arXiv:2608.29029v3 Announce Type: replace-cross
Abstract: Joint-Embedding Predictive Architectures (JEPAs) provide a powerful framework for latent world modeling and planning in a reconstruction-free...
By Yanchen Huo, Ziying Song, Yadan Luo
The paper introduces Commute-Time-Preserving World Models (CTWMs), which learn latent representations that reflect commute-times in an environment by using a latent displacement predictor and a log-determinant regularizer. This approach addresses the issue that existing self-supervised methods degrade the necessary eigenvalue-dependent scaling for accurate commute-time representation. In experiments, CTWMs outperform the task-agnostic baseline LeWM on several continuous goal-reaching benchmarks while using only half the parameters.
By Michael Hauri, Peter Buttaroni, Fabian A. Mikulasch, Friedemann Zenke
The paper introduces ATLAS, a training objective that preserves relational geometry in latent world models while calibrating the global latent distribution. By transferring normalized pairwise structure from an informative encoder to the planning latent and applying Wasserstein embedding matching, ATLAS improves goal‑reaching success on tasks such as PushT, TwoRoom, and OGBench‑Cube, especially on higher‑novelty episodes. Diagnostics show stronger novelty‑related structure, better marginal calibration, and lower multi‑step prediction error in the planning latent.
By Ke Fang, Yupu Yao, Lu Cheng
Orthogonal JEPA introduces a latent world‑modeling framework that factorizes predictive states into orthogonal components. By learning basis matrices and dedicated prediction branches, the method reduces redundancy and improves gradient signals for less dominant predictive structures. The factorized states can be synthesized into complete latent representations for downstream tasks such as decoding, planning, or autoregressive rollout, and are evaluated across vision, biology, health, control, and molecular dynamics domains.
arXiv:2608. 20065v1 Announce Type: new Abstract: World models construct latent states that support prediction, planning, and reasoning about an underlying system.
By Taoyong Cui, Pheng Ann Heng, Wanli Ouyang
arXiv:2607. 23337v1 Announce Type: new Abstract: Neural operators provide data-driven mappings for modeling dynamical systems.
By Zituo Chen, Qiaofeng Li, Jiaxin Hu, Sili Deng