OIBench

# 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 ( [e-mail] )

# OIBench数据集

## 数据集概览

[OIBench](https://arxiv.org/abs/2506.10481) 是一款高质量、私有且极具挑战性的奥林匹克级信息学基准数据集，包含250道经过精心筛选的原创试题。

**OIBench数据集**的Hugging Face仓库收录了算法试题描述、题解以及相关元数据，例如测试用例、伪代码与难度等级。本数据集已完成预处理，并以Parquet格式存储，以实现高效的访问与分析。

本数据集提供全部250道试题的完整信息，可通过`dataset = load_dataset("AGI-Eval/OIBench")`进行加载；由于测试用例体积较大，Hugging Face的默认数据集查看器可能无法完整展示所有信息。

我们在`human_participants_data.parquet`中提供了人类参赛者的竞赛记录。详细使用方法请参考：https://github.com/AGI-Eval-Official/OIBench

## 数据集结构

本数据集包含以下字段：
- **`id`**：试题ID（例如`000`、`001`……`249`）
- **`prob_zh`**：中文试题描述
- **`prob_en`**：英文试题描述
- **`algorithm_tag_zh`**：中文算法标签
- **`algorithm_tag_en`**：英文算法标签
- **`level`**：试题难度等级
- **`canonical_solution`**：C++语言编写的官方题解代码
- **`test_case`**：测试用例列表，每个测试用例包含`input`（输入数据）与`output`（输出结果）。
- 每个测试用例为包含以下字段的对象：
- `input`：测试用例的输入数据
- `output`：测试用例的输出结果
- **`pseudo_code`**：对应算法的伪代码
- **`buggy_code`**：该试题的存在缺陷的代码
- **`corrupted_code`**：该试题的不完整代码

## 使用方法

你可以通过以下示例代码在Python中加载本数据集：
python
from datasets import load_dataset

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

更多使用细节请参考我们的GitHub仓库：https://github.com/AGI-Eval-Official/OIBench

## 引用格式

@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},
}

通讯作者：林秋（[e-mail]）