arXiv AI
LeHami: Symplectic Latent Dynamics for World Models
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arXiv AI
Jul 3
Physically Native World Models: A Hamiltonian Perspective on Generative World Modeling
arXiv:2605. 00412v3 Announce Type: replace Abstract: World models have recently re-emerged as a central paradigm for embodied intelligence, robotics, autonomous driving, and model-based reinforcement learning.
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 19
Sensorimotor World Models: Perception for Action via Inverse Dynamics
arXiv:2606. 20104v1 Announce Type: cross Abstract: Perception for action suggests that representations of the world should be shaped not by visual fidelity alone, but by their relevance for actions.
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.
arXiv Computer Vision
Sep 28
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 AI
Jun 16
Akasha 2: Hamiltonian State Space Duality and Visual-Language Joint Embedding Predictive Architectur
arXiv:2601. 06212v2 Announce Type: replace-cross Abstract: We present Akasha 2, a state-of-the-art multimodal architecture that integrates Hamiltonian State Space Duality (H-SSD) with Visual-Language Joint Embedding Predictive Architecture (VL-JEPA).