World Action Models (WAMs) typically adapt video foundation models to robotic control through joint video and action prediction. However, task-level instructions may leave the next physical interaction underspecified, requiring semantic grounding and state-dependent motion selection. To this end, we introduce Atomic-WAM, an understand-then-execute framework that connects task reasoning to action generation through atomic-motion descriptions. Specifically, a vision-language reasoner describes the next local motion from the task instruction and visual context, guiding a WAM to predict future video and an action chunk. Furthermore, an automatic annotation pipeline curates temporally aligned, contact-enriched supervision from multi-view DROID demonstrations. To make atomic-motion predictions executable by the WAM, we post-train the reasoner with Execution-Grounded GRPO (EG-GRPO). With the WAM fixed, EG-GRPO adapts the reasoner through rewards for motion appropriateness and execution success. Finally, Atomic-WAM achieves 39.6% task success under default instructions and 31.6% under vague instructions in a real-to-sim evaluation on RoboLab. Project page: https://ssj9596.github.io/Atomic-WAM-project
G$^2$WAM: World Action Models for Efficient and Geometry Grounded Image-Goal Navigation
College of Intelligence Science and Technology, National University of Defense Technology（国防科技大学智能科学学院）；School of Future Technology, National University of Defense Technology（国防科技大学未来技术学院）
Memory as Plans: World-Action Modeling with Memory-Grounded Planning
Harbin Institute of Technology（哈尔滨工业大学）；Nanyang Technological University（南洋理工大学）；Shandong University（山东大学）；Harbin Institute of Technology (Weihai) Qingdao Research Institute（哈尔滨工业大学（威海）青岛研究院）
HiTac-WAM: A Hierarchical Tactile World Action Model for Contact-Rich Robot Manipulation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；ImprintX Robotics（ImprintX机器人公司）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）
MV-WAM: Manifold-Aware World Action Model with Value Augmentation
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University（北京大学计算机学院多媒体信息处理国家重点实验室）；Beijing Innovation Center of Humanoid Robotics（北京人形机器人创新中心）
Causal-History Test-Time Scaling for Failure Recovery in Autoregressive World-Action Models
The Hong Kong University of Science and Technology (HKUST)（香港科技大学）；ACCESS – AI Chip Center for Emerging Smart Systems（ACCESS——新兴智能系统AI芯片中心）；Zhejiang University（浙江大学）
AR-WAM: A Visual-Conditioned Agent-Ready World Action Model for Robotic Manipulation
The Hong Kong University of Science and Technology（香港科技大学）；Shenzhen University of Advanced Technology（深圳理工大学）；Sangfor Technologies Inc.（深信服科技股份有限公司）；Mininglamp Technology（明略科技）；The Chinese University of Hong Kong（香港中文大学）；The University of British Columbia（不列颠哥伦比亚大学）；Astribot
PACT-WAM: Predicting Actions and Visual Foresight with Compact Temporal Encoding for Robot Manipulation
Department of Automation, Tsinghua University（自动化系，清华大学）；Shenzhen International Graduation School, Tsinghua University（深圳国际研究生院，清华大学）；Department of Computer Science and Technology, Tsinghua University（计算机科学与技术系，清华大学）；School of Mechanical and Aerospace Engineering, Nanyang Technological University（机械与航空航天工程学院，南洋理工大学）
HarnessWAM: Bridging Prediction and Deliberation in World Action Models
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Yinwang Intelligent Technology Co., Ltd.（银湾智能科技有限公司）；Beijing Institute of Technology（北京理工大学）；Wuhan AI Research（武汉人工智能研究院）
TemporalFlow-VLA: Learning Physically Grounded Execution History for Long-Horizon Robot Manipulation
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Zhejiang University（浙江大学）；Simon Fraser University（西蒙菲莎大学）；AgiBot（云圣智能科技（上海）有限公司）
Efficient World Action Model Inference with Adaptive Intermediate States
Xiamen University（厦门大学）；Institute for AI Industry Research, Tsinghua University（清华大学人工智能产业研究院）；School of Computer Science, Peking University（北京大学计算机学院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Alibaba Group（阿里巴巴集团）
StreamingWAM: Streaming World Action Modeling via Chunk-Wise Staircase Flow Matching
University of Chinese Academy of Sciences（中国科学院大学）；Beijing Zhongguancun Academy（北京中关村学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Science and Technology of China（中国科学技术大学）；Nankai University（南开大学）；Harbin Institute of Technology（哈尔滨工业大学）；Eastern Institute of Technology, Ningbo（宁波工程学院）
针对现有WAM机器人控制方法未同时解决推理效率与动作连续性的问题，提出StreamingWAM流扩散框架，在多机器人任务上提升动作连续性，降低重规划延迟并减少对执行 horizon K 的敏感性。
