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 a compositional continual learning benchmark for world models in robot manipulation, designed to isolate knowledge reuse from learning speed and capacity. Tasks are curated to combine previously seen action and perception components, allowing analysis of how different modalities affect reuse. Experiments show that modular world models better balance reuse and forgetting than conventional methods, yet none fully solve the challenge, highlighting the need for models explicitly built to reuse knowledge without forgetting.
By Haoyu Zhou, Joe Watson, Anson Lei, Ingmar Posner
arXiv:2607. 03964v1 Announce Type: cross Abstract: World models are rapidly becoming a core infrastructure for embodied intelligence and interactive agents: they provide controllable simulators in which agents can perceive, act, forecast, and acquire scalable experience.
By Jianjie Fang, Yongyan Xu, Ziyou Wang, Chen Gao, Yuchao Huang, Zhaolu Wang, Rongze Tang, Mingyuan Jia, Baining Zhao, Weichen Zhang, Xin Zhang, Haisheng Su, Yu Shang, Wei Wu, Xinlei Chen, Yong Li
arXiv:2607. 04409v1 Announce Type: new Abstract: Learning and planning in imagination using world models provides an effective paradigm for training agents for decision-making.
By Fan Feng, Yujia Zheng, Minghao Fu, Yongqiang Chen, Guangyi Chen, Kevin Murphy, Biwei Huang, Kun Zhang
arXiv:2607. 18715v1 Announce Type: new Abstract: Latent world models underpin much of modern model-based control, yet current action-conditioned formulations supervise the next-latent transition with a single, undifferentiated target, forcing a monolithic learning signal to absorb every source of state change.
By Yi-Ge Zhang, Tianqi Du, Qi Zhang, Yisen Wang
The paper introduces the Latent Evolution Operator Network (LEON), a new architecture for modeling latent state transitions in World Action Models (WAMs). LEON uses a learned observable space with context‑modulated operator‑based propagation and additive forcing, grounded in a controlled Koopman generator framework. Experiments on two WAM formulations show that LEON improves closed‑loop performance and robustness, demonstrating that the way transitions are realized is a crucial architectural choice in latent WAMs.
By Xiaoxiao Lu, Yunlong Dong, Jiahao Shi, Ye Yuan