---
license: apache-2.0
language:
- zh
- en
tags:
- vlm
- benchmark
- graphic-reasoning
- intelligence-test
---
# 🧠 ReasonBench: Benchmarking and Improving Visual Language Models for Complex Graphic Reasoning


<img src="https://huggingface.co/datasets/cistine/ReasonBench/resolve/main/image_1.jpg" 
       alt="background" 
       width="50%"/>
  <p style="font-style:italic">image：background</p>


## 🌐 Overview
**ReasonBench** is a comprehensive benchmark designed to evaluate Visual Language Models (VLMs) on complex graphical reasoning tasks. It contains **1,613 problems** collected from real-world intelligence tests, covering **11 core cognitive dimensions** and **29 task types**. This benchmark provides a robust framework for assessing VLMs' spatial, relational, and abstract reasoning capabilities.

**Dataset Type**: Visual Language Reasoning · Graphical Reasoning · Benchmark Evaluation

**Paper Link**:[https://arxiv.org/abs/2508.00323](https://arxiv.org/abs/2508.00323)

## 📊 Dataset Structure
### Core Cognitive Dimensions & Task Types
| Cognitive Dimension      | Task Type                   | Count |
|--------------------------|-----------------------------|-------|
| **Positional Patterns**  | Translation                 | 94    |
|                          | Rotation                    | 56    |
|                          | Combination                 | 30    |
| **Stylistic Patterns**   | Crossing                    | 54    |
|                          | Addition/Subtraction        | 67    |
|                          | Black/White Operation       | 63    |
| **Attribute Patterns**   | Symmetry                    | 109   |
|                          | Open/Close State            | 19    |
|                          | Combination                 | 6     |
| **Quantitative Patterns**| Lines                       | 173   |
|                          | Faces                       | 137   |
|                          | Points                      | 66    |
|                          | Elements                    | 94    |
|                          | Combination                 | 50    |
| **Spatial Patterns**     | Cubes                       | 109   |
|                          | 3D                          | 46    |
|                          | Polyhedrons                 | 17    |
|                          | Three Views                 | 40    |
|                          | Cross-Sections              | 35    |
|                          | Spatial Quantitative Trans. | 10    |
| **Special Patterns**     | 2D Combination              | 31    |
|                          | Figure Relations            | 40    |
| **Alphanumeric**         | Alphanumeric                | 27    |
| **B&W Blocks**           | Black & White Blocks        | 32    |
| **Other Patterns**       | Comprehensive               | 34    |
| **MENSA**                | Task 1                      | 35    |
|                          | Task 2                      | 39    |
| **Raven**                | Task 1                      | 40    |
|                          | Task 2                      | 60    |

### 🖼️ Input Formats
| Format                | Description |
|-----------------------|-------------|
| **Integrated Format** | Presents questions and options in a single image for holistic processing |
| **Separated Format**  | Splits questions and options into multiple images for step-by-step reasoning |

## 🔍 Key Features
- **Multi-format Evaluation**: Supports both integrated and separated input formats
- **Full Accessibility**: Provides public URLs for all images (questions, options, and combined sets)
- **Human Baseline**: Includes human performance metrics for comparison
- **Diverse Tasks**: Covers 29 distinct reasoning task types across 11 cognitive dimensions

## 🚀 Usage（GPT-4o example）
```python
import base64
import requests
import os
from openai import OpenAI  # Requires openai>=1.0.0

# Configuration
api_key = os.getenv("OPENAI_API_KEY")
if not api_key:
    raise ValueError("Missing OPENAI_API_KEY environment variable")

# Initialize client (official SDK approach)
client = OpenAI(api_key=api_key)

def process_image_question(image_path: str, question: str, max_tokens=300):
    """Send image and question to GPT-4o API"""
    # Encode image to base64
    base64_image = base64.b64encode(open(image_path, "rb").read()).decode("utf-8")

    # Construct messages payload
    messages = [
        {
            "role": "user",
            "content": [
                {"type": "text", "text": question},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:image/jpeg;base64,{base64_image}",
                        "detail": "auto"  # Options: low, high, auto
                    }
                }
            ]
        }
    ]

    # Make API request
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=messages,
        max_tokens=max_tokens
    )
    
    return response.choices[0].message.content

# Example usage
if __name__ == "__main__":
    image_path = "path/to/your/image.jpg"  # Update with actual path
    user_question = "What's in this image?"  # Customize your question
    
    try:
        answer = process_image_question(image_path, user_question)
        print("AI Response:", answer)
    except Exception as e:
        print(f"Error: {str(e)}")
```

# 🧠 ReasonBench：复杂图形推理的视觉语言模型评估基准

## 🌐 概述
**ReasonBench** 是一个用于评估视觉语言模型（VLMs）在复杂图形推理任务表现的基准测试。数据集包含从真实智力测试中收集的 **1,613个问题**，覆盖**11个核心认知维度**和**29种任务类型**，为评估VLMs的空间、关系和抽象推理能力提供综合框架。

**数据集类型**：视觉语言推理 · 图形推理 · 基准评估

**论文地址**:[https://arxiv.org/abs/2508.00323](https://arxiv.org/abs/2508.00323)

## 📊 数据结构
### 核心认知维度与任务类型
| 认知维度            | 任务类型               | 数量 |
|---------------------|------------------------|------|
| **位置规律**        | 平移                  | 94   |
|                     | 旋转                  | 56   |
|                     | 组合                  | 30   |
| **样式规律**        | 穿越                  | 54   |
|                     | 加减法                | 67   |
|                     | 黑白运算              | 63   |
| **属性规律**        | 对称                  | 109  |
|                     | 开闭状态              | 19   |
|                     | 组合                  | 6    |
| **数量规律**        | 线                    | 173  |
|                     | 面                    | 137  |
|                     | 点                    | 66   |
|                     | 元素                  | 94   |
|                     | 组合                  | 50   |
| **空间规律**        | 立方体                | 109  |
|                     | 3D                    | 46   |
|                     | 多面体                | 17   |
|                     | 三视图                | 40   |
|                     | 剖视图                | 35   |
|                     | 空间数量变换          | 10   |
| **特殊规律**        | 2D组合                | 31   |
|                     | 图形关系              | 40   |
| **字母数字**        | 字母数字              | 27   |
| **黑白块**          | 黑白块                | 32   |
| **其他规律**        | 综合                  | 34   |
| **门萨**            | 任务1                 | 35   |
|                     | 任务2                 | 39   |
| **瑞文**            | 任务1                 | 40   |
|                     | 任务2                 | 60   |

### 🖼️ 输入格式
| 格式                | 描述 |
|---------------------|------|
| **集成格式**        | 问题与选项呈现在单个图形中，便于模型整体处理 |
| **分离格式**        | 将问题与选项拆分为多个图形，测试分步推理能力 |

## 🔍 核心特性
- **多格式评估**：支持整体式和分隔式两种输入格式
- **完全开放**：公开所有格式的图片URL（题目、选项、题目+选项）
- **人类基准**：提供人类准确率作为参考基准
- **多样化任务**：覆盖11个认知维度的29种推理任务

## 🚀 使用示例(以openai GPT-4o为例)
```python
import base64
import requests
import os

# 配置OpenAI API密钥
api_key = os.getenv("OPENAI_API_KEY")  # 建议将密钥存储在环境变量中
if not api_key:
    raise ValueError("请设置OPENAI_API_KEY环境变量")

# 图像处理函数
def encode_image(image_path):
    """将本地图像编码为base64字符串"""
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode('utf-8')

# 示例图像路径和问题
image_path = "path/to/your/image.jpg"  # 替换为你的图像路径
question = "描述这张图片的内容"  # 替换为你的问题

# 构建API请求
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {api_key}"
}

payload = {
    "model": "gpt-4o",  # 使用支持图像的模型
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": question
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:image/jpeg;base64,{encode_image(image_path)}"
                    }
                }
            ]
        }
    ],
    "max_tokens": 300  # 控制响应长度
}

# 发送请求
response = requests.post(
    "https://api.openai.com/v1/chat/completions",
    headers=headers,
    json=payload
)

# 处理响应
if response.status_code == 200:
    result = response.json()
    answer = result['choices'][0]['message']['content']
    print("AI回答:", answer)
else:
    print("请求失败，状态码:", response.status_code)
    print("错误信息:", response.text)
```

如果需要引用，请引用下列内容
```
{
  author        = {Jianyi Zhang and Xu Ji and Ziyin Zhou and Yuchen Zhou and Shubo Shi and Haoyu Wu and Zhen Li and Shizhao Liu},
  title         = {Oedipus and the Sphinx: Benchmarking and Improving Visual Language Models for Complex Graphic Reasoning},
  howpublished  = {arXiv preprint},
  archivePrefix = {arXiv},
  eprint        = {2508.00323},
  primaryClass  = {cs.AI},
  year          = {2025},
  note          = {arXiv:2508.00323v1 [cs.AI]},
  url           = {https://arxiv.org/abs/2508.00323}
}
```