Add dataset card for GeoFocus-test

README.md

@@ -0,0 +1,38 @@
+---
+task_categories:
+- image-text-to-text
+tags:
+- geometry
+- mathematical-reasoning
+- multimodal
+---
+
+# GeoFocus-test
+
+[**Paper**](https://huggingface.co/papers/2602.08524) | [**GitHub**](https://github.com/dle666/GeoFocus)
+
+This repository contains the test and evaluation data for **GeoFocus**, a framework for multimodal geometry problem-solving. GeoFocus addresses the challenge of geometry reasoning by blending efficient global and local perception through two core modules:
+
+1. **Critical Local Perceptor**: Automatically identifies and emphasizes critical local structures (e.g., angles, parallel lines, comparative distances) through thirteen theory-based perception templates.
+2. **VertexLang**: A compact topology formal language that encodes global figures through vertex coordinates and connectivity relations, reducing global perception training time while improving topology recognition accuracy.
+
+This dataset is used to evaluate models on benchmarks including **Geo3K**, **GeoQA**, and **FormalGeo7K**, and demonstrates superior robustness in **MATHVERSE**.
+
+## Related Datasets
+
+The training data used by GeoFocus is available at the following links:
+* [Global_Perceptor_Data](https://huggingface.co/datasets/dle666/Global_Perceptor)
+* [Local_Perceptor_Data](https://huggingface.co/datasets/dle666/Local_Perceptor)
+
+## Citation
+
+If you find this dataset or the GeoFocus framework useful for your research, please cite:
+
+```bibtex
+@article{geofocus2026,
+  title={GeoFocus: Blending Efficient Global-to-Local Perception for Multimodal Geometry Problem-Solving},
+  author={Dle et al.},
+  journal={arXiv preprint arXiv:2602.08524},
+  year={2026}
+}
+```