exploration as a pretraining axis can boost generative model performance by up to 36%, revolutionizing how we approach end-to-end generation.
A 1000x larger video reasoning dataset reveals early signs of emergent generalization, offering a new foundation for training and evaluating spatiotemporal AI.
