arXiv:2607. 19719v1 Announce Type: new Abstract: Latent world models improve sample efficiency in continuous control by optimizing policies over imagined latent trajectories, but common neural transitions offer limited direct control over modal persistence and error accumulation in long rollouts.
By Jiaqi Li, Xinglong Zhang, Haibin Xie, Yixing Lan, Wei Pan, Xin Xu
arXiv:2609.37156v1 Announce Type: cross
Abstract: World models enable agents to learn and plan in imagination, but predictions beyond their experience can become unreliable and mislead decisions. Exi...
By Ziqi Wen, Ting Xu, Lianyu Wang, Xian Lin, Yanda Meng, Huazhu Fu, Meng Wang, Ching-Yu Cheng
arXiv:2507. 06722v2 Announce Type: replace-cross Abstract: Understanding how large language models (LLMs) internally represent and process their predictions is central to detecting uncertainty and preventing hallucinations.
By Sunwoo Kim, Haneul Yoo, Alice Oh
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
The paper introduces an adaptive rollout truncation method for offline world model training that uses epistemic uncertainty to decide when to stop autoregressive rollouts. By calibrating a threshold during a warm‑up phase, the approach replaces fixed‑horizon rollouts with uncertainty‑driven truncation, evaluated with ensemble and Monte Carlo dropout estimators. Experiments on ANYmal‑D and ANT demonstrate that this strategy matches or surpasses fixed‑horizon training while reducing cumulative rollout steps by about 72%.
By Nikodem Sebastian Zymla, Laurin Thiele, Johannes Pitz
arXiv:2607. 14180v1 Announce Type: cross Abstract: World models are widely used in offline reinforcement learning (RL) to improve sample efficiency and generate experience beyond a fixed dataset.
By Logan Mondal Bhamidipaty, Mykel Kochenderfer, Subramanian Ramamoorthy