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README.md

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 ---
 license: cc-by-4.0
+language:
+- zh
+- en
+tags:
+- multimodal-mathematical-reasoning
+- geometry
+- tikz
+- latex
+- image-to-tikz
+- benchmark
+size_categories:
+- 10K<n<100K
+task_categories:
+- image-to-text
+- visual-question-answering
+- text-generation
+pretty_name: TriGeoBench
 ---
+
+# TriGeoBench
+
+TriGeoBench is a geometry-centric multimodal mathematics benchmark designed for evaluating mathematical reasoning with visual diagrams and image-to-TikZ generation. The dataset contains mathematical problems, solutions, diagram images, and corresponding TikZ annotations.
+
+This repository is anonymized for peer review. Author and institution information will be added upon acceptance.
+
+## Dataset Files
+
+The dataset contains four Parquet files:
+
+```text
+image2tikz_train.parquet
+image2tikz_test.parquet
+question_train.parquet
+question_test.parquet
+````
+
+The dataset supports two tasks:
+
+1. **Image-to-TikZ generation**: generating TikZ code from a geometric diagram image.
+2. **Multimodal mathematical reasoning**: solving math problems with textual questions, solutions, and associated figures.
+
+## Image-to-TikZ Data
+
+Files:
+
+```text
+image2tikz_train.parquet
+image2tikz_test.parquet
+```
+
+Each row corresponds to one diagram image and its ground-truth TikZ code.
+
+### Fields
+
+| Field        | Description                                                  |
+| ------------ | ------------------------------------------------------------ |
+| `key`        | Unique figure identifier. It is composed of `<problem_id>_<position>_<figure_index>`, where `position` indicates whether the figure appears in the question or the solution. This key can be linked to the corresponding problem in the question-level data. |
+| `image`      | Base64-encoded image.                                        |
+| `latex_gt`   | Ground-truth TikZ code corresponding to the image.           |
+| `difficulty` | Figure complexity level. Possible values are `容易`, `中等`, and `困难`. |
+
+## Question-Level Data
+
+Files:
+
+```text
+question_train.parquet
+question_test.parquet
+```
+
+Each row corresponds to one mathematical problem, including the problem text, solution, metadata, and associated figures.
+
+### Fields
+
+| Field             | Description                                                  |
+| ----------------- | ------------------------------------------------------------ |
+| `sample_id`       | Unique problem identifier. It can be linked to the `key` field in the image-to-TikZ data. |
+| `difficulty`      | Problem difficulty level. Possible values are `容易`, `中等`, and `困难`. |
+| `question_type`   | Problem type. Possible values include `选择题`, `填空题`, `解答题`, and `证明题`. |
+| `knowledge_point` | Main mathematical knowledge area. Possible values include `向量`, `函数`, `平面几何`, `立体几何`, and `解析几何`. |
+| `question`        | Problem statement in LaTeX format.                           |
+| `solution`        | Solution or answer in LaTeX format.                          |
+| `q_figX`          | Base64-encoded image of the X-th figure appearing in the question. |
+| `q_figX_latex_gt` | Ground-truth TikZ code of the X-th question figure.          |
+| `s_figY`          | Base64-encoded image of the Y-th figure appearing in the solution. |
+| `s_figY_latex_gt` | Ground-truth TikZ code of the Y-th solution figure.          |
+
+Here, `X` and `Y` denote figure indices. A problem may contain different numbers of question-side and solution-side figures.
+
+## Data Splits
+
+The dataset is split into training and test sets for both tasks:
+
+| Task                   | Train File                 | Test File                 |
+| ---------------------- | -------------------------- | ------------------------- |
+| Image-to-TikZ          | `image2tikz_train.parquet` | `image2tikz_test.parquet` |
+| Mathematical Reasoning | `question_train.parquet`   | `question_test.parquet`   |
+
+## Loading the Dataset
+
+The Parquet files can be loaded with `pandas`:
+
+```python
+import pandas as pd
+
+image2tikz_train = pd.read_parquet("image2tikz_train.parquet")
+image2tikz_test = pd.read_parquet("image2tikz_test.parquet")
+
+question_train = pd.read_parquet("question_train.parquet")
+question_test = pd.read_parquet("question_test.parquet")
+```
+
+Base64-encoded images can be decoded as follows:
+
+```python
+import base64
+from io import BytesIO
+from PIL import Image
+
+def decode_base64_image(image_base64):
+    image_bytes = base64.b64decode(image_base64)
+    return Image.open(BytesIO(image_bytes)).convert("RGB")
+
+img = decode_base64_image(image2tikz_train.iloc[0]["image"])
+img.show()
+```
+
+## Intended Use
+
+TriGeoBench is intended for research on:
+
+* multimodal mathematical reasoning;
+* geometry-centric visual question answering;
+* image-to-TikZ generation;
+* evaluating whether models can reason over precise geometric structures;
+* studying the interaction between textual math problems, visual diagrams, and symbolic diagram representations.
+
+## Limitations
+
+The dataset focuses on geometry-centric middle- and high-school mathematics problems. The annotations include LaTeX-formatted problem texts and TikZ code for figures. Although the dataset has been processed and checked, residual annotation errors may remain.
+
+## Anonymous Review Notice
+
+This repository is anonymized for peer review. Please do not attempt to identify the authors during the review process.

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question/test.parquet

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question/train.parquet

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