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Genie 4: A new generation of world simulation

Genie 4 is a new world model developed by DeepMind that enables real‑time simulation with unprecedented realism. The model represents a significant advancement in creating immersive, lifelike virtual environments. It promises to enhance applications that rely on accurate, dynamic world simulations.

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Hugging Face Trending Papers
Jul 7

A Definition and Roadmap for World Models

World models -- internal simulators that learn the structure and dynamics of an environment -- have become one of the most actively debated concepts in AI. From model-based reinforcement learning and video generation to embodied robotics and ultimately, physical AI, researchers across AI subfields are building systems that they call "world models", yet there is no consensus on what a world model fundamentally is, what it should predict, or how it should be built.

diffusionreinforcement-learningrobotics
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arXiv AI
1d ago

Interactive World Simulation with Cosmos for Physical AI

The paper introduces a shared framework that adapts Cosmos world foundation models for real‑time interactive world simulation in Physical AI. By streaming visual observations in response to actions and scene controls, the framework allows users and embodied agents to interact with evolving virtual environments, supporting both prediction of visual consequences and generation of observations from an external simulator’s physical state. The authors demonstrate the approach in camera navigation, robot manipulation, controllable video transfer, and multi‑view autonomous vehicle simulation, providing a common foundation for developing interactive virtual environments for Physical AI training and evaluation.

By NVIDIA, :, Anis Ahmad, Jon Allen, Malik Aqeel Anwar, Jesse Archer, Maciej Bala, Piotr Barejko, Aarti Basant, Dan Blick, Taras Bolzhelarskyi, Henry Che, Jin Chen, Yukang Chen, Tae Eun Choe, Yuzhu Dong, Nat Duca, Liang Feng, Francesco Ferroni, Xiao Fu, Mohit Gambhir, Vivek Goel, Rony Goldenthal, Aryaman Gupta, Ali Hassani, Nathan Hayes-Roth, Yong He, Michael Honke, Shengyu Huang, Wei Huang, DeLesley Hutchins, Suneel Indupuru, Pooya Jannaty, Jingyi Jin, Janmesh Kalra, Ashna Khetan, Xin Kong, Aleksandr Korovko, Hui Kuang, Zhengfei Kuang, George Kurian, Jean-Francois Lafleche, Jayjun Lee, Justin Leung, William Lew, Xiang Li, Zhaoshuo Li, Chen-Hsuan Lin, Ming-Yu Liu, Xiangyu Lu, Yuhang Lu, Martin Ding Ma, Qianli Ma, Aditya Mahajan, Miguel Martin, Janarbek Matai, Jonathan McCaffrey, Qing Miao, Ashkan Mirzaei, Mustafa Munir, Sangmin Oh, David Pankratz, Trung Pham, Marilyn Reeb, Gavriel State, Jiaxiang Tang, Shitao Tang, Yue Tang, Tolou Tavakkoli, Alexander Temirov, Hweimi Tsou, Sergei Vasilev, Thomas Volk, Andrew Z. Wang, Hao Wang, Shijie Wang, Zhonghao Wang, David Wehr, Shumian Xin, Mengyao Xu, Yao Xu, Shuai Yang, Sophia Zalewski, Pengcuo Zeren, Cindy Zha, Jing Zhang, Yuliya Zhautouskaya, Kaiwen Zheng, Andrew Zhu, Rick Zhuang
agentsrobotics
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arXiv AI
Jun 2

Coding Agent Is Good As World Simulator

arXiv:2605. 14398v2 Announce Type: replace Abstract: World models have emerged as a powerful paradigm for building interactive simulation environments, with recent video-based approaches demonstrating impressive progress in generating visually plausible dynamics.

By Hongyu Wang, Jingquan Wang, Bocheng Zou, Radu Serban, Dan Negrut
agentsrobotics
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arXiv Computer Vision
Oct 15

Generative World Simulation with DAO and JING

arXiv:2610.19726v1 Announce Type: new Abstract: We present a Generative World Simulation framework that couples persistent shared world state with continuous first-person audiovisual generation. The...

By XGEN Team, Guohao Cai, Haoxuan Che, Frank Fan, Chenran Huang, Junjia Huang, Jiayi Jiang, Yanheng Li, Leon Liu, Shaoteng Liu, Xi Liu, Feng Nie, Zhefan Rao, An Wang, Hanwen Wang, Pengxiang Yan, Jieming Yu, Qian Zha, Wentao Zheng
agents
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arXiv AI
Aug 21

ChronoAgentic: A Code-based Multi-Agent World Simulator for Physically Grounded Simulation Construction

arXiv:2605. 14398v3 Announce Type: replace Abstract: Video-based world models generate visually plausible rollouts, but since they infer dynamics in latent states, they enforce no explicit physical constraints: contacts drift, shapes distort, and motion loses consistency.

By Hongyu Wang, Jingquan Wang, Ashvin Anilkumar, Bocheng Zou, Radu Serban, Dan Negrut
agentsdiffusionbenchmarks
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arXiv Computer Vision
Sep 23

Code Plans, Diffusion Renders: Open-Ended Generative World Modeling

The paper introduces CoDeR, a new paradigm for world modeling that explicitly builds an executable world using code rather than relying solely on visual observations. CoDeR translates high‑level concepts into structured world rules, executable dynamics, and perceptual observations through five complementary roles, enabling long‑term memory, open‑ended interactions, autonomous world evolution, and persistent multi‑agent dynamics. Experiments show that this framework extends the capabilities of existing world models and achieves state‑of‑the‑art performance across multiple evaluation settings.

By Zixun Fang, Yawen Shao, Kai Zhu, Jie Xiao, Shihan Chen, Yu Liu, Xueyang Fu, Yang Cao, Wei Zhai, Zheng-Jun Zha
agentsdiffusionbenchmarks
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