Adrien Ramanana Rahary, Yutong Bai, Nicolas Dufour, Patrick Pérez, David Picard · 2026 · arXiv
DOI: 10.48550/arXiv.2610.30129 · open-access PDF
Video world models predict future frames from past frames and actions. They serve as simulators in which agents train, plan or are evaluated, and that humans can control interactively. They generate in the latent space of a video autoencoder, whose compression sets their cost per frame and how long each action waits to take effect. Yet world models often reuse the autoencoders of text-to-video generators, built for offline generation with no agent waiting and, without their training data and code, not retrainable at another compression. We therefore ask how, where and how much a world model sh
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