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NVIDIA Research introduces Hydra-0, a generalist world model that conditions on action flow to represent robot actions as pixel motion, enabling learning across diverse embodiments with significant error reductions and zero-shot capabilities.
Nvidia's research demonstrates that a well-designed harness around an AI model, rather than the model itself, significantly improves performance on long-horizon tasks, with Claude Opus 5 achieving a perfect score on the ARC-AGI-3 benchmark.
NVIDIA Research introduces SANA-Video 2.0, a hybrid video diffusion transformer that generates high-quality 720p video on a single GPU, achieving up to 120× speedup over Wan 2.2-14B via hybrid linear-softmax attention and block attention residuals.
This blog from NVIDIA Research discusses how sequence parallelism can scale long-video training systems for both understanding and generation, addressing the challenge of fitting very long video sequences across multiple GPUs.
NVIDIA introduces PiD, a Pixel Diffusion Decoder that replaces traditional VAE/RAE decoders in latent diffusion models, enabling fast, high-resolution decoding with up to 6× speedup and improved visual fidelity.