{"ID":3083835,"CreatedAt":"2026-06-05T06:46:15.197025399Z","UpdatedAt":"2026-06-07T05:32:54.120957816Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.05979","arxiv_id":"2606.05979","title":"World-Language-Action Model for Unified World Modeling, Language Reasoning, and Action Synthesis","abstract":"We propose world-language-action (WLA) models as a new class of embodied foundation models. WLA takes textual instructions, images, and robot states as inputs to jointly predict textual subtasks, subgoal images, and robot actions, conjoining the \\emph{world modeling interface} to learn from extensive egocentric videos as in the world-action model (WAM) and the \\emph{language reasoning} capacities to solve complex long-horizon tasks as in vision-language-action (VLA) models. At the core of WLA lies an \\emph{autoregressive (AR)} Transformer backbone, instead of a bidirectional diffusion Transformer as in WAMs, to predict the \\emph{next state}, comprising the \\emph{semantic-level} textual intention and complementary \\emph{fine-grained} physical dynamics. The physical dynamics are supervised by the world modeling objective based on a dedicated World Expert, and are leveraged to ease the characterization of the state-action correlation for the Action Expert. WLA leverages meta-queries to make the world prediction \\emph{implicitly} impact the action generation so that the former can be disabled during inference. The world prediction can also be activated to enable test-time scaling for improved robot control. Our WLA-0 prototype, with 2B active parameters, achieves 40 ms per inference on an NVIDIA RTX 5090. Evaluations across simulated and real-world environments demonstrate that WLA-0 achieves state-of-the-art multi-task and long-horizon learning abilities, e.g., 92.94\\% success rate on RoboTwin2.0 Clean and 56.5\\% success rate on RMBench. WLA-0 also holds the promise to learn novel tasks directly from \\emph{cross-embodiment robot videos} without action annotations.","short_abstract":"We propose world-language-action (WLA) models as a new class of embodied foundation models. WLA takes textual instructions, images, and robot states as inputs to jointly predict textual subtasks, subgoal images, and robot actions, conjoining the \\emph{world modeling interface} to learn from extensive egocentric videos...","url_abs":"https://arxiv.org/abs/2606.05979","url_pdf":"https://arxiv.org/pdf/2606.05979v1","authors":"[\"Yi Yang\",\"Zhihong Liu\",\"Siqi Kou\",\"Yiyang Chen\",\"Yanzhe Hu\",\"Jianbo Zhou\",\"Boyuan Zhao\",\"Zhijie Wei\",\"Xiao Xia\",\"Xueqi Li\",\"Pengfei Liu\",\"Zhijie Deng\"]","published":"2026-06-04T10:23:01Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\"]","methods":"[\"Diffusion Model\",\"Transformer\"]","has_code":false}