README.md

---
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). 
You need firstly download and decompress the data.
The overall file structure after decompressing is as follows:
```
PreproduceBench
|-- PreproduceBench
    |-- itransformer
        |-- source/ # contain the dataset
        |-- dataloader.py
        |-- run_itransformer.py
    |-- lsm
        |-- source/ # contain the dataset
        |-- 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}, 
}
```