Add dataset card, paper link, and task category

README.md

@@ -0,0 +1,41 @@
+---
+task_categories:
+- image-text-to-text
+language:
+- en
+tags:
+- geometry
+- multimodal
+- geometry-problem-solving
+---
+
+# GeoFocus
+
+[Paper](https://huggingface.co/papers/2602.08524) | [Code](https://github.com/dle666/GeoFocus)
+
+GeoFocus is a novel framework for Multimodal Geometry Problem-Solving (MGPS). It addresses the challenges of recognizing global shapes and intricate local geometric relationships through two core components:
+
+1.  **Critical Local Perceptor**: Automatically identifies and emphasizes critical local structures (e.g., angles, parallel lines, comparative distances) through thirteen theory-based perception templates, boosting local feature coverage.
+2.  **VertexLang**: A compact topology formal language that encodes global figures using vertex coordinates and connectivity relations, reducing training time while improving topology recognition accuracy.
+
+## Dataset Description
+
+The GeoFocus project involves several data splits used for training and evaluation:
+- **Global_Perceptor_Data**: Training data focused on global figure recognition using the VertexLang encoding.
+- **Local_Perceptor_Data**: Training data featuring fine-grained visual attribute annotations for critical local structures.
+- **Geo_test**: Evaluation datasets covering benchmarks such as Geo3K, GeoQA, and FormalGeo7K.
+
+The models trained on this data, GeoFocus-3B and GeoFocus-7B, demonstrate superior performance and robustness in geometry reasoning tasks.
+
+## Citation
+
+If you use this work or dataset in your research, please cite the original paper:
+
+```bibtex
+@article{geofocus2026,
+  title={GeoFocus: Blending Efficient Global-to-Local Perception for Multimodal Geometry Problem-Solving},
+  author={...},
+  journal={arXiv preprint arXiv:2602.08524},
+  year={2026}
+}
+```