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REZE achieves a new state of the art in training-free video temporal grounding, outperforming fully supervised models in highlight detection and moment retrieval.
Retaining over 64% of in-context learning gains without further environment interactions could revolutionize how agents learn from their experiences.
Learning from real-world environments follows a precise log-sigmoid scaling law, with agent performance and learning speed improving dramatically over time.
Generative video models can now simulate a continuously evolving world, even when objects are out of sight, thanks to a new framework that maintains persistent global state.
Open-source VQ-VA models just got a massive boost: a new dataset and benchmark close the gap with proprietary systems on visual question-visual answering.
