Update README.md

README.md

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   - split: train
     path: data/train-*
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+
+# 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)
+
+## 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}
+}
+```