EvoState: Predicting Spatiotemporal Physical Dynamics with Hierarchical Latent States

Autoregressive prediction of physical dynamics raises a basic design question: which latent states should persist across steps, and which can be recomputed? We introduce EvoState, which separates hierarchical representation learning, latent evolution, and physical readout. Its transition model jointly advances Fine, Middle, and Coarse states, while a fixed readout reconstructs physical fields from Fine. On The Well's shear-flow benchmark, the 11.2M-parameter system achieves Test VRMSE of 0.349 over steps 6–12 and 0.657 over steps 13–30, below the published benchmark baselines. On a common validation panel, EvoState also achieves lower rollout error from step eight onward than three 140M-parameter Walrus-style models trained on shear flow alone, reaching 0.608 NRMSE at step 64 compared with 1.161–2.412. In a two-epoch, single-seed comparison under a matched training schedule, recomputing coarse states from Fine or omitting them increases mean paired per-window 64-step NRMSE by 20.9% and 23.7%, respectively, relative to carrying all three levels. Recomputing coarse states at inference brings them closer to true-frame encodings but worsens physical predictions. These results favor carrying jointly evolved states under the evaluated training recipe and show that same-frame latent accuracy alone is insufficient to assess their utility for an already-trained transition.