README.md

---
license: mit
size_categories:
- 1K<n<10K
task_categories:
- visual-question-answering
pretty_name: VisualPuzzles
dataset_info:
  features:
  - name: id
    dtype: int64
  - name: image
    dtype: image
  - name: question
    dtype: string
  - name: options
    sequence: string
  - name: answer
    dtype: string
  - name: category
    dtype: string
  - name: difficulty
    dtype: string
  splits:
  - name: train
    num_bytes: 143955058.0
    num_examples: 1168
  download_size: 142706932
  dataset_size: 143955058.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# VisualPuzzles: Decoupling Multimodal Reasoning Evaluation from Domain Knowledge

[🏠 Homepage](https://neulab.github.io/VisualPuzzles/) | [📊 VisualPuzzles](https://huggingface.co/datasets/neulab/VisualPuzzles) | [💻 Github](https://github.com/neulab/VisualPuzzles) | [📄 Arxiv](https://arxiv.org/abs/2504.10342) | [📕 PDF](https://arxiv.org/pdf/2504.10342) | [🖥️ Zeno Model Output](https://hub.zenoml.com/project/2e727b03-a677-451a-b714-f2c07ad2b49f/VisualPuzzles)

![Puzzle Teaser](https://neulab.github.io/VisualPuzzles/static/img/human_performance.png)

## Overview
**VisualPuzzles** is a multimodal benchmark specifically designed to evaluate **reasoning abilities** in large models while deliberately minimizing reliance on domain-specific knowledge.

Key features:
- 1168 diverse puzzles
- 5 reasoning categories: Algorithmic, Analogical, Deductive, Inductive, Spatial
- Difficulty labels: Easy, Medium, Hard
- Less knowledge-intensive than existing benchmarks (e.g., MMMU)
- More reasoning-complex than existing benchmarks (e.g., MMMU)

## Key Findings
- All models perform worse than humans; most can't surpass even 5th-percentile human performance.
- Strong performance on knowledge-heavy benchmarks does not transfer well.
- Larger models and structured "thinking modes" don't guarantee better results.
- Scaling model size does not ensure stronger reasoning

## Usage

To load this dataset via Hugging Face’s `datasets` library:

```python
from datasets import load_dataset

dataset = load_dataset("neulab/VisualPuzzles")
data = dataset["train"]

sample = data[0]
print("ID:", sample["id"])
print("Category:", sample["category"])
print("Question:", sample["question"])
print("Options:", sample["options"])
print("Answer:", sample["answer"])
```

## Citation

If you use or reference this dataset in your work, please cite:

```bibtex
@article{song2025visualpuzzles,
  title         = {VisualPuzzles: Decoupling Multimodal Reasoning Evaluation from Domain Knowledge},
  author        = {Song, Yueqi and Ou, Tianyue and Kong, Yibo and Li, Zecheng and Neubig, Graham and Yue, Xiang},
  year          = {2025},
  journal       = {arXiv preprint arXiv:2504.10342},
  url           = {https://arxiv.org/abs/2504.10342}
}
```