{"ID":3084808,"CreatedAt":"2026-06-05T06:46:15.197025399Z","UpdatedAt":"2026-06-07T03:05:32.813677833Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.05645","arxiv_id":"2606.05645","title":"Discrete-WAM: Unified Discrete Vision-Action Token Editing for World-Policy Learning","abstract":"Autonomous driving requires reasoning about how ego actions shape the evolution of the surrounding world. However, most end-to-end methods rely on direct state-to-action mappings, capturing correlations without explicitly modeling action-conditioned dynamics. Conversely, continuous-latent world models often lack compositional structure for causal reasoning across counterfactual futures. We introduce Discrete-WAM, a unified latent vision-action world policy that represents future visual states and ego actions as aligned discrete tokens, enabling compositional causal reasoning across alternative futures. Built upon this unified discrete alignment, Discrete-WAM establishes a shared discrete diffusion framework with unified generative tasks, jointly formulating world modeling, world-action policy, and hierarchical decision-enabled policy, supporting compositional generalization across diverse driving scenarios. Experiments on large-scale autonomous-driving benchmarks show that Discrete-WAM achieves competitive performance while supporting controllable generation and counterfactual reasoning, offering a principled path toward more reliable decision-making.","short_abstract":"Autonomous driving requires reasoning about how ego actions shape the evolution of the surrounding world. However, most end-to-end methods rely on direct state-to-action mappings, capturing correlations without explicitly modeling action-conditioned dynamics. Conversely, continuous-latent world models often lack compos...","url_abs":"https://arxiv.org/abs/2606.05645","url_pdf":"https://arxiv.org/pdf/2606.05645v1","authors":"[\"Ziyang Yao\",\"Haochen Liu\",\"Yuncheng Jiang\",\"Zeyu Zhu\",\"Zibin Guo\",\"Jingru Wang\",\"Tianle Liu\",\"Jianwei Cui\",\"Kuiyuan Yang\",\"Hongwei Xie\",\"Jingwei Zhao\",\"Guang Chen\",\"Hangjun Ye\"]","published":"2026-06-04T03:16:31Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Diffusion Model\"]","has_code":false}