Accelerating Video World Models at the Edge via Defect-Adaptive Reanchoring

Video world models require many expensive neural evaluations along their sampling trajectories. Caching skips some of them, but each approximate output alters the subsequent trajectory, and its error is unobservable precisely when the evaluation is skipped. We introduce the Defect-Adaptive Reanchoring Controller (DARC), a training-free method that uses every fresh evaluation twice: as an anchor for predicting complete model outputs, and as feedback on the forecast it replaces. DARC predicts outputs from recent anchors using latent-state and timestep geometry, calibrates this predictor from pre-call forecast errors, and reanchors once accumulated exposure indicates that further prediction is unreliable. It adds no neural evaluation on skipped steps and leaves the native schedule and solver unchanged. Its routing signal tracks the true reuse error with Spearman $\rho =0.95$, versus at most $0.50$ for prior caches. Across four world-model families and seven benchmarks, DARC is faster and more energy-efficient than prior caches on every family, reaching 2.4-3.0$\times$ end-to-end speedup with 59-70% lower energy on the edge-class Jetson AGX Thor and up to 3.8$\times$ across five GPU platforms, with near-reference quality. Our code and models will be publicly released.