Add paper link, GitHub link, and improve dataset card documentation

README.md

@@ -1,3 +1,23 @@
+---
+task_categories:
+- other
+---
+
+# ReproduceBench
+
+ReproduceBench is a benchmark featuring verified implementations and comprehensive metrics for evaluating both reproduction and execution fidelity of AI experiments. It was introduced in the paper [AutoReproduce: Automatic AI Experiment Reproduction with Paper Lineage](https://huggingface.co/papers/2505.20662).
+
+**GitHub Repository:** [https://github.com/AI9Stars/AutoReproduce](https://github.com/AI9Stars/AutoReproduce)
+
+## Download Datasets
+
+All the datasets and human-curated reference code are available here. You can download the dataset using the Hugging Face CLI:
+
+```bash
+pip install -U huggingface_hub
+huggingface-cli download --repo-type dataset --resume-download ai9stars/ReproduceBench --local-dir ReproduceBench
+```
+
 ## Data Preparation
 
 All the datasets are included in the [reproducebench.tar.gz](https://huggingface.co/datasets/ai9stars/ReproduceBench/blob/main/reproducebench.tar.gz). 
@@ -15,4 +35,20 @@ PreproduceBench
         |-- dataloader.py
         |-- run_itransformer.py
     ...
+```
+
+## Citation
+
+If you find this work useful, please cite our paper:
+
+```bibtex
+@misc{zhao2025autoreproduceautomaticaiexperiment,
+      title={AutoReproduce: Automatic AI Experiment Reproduction with Paper Lineage}, 
+      author={Xuanle Zhao and Zilin Sang and Yuxuan Li and Qi Shi and Shuo Wang and Duzhen Zhang and Xu Han and Zhiyuan Liu and Maosong Sun},
+      year={2025},
+      eprint={2505.20662},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/2505.20662}, 
+}
 ```