ODEWorld: A Continuous Predictive Architecture via Physical-Time Flow

[2607.27924] ODEWorld: A Continuous Predictive Architecture via Physical-Time Flow Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2607.27924 (cs) [Submitted on 30 Jul 2026 ( v1 ), last revised 14 Aug 2026 (this version, v3)] Title: ODEWorld: A Continuous Predictive Architecture via Physical-Time Flow Authors: Dongxiu Liu , Haoyi Niu , Peng Cheng , Yuan Gao , Xirui Kang , Sangli Teng , Koushil Sreenath , Xianyuan Zhan View a PDF of the paper titled ODEWorld: A Continuous Predictive Architecture via Physical-Time Flow, by Dongxiu Liu and Haoyi Niu and Peng Cheng and Yuan Gao and Xirui Kang and Sangli Teng and Koushil Sreenath and Xianyuan Zhan View PDF HTML (experimental) Abstract: In the physical world we inhabit, space and time are fundamentally continuous. However, existing machine learning paradigms for world modeling are largely confined to discrete-time prediction, thereby exhibiting significant inefficiency in capturing the dynamics of physical world. We introduce Physical-Time Flow (PT-Flow), a novel approach that learns a continuous latent velocity field operating in physical time. Crucially, the underlying dynamics of sequential data are parameterized by an ordinary differential equation (ODE) embedded in a well-structured representation space. Under this paradigm, the prediction of future can be recast as temporal integration via an ODE solver in the compressed latent space. Building upon PT-Flow, we construct ODEWorld, a continuous-time latent world model that is both efficient and versatile. By extracting time-variant features and enforcing ODE properties on both the dynamical representation space and the latent velocity field, ODEWorld effectively addresses the long-standing representation collapse issue in latent world model literature. This also enables high-quality image reconstruction even after long-horizon prediction. Moreover, its continuous nature allows for arbitrary temporal resolution and even backward prediction, which is impossible for most discrete-time models. Lastly, ODEWorld can provide rich planning-oriented information to facilitate downstream policy learning. Comprehensive experiments demonstrate that ODEWorld successfully reconciles planning-conducive dynamics abstraction with visual realism, excelling in both video generation and robotic control. Project page: this https URL . Subjects: Machine Learning (cs.LG) ; Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO) Cite as: arXiv:2607.27924 [cs.LG] (or arXiv:2607.27924v3 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.27924 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Dongxiu Liu [ view email ] [v1] Thu, 30 Jul 2026 09:37:30 UTC (5,284 KB) [v2] Fri, 7 Aug 2026 03:27:58 UTC (5,284 KB) [v3] Fri, 14 Aug 2026 23:51:40 UTC (5,280 KB) Full-text links: Access Paper: View a PDF of the paper titled ODEWorld: A Continuous Predictive Architecture via Physical-Time Flow, by Dongxiu Liu and Haoyi Niu and Peng Cheng and Yuan Gao and Xirui Kang and Sangli Teng and Koushil Sreenath and Xianyuan Zhan View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2026-07 Change to browse by: cs cs.CV cs.RO References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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