The paper introduces the concept of decision‑metric alignment, which ensures that Euclidean distance to a goal latent in JEPA‑style latent world models correctly ranks action sequences for model‑predictive control. It proposes two metrics—Plan‑Real Spearman and CEM‑stage Spearman—to evaluate latent–real rank agreement, and identifies encoder distortion, terminal rollout error, and candidate margins as key factors affecting alignment. Building on these insights, the authors present DA‑LeWM, an enhanced latent world model that incorporates inverse‑dynamics and demonstration‑conditioned goal‑action heads, leading to faster convergence and higher online success rates compared to the baseline LeWM while maintaining similar probe scores.
By Jiawei Wang, Ke Rui, Yushen Zuo, Yichun Feng, Minglei Li
arXiv:2608. 14125v1 Announce Type: new Abstract: LeWM is a lightweight visual world model that learns latent dynamics end-to-end from pixels and ranks candidate action sequences by the distance between their predicted endpoints and the goal.
By Xiaodi Huang, Ziyi Ding, Jingtian Wan, Yuchen Liu, Yuan Zhang, Xiao-Ping Zhang, Jiayu Chen, Zhang Zhang, Tao Huang
arXiv:2611.05847v1 Announce Type: cross
Abstract: Joint-Embedding Predictive Architectures (JEPAs) support planning by comparing predicted latent states with an encoded goal. For this comparison to g...
By Ruilong Yu, Fei Ye
arXiv:2608. 16287v1 Announce Type: new Abstract: Joint-embedding predictive world models plan by scoring predicted terminal embeddings against a goal embedding using a cost defined on the representation itself.
By Jiaming Hu, Yan Zheng, Tian Wang
arXiv:2609.24749v1 Announce Type: cross
Abstract: Latent world models predict the consequences of actions, but accurate prediction does not guarantee that latent distance reflects which candidate wil...
By Shuaijun Liu, Chengyu Wu, Qifu Wen, Feiyang You, Chenglong Zhang, Shuyang Hao, Xi Lin, Ningxin Su
The paper introduces TEMPO, a temporal-distance planning objective that augments latent world models by learning a map of the frozen latent space where distances correspond to the number of environment steps between states. TEMPO blends this learned temporal distance into the planner’s cost, requiring no additional rewards or policies, and adds negligible computational overhead. Experiments on eleven simulated environments show that TEMPO consistently improves planner performance across all goal distances, notably raising LeWM’s success rate from 36% to 99% on the TwoRoom task.
By Lama Moukheiber, Haotian Xue, Yongxin Chen