Latent Energy Action Planning (LEAP) improves model predictive control by treating the entire action horizon as a differentiable variable and optimizing it using a frozen LeWorldModel (LeWM). LEAP couples terminal latent goal matching with a terminal-window state energy, ensuring both the predicted terminal latent and the decoder-predicted terminal descriptor align with the goal. In four control domains, LEAP raises mean success from 77.5% (LeWM+CEM) to 94.8%, a 17.3‑percentage‑point improvement while keeping the frozen LeWM representation.
arXiv:2604. 03208v2 Announce Type: replace Abstract: World models are a promising path to zero-shot embodied control through planning.
By Wancong Zhang, Basile Terver, Artem Zholus, Soham Chitnis, Harsh Sutaria, Mido Assran, Randall Balestriero, Amir Bar, Adrien Bardes, Yann LeCun, Nicolas Ballas
The paper introduces the Dual-Latent World Model (Dual-WM), which separates local execution and long-range planning into distinct latent spaces and dynamics models. A new learning method, Long-Horizon Representation Learning with Weighted Rollout (LoRe), supervises predictions at both levels using exponential horizon weights. Experiments on five goal-conditioned visual control tasks show that Dual-WM improves success rates over strong baselines, especially at longer horizons.
By Delin Zhao, Zhengrong Yue, Shaobin Zhuang, Junlin He, Xiaoyu Chen, Zikang Wang, Yuxin Liu, Limin Wang, Yali Wang
arXiv:2607. 17973v1 Announce Type: new Abstract: Latent world models have emerged as a powerful planning paradigm by learning action-conditioned predictive dynamics and using them as internal simulators to imagine and evaluate candidate action sequences.
By Letian Cheng, Qi Zhang, Yisen Wang
arXiv:2606. 26217v1 Announce Type: new Abstract: Joint-Embedding Predictive Architectures (JEPAs), including recent LeWorldModel (LeWM), have become a promising foundation for reconstruction-free visual world models.
By Yuntian Gao, Xiangyu Xu
arXiv:2607. 12547v1 Announce Type: cross Abstract: We investigate whether temporal hierarchy can improve LeWorldModel on long-horizon goal-conditioned control.
By Niccol\`o Caselli, Salvatore Lo Sardo, Francesco Massafra, Ippokratis Pantelidis, Samuele Punzo, Sathya Kamesh Bhethanabhotla