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language:
- en
license: cc-by-4.0
size_categories:
- n<1K
pretty_name: Rocket Science
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- image-text-to-text
tags:
- spatial-reasoning
- vlm
- benchmark
dataset_info:
features:
- name: text1
dtype: string
- name: text2
dtype: string
- name: Label
dtype: string
- name: Category
dtype: string
- name: image1
dtype: image
- name: image2
dtype: image
splits:
- name: train
num_bytes: 772658832
num_examples: 241
download_size: 771816767
dataset_size: 772658832
---
# Dataset Card for Rocket Science
Rocket Science is a benchmark that tests for understanding of spatial relations in Vision Language Models.
# Dataset Description
We propose RocketScience, an open-source contrastive VLM benchmark that tests for spatial relation understanding. It is comprised of entirely new real-world image-text pairs covering mostly relative spatial understanding and the order of objects. The benchmark is designed to be very easy for humans and hard for the current generation of VLMs, and this is empirically verified. Our results show a striking lack of spatial relation understanding in open source and frontier commercial VLMs and a surprisingly high performance of reasoning models. Additionally, we perform a disentanglement analysis to separate the contributions of object localization and spatial reasoning in chain-of-thought-based models and find that the performance on the benchmark is bottlenecked by spatial reasoning and not object localization capabilities.
- **Curated by:** Nils Hoehing
- **Language(s) (NLP):** English
- **License:** CC-BY-4.0
# Dataset Sources
- **Repository:** https://github.com/nilshoehing/rocketscience
- **Paper:** https://huggingface.co/papers/2509.02175
# Sample Usage
The dataset can be loaded using the Hugging Face `datasets` library:
```python
from datasets import load_dataset
dataset = load_dataset("nilshoehing/rocketsciencebench")
```
# Citation
Please cite the following paper if you use this dataset:
```bibtex
@article{hoehing2025understanding,
title={Understanding Space Is Rocket Science - Only Top Reasoning Models Can Solve Spatial Understanding Tasks},
author={Hoehing, Nils and Likhitha, D and Woehler, Thomas and Beham, Michael and Kuehne, Axel},
journal={arXiv preprint arXiv:2509.02175},
year={2025},
url={https://huggingface.co/papers/2509.02175}
}
``` |