QUASAR: STRUCTURE-PRESERVING DENSITY-STATE DYNAMICS FOR RECURSIVE VIDEO PREDICTION

Visual world models increasingly rely on structured latent dynamics, yet they are still evaluated mainly through decoded frames. In recursive prediction, latent states are fed back into later forecasts, so a model can produce accurate frames while its states leave the space its representation declares valid, a failure that standard forecasting metrics cannot detect. We introduce QUASAR, a video predictor whose local latent states are density matrices: symmetric, positive semidefinite, and unit trace. Motion-conditioned orthogonal transport preserves each state’s spectrum, and learned Kraus channels, made complete by thin QR factorization, redistribute spectral mass. Every recurrent update is therefore completely positive and trace preserving (CPTP), so states remain admissible by construction rather than by post-hoc correction. Experiments show that forecast accuracy and state validity are distinct properties. On Human3.6M, an unconstrained transition matches QUASAR’s four-frame mean squared error (MSE), yet its minimum eigenvalue reaches−1.36×10−3, versus−8.79×10−9 for CPTP. Among matched transition controls under 16-frame recursion, CPTP gives the lowest mean MSE and highest mean structural similarity (SSIM) on both evaluation cohorts, and the lowest mean MSE at all three tested temporal rates. As a predictor, QUASAR reduces Human3.6M MSE by 17.95% relative to the strongest reproduced baseline and attains the highest SSIM on TaxiBJ and KITTI/Caltech, though not the lowest MSE on either. Added to PredFormer, the density-dynamics module reduces MSE by 14.69% with about 1.5% more parameters, although its gains vary across metrics. Density entropy is moderately associated with an occlusion proxy but only weakly with prediction error. These results indicate that admissible latent dynamics can be enforced without sacrificing forecast quality, and that latent states merit auditing alongside decoded frames.