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A frozen world-model planner's performance can be drastically improved by adapting its latent rollouts to controller-induced execution shifts, rather than directly substituting predicted trajectories.
Robots can now reliably avoid forbidden actions, achieving near-perfect success rates on negated instructions with a lightweight, plug-and-play module.