---
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}
}
```