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.
Variational Streaming Flow: Probabilistic Forecasting in Physical Time
Variational Streaming Flow (VSF) extends the efficient Streaming Flow (SF) framework by learning a latent distribution conditioned on system dynamics, enabling probabilistic forecasting in physical time. Unlike SF’s deterministic velocity field, VSF produces multiple plausible future trajectories, improving predictive accuracy and distributional fidelity across deterministic and stochastic dynamical systems. The method supports long‑horizon rollouts over 1,000 steps, handles bifurcating dynamics, and can be integrated as a plug‑and‑play predictor into Joint‑Embedding Predictive Architecture (JEPA) world models to enhance navigation, motion planning, and manipulation tasks.
Related stories
Hugging Face Trending Papers
Jul 30
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.
arXiv AI
Aug 11
Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning
arXiv:2608. 09876v1 Announce Type: cross Abstract: Physically consistent motion planning remains a fundamental challenge in embodied AI, as generated trajectories must strictly conform to real-world execution dynamics.
arXiv Computer Vision
Aug 26
LeFlow: Generative Latent Flow Planning for World Models
arXiv:2608.24855v1 Announce Type: new Abstract: Latent world models are inherently strong encoders that transform image pixel to latent embedding, yet existing world models still rely on online traje...
arXiv AI
4d ago
Flow-JEPA: Robust Latent Dynamics for JEPA World Models via Flow Matching
arXiv:2608.29029v3 Announce Type: replace-cross Abstract: Joint-Embedding Predictive Architectures (JEPAs) provide a powerful framework for latent world modeling and planning in a reconstruction-free...
arXiv Computer Vision
6d ago
WALT: Learning World-Model-Aligned Latent Trajectories for Autonomous Driving
WALT introduces a method to align latent trajectories with pretrained driving world models, creating a compact generative trajectory space that preserves action-relevant semantics without altering the original model. The approach uses a dual-branch autoencoder to map raw waypoints into this latent space and transfers visual world knowledge into trajectory representations. Experiments on NAVSIM benchmarks show modest performance gains and a 30.5% reduction in planner FLOPs, indicating that maintaining world representations while extracting action-relevant information can improve trajectory planning efficiency.
arXiv Machine Learning
Jul 28
Autoregressive One-Step Generative Modeling for Dynamical System Forecasting
arXiv:2605. 05540v2 Announce Type: replace Abstract: Fast surrogate modeling for high-dimensional physical dynamics requires more than low short-term error: useful models must roll out efficiently while preserving the statistical structure of long trajectories.
arXiv Computer Vision
Sep 4
Drive-HWM: Hierarchical World Models for Dynamic-Latent Guided Autonomous Driving
Drive‑HWM introduces a hierarchical slow‑fast world modeling framework for autonomous driving. The slow model predicts multi‑step future representations, while the fast model jointly predicts the next frame and immediate action using a lightweight multimodal backbone and an autoregressive expert. Dynamic‑Aware Latents, learned through optical‑flow prediction, explicitly capture motion dynamics, and experiments on NAVSIM v1 and v2 show strong driving performance with validated ablation studies.
arXiv Machine Learning
Jun 15
FlowMo-WM: A World Model with Object Momentum and Hidden Ambient Drift
arXiv:2606. 13817v1 Announce Type: cross Abstract: World models in robot learning predict future states from visual observations and actions, enabling agents to reason about the consequences of their controls.
arXiv AI
Aug 24
WA-JEPA: Rethinking the Video JEPA Paradigm for World-Action Modeling in Autonomous Driving
arXiv:2608.20974v1 Announce Type: cross Abstract: Video Joint Embedding Predictive Architecture (V-JEPA) learns powerful spatiotemporal representations from video through self-supervised latent featu...
arXiv Machine Learning
Jul 10
Unlocking Temporal Generalization in Hamiltonian Video Dynamics Models
arXiv:2607. 07763v1 Announce Type: new Abstract: World models are typically trained to predict discrete-time physical dynamics with a fixed step size baked into the model weights, preventing prediction at variable temporal resolutions.
arXiv AI
Jun 30
Flow Matching in Feature Space for Stochastic World Modeling
arXiv:2606. 29059v1 Announce Type: cross Abstract: World modeling requires forecasting uncertain futures while preserving information useful for downstream perception.