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Latent world models scale predictably with compute via a second-order power law, enabling accurate forecasting of robotic planning performance and zero-shot task execution on real hardware.
Incorporating local context into discrete flow matching can reduce generative perplexity by up to 63%, revolutionizing how we approach generative modeling.
