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title: "Unlocking Temporal Generalization in Hamiltonian Video Dynamics Models — Stuff That Spins"
description: "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…"
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date: "2026-07-10T04:00:00+00:00"
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# Unlocking Temporal Generalization in Hamiltonian Video Dynamics Models

**Source:** Unknown  
**Published:** July 10, 2026  
**Original:** https://arxiv.org/abs/2607.07763  

## On this page

- [Overview](#overview)

<a id="overview"></a>

## Overview

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

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