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---
license: cc
task_categories:
- question-answering
language:
- en
tags:
- math
pretty_name: Can Vision-Language Models Solve Visual Math Equations?
size_categories:
- 1K<n<10K
---
# Visual Equation Solving Benchmark
This repository contains the dataset introduced in the paper:
**Can Vision-Language Models Solve Visual Math Equations?** which is currently accepted in EMNLP 2025 (Main)
> Despite strong performance in vision and language understanding, Vision-Language Models (VLMs) struggle on tasks requiring integrated perception and symbolic reasoning. This benchmark evaluates VLMs on **visual equation solving**, where systems of linear equations are represented using **object icons** as variables and **icon repetition** as coefficients.
---
## 📖 Overview
The **Visual Equation Solving Benchmark** tests whether VLMs can:
1. **Recognize variables** represented as object icons.
2. **Count coefficients** by inferring from repeated instances of icons.
3. **Integrate recognition with symbolic reasoning** to solve equations.
We provide multiple settings:
- **Symbolic equations** (textual form, rendered as images).
- **Visual-symbolic equations** (icons for variables, numeric text for coefficients).
- **Fully visual equations** (both variables and coefficients represented visually).
---
Example:
```
🍎🍎🍎 + 🍌🍌 = 10
🍎 + 🍌🍌🍌🍌🍌 = 15
```
---
## 📂 Dataset Structure
There are 2 variants of the dataset based on number of variables used -
2 variables and 3 variables which can be found in the respective zip files.
Once you extract any of them you will see the following tree -
```
├── char_only
│ └── metadata.csv
│ └── *.png
├── counting
│ └── metadata.csv
│ └── *.png
├── icon_only
│ └── metadata.csv
│ └── *.png
├── icon_partial
│ └── metadata.csv
│ └── *.png
└── [two/three]-vars.txt
```
The char_only, icon_only, icon_partial, counting points to the
symbolic, visual, visual-symbolic and counting datasets mentioned in the paper
respectively. Each of them consist of the following metadata -
1. file_path to corresponding image
2. solution to variable
3. mapping to symbolic variable (in case of visual, visual-symbolic, counting dataset)
The base equations which are used to create the same are attached in the respective .txt file in the root level directory.
---
## 📜 License
This dataset is released under the **CC BY 4.0 License**.
You are free to share, adapt, and build upon the data with attribution.
---
## 📚 Citation
If you use this dataset, please cite:
```bibtex
@inproceedings{anonymous2025vlm-math,
title = {Can Vision-Language Models Solve Visual Math Equations?},
author = {Anonymous},
booktitle = {ACL (under review)},
year = {2025}
}
``` |