---
license: apache-2.0
dataset_info:
  features:
  - name: image_path
    dtype: string
  - name: label
    dtype: string
  - name: question
    dtype: string
  - name: tag
    dtype: string
  - name: id
    dtype: string
task_categories:
- visual-question-answering
language:
- en
size_categories:
- 1K<n<10K
tags:
- agent
---
# VisuLogic: A Benchmark for Evaluating Visual Reasoning in Multi-modal Large Language Models

**A Challenging Visual-centric Benchmark for Evaluating Multimodal Reasoning in MLLMs!**

This is the Benchmark data repo of [VisuLogic](https://visulogic-benchmark.github.io/VisuLogic).

For more details, please refer to the project page with dataset exploration and visualization tools: [https://visulogic-benchmark.github.io/VisuLogic/](https://visulogic-benchmark.github.io/VisuLogic/).

# VisuLogic Resouces

[**🌐 Homepage**](https://visulogic-benchmark.github.io/VisuLogic) | [**🏆 Leaderboard**](https://visulogic-benchmark.github.io/VisuLogic/) | [**📖 Paper**](https://arxiv.org/abs/2504.15279) | [**🤗 Benchmark**](https://huggingface.co/datasets/VisuLogic/VisuLogic) | [**🤗 Train Data**](https://huggingface.co/datasets/VisuLogic/VisuLogic-Train) 

[**💻 Eval Code**](https://github.com/VisuLogic-Benchmark/VisuLogic-Eval) |  [**💻 Train Code**](https://github.com/VisuLogic-Benchmark/VisuLogic-Train) | [**🤗 Checkpoint (7B)**](https://huggingface.co/VisuLogic/qwen2_5vl_7b_rloo_80steps_hf) | [**🤗 Checkpoint (38B)**](https://huggingface.co/VisuLogic/internvl2_5_38b_rloo_100steps_hf)


## 🔔News
- **🔥[2025-06-28] Release the [SFT data](https://huggingface.co/datasets/VisuLogic/VisuLogic-Train)! 🚀**
- **🔥[2025-04-26] [VisuLogic](https://github.com/open-compass/VLMEvalKit/pull/944) has been merged into [VLMEvalkit](https://github.com/OpenCompass/VLMEvalkit). You can evaluate your model on VisuLogic with it ! Usage see [VLMEvalkit](https://github.com/open-compass/VLMEvalKit/blob/main/docs/en/Quickstart.md) ! 🚀** 
- **🔥[2025-04-22] Release the paper, training data and training code! 🚀**
- **🔥[2025-04-08] Release the benchmark and the code! 🚀**
## ✅ To-do
- [x] Release the benchmark dataset and eval code
- [x] Release training code
- [x] Release the paper
- [x] Release the training dataset
- [x] Release model ckpts

## 📖 Introduction
VisuLogic is a newly designed benchmark aimed at evaluating the visual reasoning capabilities of Multi-modal Large Language Models (MLLMs), independent of textual reasoning processes. It features carefully constructed visual reasoning tasks spanning multiple categories, divided into six types based on required reasoning skills (e.g., Quantitative Reasoning, which involves understanding and deducing changes in the quantity of elements in images). Unlike existing benchmarks, VisuLogic is a challenging visual reasoning benchmark that is inherently difficult to articulate using language, providing a more rigorous evaluation of the visual reasoning capabilities of MLLMs. Most models score below 30\% accuracy—only slightly above the 25\% random baseline and far below the 51.4\% achieved by humans—revealing significant gaps in visual reasoning.
![Overview](assets/overview4.png)
## 🌟 Key Features

- 🚀 **Visuo-Logical Challenge**  
  The first benchmark to integrate **visual perception** with **logical reasoning**, enabling authentic multimodal evaluation. Most models score below **30%** accuracy—only slightly above the 25% random baseline and far below the 51.4% achieved by humans—revealing significant gaps in visual reasoning.
  
- 🛠️ **Rigorous Design**  
  Includes **1,000 meticulously curated questions**, spanning **6 domains** and **24 subcategories**, for comprehensive performance evaluation.
  
- 📝 **Anti-Linguistic Shortcut**  
  Designed to avoid linguistic reasoning, ensuring tasks rely on **genuine visual reasoning** rather than shortcuts.

- 💡 **RL Exploration**  
  We identify the  RL technique as a promising direction for improving the visual reasoning capabilities of MLLMs. Through RL method, models reach **SOTA** in VisuLogic!

- ✅ **Fully Open-source**  
  We **open-source** all the evaluation code, training scripts, and datasets associated with this work to promote further research and innovation.

## 🖼️  Examples of VisuLogic
![Examples of VisuLogic](assets/examples_in_benchmarks.png)

## 📊 Eval

Please refer to [VisuLogic-Eval](https://github.com/VisuLogic-Benchmark/VisuLogic-Eval.git) for eval code.

## 📦 Training

Please refer to [VisuLogic-Train](https://github.com/VisuLogic-Benchmark/VisuLogic-Train.git) for training code.

## 📩 Contact
- Weiye Xu: ustcxwy0271@mail.ustc.edu.cn
- Jiahao Wang: wjhwdscience@stu.xjtu.edu.cn

## 📜 Citation

**BibTeX:**
```bibtex
@article{xu2025visulogic,
  title={VisuLogic: A Benchmark for Evaluating Visual Reasoning in Multi-modal Large Language Models},
  author={Xu, Weiye and Wang, Jiahao and Wang, Weiyun and Chen, Zhe and Zhou, Wengang and Yang, Aijun and Lu, Lewei and Li, Houqiang and Wang, Xiaohua and Zhu, Xizhou and Wang, Wenhai and Dai, Jifeng and Zhu, Jinguo},
  journal={arXiv preprint arXiv:2504.15279},
  year={2025},
  url={https://arxiv.org/abs/2504.15279}
}
```
🎉 Thank you for your interest in VisuLogic! We hope this benchmark helps drive advancements in multimodal reasoning! 🚀