Pathwise Test-Time Correction for Autoregressive Long Video Generation

Distilled autoregressive di!usion models facilitate real-timeshort video synthesis but su!er from severe error accumulation duringlong-sequence generation. While existing Test-Time Optimization (TTO)methods prove e!ective for images or short clips, we identify that they failto mitigate drift in extended sequences due to unstable reward landscapesand the hypersensitivity of distilled parameters. To overcome these limi-tations, we introduce Test-Time Correction (TTC), a training-free alter-native. Specifically, TTC utilizes the initial frame as a stable referenceanchor to calibrate intermediate stochastic states along the sampling tra-jectory. Extensive experiments demonstrate that our method seamlesslyintegrates with various distilled models, extending generation lengthswith a slight overhead while matching the quality of resource-intensivetraining-based methods on 30-second benchmarks.