New paper advised by Yann LeCun! "H-JEPA: End-to-End Learning of Hierarchical World Models for Visual Planning" Most world models plan in a single latent space and at one timescale, which makes long-horizon planning expensive and forces one representation to handle both low-level motion and high-level goals. H-JEPA instead stacks JEPA world models across multiple timescales, with higher levels predicting farther into the future. The highest level first figures out roughly where the agent should go, then each lower level turns that into increasingly concrete subgoals until the bottom level outputs actual actions. On Visual AntMaze, this 3-level setup increases success from 18% to 73% while using less planner compute.