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EditWM: Event-Decomposed World Modeling with Incremental Correction for End-to-End Autonomous Driving

arXiv · AI, language, vision and robotics · article · Sep 15, 2026 · UTC

World models support autonomous driving by predicting the scene evolution associated with candidate trajectories. Driving dynamics differ in predictability, motivating a distinction between regular evolution and event-induced deviations that call for selective correction. We propose EditWM, a world model that decomposes future prediction into normal evolution and event-driven incremental correction in compact visual feature space. A trajectory-conditioned normal predictor provides the base forecast and is then frozen for correction learning. A correction decoder compares this forecast with obs

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