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Beyond One-Step Accuracy: State-Affine Latent Transition for Reliable Visual Planning
Joint-embedding world models enable visual planning by learning action-conditioned dynamics in latent space. Yet they are commonly trained for one-step prediction on encoded states, while planning recursively applies the learned transition to its own predictions. One-step accuracy therefore does not
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