Discrete-WAM unifies world modeling and policy learning for autonomous driving by representing observations, states, decisions, and actions as tokens in one space and using hierarchical token editing for planning.
Dap: A discrete-token autoregressive planner for autonomous driving.arXiv preprint arXiv:2511.13306, 2025
2 Pith papers cite this work. Polarity classification is still indexing.
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IRR-Drive adds an adaptive multimodal reflection step (text intention plus predicted future BEV) that lets a VLA model self-correct its trajectory plan according to scene complexity and reports SOTA on NAVSIM.
citing papers explorer
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Intend, Reflect, Refine: An Adaptive Multimodal Reflection Framework for Autonomous Driving
IRR-Drive adds an adaptive multimodal reflection step (text intention plus predicted future BEV) that lets a VLA model self-correct its trajectory plan according to scene complexity and reports SOTA on NAVSIM.