arXiv AI August 17, 2026 By Zian Meng, Zhen Li, Chuanhao Li, Qiang Li, Kaipeng Zhang

Marionette: Predicting World States, Rendering Geometry, Painting Appearance

arXiv:2608. 14530v1 Announce Type: cross Abstract: Interactive game world models typically autoregress visual observations directly in pixel or latent space, forcing structured properties such as pose, geometry, and occlusion to be implicitly maintained by the same generative sequence.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

diffusion

arXiv AI

Jul 3

Physically Native World Models: A Hamiltonian Perspective on Generative World Modeling

arXiv:2605. 00412v3 Announce Type: replace Abstract: World models have recently re-emerged as a central paradigm for embodied intelligence, robotics, autonomous driving, and model-based reinforcement learning.

By Sen Cui, Jingheng Ma

agents reinforcement-learning robotics safety

arXiv Machine Learning

Jul 31

ShadowDancer: Teaching Video World Models Any Action by Learning Unified Dynamics Representations from a Video and Its Shadow

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

fine-tuning

arXiv AI

Jul 7

Mask2Real-WM: Segmentation Masks as a Sim-to-Real Bridge for Controllable Dexterous World Models

arXiv:2607. 04546v1 Announce Type: cross Abstract: Action-conditioned world models allow robots to predict the future consequences of candidate actions without additional physical interaction, supporting policy evaluation, planning, and data augmentation.

By Riccardo O. Feingold, Davide Liconti, Chenyu Yang, Robert K. Katzschmann

diffusion computer-vision robotics fine-tuning benchmarks

arXiv AI

Jul 2

From World Models to World Action Models: A Concise Tutorial for Robotics

arXiv:2607. 00836v1 Announce Type: cross Abstract: World models are increasingly used in embodied intelligence and generative simulation, yet their scope remains ambiguous across communities.

By Xiaoxiong Zhang, Xiong Zeng, Wei Zhang

robotics safety

arXiv AI

Jun 10

Can Image Models Imagine Time? ImageTime: A Novel Benchmark for Probing Visual World Modeling Through Spatiotemporal Consistency

arXiv:2606. 10620v1 Announce Type: cross Abstract: Image generation models now produce high-quality static images, yet their ability to represent how a visual world changes over time remains poorly understood.

By Xinrui Wu, Lichen Huang

llms diffusion multimodal benchmarks safety

arXiv Machine Learning

Jun 24

Autonomous Video Generation with Counterfactual Controllability for Self-Evolving World Models

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

agents diffusion robotics

Related stories

Physically Native World Models: A Hamiltonian Perspective on Generative World Modeling

ShadowDancer: Teaching Video World Models Any Action by Learning Unified Dynamics Representations from a Video and Its Shadow

Mask2Real-WM: Segmentation Masks as a Sim-to-Real Bridge for Controllable Dexterous World Models

From World Models to World Action Models: A Concise Tutorial for Robotics

Can Image Models Imagine Time? ImageTime: A Novel Benchmark for Probing Visual World Modeling Through Spatiotemporal Consistency

Autonomous Video Generation with Counterfactual Controllability for Self-Evolving World Models