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---
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}
}
``` |