OneWorld introduces a shared‑mechanism counterfactual generation framework that jointly models multiple action‑conditioned futures using a common latent physical mechanism. By inferring distributions over latent mechanisms for each action‑outcome branch and aggregating them into shared‑world evidence, the model enforces consistency across interventions while preserving distinct action outcomes. Experiments in controlled environments demonstrate that OneWorld improves cross‑intervention physical consistency without sacrificing single‑rollout prediction quality.
By Ke He, Yichen Ding, Bin Yang
arXiv:2608. 04964v1 Announce Type: new Abstract: Interactive video world models are essential for long-horizon planning and exploration, yet they suffer from compounding errors.
By Bohai Gu, Yueyang Yuan, Taiyi Wu, Dazhao Du, Jian Liu, Xiaoyi Pang, Jie Zhang, Xiaocheng Lu, Haobin Zhong, Xiaotong Zhao, Alan Zhao, Song Guo
arXiv:2608. 10232v1 Announce Type: cross Abstract: Recent world-action models (WAMs) show that co-training policies with future prediction can provide physical priors for action generation.
By Quanquan Peng, Yutong Liang, Rui Yan, Nicklas Hansen, Xiaolong Wang
arXiv:2607. 28362v1 Announce Type: cross Abstract: We present ShadowDancer, a novel approach to any-action, frame-level control of interactive video world models.
By Jin Cao, Zian Meng, Kaipeng Zhang
arXiv:2606. 24152v1 Announce Type: cross Abstract: Existing literature claims that video generation essentially is world modelling.
By Xin Wang, Wenxuan Liu, Tongtong Feng, Wenwu Zhu
arXiv:2609.36851v1 Announce Type: new
Abstract: End-to-end autonomous driving policies are commonly trained via imitation learning on logged demonstrations without observing the consequences of their...
By Hongbin Lin, Chaoda Zheng, Yiming Yang, Xiangyu Li, Shijia Chen, Jinhao Deng, Kangjie Chen, Dongbin Zhang, Jie Feng, Yu Zhang, Xianming Liu, Shuguang Cui, Boyang Wang, Zhen Li
The paper introduces Statebench, a benchmark for evaluating how well video generators track world states across segments, focusing on past-visible, occluded-process, and complex-transition states. It also proposes Stateagent, a method that maintains an explicit entity-state representation, updates it with new prompts, and uses the resulting state to guide video continuation. Experiments show Stateagent raises the overall state score from 45.2 to 69.3 and improves one‑minute story generation.
By Yingmao Miao, Pengfei Zhang, Chaoran Xu, Meng Yu, Jing Tang, Xiangxiang Chu, Chao Shen, Chenhao Lin
The paper investigates whether a small, directly addressable change in the hidden state of a learned world model can steer its future predictions along a desired counterfactual trajectory. Using a 192‑dimensional recurrent model in a two‑object collision setting, the authors identify low‑rank latent carriers—specifically a rank‑4 patch—that, when applied, successfully redirect a 12‑step autonomous rollout without further intervention. The study demonstrates that this compact intervention interface consistently works across independently trained checkpoints and intervention times, while various control experiments confirm the specificity of the effect.
By Yang Liu, Yuming Chen
arXiv:2608.29904v1 Announce Type: new
Abstract: Modern video generators routinely fail at physical dynamics: objects float, trajectories violate gravity, contacts vanish. Standard denoising and flow-...
By Hai Nguyen-Truong, Tuan-Anh Vu, Dang Huynh
arXiv:2608.24885v1 Announce Type: cross
Abstract: Action-conditioned world models are increasingly used as learned simulators for policy evaluation and improvement, yet their effectiveness rests on a...
By Sixiang Chen, Jiaming Liu, Jixian Wu, Yichen Guo, Tinghao Wang, Siyuan Qian, Hao Chen, Jiajun Cao, Jian Tang, Shanghang Zhang
The paper introduces a reinforcement learning post‑training scheme that trains robot world models on their own autoregressive rollouts, using a contrastive RL objective adapted from diffusion models. It also proposes a training protocol that compares multiple variable‑length futures, a multi‑view visual fidelity reward, and demonstrates state‑of‑the‑art rollout fidelity on the DROID dataset, outperforming baselines on LPIPS, SSIM, and human preference tests.
By Jai Bardhan, Patrik Drozdik, Josef Sivic, Vladimir Petrik
PAWBench introduces a benchmark to evaluate whether video generation models can act as probabilistically aligned world models, meaning they should reproduce not just plausible trajectories but the full distribution of possible behaviors from the same initial conditions. The authors formalize probabilistic alignment as a distributional criterion and provide PAWEval, an outcome-level protocol that turns repeated video rollouts into empirical distributions over physical behaviors. Across 50 scenarios and eleven current systems, none consistently matched reference probabilities or captured the full range of valid behaviors, highlighting a significant gap in current video generators.
By Yuandong Pu, Le Zhuo, Sayak Paul, Gabriel Jorge Menezes, Avram {\DJ}or{\dj}evi\'c, Shiyang Li, Yifan Zhou, Bin Fu, Wenlong Zhang, Junjun He, Yu Qiao, Yihao Liu, Jingbo Xing, Xi Chen