这篇论文梳理了从世界动作模型到具身大脑的路线图，对搞具身智能的研究者很有参考价值。
这篇论文回顾了世界动作模型（WAMs）的演变，指出当前研究存在三个耦合差距：模型角色和表示、目标和标准化、系统组合。基于此，提出以embodied brain为核心的协同进化路线图，这是一个长期模型目标，能整合多模态上下文、比较候选干预、发出状态转换或能力请求。论文还介绍了物理束缚（physical harness）、共享合同和闭环后训练等组件，构建了一个模块化的物理智能堆栈。
From World Action Models to Embodied Brains: A Roadmap for Open-World Physical Intelligence
Artificial general intelligence ultimately requires agents that can reason and act in the physical world. Action models, vision-language-action policies, and world models have advanced this goal, while World Action Models (WAMs) are particularly promising because they connect candidate interventions with predicted consequences. However, progress remains fragmented: models use incompatible action spaces and prediction targets, datasets and tasks follow different conventions, and runtime systems expose limited interfaces for reuse and evaluation. We review the evolution toward WAMs and organize these limitations into three coupled gaps: model roles and representations, objectives and standardization, and system composition. Building on this analysis, we propose a co-evolution roadmap for physical intelligence centered on the \emph{embodied brain}, a long-term model target for integrating multimodal context, comparing candidate interventions, and issuing state-transition or capability requests rather than direct actuator commands. WAMs provide promising prototypes for its predictive functions, while a physical harness grounds model outputs through tools, controllers, verification, and trace logging. Shared contracts align heterogeneous models, data, tasks, and embodiments, and closed-loop post-training converts verified interaction into reusable experience. Together, these components define a modular physical-intelligence stack for adaptive and self-improving embodied agents.