arXiv: cs.AI preprintsInternational5 November 2026
Readable Is Not Steerable: Pre-Registered Tests of Physical Structure in Learned Fluid Representations
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arXiv:2611.05577v1 Announce Type: cross Abstract: Self-supervised world models are often said to organize their latents around physical quantities. We test this on 2D decaying Navier-Stokes flow in three pre-registered studies, each tested once on held-out data. In Study 1, a joint-embedding predictive architecture (JEPA) trained to predict the flow fails the pre-registered criteria for organizing its latent around fluid quantities better than an untrained network of the same architecture (not supported); a JEPA trained on non-physical linear dynamics and a static invariance model that never s
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