Cloning Deterministic Worlds: The Critical Role of Latent Geometry in Long-Horizon World Models

TL;DR

This paper introduces a novel approach called Geometrically-Regularized World Models (GRWM) that enhances the fidelity of deterministic 3D world cloning by shaping latent space geometry using contrastive learning.

Contribution

It demonstrates that latent space geometry is key to high-fidelity long-horizon world modeling and proposes a simple regularization method to improve latent representations.

Findings

High-fidelity cloning is feasible with proper latent geometry.

Contrastive regularization improves latent space stability.

GRWM enhances long-term world model accuracy.

Abstract

A world model is an internal model that simulates how the world evolves. Given past observations and actions, it predicts the future physical state of both the embodied agent and its environment. Accurate world models are essential for enabling agents to think, plan, and reason effectively in complex, dynamic settings. However, existing world models often focus on random generation of open worlds, but neglect the need for high-fidelity modeling of deterministic scenarios (such as fixed-map mazes and static space robot navigation). In this work, we take a step toward building a truly accurate world model by addressing a fundamental yet open problem: constructing a model that can fully clone a deterministic 3D world. 1) Through diagnostic experiment, we quantitatively demonstrate that high-fidelity cloning is feasible and the primary bottleneck for long-horizon fidelity is the geometric structure of the latent representation, not the dynamics model itself. 2) Building on this insight, we show that applying temporal contrastive learning principle as a geometric regularization can effectively curate a latent space that better reflects the underlying physical state manifold, demonstrating that contrastive constraints can serve as a powerful inductive bias for stable world modeling; we call this approach Geometrically-Regularized World Models (GRWM). At its core is a lightweight geometric regularization module that can be seamlessly integrated into standard autoencoders, reshaping their latent space to provide a stable foundation for effective dynamics modeling. By focusing on representation quality, GRWM offers a simple yet powerful pipeline for improving world model fidelity.