README.md

metadata

task_categories:
  - image-text-to-text
language:
  - en
tags:
  - geometry
  - multimodal
  - geometry-problem-solving

GeoFocus

Paper | Code

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:

@article{geofocus2026,
  title={GeoFocus: Blending Efficient Global-to-Local Perception for Multimodal Geometry Problem-Solving},
  author={...},
  journal={arXiv preprint arXiv:2602.08524},
  year={2026}
}