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README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+
+# GeoGramBench: Benchmarking the Geometric Program Reasoning in Modern LLMs
+
+GeoGramBench is a tailored benchmark dataset designed for evaluating the geometric spatial reasoning capabilities of large language models (LLMs) over procedural programmatic code. The dataset introduces a novel task, **Program-to-Geometry**, that requires models to transform programmatic drawing code into abstract geometric reasoning for problem-solving. 
+
+## Features of GeoGramBench
+
+- **500 Curated Problems:** Each sample includes procedural drawing code and associated geometry reasoning problems. These problems are rigorously curated to ensure quality, fairness, and diversity.
+- **Taxonomy-Based Evaluation:** Problems are categorized into three difficulty levels:
+  - **Primitive Recognition:** Basic geometric problems requiring direct recognition of a few elements.
+  - **Local Relation Composition:** Involves reasoning about relationships between multiple geometric components.
+  - **Global Abstract Integration:** Complex problems requiring global spatial synthesis, parameterization, or 3D reasoning.
+- **Six Subtypes:** Problems span six mathematical subfields: `Angle`, `Length`, `Area`, `Volume`, `Ratio`, and `Count`, supporting fine-grained diagnostics.
+
+## Benchmark Highlights
+
+- GeoGramBench differs from traditional math benchmarks by emphasizing the symbolic-to-spatial abstraction capabilities of LLMs, leveraging procedural code expressed in formats such as `Asymptote`.
+- Initial evaluation using 17 state-of-the-art LLMs revealed substantial gaps, particularly for higher abstraction tasks:
+  - Models achieved less than **50%** accuracy on the most challenging **Global Abstract Integration** category.
+  - Even advanced models struggle to bridge procedural code with reliable spatial reasoning.
+
+## Dataset Composition
+
+| Complexity Level           | Problem Count | Example Tasks                     |
+|----------------------------|---------------|-----------------------------------|
+| **Primitive Recognition**  | 102           | Compute the area of a triangle.  |
+| **Local Relation Composition** | 279       | Solve for angles in composite diagrams. |
+| **Global Abstract Integration** | 119       | Analyze 3D projections and symmetry. |
+
+### Subtype Distribution Across Levels
+
+| Subtype   | Primitive | Compositional | Abstract |
+|-----------|-----------|---------------|----------|
+| Angle     | 22        | 20            | 7        |
+| Length    | 25        | 88            | 20       |
+| Area      | 26        | 89            | 46       |
+| Ratio     | 14        | 51            | 4        |
+| Count     | 15        | 31            | 15       |
+| Volume    | 0         | 0             | 27       |
+
+## Use Cases
+
+GeoGramBench is designed for:
+- Researchers developing **geometry-aware LLMs** or fine-tuning models for symbolic-to-spatial reasoning.
+- Model diagnostics to pinpoint weaknesses in handling code-driven geometric reasoning or abstract spatial relations.
+- Evaluation and advancement of LLMs' performance on tasks involving spatial reasoning.
+
+
+## Citation
+
+If you use GeoGramBench in your research, please cite:
+
+```bibtex
+@article{luo2025geogrambench,
+  title={Geogrambench: Benchmarking the geometric program reasoning in modern llms},
+  author={Luo, Shixian and Zhu, Zezhou and Yuan, Yu and Yang, Yuncheng and Shan, Lianlei and Wu, Yong},
+  journal={arXiv preprint arXiv:2505.17653},
+  year={2025}
+}
+```
+```