arXiv:2608.24855v1 Announce Type: new
Abstract: Latent world models are inherently strong encoders that transform image pixel to latent embedding, yet existing world models still rely on online traje...
By Hsiang-Wei Huang, Jianxu Shangguan, Junbin Lu, Jenq-Neng Hwang
arXiv:2609.38383v1 Announce Type: cross
Abstract: Random exploration reveals how an environment can be traversed before a goal is specified. Can this experience support long-range planning without po...
By Deqian Kong, Guangyan Sun, Sheng Cheng, Sirui Xie, Bo Pang, Jianwen Xie, Tony Geng, Caiwen Ding, Ying Nian Wu
arXiv:2607. 00917v1 Announce Type: cross Abstract: World models can enable Model Predictive Control (MPC), but this requires dynamics prediction that is both fast enough for online use and expressive enough to represent uncertain futures.
By Christopher Lindenberg, Kashyap Chitta
RD‑JEPA is a joint‑embedding predictive architecture designed for self‑supervised pretraining on reaction‑diffusion trajectories. The model is pretrained on five parameterized systems and then adapted to three held‑out systems that were not seen during pretraining. Using as few as one, five, or ten complete trajectories from a held‑out system, RD‑JEPA outperforms five supervised surrogate baselines, an independently trained control that removes the trajectory‑dependent predictive latent pathway, and an architecture‑matched model trained from scratch, achieving lower mean relative discrete β field error and mean absolute spatial first‑difference error across various output resolutions, forecast horizons, and adaptation trajectory choices.
By Chenhao Si, Ming Yan
arXiv:2602. 07339v2 Announce Type: replace Abstract: Diffusion-based trajectory planners can model multi-modal driving behavior, but their iterative denoising process introduces a latency bottleneck for real-time closed-loop deployment.
By Ruturaj Reddy, Hrishav Bakul Barua, Junn Yong Loo, Thanh Thi Nguyen, Ganesh Krishnasamy
JEPA‑TTT is a method that continuously adapts the latent dynamics predictor of a pretrained Joint‑Embedding Predictive Architecture (JEPA) world model during test time. It performs self‑supervised updates across episodes while keeping the visual encoder and reward head fixed, using dense replay to sample prediction windows from a growing buffer. In experiments on eight dynamics shifts across four continuous‑control environments, JEPA‑TTT reduces latent prediction error by 83% and improves planning performance by 153% compared to the frozen model.
By Zheyuan Zhang, Suyu Ye, Nakul Agarwal, Hossein Nourkhiz Mahjoub, Ehsan Moradi Pari, Daniel Khashabi, Tianmin Shu, Vaishnav Tadiparthi