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ARC-Bench: Closed-Loop Replanning Masks Broken Action Ranking in Frozen JEPA World Models

This story is from 2026-09-09. It is preserved in the archive; the latest stories are on the live feed.

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 latent closeness is acti…

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  1. 2026-09-09 04:00 · arXiv cs.AI
    ARC-Bench: Closed-Loop Replanning Masks Broken Action Ranking in Frozen JEPA World Models

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