{"ID":2851765,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.19654","arxiv_id":"2510.19654","title":"From Forecasting to Planning: Policy World Model for Collaborative State-Action Prediction","abstract":"Despite remarkable progress in driving world models, their potential for autonomous systems remains largely untapped: the world models are mostly learned for world simulation and decoupled from trajectory planning. While recent efforts aim to unify world modeling and planning in a single framework, the synergistic facilitation mechanism of world modeling for planning still requires further exploration. In this work, we introduce a new driving paradigm named Policy World Model (PWM), which not only integrates world modeling and trajectory planning within a unified architecture, but is also able to benefit planning using the learned world knowledge through the proposed action-free future state forecasting scheme. Through collaborative state-action prediction, PWM can mimic the human-like anticipatory perception, yielding more reliable planning performance. To facilitate the efficiency of video forecasting, we further introduce a dynamically enhanced parallel token generation mechanism, equipped with a context-guided tokenizer and an adaptive dynamic focal loss. Despite utilizing only front camera input, our method matches or exceeds state-of-the-art approaches that rely on multi-view and multi-modal inputs. Code and model weights will be released at https://github.com/6550Zhao/Policy-World-Model.","short_abstract":"Despite remarkable progress in driving world models, their potential for autonomous systems remains largely untapped: the world models are mostly learned for world simulation and decoupled from trajectory planning. While recent efforts aim to unify world modeling and planning in a single framework, the synergistic faci...","url_abs":"https://arxiv.org/abs/2510.19654","url_pdf":"https://arxiv.org/pdf/2510.19654v2","authors":"[\"Zhida Zhao\",\"Talas Fu\",\"Yifan Wang\",\"Lijun Wang\",\"Huchuan Lu\"]","published":"2025-10-22T14:57:51Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"cs.CL\",\"cs.RO\"]","methods":"[\"LoRA\"]","has_code":false,"code_links":[{"ID":607935,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2851765,"paper_url":"https://arxiv.org/abs/2510.19654","paper_title":"From Forecasting to Planning: Policy World Model for Collaborative State-Action Prediction","repo_url":"https://github.com/6550Zhao/Policy-World-Model","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}