README.md

metadata

task_categories:
  - question-answering
language:
  - en
tags:
  - code
pretty_name: PFPdatasets
size_categories:
  - 100K<n<1M
license: apache-2.0

Paper Folding Puzzles: A Benchmark for Evaluating Spatial Reasoning in Multimodal Large Language Models

🌐 Homepage   |    💻 GitHub   |    🤗 Hugging Face  

👋 Introduction

Recent advancements in multimodal large language models (MLLMs) have shown remarkable progress in various reasoning tasks. However, spatial reasoning, particularly in paper folding scenarios, remains a significant challenge due to limitations in understanding geometric transformations and spatial relationships. To address this gap, we present Paper Folding Puzzles (PFP), a comprehensive benchmark designed to evaluate and enhance spatial reasoning capabilities in MLLMs. Our benchmark systematically covers five distinct task types, from basic single-step transformations to complex 3D spatial visualization, providing a rigorous framework for assessing spatial intelligence in AI systems.

📌 Highlights

• We introduce Paper Folding Puzzles (PFP), a multi-dimensional benchmark for spatial reasoning. It systematically covers five key task types—Single-Step, Inverse, Multi-Step, 3D-Folding, and 2D-Unfolding—addressing different aspects of spatial intelligence.

• Comprehensive scale with 153,000 carefully curated samples. The dataset includes 150,000 training samples and 3,000 test samples, ensuring robust evaluation across all task categories.

• Structured difficulty levels within complex tasks. The 3D-Folding and 2D-Unfolding categories include easy and hard sub-levels, enabling granular assessment of model capabilities.

• Standardized format for easy integration. The dataset uses parquet format with consistent JSON structure, facilitating seamless integration with existing MLLM frameworks.

Dataset Structure

The structure of Paper Folding Puzzles is shown as follows:

PFP_dataset/
├── train/
│   ├── Single-Step.parquet
│   ├── Inverse.parquet
│   ├── Multi-Step.parquet
│   ├── 3D-Folding/
│   │   ├── _2DTo3D_N.parquet
│   │   └── _2DTo3D_Y.parquet
│   └── 2D-Unfolding/
│       ├── _3DTo2D_N.parquet
│       └── _3DTo2D_Y.parquet
└── test/
    ├── Single-Step.parquet
    ├── Inverse.parquet
    ├── Multi-Step.parquet
    ├── 3D-Folding.parquet
    └── 2D-Unfolding.parquet

Data Instances

For each instance in the dataset, the following fields are provided:

{
    "image": "circle_001.png",
    "answer": "D"
}

Data Fields

• image: a string containing the relative path to the paper folding puzzle image (e.g., "circle_001.png")
• answer: a string indicating the correct answer option (A, B, C, or D)

🚀 Quick Start

Loading the Dataset

from datasets import load_dataset

# Load the entire dataset
dataset = load_dataset("hznuer/PFP_datasets")

# Or load specific splits
train_dataset = load_dataset("hznuer/PFP_datasets", split="train")
test_dataset = load_dataset("hznuer/PFP_datasets", split="test")

# Load specific task types
single_step_data = load_dataset("hznuer/PFP_datasets", "Single-Step")

Basic Usage Example

# Example of processing the dataset
dataset = load_dataset("hznuer/PFP_datasets", split="train")

for sample in dataset:
    image_path = sample["image"]
    correct_answer = sample["answer"]
    # Process your paper folding puzzle here

✒️ Citation

If you find Paper Folding Puzzles helpful, please consider giving this repo a :star: and citing:

@inproceedings{zhou2026paperfolding,
  title={Paper Folding Puzzles: A Benchmark for Evaluating Spatial Reasoning in Multimodal Large Language Models},
  author={Zhou, Dibin and Xu, Yantao and Huang, Zongming and Yan, Zengwei and Liu, Wenhao and Miao, Yongwei and Ren, Jianfeng and Liu, Fuchang},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2026}
}

👥 Authors

Dibin Zhou, Yantao Xu, Zongming Huang, Zengwei Yan, Wenhao Liu, Yongwei Miao, Jianfeng Ren, Fuchang Liu

Affiliation: School of Information Science and Technology, Hangzhou Normal University & The Digital Port Technologies Lab, School of Computer Science, University of Nottingham Ningbo China

📞 Contact

For questions or issues regarding this dataset:

• Open an issue on the GitHub repository
• Contact the authors through the paper correspondence

---

Paper Folding Puzzles: Advancing spatial reasoning evaluation for multimodal AI systems 🧠