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Teaching VLMs to predict depth maps during pre-training unlocks surprisingly large gains in real-world robot task execution.
Forget fixed agent slots and quadratic attention: Gamma-World uses simplex embeddings and sparse hubs to generate interactive multi-agent environments with better fidelity and control, even generalizing from 2 to 4 players without retraining.
