Mixture-of-Transformers

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12 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

DriveWLA: Unifying Future Dynamics and Scene Semantics through World-Language-Action Modeling for Autonomous Driving
Oct 13, 2026Shuzheng Wang, Haoxuan Li, Shengzhen Li +5Mixture-of-Transformers Autonomous Driving Planning
Huazhong University of Science and Technology · Yinwang Intelligent Technology Co. Ltd.
Devol-ONE: One Autoregressive Mixture of Transformers to Unify Vision-Language-Action and Latent World Modeling
Sep 26, 2026Hongyi Cai, Yi Herng Ong, Tingshiuan C. Wu +4Mixture-of-Transformers Robotic Manipulation
Devol Robots, San Francisco, CA, United States
Generation as Auxiliary Supervision: Enhancing Visual Understanding at Zero Inference Overhead via Decoupled Embedding Prediction
Aug 12, 2026Zhongbin Guo, Jiahao Xie, Dongling Xiao +5Multimodal Pretraining Mixture-of-Transformers
ByteDance
GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch
Jul 15, 2026GigaWorld Team, Angen Ye, Angyuan Ma +26Robot Policy Learning World Model Learning
GigaAI Tsinghua University
Mural: Transferring LLM knowledge to image generation via Mixture-of-Transformers
Jun 27, 2026Achin Jain, Jie An, Siddharth Chaudhary +1T2I Generation Mixture-of-Transformers
Amazon AGI
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 Mixture-of-Transformers
Fudan University · Shanghai Innovation Institute · The University of Hong Kong +5
EVA01: Unified Native 3D Understanding and Generation via Mixture-of-Transformers
May 16, 2026Zongyuan Yang, Mingjing Yi, Wanli Ma +83D Mesh Generation 3D Shape Editing
SeeleAI Team
HY-Embodied-0.5: Embodied Foundation Models for Real-World Agents
Apr 8, 2026Tencent Robotics X, HY Vision Team, : +20Mixture-of-Transformers Robot Foundation Models
UniDriveVLA: Unifying Understanding, Perception, and Action Planning for Autonomous Driving
Apr 2, 2026Yongkang Li, Lijun Zhou, Sixu Yan +11Mixture-of-Transformers Vision-Language-Action Models
Huazhong University of Science and Technology · Xiaomi EV · SKL-IOTSC, University of Macau
AutoMoT: A Unified Vision-Language-Action Model with Asynchronous Mixture-of-Transformers for End-to-End Autonomous Driving
Mar 16, 2026Wenhui Huang, Songyan Zhang, Qihang Huang +6Mixture-of-Transformers End-to-End Autonomous Driving
Nanyang Technological University, Singapore · Harvard University, US · Xiaomi EV, China
Brain-WM: Brain Glioblastoma World Model
Mar 8, 2026Chenhui Wang, Boyun Zheng, Liuxin Bao +4Mixture-of-Transformers Clinical Prediction
Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai 200433, China · Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China · School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China +1
Laplacian Multi-scale Flow Matching for Generative Modeling
Feb 23, 2026Zelin Zhao, Petr Molodyk, Haotian Xue +1Mixture-of-Transformers Generative Modeling
Georgia Institute of Technology Atlanta, GA 30332, USA

Oct 13, 2026J/K move · Enter open · S save

DriveWLA: Unifying Future Dynamics and Scene Semantics through World-Language-Action Modeling for Autonomous Driving

Shuzheng Wang, Haoxuan Li, Shengzhen Li, Chenxi Wu+4

Huazhong University of Science and Technology · Yinwang Intelligent Technology Co. Ltd.

Mixture-of-Transformers Autonomous Driving Planning VLMs for Autonomous Driving World Models for Autonomous Driving

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Vision-Language Models (VLMs) and world models have demonstrated strong capabilities in semantic understanding and future scene evolution for autonomous driving. However, effectively integrating both capabilities for trajectory generation remains challenging. In this work, we propose DriveWLA, a unified World-Language-Action model based on Mixture-of-Transformers (MoT) that jointly models semantic understanding, future scene dynamics, and trajectory generation. DriveWLA consists of three experts: a World Expert for future prediction, a Language Expert for semantic understanding, and an Action Expert for trajectory generation. We further introduce Cross-Expert Attention, which enables controlled information flow across experts while aligning semantic and dynamic representations. To improve trajectory generation beyond imitation learning, we apply Flow-GRPO reinforcement learning post-training to refine the Action Expert. Extensive experiments show that DriveWLA achieves superior performance in both open-loop and closed-loop evaluation. DriveWLA achieves 92.3 PDMS on NAVSIM v1 and 90.1 EPDMS on NAVSIM v2, demonstrating strong performance among VLM-based and world-model-based planners. Without specific fine-tuning, DriveWLA further demonstrates strong zero-shot generalization to HUGSIM, nuScenes, and Bench2Drive. These results validate the effectiveness of our unified World-Language-Action modeling, establishing DriveWLA as a generalizable framework for autonomous driving.