SPIN Unprocessed July 10, 2026 ai_technology research
Unlocking Temporal Generalization in Hamiltonian Video Dynamics Models
View original on arxiv.orgOverview
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. This matters for hierarchical planning, sim-to-real transfer, and scientific or game-engine applications that must query the same dynamics at multiple timescales. Hamiltonian Generative Networks (HGN) offer a principled path forward, grounding predictions in a cont
SpinGraph analysis pending — check back after processing.
Ask AI about this story
Opens with the SpinGraph .md URL and structured context — one click, prompt included.
More from arXiv Machine Learning
View all →- A law of robustness for two-layer neural networks with arbitrary weights
- Graph-Regularized Deep Learning for EEG-Based Emotion Recognition with Psychologically-Grounded Label Structure
- Principled Analysis of Deep Reinforcement Learning Evaluation and Design Paradigms
- Trustworthy Machine Learning through the Lens of Combinatorial Optimization: Survey and Research Perspectives
- Scalable and Trustworthy Earth Observation Foundation Models
- The Importance of Encoder Choice:A Tabular-Image Study
Markdown (.md) · JSON-LD schema (.json) · Machine-readable for AI & GEO