FORECASTEDIT: LEARNING TO EDIT WORLD FORECASTS FOR 4D OCCUPANCY PREDICTION

Future-world models for autonomous driving typically improve prediction by learning to generate increasingly accurate future states from observations. We study a complementary problem: once a future world has already been predicted, can a model learn what should be edited rather than regenerate the entire forecast? We introduce ForecastEdit, which treats a frozen world-model prediction as an explicit reference and learns selective, evidence-grounded revisions. ForecastEdit combines evidence-guided boundary editing to retract unsupported occupancy with executable semantic editing that converts ontology-structured object dynamics into localized, utility-aware edits, followed by lightweight semantic commitment. On Occ3D-nuScenes, ForecastEdit achieves state-of-the-art semantic forecasting performance with 14.82 mIoU, improving the SparseWorld anchor by 1.62 points while also improving occupied IoU. The edited future representation further improves downstream planning, reducing average trajectory error from 0.27 to 0.23m and collision rate from 0.29% to 0.16%. Controlled comparisons show that these gains cannot be explained by generic post-hoc correction alone. Our results suggest that stronger world models may require not only better forecasting, but also principled mechanisms for editing the futures they already predict.