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Iterative refinement in data space, not flow space, unlocks fast, high-fidelity generative sampling without manual timestep tuning.
Training a single model across text, images, video, 3D geometry, and hidden representations unlocks "Context Unrolling," where the model reasons across modalities to improve reasoning fidelity.
