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)
arXiv:2608. 12939v1 Announce Type: new Abstract: Joint-embedding predictive architectures (JEPAs) learn world models that predict in a compact latent space rather than in pixels, reducing the pressure to model nuisance appearance.
By Guo An, Zijing Wu, Honghua Dong, Yuhao Yan, Zixuan Gui, Haochong Chen, Shanzhao Ruan, Xiang Wang, Yurong Ling, Qi Tian
arXiv:2608. 11605v1 Announce Type: new Abstract: World Action Models (WAMs) couple future visual prediction with robot action generation, enabling policies to model how the physical world evolves during interaction.
By Jiakai Huang, Zhongbo Wu, Zheng Zhang, Zihan Wang, Shan You, Tao Huang
arXiv:2608. 20009v1 Announce Type: new Abstract: Understanding object dynamics requires not only predicting future trajectories but also examining whether a model captures the physical properties that govern motion.
By Rui Wang, Yeteng Wu, Xianlin Zhang, Mengshi Qi
arXiv:2608. 01049v1 Announce Type: cross Abstract: World models have attracted significant attention for their ability to capture and predict the structure and dynamics of the physical world.
By Kapil Wanaskar, Gaytri Jena, Aman Chadha, Vinija Jain, Vasu Sharma, Amitava Das
Underwater C³-JEPA is an object‑centric, cross‑view predictive world model designed for near‑field heavy‑load ROV salvage. It encodes synchronized multi‑camera RGB observations and vehicle control signals into task‑object and context tokens, fuses cross‑camera evidence via held‑out‑view attention, and predicts future states conditioned on control without using contact sensors. The model demonstrates superior transfer of task‑relevant information to downstream probes compared to a reconstruction‑free baseline, supports model‑predictive control and imagined‑rollout training, and validates its effectiveness on real underwater video by accurately recovering withheld camera states and maintaining predictive lead over persistence.
By Yuncong Yang, Jinlong Li, Yulong Xue, Feng Wu, Chunwen Zhang, Lei Qiao, Xuyang Wang
arXiv:2607. 27017v1 Announce Type: new Abstract: A central premise of latent world models is that predicting the future forces a representation to internalize the physics of its environment.
By Kaizhen Tan (New York University, Carnegie Mellon University), Xin Xu (Carnegie Mellon University), Siru Tao (Carnegie Mellon University), Hanzhe Hong (Carnegie Mellon University), Yang Feng (Columbia University), Heqing Du (Columbia University)