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Generative world action models can run up to 3× faster in closed-loop control without fine-tuning or task degradation by dynamically repairing and reusing intermediate computational states instead of recomputing them from scratch.
Instantaneous atomistic snapshots discard critical dynamical context; restoring this hidden state through multi-scale temporal memory enables neural world models to guide physical KMC simulations and transfer zero-shot across unseen material regimes.
