Exact Feedback Need Not Help: Correction Compatibility in Frozen Neural Simulators

Exact observations improve the coordinates they replace yet can worsen an autoregressive neural simulator's later forecasts. We study correction compatibility: whether a correction improves a frozen simulator's later forecasts. For exact linear dynamics, harm is possible if and only if the propagated, scored error couples corrected and uncorrected coordinates, so exact partial replacement can hurt even a perfect model (related shock from direct insertion is known in data assimilation), and we show that such harm occurs in released neural simulators. A Rayleigh-Bénard checkpoint that beats persistence by about 40% is harmed by two exact corrections selected as harmful on validation, and the harm replicates, with intervals excluding zero, on a previously unused trajectory from the same test cells; the effect is small (about 1.5% of delayed RMSE) and shown for one checkpoint, an existence result. On Supernova simulations, more exact correction helps on average, and all-channel correction helps in every event; in a post-hoc comparison, a channel-restricted exact correction does worse over one step but better over seven than a fraction-scaled all-channel correction with equal immediate repair. How the corrected state is emitted and fed back is a design choice: development data select opposite interfaces for two acoustic checkpoints, and both hold on 200 untouched trajectories. On a turbulence simulator, with the emission mapping fixed, feeding back a learned, constraint-preserving adjustment improves on discarding it, but the approach harms one other system and adds nothing on another. Feedback should be validated by how it enters recurrence, not by its immediate accuracy.