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
dataset_info:
  features:
    - name: image
      dtype: image
    - name: c_count
      dtype: string
    - name: b_icon_type
      dtype: string
    - name: b
      dtype: string
    - name: a
      dtype: string
    - name: a_value
      dtype: string
    - name: b_icon
      dtype: string
    - name: a_icon_type
      dtype: string
    - name: b_count
      dtype: string
    - name: b_value
      dtype: string
    - name: c_icon_type
      dtype: string
    - name: c_value
      dtype: string
    - name: a_icon
      dtype: string
    - name: equation
      dtype: string
    - name: c
      dtype: string
    - name: c_icon
      dtype: string
    - name: a_count
      dtype: string
  splits:
    - name: two_variables_char_only
      num_bytes: 25833275
      num_examples: 1000
    - name: two_variables_icon_only
      num_bytes: 16183171
      num_examples: 1000
    - name: two_variables_icon_partial
      num_bytes: 16099172
      num_examples: 1000
    - name: two_variables_counting
      num_bytes: 7730753
      num_examples: 1000
    - name: three_variables_char_only
      num_bytes: 50892199
      num_examples: 1000
    - name: three_variables_icon_only
      num_bytes: 34912838
      num_examples: 1000
    - name: three_variables_icon_partial
      num_bytes: 34540943
      num_examples: 1000
    - name: three_variables_counting
      num_bytes: 11478229
      num_examples: 1000
  download_size: 194320410
  dataset_size: 197670580
configs:
  - config_name: default
    data_files:
      - split: two_variables_char_only
        path: data/two_variables_char_only-*
      - split: two_variables_icon_only
        path: data/two_variables_icon_only-*
      - split: two_variables_icon_partial
        path: data/two_variables_icon_partial-*
      - split: two_variables_counting
        path: data/two_variables_counting-*
      - split: three_variables_char_only
        path: data/three_variables_char_only-*
      - split: three_variables_icon_only
        path: data/three_variables_icon_only-*
      - split: three_variables_icon_partial
        path: data/three_variables_icon_partial-*
      - split: three_variables_counting
        path: data/three_variables_counting-*

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:

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