update docs

Author: WCJ-BERT

README.md

@@ -40,7 +40,6 @@
       - [SimEngine - Photorealistic Closed-Loop Simulation](#simengine---photorealistic-closed-loop-simulation)
       - [AlgEngine - End-to-End Model Training \& Fine-Tuning](#algengine---end-to-end-model-training--fine-tuning)
       - [RenderEngine - Multi-Traversal Scene Reconstruction](#renderengine---multi-traversal-scene-reconstruction)
-  - [FAQ](#faq)
   - [Citation](#citation)
   - [Contributing](#contributing)
   - [License](#license)
@@ -57,10 +56,8 @@
 
 
 ## News
-
-- **[2026/04/08]** Official code repository established. Data publication under preparation.
 - **[2026/04/09]** Official dataset released. See [OpenDriveLab/WorldEngine](https://huggingface.co/datasets/OpenDriveLab/WorldEngine) or [OpenDriveLab/WorldEngine (ModelScope)](https://www.modelscope.cn/datasets/OpenDriveLab/WorldEngine)
-
+- **[2026/04/10]** Official code repository established.
 
 
 ## Benchmark
@@ -136,11 +133,10 @@ WorldEngine consists of two tightly coupled subsystems:
 - [x] Multi-GPU distributed simulation and training
 - [x] Rare case extraction and fine-tuning pipeline
 - [x] Comprehensive documentation and usage guides
+- [x] Hugging Face / ModelScope dataset
 - [ ] Open-source release (code, data, pre-trained models)
 - [ ] arXiv preprint
-- [ ] Hugging Face / ModelScope dataset and model hosting
 - [ ] Demo webpage with interactive visualization
-- [ ] Additional model architectures (UniAD, HydraMDP baselines)
 - [ ] Behavior World Model integration
 
 
@@ -207,7 +203,7 @@ Learn how to run simulations, generate rollouts, and test models:
 
 Learn how to train models, extract rare cases, and fine-tune:
 
-- **Training from scratch** (VADv2, UniAD, HydraMDP)
+- **Training from scratch**
 - **Open-loop evaluation** on test sets
 - **Rare case extraction** from evaluation failures
 - **RL-based fine-tuning** on long-tail scenarios
@@ -226,50 +222,6 @@ WorldEngine's simulation environments are powered by 3D Gaussian Splatting (MTGS
 **[MTGS Repository](https://github.com/OpenDriveLab/MTGS)**
 
 
-## FAQ
-
-<details>
-<summary><b>Q1: Do I need both conda environments?</b></summary>
-
-**A:** It depends on your use case:
-- **For simulation only:** Install `simengine` environment
-- **For training only:** Install `algengine` environment  
-- **For complete pipeline:** Install both environments
-
-The two environments are needed because SimEngine requires Python 3.9 (for tiny-cuda-nn) while AlgEngine requires Python 3.8 (for MMCV 1.6.2).
-</details>
-
-<details>
-<summary><b>Q2: Where can I download the data and models?</b></summary>
-
-**A:** Datasets and pre-trained models will be available on:
-- **Hugging Face:** `OpenDriveLab/WorldEngine-Data` and `OpenDriveLab/WorldEngine-Models` (URLs coming soon)
-- **ModelScope:** For users in China (URLs coming soon)
-
-Check our [News section](#news) for download links when available.
-</details>
-
-<details>
-<summary><b>Q3: What's the difference between NR and R modes?</b></summary>
-
-**A:** These are reactive modes for other agents in simulation:
-- **NR (Non-Reactive):** Other agents replay their logged trajectories (default, easier)
-- **R (Reactive):** Other agents use IDM policy to react to ego vehicle (harder, more realistic)
-
-Use NR for initial testing, R for robustness evaluation.
-</details>
-
-<details>
-<summary><b>Q4: How do I report bugs or request features?</b></summary>
-
-**A:** 
-- **Bugs:** Open an [Issue](https://github.com/OpenDriveLab/WorldEngine/issues)
-- **Features:** Start a [Discussion](https://github.com/OpenDriveLab/WorldEngine/discussions)
-- **Questions:** Check [documentation](docs/) first, then ask in Discussions
-
-</details>
-
-
 ## Citation
 
 If any parts of our work help your research, please consider citing us and giving a star to our repository:
@@ -288,14 +240,30 @@ If you use the Render Assets (MTGS), please also cite:
   year={2025}
 }
 ```
+If you use the augmented scenarios data, please cite as well:
+```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}
+}
+```
 
 
 ## Contributing
 
 We welcome contributions from the community! Whether you want to:
 
 - **Report bugs** - Open an [Issue](https://github.com/OpenDriveLab/WorldEngine/issues)
-- **Suggest features** - Start a [Discussion](https://github.com/OpenDriveLab/WorldEngine/discussions)
 - **Improve documentation** - Submit a [Pull Request](https://github.com/OpenDriveLab/WorldEngine/pulls)
 - **Contribute code** - Fork, develop, and submit a PR
 
@@ -304,7 +272,6 @@ Please read our contributing guidelines before submitting PRs.
 **For questions:**
 1. Check the [documentation](docs/) first
 2. Search existing [Issues](https://github.com/OpenDriveLab/WorldEngine/issues)
-3. Ask in [Discussions](https://github.com/OpenDriveLab/WorldEngine/discussions)
 
 
 ## License
@@ -338,6 +305,7 @@ We acknowledge all the open-source contributors for the following projects to ma
 | [![nerfstudio](https://img.shields.io/badge/nerfstudio-NeRF_Framework-green?style=flat-square&logo=github)](https://github.com/nerfstudio-project/nerfstudio) | Collaboration-friendly NeRF toolkit |
 | [![MMDetection3D](https://img.shields.io/badge/MMDetection3D-3D_Detection-orange?style=flat-square&logo=github)](https://github.com/open-mmlab/mmdetection3d) | 3D detection framework |
 | [![UniAD](https://img.shields.io/badge/UniAD-End--to--End_AD-red?style=flat-square&logo=github)](https://github.com/OpenDriveLab/UniAD) | End-to-end autonomous driving framework |
+| [![VADv2](https://img.shields.io/badge/VADv2-End--to--End_AD-crimson?style=flat-square&logo=github)](https://github.com/priest-yang/VADv2) | Vectorized autonomous driving framework |
 | [![NAVSIM](https://img.shields.io/badge/NAVSIM-AD_Benchmark-teal?style=flat-square&logo=github)](https://github.com/autonomousvision/navsim) | Non-reactive autonomous vehicle simulation benchmark |
 | [![nuPlan](https://img.shields.io/badge/nuPlan-Dataset-purple?style=flat-square&logo=github)](https://www.nuscenes.org/nuplan) | Large-scale autonomous driving dataset |
 | [![MetaDrive](https://img.shields.io/badge/MetaDrive-Driving_Simulation-ff69b4?style=flat-square&logo=github)](https://github.com/metadriverse/metadrive) | Compositional driving simulation platform |

docs/data_organization.md

@@ -21,7 +21,7 @@ WorldEngine/
 
 ## Data Download
 
-We provide pre-processed datasets and model checkpoints via **ModelScope** and **Hugging Face**.
+We provide pre-processed datasets and model checkpoints via **[ModelScope](https://www.modelscope.cn/datasets/OpenDriveLab/WorldEngine)** and **Hugging Face[https://huggingface.co/datasets/OpenDriveLab/WorldEngine]**.
 
 ### Option 1: Download from ModelScope (Recommended for Users in China)
 
@@ -33,11 +33,14 @@ modelscope download --dataset OpenDriveLab/WorldEngine
 ### Option 2: Download from Hugging Face
 
 ```bash
-pip install huggingface-hub
-hf download OpenDriveLab/WorldEngine
+# Install Hugging Face CLI
+curl -LsSf https://hf.co/cli/install.sh | bash
+
+# Download the dataset
+hf download OpenDriveLab/WorldEngine --repo-type dataset --local-dir /path/to/your/WorldEngine_repo
 ```
 
-> **Note:** Dataset and model URLs will be announced once the official release is ready. Stay tuned to our [News section](../README.md#-news).
+> **Note:** Stay tuned to our [News section](../README.md#-news).
 
 ---