AD-WM is a new action‑discriminative joint‑embedding world model designed for counterfactual model predictive control. It augments residual latent dynamics with action‑recovery regularization based on inverse dynamics and conditional mutual information, while discarding auxiliary heads at test time so that MPC remains unchanged. Experiments on OGBench‑Cube and other simulation environments show substantial gains in hard‑start success and mean success, and zero‑shot transfer to a Franka robot improves pick‑and‑place success from 42.2% to 71.1%.
By Jiabin Qiu, Zixuan Chen, Hongye Cao, Jieqi Shi, Jing Huo, Yang Gao
The paper introduces a lightweight Fourier auxiliary head to enforce physically-informed structuring of latent states in JEPA-style world models, addressing a newly identified failure mode called physical representation laziness that hampers planning in dynamic environments. Experiments show that this auxiliary supervision improves planning success rates, enhances latent space correlations with key physical properties, and boosts data efficiency, even when the baseline model does not exhibit laziness.
By Penghao Zhu, Salvatore Penachio, Kaustav Mukherjee, Aneesh Jonelagadda
The paper introduces the Dual-Latent World Model (Dual-WM), which separates local execution and long-range planning into distinct latent spaces and dynamics models. A new learning method, Long-Horizon Representation Learning with Weighted Rollout (LoRe), supervises predictions at both levels using exponential horizon weights. Experiments on five goal-conditioned visual control tasks show that Dual-WM improves success rates over strong baselines, especially at longer horizons.
By Delin Zhao, Zhengrong Yue, Shaobin Zhuang, Junlin He, Xiaoyu Chen, Zikang Wang, Yuxin Liu, Limin Wang, Yali Wang
arXiv:2512.03400v2 Announce Type: replace-cross
Abstract: We study how explicit world-modeling objectives affect the internal representations and downstream capability of Transformers, using Rubik's...
By Prakhar Gupta, Henry Conklin, Sarah-Jane Leslie, Andrew Lee
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
The paper investigates how latent world models (specifically JEPA-style models) use Euclidean distance to a goal latent as a cost for model‑predictive control (MPC). It introduces two metrics—Plan‑Real Spearman and CEM‑stage Spearman—to evaluate how well latent‑space distances align with real‑task progress, a property termed decision‑metric alignment. By identifying encoder distortion, terminal rollout error, and candidate margins as key factors, the authors propose DA‑LeWM, which augments the base model with inverse‑dynamics and demonstration‑conditioned goal‑action heads, leading to faster convergence and higher online success while maintaining similar probe scores.
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
The paper presents an end‑to‑end JEPA world model that enhances latent prediction with inverse dynamics and state alignment to improve goal‑conditioned robotic planning. By preventing latent collapse and grounding representations in physical configuration, the model achieves top success rates on tasks such as TwoRoom, PushT, and OGBench‑Cube, outperforming the baseline LeWorldModel. Ablation studies confirm that state alignment consistently boosts planning success over inverse dynamics alone across all four benchmark tasks.
By Muyuan Liu (GENISOM AI, Beijing, China), Yue Huang (GENISOM AI, Beijing, China), Zheng Liang (GENISOM AI, Beijing, China), Xiang Gao (GENISOM AI, Beijing, China)
arXiv:2607. 17973v1 Announce Type: new Abstract: Latent world models have emerged as a powerful planning paradigm by learning action-conditioned predictive dynamics and using them as internal simulators to imagine and evaluate candidate action sequences.
By Letian Cheng, Qi Zhang, Yisen Wang
Subspace-Decomposed JEPAs (SD-JEPA) split the latent space of Joint-Embedding Predictive Architectures into two orthogonal subspaces: a low-dimensional progression subspace trained with a cosine-margin triplet loss and a high-dimensional content subspace regularised by SIGReg. The authors prove that the anti-collapse forces act on disjoint coordinates, allowing additive composition rather than competition. SD-JEPA outperforms the LeWM baseline on most control benchmarks and the strongest non-LeWM JEPA baseline on Push‑T, with a subspace-ablation confirming the split as essential. The 1‑D angular progression coordinate serves as a scene-aware compass, advancing with task progress, regressing on backtracking, and relocalising under perturbations to separate surprise from meaning.
By Lucas Thil, Jesse Read, Rim Kaddah, Guillaume Doquet
The paper introduces Discriminative World Models for Web Agents, proposing a predicted-state matching objective that trains world models to produce representations that can distinguish the true resulting state from those of alternative actions. Using a branching dataset from WebArena Go-Browse, the authors demonstrate that this approach outperforms traditional supervised next-state prediction on a held‑out benchmark and improves action ranking on WebPRMBench. Additionally, employing the discriminative world model for test‑time action selection boosts end‑to‑end task success on WebArena‑Lite.
By Kelvin Li, Dhruv Pendharkar, Anish Pahilajani, Chuyi Shang, Leon Oks, Leonid Karlinsky, Rogerio Feris, Trevor Darrell, Roei Herzig
arXiv:2608.29434v1 Announce Type: cross
Abstract: JEPA world models make latent-space planning a practical route to control, but they are built almost exclusively on images. Whether latent prediction...
By Fabio F. Oberweger, Michael Schwingshackl