Update README.md

README.md

@@ -9,8 +9,11 @@ tags:
 <img src="docs/imgs/WE_title.png" width="800px">
 
 > *The missing infrastructure for Physical AI post-training in AD. Open-source. Production-validated.*
-
+>
+[![Paper](https://img.shields.io/badge/arXiv-2606.19836-b31b1b.svg?style=for-the-badge&logo=arxiv)](https://arxiv.org/abs/2606.19836)
+[![YouTube](https://img.shields.io/badge/YouTube-Video-FF0000.svg?style=for-the-badge&logo=youtube)](https://www.youtube.com/watch?v=P1zEyfqa1uY)
 [![License](https://img.shields.io/badge/License-CC--BY--NC--SA--4.0-blue.svg?style=for-the-badge)](https://creativecommons.org/licenses/by-nc-sa/4.0/)
+
 [![ModelScope](https://img.shields.io/badge/ModelScope-Dataset-orange.svg?style=for-the-badge)](https://www.modelscope.cn/datasets/OpenDriveLab/WorldEngine)
 [![GitHub](https://img.shields.io/badge/GitHub-Repository-black.svg?style=for-the-badge&logo=github)](https://github.com/OpenDriveLab/WorldEngine)
 [![Hugging Face](https://img.shields.io/badge/Hugging_Face-Dataset-ffc107.svg?style=for-the-badge&logo=huggingface)](https://huggingface.co/datasets/OpenDriveLab/WorldEngine)
@@ -33,6 +36,7 @@ tags:
 - **Production-scale validation**: Deployed on a mass-produced ADAS platform trained on 80,000+ hours of driving logs, reducing collision rate by up to **45.5%** and achieving zero disengagements in a 200 km on-road test.
 
 ## News
+- **[2026/06/19]** Paper released on arXiv. See [World Engine: Towards the Era of Post-Training for Autonomous Driving](https://arxiv.org/abs/2606.19836).
 - **[2026/04/09]** Official data release.
 
 ---
@@ -243,52 +247,15 @@ After dataset setup, refer to the main project documentation:
 If this project is helpful to your research, please consider citing:
 
 ```bibtex
-
-```
-
-If you use the Render Assets (MTGS), please also cite:
-```bibtex
-@article{li2025mtgs,
-  title={MTGS: Multi-Traversal Gaussian Splatting},
-  author={Li, Tianyu and Qiu, Yihang and Wu, Zhenhua and Lindstr{\"o}m, Carl and Su, Peng and Nie{\ss}ner, Matthias and Li, Hongyang},
-  journal={arXiv preprint arXiv:2503.12552},
-  year={2025}
-}
-```
-
-If you use the scenario data generated by Behavior World Model (BWM), please also cite:
-```bibtex
-@inproceedings{zhou2025nexus,
-  title={Decoupled Diffusion Sparks Adaptive Scene Generation},
-  author={Zhou, Yunsong and Ye, Naisheng and Ljungbergh, William and Li, Tianyu and Yang, Jiazhi and Yang, Zetong and Zhu, Hongzi and Petersson, Christoffer and Li, Hongyang},
-  booktitle={ICCV},
-  year={2025}
-}
-```
-
-```bibtex
-@article{li2025optimization,
-  title={Optimization-Guided Diffusion for Interactive Scene Generation},
-  author={Li, Shihao and Ye, Naisheng and Li, Tianyu and Chitta, Kashyap and An, Tuo and Su, Peng and Wang, Boyang and Liu, Haiou and Lv, Chen and Li, Hongyang},
-  journal={arXiv preprint arXiv:2512.07661},
-  year={2025}
-}
+@misc{li2026worldengineeraposttraining,
+      title={World Engine: Towards the Era of Post-Training for Autonomous Driving}, 
+      author={Tianyu Li and Li Chen and Caojun Wang and Haochen Liu and Kashyap Chitta and Zhenjie Yang and Yuhang Lu and Naisheng Ye and Yihang Qiu and Yufei Wang and Luoxi Zou and Jiaxin Peng and Jin Pan and Zhaoyu Su and Andrei Bursuc and Shengbo Eben Li and Andreas Geiger and Peng Su and Hongyang Li},
+      year={2026},
+      eprint={2606.19836},
+      archivePrefix={arXiv},
+      primaryClass={cs.RO},
+      url={https://arxiv.org/abs/2606.19836}, 
 ```
-
-If you find AlgEngine well, please cite as well:
-```bibtex
-@ARTICLE{11353028,
-  author={Liu, Haochen and Li, Tianyu and Yang, Haohan and Chen, Li and Wang, Caojun and Guo, Ke and Tian, Haochen and Li, Hongchen and Li, Hongyang and Lv, Chen},
-  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
-  title={Reinforced Refinement With Self-Aware Expansion for End-to-End Autonomous Driving}, 
-  year={2026},
-  volume={48},
-  number={5},
-  pages={5774-5792},
-  keywords={Adaptation models;Self-aware;Autonomous vehicles;Pipelines;Planning;Training;Reinforcement learning;Uncertainty;Data models;Safety;End-to-end autonomous driving;reinforced finetuning;imitation learning;motion planning},
-  doi={10.1109/TPAMI.2026.3653866}}
-```
-
 ---
 
 ## 📄 License