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---
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license: cc-by-4.0
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language:
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- zh
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- en
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tags:
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- multimodal-mathematical-reasoning
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- geometry
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- tikz
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- latex
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- image-to-tikz
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- benchmark
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size_categories:
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- 10K<n<100K
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task_categories:
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- image-to-text
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- visual-question-answering
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- text-generation
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pretty_name: TriGeoBench
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---
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# TriGeoBench
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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.
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This repository is anonymized for peer review. Author and institution information will be added upon acceptance.
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## Dataset Files
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The dataset contains four Parquet files:
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```text
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```
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*
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##
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---
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license: cc-by-4.0
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language:
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- zh
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- en
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tags:
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- multimodal-mathematical-reasoning
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- geometry
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- tikz
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- latex
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- image-to-tikz
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- benchmark
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size_categories:
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- 10K<n<100K
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task_categories:
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- image-to-text
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- visual-question-answering
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- text-generation
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pretty_name: TriGeoBench
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---
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# TriGeoBench
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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.
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This repository is anonymized for peer review. Author and institution information will be added upon acceptance.
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## Dataset Files
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The dataset contains four Parquet files:
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```text
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TriGeoBench
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├── README.md
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├── image2tikz/
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│ ├── train.parquet
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│ └── test.parquet
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└── question/
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├── train.parquet
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└── test.parquet
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````
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The dataset supports two tasks:
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1. **Image-to-TikZ generation**: generating TikZ code from a geometric diagram image.
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2. **Multimodal mathematical reasoning**: solving math problems with textual questions, solutions, and associated figures.
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## Image-to-TikZ Data
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Files:
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```text
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image2tikz/train.parquet
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image2tikz/test.parquet
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```
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Each row corresponds to one diagram image and its ground-truth TikZ code.
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### Fields
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| Field | Description |
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| ------------ | ------------------------------------------------------------ |
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| `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. |
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| `image` | Base64-encoded image. |
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| `latex_gt` | Ground-truth TikZ code corresponding to the image. |
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| `difficulty` | Figure complexity level. Possible values are `容易`, `中等`, and `困难`. |
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## Question-Level Data
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Files:
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```text
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question/train.parquet
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question/test.parquet
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```
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Each row corresponds to one mathematical problem, including the problem text, solution, metadata, and associated figures.
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### Fields
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| Field | Description |
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| ----------------- | ------------------------------------------------------------ |
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| `sample_id` | Unique problem identifier. It can be linked to the `key` field in the image-to-TikZ data. |
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| `difficulty` | Problem difficulty level. Possible values are `容易`, `中等`, and `困难`. |
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| `question_type` | Problem type. Possible values include `选择题`, `填空题`, `解答题`, and `证明题`. |
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| `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`. |
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| `question` | Problem statement in LaTeX format. |
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| `solution` | Solution or answer in LaTeX format. |
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| `q_figX` | Base64-encoded image of the X-th figure appearing in the question. |
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| `q_figX_latex_gt` | Ground-truth TikZ code of the X-th question figure. |
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| `s_figY` | Base64-encoded image of the Y-th figure appearing in the solution. |
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| `s_figY_latex_gt` | Ground-truth TikZ code of the Y-th solution figure. |
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Here, `X` and `Y` denote figure indices. A problem may contain different numbers of question-side and solution-side figures.
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## Data Splits
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The dataset is split into training and test sets for both tasks:
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| Task | Train File | Test File |
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| ---------------------- | -------------------------- | ------------------------- |
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| Image-to-TikZ | `image2tikz_train.parquet` | `image2tikz_test.parquet` |
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| Mathematical Reasoning | `question_train.parquet` | `question_test.parquet` |
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## Loading the Dataset
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The Parquet files can be loaded with `pandas`:
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```python
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import pandas as pd
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image2tikz_train = pd.read_parquet("image2tikz_train.parquet")
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image2tikz_test = pd.read_parquet("image2tikz_test.parquet")
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question_train = pd.read_parquet("question_train.parquet")
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question_test = pd.read_parquet("question_test.parquet")
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```
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Base64-encoded images can be decoded as follows:
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```python
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import base64
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from io import BytesIO
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from PIL import Image
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def decode_base64_image(image_base64):
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image_bytes = base64.b64decode(image_base64)
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return Image.open(BytesIO(image_bytes)).convert("RGB")
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img = decode_base64_image(image2tikz_train.iloc[0]["image"])
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img.show()
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```
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## Intended Use
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TriGeoBench is intended for research on:
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* multimodal mathematical reasoning;
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* geometry-centric visual question answering;
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* image-to-TikZ generation;
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* evaluating whether models can reason over precise geometric structures;
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* studying the interaction between textual math problems, visual diagrams, and symbolic diagram representations.
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## Limitations
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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.
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## Anonymous Review Notice
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This repository is anonymized for peer review. Please do not attempt to identify the authors during the review process.
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