标题为英语ARC-Bench: Closed-Loop Replanning Masks Broken Action Ranking in Frozen JEPA World Models
arXiv:2609.05461v1 Announce Type: new Abstract: 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…
AI 摘要：要点
- arXiv:2609.05461v1 Announce Type: new Abstract: 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…
ARC-Bench: Closed-Loop Replanning Masks Broken Action Ranking in Frozen JEPA World Models
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