Recent World-Action Models

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6 papers in the last four weeks, up 20% on the four weeks before. 0.1% of all new papers.

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Latest papers 24

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ReWAM: Reciprocal World Action Models for Interactive Autonomous Driving
Sep 30, 2026Benshan Ma, Pei Liu, Ruiguo Zhong +4Efficient World-Action Model Recent World-Action Models
The Hong Kong University of Science and Technology (Guangzhou) · Leapmotor · The Hong Kong University of Science and Technology
V-JEPA Policy: Building Effective World-Action Models on Predictive Visual Latents
Sep 29, 2026Yang Zhang, Jiangyuan Zhao, Chenyou Fan +4Video Joint Embedding Predictive Architecture Recent World-Action Models
Tsinghua University · Shanghai Jiao Tong University · Fudan University +4
V2X-WAM: A Cooperative World Action Model for End-to-End Autonomous Driving
Sep 29, 2026Junwei You, Weizhe Tang, Can Wang +6V2X Autonomous Driving
ITS Center, Research Institute of Highway Ministry of Transport, Beijing, 100088, China · State Key Lab of Intelligent Transportation System, Research Institute of Highway Ministry of Transport, Beijing, 100088, China · Department of Civil and Environmental Engineering, University of Wisconsin–Madison, Madison, WI, 53706, USA +4
ReSync: Re-Aligning the Two Clocks of Asynchronous World-Action Models
Sep 27, 2026Xi Lin, Feihong Zhang, Yulong Shi +6Recent World-Action Models World Models
Johns Hopkins University · Tsinghua University · Yinwang Intelligent Technology Co., Ltd. +1
AnyStep-WAM: Budget-Aligned Distillation and Adaptive Inference for World Action Models
Sep 27, 2026Rui Wang, Xiangyu Wang, Donglin Yang +4Recent World-Action Models Faster-Wam
Southern University of Science and Technology · Shenzhen Loop Area Institute · The University of Hong Kong
DeltaWAM: Delta World Action Models for Bimanual Manipulation
Sep 23, 2026Han Yan, Zishang Xiang, Haokai Jiang +6Recent World-Action Models World Models
School of Computer Science, Peking University · AI2 Robotics
What Matters in Designing World Action Models: An Empirical Study
Sep 21, 2026Chao Tang, Haoqing Wang, Zilang Cen +9Recent World-Action Models World Models
Samsung Robotics eXperience · Samsung R&D Institute China–Beijing · Zhongguancun Academy +2
WholeBodyWAM: Learning Whole-Body World Action Models with Scalable Motion Priors
Sep 16, 2026Bowei Zhang, Qiyao Zhang, Shuanghao Bai +8Whole-Body Motion Whole-Body
Nankai University · Beijing Innovation Center of Humanoid Robotics · Beijing Institute of Technology +1
ZimaBlue: Evolving Generalizable World Action Models through Scalable Video Pre-training
Aug 31, 2026Xionghao Wu, Yijun Yang, Shiyang Zhou +17Recent World-Action Models Egocentric Video
Joy Future Academy
GlanceWAM: Sparse Test-Time Imagination for World-Action Models
Aug 25, 2026Linhan Wang, Zijian An, Mingyuan Zhang +8Recent World-Action Models Imagination
Virginia Tech · Drexel University · Northeastern University +1
JEPA-WAM: Stage-Level Joint-Embedding Prediction for World-Action Models in Robot Manipulation
Aug 11, 2026Xiao Liu, Yuguang Yang, Xi Wang +6Recent World-Action Models Robotic Manipulation
Institute for AI Industry Research (AIR), Tsinghua University · School of Electronic Information Engineering, Beihang University · AIR Wuxi Innovation Center, Tsinghua University +3
SimWAM: A Simple World Action Model for End-to-End Autonomous Driving
Aug 7, 2026Zongchuang Zhao, Xin Zhou, Tianyang Xu +6Recent World-Action Models Autonomous Driving
Huazhong University of Science & Technology · Dongfeng Research & Development Institute
Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models
Aug 6, 2026Haodong Yan, Junfeng Li, Junjie He +12Recent World-Action Models Faster-Wam
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · Beihang University, Beijing, China · Huawei Foundation Model Department
Faster-WAM: Efficient Inference-Time Future Conditioning for Robust World Action Models
Aug 5, 2026Weiheng Zhao, Haoyi Jiang, Xin Shi +5Efficient World-Action Model Recent World-Action Models
Huazhong University of Science and Technology · D-Robotics · Horizon Robotics +1
AeroAct: Action-Centered World-Action Models for Language-Conditioned Quadrotor Flight
Jul 16, 2026Xinhong Zhang, Qiyuan Zhu, Yubo Huang +8Aerial Vision-Language Navigation Quadrotor
School of Automation, Beijing Institute of Technology · School of Mechanical Engineering, Beijing Institute of Technology · Independent researcher
ABot-M0.5: Unified Mobility-and-Manipulation World Action Model
Jul 1, 2026Ronghan Chen, Yandan Yang, Zuojin Tang +18Efficient World-Action Model Recent World-Action Models
AMAP CV Lab Alibaba Group
World Action Models: A Survey
Jun 18, 2026Qiuhong Shen, Shihua Zhang, Yue Liao +5Recent World-Action Models World Models
National University of Singapore
EWAM: An Enhanced World Action Model for Closed-Loop Online Adaptation in Embodied Intelligence
Jun 10, 2026Xin Zhou, Cong MiaoEfficient World-Action Model Recent World-Action Models
Astronex Robotics · Nanjing University of Information Science and Technology
Unified Video-Action Joint Denoising for Dexterous Action and Data Generation
Jun 2, 2026Dingrui Wang, YuAn Wang, Jinkun Liu +4Camera-Conditioned Video Generation Human Motion Generation
1Technical University of Munich · 2ByteDance · 3Tsinghua University
World Action Models: The Next Frontier in Embodied AI
May 12, 2026Siyin Wang, Junhao Shi, Zhaoyang Fu +11Recent World-Action Models World Models
1Fudan University · 2Shanghai Innovation Institute · 3National University of Singapore
Learning Visual Feature-Based World Models via Residual Latent Action
May 8, 2026Xinyu Zhang, Zhengtong Xu, Yutian Tao +3Latent World Models Latent Action Models
1Rutgers University · 2Purdue University · University of Wisconsin-Madison
CKT-WAM: Parameter-Efficient Context Knowledge Transfer Between World Action Models
May 7, 2026Yuhua Jiang, Yijun Guo, Hongbing Yang +7Efficient World-Action Model Recent World-Action Models
Tsinghua University · LivsynRobotics · Shanghai AI Laboratory
EA-WM: Event-Aware Generative World Model with Structured Kinematic-to-Visual Action Fields
May 7, 2026Zhaoyang Yang, Yurun Jin, Lizhe Qi +2Efficient World-Action Model Recent World-Action Models
Fudan University · Zhongguancun Institute of Artificial Intelligence · University of Science and Technology of China +2
Motubrain: An Advanced World Action Model for Robot Control
Apr 30, 2026Motubrain Team, Chendong Xiang, Fan Bao +17Diffusion-Based Vision-Language-Actions Recent World-Action Models
Motubrain Team

Sep 30, 2026, cs.ROJ/K move · Enter open · S save

ReWAM: Reciprocal World Action Models for Interactive Autonomous Driving

Benshan Ma, Pei Liu, Ruiguo Zhong, Lang Zhang+3

The Hong Kong University of Science and Technology (Guangzhou) · Leapmotor · The Hong Kong University of Science and Technology

Efficient World-Action Model Recent World-Action Models Interaction Prediction Autonomous Driving World Models Cross-Attention

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In interactive scenarios, an autonomous driving system is required to generate ego actions under the influence of other agents' behaviors. Existing World Action Models (WAMs) typically model other agents as components of the world model rather than as decision-makers that fundamentally shape the action of the ego agent, which impairs their performance in dense interaction scenarios. We introduce Reciprocal World Action Models (ReWAM), a game-theoretic world action modeling framework that captures the reciprocal influence between the ego agent and other agents by representing them as conditional responders whose actions are mutually influenced. We instantiate this framework with a Level-

k

response hierarchy, where role-specific ego and other action DiTs exchange compact strategy tokens through cross-agent attention while remaining grounded in a shared representation of the future driving world. To learn the response policy of the ego agent from demonstrations, we formulate expert actions as samples from the best response distribution and jointly optimize the entire hierarchy using conditional flow matching. Our framework is evaluated on the NAVSIM dataset and achieves state-of-the-art performance compared to baselines. The improvement is particularly significant in interactive scenarios, validating that modeling reciprocal responses provides a more effective foundation for interaction-aware world action generation.

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