ODEWorld: A Continuous Predictive Architecture via Physical-Time Flow

• URL: http://arxiv.org/abs/2607.27924v1
• Date: Thu, 30 Jul 2026 09:37:30 GMT
• Title: ODEWorld: A Continuous Predictive Architecture via Physical-Time Flow
• Abstract summary: We introduce Physical-Time Flow (textbfPT-Flow), a novel approach that learns a continuous latent velocity field operating in physical time.<n>Under PT-Flow, the prediction of future can be recast as temporal integration via an ODE solver in the compressed latent space.<n>We construct textbfODEWorld, a continuous-time latent world model that is both efficient and versatile.
• Score: 26.147123396344128
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 (\textbf{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 \textbf{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. \href{https://dstate.github.io/odeworld_website/}{Project Website}.

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