arXiv Machine Learning
Jul 31
ODEWorld: A Continuous Predictive Architecture via Physical-Time Flow
arXiv:2607. 27924v1 Announce Type: new Abstract: In the physical world we inhabit, space and time are fundamentally continuous.
ODEWorld: A Continuous Predictive Architecture via Physical-Time Flow
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.
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Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning
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Physically Native World Models: A Hamiltonian Perspective on Generative World Modeling
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Unlocking Temporal Generalization in Hamiltonian Video Dynamics Models
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arXiv Machine Learning
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FlowMo-WM: A World Model with Object Momentum and Hidden Ambient Drift
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arXiv AI
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arXiv AI
Jun 9
Latent Diffusion Policy: Shaping Latent Spaces for Diffusion-Based Robotic Manipulation
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arXiv AI
Jul 14
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arXiv AI
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A Geometric Theory of Cognition for Machine Intelligence
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arXiv Machine Learning
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arXiv Machine Learning
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Fast LeWorldModel
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