arXiv:2607. 11964v1 Announce Type: new Abstract: Autonomous driving requires long-horizon closedloop decision making in dynamic traffic environments.
By Yongzhi Liu, Yang Xiao, Zhong Cao, Zeng Kang, Sunan Zhang, Zhaozhi Dong, Guojun Yu, Weichao Zhuang
Autonomous driving requires long-horizon closedloop decision making in dynamic traffic environments. Latent world models offer an effective framework for this problem by enabling imagination-based decision making in compact latent spaces.
arXiv:2606. 20104v1 Announce Type: cross Abstract: Perception for action suggests that representations of the world should be shaped not by visual fidelity alone, but by their relevance for actions.
By Petr Ivashkov, Randall Balestriero, Bernhard Sch\"olkopf
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
arXiv:2604. 25416v2 Announce Type: replace Abstract: Model-based reinforcement learning distinguishes between dynamics models operating on proprioceptive states and latent dynamics models typically operating on high-dimensional image observations.
By Julia Berger, Bernd Frauenknecht, Sebastian Trimpe, Bastian Leibe
arXiv:2608. 04471v1 Announce Type: cross Abstract: Time series in real-world applications are often generated by nonlinear dynamical systems, making accurate forecasting challenging.
By Mengzhou Gao, Huangqian Yu, Pengfei Jiao