Progression as Latent Drift: Generative Forecasting of Slow-Evolving Pathologies

Forecasting the future anatomy of slow-evolving neurode-generative diseases could enable earlier, more targeted intervention andimprove clinical trial design, but it remains challenging because true pro-gression signals are subtle in longitudinal MRI. In this low-signal regime,transferring modern generative sequence models directly is unreliable:training is dominated by stable baseline anatomy and confounded bydense, sample-specific nuisance variation. We first provide a theoreticalanalysis that explains these failures through two modes. Identity col-lapse occurs when optimization is driven toward reproducing the currentanatomy, which prevents the model from learning faint temporal change.The continuous interpolation trap arises when standard smooth networkscannot separate localized biological drift from pervasive noise, whichleads to spurious changes that diffuse across the volume. To addressboth issues, we propose Latent Drift, a progressive generative frame-work that learns change in a compressed semantic representation ratherthan synthesizing full-resolution anatomy. This design removes pixel-levelidentity from the prediction target and concentrates model capacity onprogression-relevant dynamics. We further apply Finite Scalar Quan-tization to the learned change representation, which suppresses small,high-frequency nuisance fluctuations while preserving consistent struc-tural drift. Experiments on longitudinal 3D brain MRI show that LatentDrift improves patient-specific neuro-forecasting over diffusion and au-toregressive transformer baselines across generative fidelity and clinicallyrelevant evaluation metrics.