Add dataset card, link to paper and task category

README.md

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+---
+task_categories:
+- image-text-to-text
+---
+
+# Global Perceptor Data
+
+This dataset contains the training data for the **Global Perceptor** module of the **GeoFocus** framework, as presented in the paper [GeoFocus: Blending Efficient Global-to-Local Perception for Multimodal Geometry Problem-Solving](https://huggingface.co/papers/2602.08524).
+
+[**Project Page**](https://github.com/dle666/GeoFocus) | [**Paper**](https://huggingface.co/papers/2602.08524) | [**Code**](https://github.com/dle666/GeoFocus)
+
+## Introduction
+GeoFocus is a novel framework designed for multimodal geometry problem-solving. It addresses the challenges of geometry reasoning by combining two core modules:
+1. **Critical Local Perceptor**: Automatically identifies and emphasizes critical local structures (e.g., angles, parallel lines) through theory-based perception templates.
+2. **Global Perceptor**: Uses **VertexLang**, a compact topology formal language, to encode global figures through vertex coordinates and connectivity relations.
+
+This repository provides the data necessary for the global perception training component of the model.
+
+## Related Datasets
+- [Local_Perceptor_Data](https://huggingface.co/datasets/dle666/Local_Perceptor)
+- [Geo_test](https://huggingface.co/datasets/dle666/GeoFocus-test)
+
+## Citation
+If you find this dataset useful for your research, please cite the original paper:
+```bibtex
+@article{geofocus2026,
+  title={GeoFocus: Blending Efficient Global-to-Local Perception for Multimodal Geometry Problem-Solving},
+  author={Anonymous Authors},
+  journal={arXiv preprint arXiv:2602.08524},
+  year={2026}
+}
+```