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ARC-Bench: Closed-Loop Replanning Masks Broken Action Ranking in Frozen JEPA World Models
Reward-free latent world models plan by scoring candidate actions with distances in a frozen latent space: an action is preferred if its predicted future embedding lands closer to the goal embedding. This silently assumes that latent closeness is action-rankable, i.e., that ordering candidates by la
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