---
license: cc-by-nd-4.0
dataset_info:
  features:
  - name: id
    dtype: string
  - name: prob_zh
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  - name: prob_en
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  - name: algorithm_tag_zh
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  - name: algorithm_tag_en
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  - name: level
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  - name: canonical_solution
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    list:
    - name: input
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    - name: output
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  - name: pseudo_code
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  - name: buggy_code
    dtype: string
  - name: corrupted_code
    dtype: string
  splits:
  - name: test
    num_bytes: 7818636649
    num_examples: 250
  download_size: 5518873050
  dataset_size: 7818636649
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
---


# OIBench Dataset

## Dataset Overview

[OIBench](https://arxiv.org/abs/2506.10481) is a high-quality, private, and challenging olympiad-level informatics benchmark consisting of 250 carefully curated original problems. 

The **OIBench Dataset**'s HuggingFace repo contains algorithm problem statements, solutions, and associated metadata such as test cases, pseudo code, and difficulty levels. The dataset has been processed and stored in Parquet format for efficient access and analysis.

We provide complete information for the 250 questions in the data (use `dataset = load_dataset("AGI-Eval/OIBench")` to access, as the test cases are large and the default Dataset Viewer on Hugging Face may not fully display the information). 


We provide the competition records of human participants in `human_participants_data.parquet`. For detailed usage, refer to https://github.com/AGI-Eval-Official/OIBench 

## Dataset Structure
The dataset includes the following fields:

- **`id`**: Problem ID (e.g., `000`, `001`, ..., `249`)
- **`prob_zh`**: Problem description in Chinese
- **`prob_en`**: Problem description in English
- **`algorithm_tag_zh`**: Algorithm tags in Chinese
- **`algorithm_tag_en`**: Algorithm tags in English
- **`level`**: Problem difficulty
- **`canonical_solution`**: Official solution code in C++
- **`test_case`**: List of test cases, each containing `input` and `output`. 
    - Each test case is structured as a list of objects containing:
      - `input`: The input for the test case
      - `output`: The output for the test case
- **`pseudo_code`**: Pseudo code for the algorithm
- **`buggy_code`**: Buggy code for the problem
- **`corrupted_code`**: Incomplete code for the problem

## Usage

You can load the dataset in your Python code using the following example:

```python
from datasets import load_dataset

dataset = load_dataset("AGI-Eval/OIBench")
print(dataset)
```

For more usage details, refer to our GitHub Repo: https://github.com/AGI-Eval-Official/OIBench 


## Citation
```
@misc{zhu2025oibenchbenchmarkingstrongreasoning,
      title={OIBench: Benchmarking Strong Reasoning Models with Olympiad in Informatics}, 
      author={Yaoming Zhu and Junxin Wang and Yiyang Li and Lin Qiu and ZongYu Wang and Jun Xu and Xuezhi Cao and Yuhuai Wei and Mingshi Wang and Xunliang Cai and Rong Ma},
      year={2025},
      eprint={2506.10481},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2506.10481}, 
}

```

Corresponding Author:  Lin Qiu   ( qiulin07@meituan.com )