Rethinking Prediction Horizons: Decoupling Model Queries from State Updates in Few-Step MeanFlow

MeanFlow predicts average velocity over a queried time interval, while standard few-step sampling ties this prediction horizon to the state-update duration. We decouple these quantities at inference, querying a longer horizon while keeping the state grid, update durations, checkpoint, and query count fixed. Across seven independently trained CIFAR-10 checkpoints at classifier-free guidance (CFG) scale 2, longer horizons worsen FID early in sampling but improve it at the midpoint. This benefit also depends on the incoming-state distribution. In held-out factorial outcomes on five previously studied checkpoints, with a hypothesis frozen before inspection, changing only preceding CFG while fixing current and subsequent guidance increases the mean horizon benefit from 0.109 to 1.308 FID. A frozen four-query schedule reduces mean CIFAR-10 FID by 2.023 without retraining or additional queries, including improvements on five checkpoints trained after rule selection. A milder frozen rule further improves mean FID by 1.041 on a stronger grid selected from 84 predefined timestep candidates and transfers without retuning to the evaluated public ImageNet-256 and AFHQ checkpoints. Under matched new calibration budgets, start-time shifting achieves lower FID/KID than horizon-only adjustment in historical CIFAR tests, while the tested Horizon allocation adds no FID benefit beyond Grid+Start on two public ImageNet models. These results identify prediction horizon as one of several useful temporal controls: the physically matched interval need not yield the best endpoint metric. Prediction horizon is a configuration-dependent inference control with no additional queries after selection; the evaluated selection procedures use target rollouts.