The paper introduces a diagnostic protocol to examine how passive object-state world models encode event-conditioned latent physical structure. It evaluates GRU, Transformer-lite, and RSSM-lite models on a dataset featuring free motion, collision, and occlusion events, finding that each architecture learns predictive dynamics and that event context shifts the emphasis among kinematic, contact, and object-permanence readouts. Functional sensitivity tests reveal contact-related structure during collisions and object-permanence structure during occlusions, supporting the presence of event-conditioned latent structure without explicit physical modules.
By Yang Liu, Yuming Chen
arXiv:2608. 09876v1 Announce Type: cross Abstract: Physically consistent motion planning remains a fundamental challenge in embodied AI, as generated trajectories must strictly conform to real-world execution dynamics.
By Yapeng Liu, Yuanzhao Zhai, Bo Ding, Huaimin Wang, Lin Wang
arXiv:2606. 13053v1 Announce Type: cross Abstract: Pretrained-feature world models provide a useful substrate for robot imagination, but visual or latent prediction alone does not determine whether an imagined future satisfies task-relevant events.
By Kailin Wang, Haoxiang Jie, Yaoyuan Yan, Jiacheng Zhou, Zhiyou Heng
JEPA-x is a cross‑predictive physics grounding method that aligns visual latent dynamics with privileged physical trajectories. By treating visual observations and physical states as two views of the same action‑conditioned trajectory and sharing a predictor, it forces the model to learn a common transition rule for both modalities. The physical branch is only used during training, so deployment incurs no extra cost, and the approach significantly reduces rollout drift and boosts control success across a multi‑task suite.
By Kehan Wen, Ziming Li, Siyuan Luo, Fan Shi
arXiv:2608.24044v1 Announce Type: new
Abstract: Latent world models plan by predicting how candidate actions transform learned representations. In self-predictive models, however, the encoder and pre...
By Kehan Wen, Ziming Li, Siyuan Luo, Fan Shi
The paper introduces an event‑anchored evaluation protocol for video world models, using 62 free‑fall recordings and 124 clips with detailed release and impact annotations. It finds that while some models (Runway, Veo) can generate release and impact events with high accuracy, they often start them late, and others (Cosmos‑Predict‑2.5, MAGI‑1) rarely produce measurable consequences. A human study shows that people’s predictions align with recorded futures but also reveal ambiguity in plausible continuations, highlighting that physical foresight requires initiating, timing, and realizing motion correctly.
By Estela Monserrat Arriaga Santana (National Autonomous University of Mexico), Julian Rosas Scull (National Autonomous University of Mexico), Eh\'ecatl Sacamch'en N\'u\~nez Rico (National Autonomous University of Mexico), Hugo Jair Escalante (University of Texas at El Paso)