World Action Models

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46 papers in the last four weeks, up 254% on the four weeks before. 0.5% of all new papers.

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ImageWAM: Do World Action Models Really Need Video Generation, or Just Image Editing?
Jun 17, 2026Yuyang Zhang, Wenyao Zhang, Zekun Qi +7Action-Conditioned World Models World Action Models
Shanghai Jiao Tong University · Tsinghua University · Tencent Robotics X +1
WAM-RL: World-Action Model Reinforcement Learning with Reconstruction Rewards and Online Video SFT
Jun 16, 2026Zezhong Qian, Xiaowei Chi, Yu Qi +3World Model Learning Action-Conditioned World Models
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University · Northeastern University · Tsinghua University
Kairos: A Regret-Aware Native World-Action Model Stack for Physical AI
Jun 15, 2026Kairos Team, Fei Wang, Shan You +21Efficient Inference Action-Conditioned World Models
Kairos Team
Metis: A Generalizable and Efficient World-Action Model for Autonomous Driving and Urban Navigation
Jun 14, 2026Jingyu Li, Zhe Liu, Dongnan Hu +10World Model Learning Autonomous Driving
Fudan University · Shanghai Innovation Institute · The University of Hong Kong +5
LaWAM: Latent World Action Models for Efficient Dynamics-Aware Robot Policies
Jun 14, 2026Jialei Chen, Kai Wang, Kang Chen +9Latent Action Learning Action-Conditioned World Models
Jilin University · Zhongguancun Academy · Nankai University +4
WAM4D: Fast 4D World Action Model via Spatial Register Tokens
Jun 12, 2026Ying Li, Xiaobao Wei, Jiajun Cao +10World Models for Robotics Robotic Manipulation
1Peking University · 2The Hong Kong University of Science and Technology · 3Beijing Innovation Center of Humanoid Robotics
RepWAM: World Action Modeling with Representation Visual-Action Tokenizers
Jun 11, 2026Junke Wang, Qihang Zhang, Shuai Yang +5Latent Action Learning World Models for Robotics
Institute of Trustworthy Embodied AI, Fudan University · 2Robbyant, Ant Group · 3Hongkong University of Science and Technology
MaskWAM: Unifying Mask Prompting and Prediction for World-Action Models
Jun 11, 2026Hanyang Yu, Haitao Lin, Jingbo Zhang +4World Model Learning Language-Conditioned Robot Manipulation
1The Hong Kong University of Science and Technology · ‡Work done during an internship at Tencent Robotics X · 2Tencent Robotics X +1
HiMem-WAM: Hierarchical Memory-Gated World Action Models for Robotic Manipulation
Jun 9, 2026Xiaoquan Sun, Ruijian Zhang, Chen Cao +12Latent Action Learning Long-Horizon Robotic Manipulation
3Huazhong University of Science and Technology · 1The University of Hong Kong · 2INFIFORCE +3
Efficient-WAM: A 1B-Parameter World-Action Model with Low-Cost Future Imagination
Jun 8, 2026Jiajun Li, Tiecheng Guo, Yifan Ye +9World Models for Robotics Efficient Inference for World Action Models
1The University of Hong Kong · 2Peking University · 3Muka Robotics +2
AHA-WAM:Asynchronous Horizon-Adaptive World-Action Modeling with Observation-Guided Context Routing
Jun 8, 2026Jisong Cai, Long Ling, Shiwei Chu +10Long-Horizon Robotic Manipulation World Models for Robotics
Shanghai Jiao Tong University · Shanghai AI Laboratory · Baidu AI Cloud +1
MotionWAM: Towards Foundation World Action Models for Real-Time Humanoid Loco-Manipulation
Jun 8, 2026Jia Zheng, Teli Ma, Yudong Fan +3Humanoid Loco-Manipulation Humanoid Robot Control
Mondo Robotics 2 HKUST (GZ) · Mondo Robotics · HKUST (GZ) 3 HKUST
C $^3$ ache: Accelerating World Action Models with Cross Inference Chunk Cache
Jun 8, 2026Weisen Zhao, Lam Nguyen, Zhicong Lu +1World Model Learning Action-Conditioned World Models
George Mason University · University of Central Florida
Dream-Tac: A Unified Tactile World Action Model for Contact-Rich Robot Manipulation
Jun 7, 2026Yunfan Lou, Yifan Ye, Yankai Fu +7World Models for Robotics Contact-Rich Robotic Manipulation
Peking University Beijing, China · Nanjing University Nanjing, China · State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University Beijing, China
Light-WAM: Efficient World Action Models with State-Fusion Action Decoding
Jun 6, 2026Ziang Li, Dongzhou Cheng, Yibin Wang +5Robot Policy Learning World Models for Robotics
Wuhan University · Shanghai Innovation Institute · Southeast University +2
Dreaming when Necessary: Advancing World Action Models with Adaptive Multi-Modal Reasoning
Jun 5, 2026Yinzhou Tang, Jingbo Xu, Yu Shang +4Efficient Multimodal Inference World Models for Robotics
Tsinghua University · Manifold AI
Flash-WAM: Modality-Aware Distillation for World Action Models
Jun 3, 2026Arman Akbari, Ci Zhang, Arash Akbari +6Video Diffusion Models Action-Conditioned World Models
1Northeastern University · University of Georgia · 3EmbodyX Inc.
GeoSem-WAM: Geometry- and Semantic-Aware World Action Models
Jun 2, 2026Fulong Ma, Daojie Peng, Wenjun Yue +4World Models for Robotics Next-Action Prediction
HKUST(GZ) · OC · HKU +4
ImagineUAV: Aerial Vision-Language Navigation via World-Action Modeling and Kinodynamic Planning
May 31, 2026Xuchen Liu, Jiawei Huang, Shihao Xia +3UAV Navigation Vision-Language Navigation
Pengcheng Laboratory, Shenzhen, Guangdong, China · School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, Guangdong, China · Southern University of Science and Technology, Shenzhen, Guangdong, China
Beyond Task Success: Behavioral and Representational Diagnostics for WAM and VLA
May 31, 2026Hung Mai, Bin Zhu, Tuan DoWorld Models for Robotics Robotic Policy Evaluation
National Economics University, Vietnam · N2TP Technology · Singapore Management University +1
DriveWAM: Video Generative Priors Enable Scalable World-Action Modeling for Autonomous Driving
May 27, 2026Chen Shi, Jinrui Xu, Shaoshuai Shi +3Video Diffusion Models End-to-End Autonomous Driving
The Chinese University of Hong Kong, Shenzhen · Voyager Research, Didi Chuxing
SANTS: A State-Adaptive Scheduler for World Action Models
May 27, 2026Yirui Sun, Guangyu Zhuge, Keliang Liu +4Action-Conditioned World Models Visuomotor Policy Learning
Fudan University · Deep Computing Era Technology Co., Ltd · Harbin Institute of Technology
Point Tracking Improves World Action Models
May 22, 2026Jiarui Guan, Wenshuai Zhao, Yue Pei +3Point Tracking Robot Policy Learning
Aalto University · ELLIS Institute Finland · Beihang University +3
WorldVLN: Autoregressive World Action Model for Aerial Vision-Language Navigation
May 15, 2026Baining Zhao, Jiacheng Xu, Weicheng Feng +13World Model Learning Vision-Language Navigation
Tsinghua University · Shandong University · Manifold AI +2
World Action Models: The Next Frontier in Embodied AI
May 12, 2026Siyin Wang, Junhao Shi, Zhaoyang Fu +11Vision-Language-Action Models World Action Models
1Fudan University · 2Shanghai Innovation Institute · 3National University of Singapore
The DAWN of World-Action Interactive Models
May 12, 2026Hongbo Lu, Liang Yao, Chenghao He +6Diffusion-Based Planning World Model-Based Planning
1COWARobot Co. Ltd · 2Shanghai Jiao Tong University · 3Hohai University
HarmoWAM: Harmonizing Generalizable and Precise Manipulation via Adaptive World Action Models
May 11, 2026Qiuxuan Feng, Jiale Yu, Jiaming Liu +8World Models for Robotics Action-Conditioned World Models
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University · The Chinese University of Hong Kong · Simplexity Robotics
NoiseGate: Learning Per-Latent Timestep Schedules as Information Gating in World Action Models
May 8, 2026Wen Huang, Haoran Sun, Yongjian Guo +8Action-Conditioned Video Generation Action-Conditioned World Models
Tsinghua University · Peking University · JDT AI Infra +1
Is the Future Compatible? Diagnosing Dynamic Consistency in World Action Models
May 8, 2026Bo-Kai Ruan, Teng-Fang Hsiao, Ling Lo +1World Models for Robotics World Model-Based Planning
National Yang Ming Chiao Tung University
OA-WAM: Object-Addressable World Action Model for Robust Robot Manipulation
May 7, 2026Yushan Liu, Peibo Sun, Shoujie Li +7World Models for Robotics Action-Conditioned World Models
Tsinghua University · Shanghai Jiao Tong University · Nanyang Technological University

Jun 17, 2026, cs.CVJ/K move · Enter open · S save

ImageWAM: Do World Action Models Really Need Video Generation, or Just Image Editing?

Yuyang Zhang, Wenyao Zhang, Zekun Qi, He Zhang+6

Shanghai Jiao Tong University · Tsinghua University · Tencent Robotics X+1

Action-Conditioned World Models World Action Models Embodied World Models

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World Action Models (WAMs) commonly rely on video generation to bridge visual world modeling and robot control. However, video-based WAMs face three coupled limitations: dense multi-frame future tokens make inference costly, full video prediction spends capacity on action-irrelevant temporal and appearance details, and long-horizon future imagination may introduce errors that mislead action prediction. These issues raise a simple question: Does world action model really need video generation? We propose ImageWAM, a simple WAM framework that repurposes pretrained image editing models for robot action prediction. In contrast to video generation, image editing provides a better-matched prior: it only needs to model a target-frame transformation, focuses on action-relevant current-to-target visual differences, and grounds task instructions to localized visual changes through edit pretraining. In practice, ImageWAM does not decode the target frame at inference time; instead, it conditions a flow-matching action expert on the KV caches produced by image-editing denoising, using them as a compact world-action context. ImageWAM outperforms standard VLA baselines and matching competitive WAMs without additional policy pretraining across different simulator and real-world experiments. It also reduces FLOPs to 1/6 and latency to 1/4 of video-based WAMs. Attention analysis further shows that editing caches focus on task-relevant change regions, supporting image editing as an effective alternative to video-based world-action modeling.

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