ODEWorld explores continuous-time world modeling by learning a latent ODE velocity field instead of discrete-time transitions. The approach provides elegant properties such as arbitrary temporal resolution and backward prediction, and shows promising results for robotic planning and video prediction.</p>\n","updatedAt":"2026-08-03T07:55:38.839Z","author":{"_id":"677259dfd3a8d679c9683f71","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/w1ee-KLiJBuZvnXmGi86G.png","fullname":"Dstate","name":"ldxxx","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.8510968685150146},"editors":["ldxxx"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/w1ee-KLiJBuZvnXmGi86G.png"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2607.27924","authors":[{"_id":"6a7048da02c90f968f48a13e","name":"Dongxiu Liu","hidden":false},{"_id":"6a7048da02c90f968f48a13f","name":"Haoyi Niu","hidden":false},{"_id":"6a7048da02c90f968f48a140","name":"Peng Cheng","hidden":false},{"_id":"6a7048da02c90f968f48a141","name":"Yuan Gao","hidden":false},{"_id":"6a7048da02c90f968f48a142","name":"Xirui Kang","hidden":false},{"_id":"6a7048da02c90f968f48a143","name":"Sangli Teng","hidden":false},{"_id":"6a7048da02c90f968f48a144","name":"Koushil Sreenath","hidden":false},{"_id":"6a7048da02c90f968f48a145","name":"Xianyuan Zhan","hidden":false}],"publishedAt":"2026-07-30T00:00:00.000Z","submittedOnDailyAt":"2026-08-03T00:00:00.000Z","title":"ODEWorld: A Continuous Predictive Architecture via Physical-Time Flow","submittedOnDailyBy":{"_id":"677259dfd3a8d679c9683f71","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/w1ee-KLiJBuZvnXmGi86G.png","isPro":false,"fullname":"Dstate","user":"ldxxx","type":"user","name":"ldxxx"},"summary":"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. https://dstate.github.io/odeworld_website/{Project Website}.","upvotes":1,"discussionId":"6a7048db02c90f968f48a146","projectPage":"https://dstate.github.io/odeworld_website/","githubRepo":"https://github.com/Dstate/ODEWorld","githubRepoAddedBy":"user","githubStars":11},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"677259dfd3a8d679c9683f71","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/w1ee-KLiJBuZvnXmGi86G.png","isPro":false,"fullname":"Dstate","user":"ldxxx","type":"user"}],"acceptLanguages":["en"],"dailyPaperRank":0,"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2607/2607.27924.md","query":{}}">
ODEWorld: A Continuous Predictive Architecture via Physical-Time Flow
Published on Jul 30
· Submitted by Dstate on Aug 3 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. https://dstate.github.io/odeworld_website/{Project Website}.
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ODEWorld explores continuous-time world modeling by learning a latent ODE velocity field instead of discrete-time transitions. The approach provides elegant properties such as arbitrary temporal resolution and backward prediction, and shows promising results for robotic planning and video prediction.
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Cite arxiv.org/abs/2607.27924 in a model README.md to link it from this page.
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