README.md

metadata

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.

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📖 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

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📂 Dataset Structure

visual-equation-solving/
│
├── train/
│   ├── images/           # equation images
│   ├── annotations.json  # ground-truth equations & solutions
│
├── test/
│   ├── images/
│   ├── annotations.json
│
└── metadata.json         # dataset metadata & icon mapping

Annotations include:

• equation: the equation in symbolic form
• variables: mapping of icons → variable names
• solution: ground truth assignments

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📜 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.

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📚 Citation

If you use this dataset, please cite:

@inproceedings{anonymous2025vlm-math,
  title     = {Can Vision-Language Models Solve Visual Math Equations?},
  author    = {Anonymous},
  booktitle = {ACL (under review)},
  year      = {2025}
}