arXiv: cs.AI preprintsInternational6 November 2026
GuidedWAM: Training-Free Energy Guidance for World Action Models in Autonomous Driving
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arXiv:2611.07912v1 Announce Type: new Abstract: World action models (WAMs) for autonomous driving couple the driving policy and the visual world model in a single generative network whose objectives are fixed post-training. Conditioning on a new driving objective requires retraining. We study energy guidance as a general mechanism and present GuidedWAM, a model-agnostic framework for injecting driving objectives into frozen WAMs at sampling time. Our framework constructs each objective as differentiable energies on the sampler's intermediate clean-action estimate, which needs only forward acce
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