Efficiency in World Models: Origami-Inspired Dimensional Reduction and Coarse-Graining

This work introduces a new theoretical framework for efficient world models in artificial intelligence. Traditional particle-based simulations are computationally expensive and scale poorly. Our approach combines origami-inspired dimensional reduction (2D -> 3D reconstruction) with coarse-graining (representing internal dynamics by contact points and centers of mass). This dual strategy reduces computational complexity while preserving structural fidelity, providing a scalable alternative to brute-force particle methods. The framework generalizes aspects of statistical dimensional reduction and molecular coarse-graining, offering efficiency guarantees and connections to practices in game technologies.