The Forecast–Reconstruction Tradeoff Is an Artifact of Coupling a Single Latent to Both Objectives

Latent dynamical models that share a single latent space between reconstruction and forecasting face a tradeoff: improving one objective degrades the other. We show this tradeoff is architectural, not inherent. For linear systems, we prove that reconstruction-optimal and prediction-optimal projections generically occupy different subspaces; empirically, we observe persistent gradient conflict on the shared encoder. We propose carrier-residual decoupling: a wide carrier handles reconstruction, a narrow residual code handles forecasting, and no parameters are shared between the two. On five dynamical systems (6-20D), a 64D spatiotemporal benchmark, three forecast heads (DMD, MLP, neural ODE), and four multi-task gradient methods, no param-matched coupled model Pareto-dominates the decoupled one.