Reduced-Order Models: The Mother of World Models
World models -- compressed latent representations of an environment that support action-conditioned prediction and planning -- are typically presented as a product of modern self-supervised learning. This paper argues that the functional anatomy of a world model was independently developed, deployed, and formally analyzed decades earlier in the model-order-reduction (MOR) and control literature, under different names and for a different purpose: the real-time operation of physical systems. We trace the anatomy across three communities. Low-dimensional models of turbulence built on proper orthogonal decomposition (POD) supplied latent dynamics learned from data of a chaotic environment; ei...
| Authors | Rajat Ghosh |
|---|---|
| Primary category | cs.LG |
| Published | 3 July 2026 |
| Last updated at source | 10 July 2026 |
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