Duplicate from MathLLMs/MathVision

Co-authored-by: MathVision <mathvision@users.noreply.huggingface.co>

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+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
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+# Audio files - compressed
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README.md

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+---
+license: mit
+annotations_creators:
+- expert-generated
+- found
+language_creators:
+- expert-generated
+- found
+task_categories:
+- question-answering
+- multiple-choice
+- visual-question-answering
+- text-generation
+- image-to-text
+- image-text-to-text
+language:
+- en
+tags:
+- mathematics
+- reasoning
+- multi-modal-qa
+- math-qa
+- figure-qa
+- geometry-qa
+- math-word-problem
+- textbook-qa
+- vqa
+- geometry-diagram
+- synthetic-scene
+- chart
+- plot
+- scientific-figure
+- table
+- function-plot
+- abstract-scene
+- puzzle-test
+- document-image
+- science
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: question
+    dtype: string
+  - name: options
+    sequence: string
+  - name: image
+    dtype: string
+  - name: decoded_image
+    dtype: image
+  - name: answer
+    dtype: string
+  - name: solution
+    dtype: string
+  - name: level
+    dtype: int64
+  - name: subject
+    dtype: string
+  splits:
+  - name: test
+    num_examples: 3040
+  - name: testmini
+    num_examples: 304
+configs:
+- config_name: default
+  data_files:
+  - split: test
+    path: data/test-*
+  - split: testmini
+    path: data/testmini-*
+pretty_name: MATH-V
+size_categories:
+- 1K<n<10K
+---
+# Measuring Multimodal Mathematical Reasoning with the MATH-Vision Dataset
+
+[[💻 Github](https://github.com/mathllm/MATH-V/)] [[🌐 Homepage](https://mathllm.github.io/mathvision/)]  [[📊 Main Leaderboard ](https://mathllm.github.io/mathvision/#leaderboard)] [[📊 Open Source Leaderboard ](https://mathllm.github.io/mathvision/#openleaderboard)] [[🌿 Wild Leaderboard ](https://mathllm.github.io/mathvision/#wildleaderboard)] [[🔍 Visualization](https://mathllm.github.io/mathvision/#visualization)] [[📖 Paper](https://proceedings.neurips.cc/paper_files/paper/2024/file/ad0edc7d5fa1a783f063646968b7315b-Paper-Datasets_and_Benchmarks_Track.pdf)]
+
+---
+
+## 🌿 NEW: MATH-Vision-Wild
+
+**MATH-Vision-Wild** is a photographic, real-world variant of MATH-Vision. The same testmini problems are **physically captured** on printed paper, iPads, laptops, and projectors under varying lighting and angles — the conditions VLMs actually face when a user holds up a phone to a math problem.
+
+📦 **Dataset**: [MathLLMs/MathVision-Wild](https://huggingface.co/datasets/MathLLMs/MathVision-Wild) · 🏆 **Leaderboard**: [mathllm.github.io/mathvision/#wildleaderboard](https://mathllm.github.io/mathvision/#wildleaderboard)
+
+**Key finding — almost every model *regresses* in the wild:**
+
+| Model | MATH-Vision (testmini) | MATH-Vision-Wild | Δ |
+|---|---:|---:|---:|
+| **o4-mini** 🥇 | 55.9 | **57.2** | **+2.33%** (only model to improve) |
+| Gemini 2.5 Pro Preview 05-06 (thinking) | 63.8 | 49.0 | **−23.20%** |
+| Gemini 2.5 Flash Preview 05-20 | 57.9 | 48.0 | −17.10% |
+| Doubao-1.5-thinking-vision-pro | 57.9 | 45.7 | −21.07% |
+| Gemini 2.5 Pro Preview 05-06 | 61.8 | 42.8 | −30.74% |
+| GPT-4.1 | 40.5 | 35.5 | −12.35% |
+| Qwen2.5-VL-72B-Instruct | 36.2 | 24.0 | −33.70% |
+| Gemini 2.0 Flash | 48.0 | 23.0 | −52.08% |
+| Gemini 1.5 Pro | 38.8 | 18.4 | −52.58% |
+
+Only **o4-mini** improves when problems are photographed; long-reasoning models degrade less than fast/non-thinking models. A full 25-model comparison with MATH-Vision-Screenshot and Δ% columns is on the [Wild Leaderboard](https://mathllm.github.io/mathvision/#wildleaderboard).
+
+```python
+from datasets import load_dataset
+
+wild = load_dataset("MathLLMs/MathVision-Wild", split="testmini_photo")
+screenshot = load_dataset("MathLLMs/MathVision-Wild", split="testmini_screenshot")
+photo_full = load_dataset("MathLLMs/MathVision-Wild", split="test_photo")  # 3,040 photos
+```
+
+---
+
+## 🚀 Data Usage
+
+
+
+<!-- **We have observed that some studies have used our MATH-Vision dataset as a training set.**
+⚠️ **As clearly stated in our paper: *"The MATH-V dataset is not supposed, though the risk exists, to be used to train models for cheating. We intend for researchers to use this dataset to better evaluate LMMs’ mathematical reasoning capabilities and consequently facilitate future studies in this area."***
+
+⚠️⚠️⚠️ **In the very rare situation that there is a compelling reason to include MATH-V in your training set, we strongly urge that the ***testmini*** subset be excluded from the training process!**
+ -->
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("MathLLMs/MathVision")
+print(dataset)
+```
+
+
+
+## 🙏 Acknowledgments
+
+We would like to thank the following contributors for helping improve the dataset quality:
+
+- [@Zhiqi Huang](https://huggingface.co/googlebrain) for correcting answers for ID 21, ID 691, ID 2522 and ID 2524
+- [@Big-Brother-Pikachu](https://github.com/Big-Brother-Pikachu) for correcting answers for ID 338 and ID 1826
+
+## 💥 News
+- **[2026.04.24]** 🌿🔥🔥 **MATH-Vision-Wild released!** A photographic variant that re-captures MATH-Vision problems on printed paper, iPads, laptops, and projectors. **o4-mini** is the **only** model whose accuracy *improves* in the wild (**57.2%**, +2.33% vs baseline); every other VLM — including Gemini 2.5 Pro, GPT-4.1, and Qwen2.5-VL-72B — regresses, often by 20-50%. Dataset: [MathLLMs/MathVision-Wild](https://huggingface.co/datasets/MathLLMs/MathVision-Wild) · Leaderboard: [#wildleaderboard](https://mathllm.github.io/mathvision/#wildleaderboard)
+- **[2025.05.16]** 💥 We now support the official open-source leaderboard! 🔥🔥🔥 [**Skywork-R1V2-38B**](https://github.com/SkyworkAI/Skywork-R1V) is the best open-source model, scoring **49.7%** on MATH-Vision. 🔥🔥🔥 [**MathCoder-VL-2B**](https://huggingface.co/MathLLMs/MathCoder-VL-2B) is the best small model on MATH-Vision, scoring **21.7%**. See the full [open-source leaderboard](https://mathllm.github.io/mathvision/#openleaderboard).
+- **[2025.05.16]** 🤗 [MathCoder-VL-2B](https://huggingface.co/MathLLMs/MathCoder-VL-2B), [MathCoder-VL-8B](https://huggingface.co/MathLLMs/MathCoder-VL-8B) and [FigCodifier-8B](https://huggingface.co/MathLLMs/FigCodifier) is available now! 🔥🔥🔥
+- **[2025.05.16]** Our MathCoder-VL is accepted to ACL 2025. 🔥🔥🔥
+- **[2025.05.13]** 🔥🔥🔥 **[Seed1.5-VL](https://github.com/ByteDance-Seed/Seed1.5-VL)** achieves **68.7%** on MATH-Vision! 🎉 Congratulations!
+- **[2025.04.11]** 💥 **Kimi-VL-A3B-Thinking achieves strong multimodal reasoning with just 2.8B LLM activated parameters!** Congratulations! See the full [leaderboard](https://mathllm.github.io/mathvision/#leaderboard).
+- **[2025.04.10]** 🔥 **SenseNova V6 Reasoner** achieves **55.39%** on MATH-Vision! 🎉 Congratulations!
+- **[2025.04.05]** 💥 **Step R1-V-Mini 🥇 Sets New SOTA on MATH-V with 56.6%!** See the full [leaderboard](https://mathllm.github.io/mathvision/#leaderboard).
+- **[2025.03.10]** 💥 **Kimi k1.6 Preview Sets New SOTA on MATH-V with 53.29%!** See the full [leaderboard](https://mathllm.github.io/mathvision/#leaderboard).
+- **[2025.02.28]** 💥 **Doubao-1.5-pro Sets New SOTA on MATH-V with 48.62%!** Read more on the [Doubao blog](https://team.doubao.com/zh/special/doubao_1_5_pro).
+- **[2025.01.26]** 🚀 [Qwen2.5-VL-72B](http://qwenlm.github.io/blog/qwen2.5-vl/) achieves **38.1%**, establishing itself as the best-performing one in open-sourced models. 🎉 Congratulations!
+- **[2025.01.22]** 💥 **Kimi k1.5  achieves new SOTA** on MATH-Vision with **38.6%**! Learn more at the [Kimi k1.5 report](https://arxiv.org/pdf/2501.12599).
+- **[2024-09-27]** **MATH-V** is accepted by NeurIPS DB Track, 2024! 🎉🎉🎉
+- **[2024-08-29]** 🔥 Qwen2-VL-72B achieves new open-sourced SOTA on MATH-Vision with 25.9! 🎉 Congratulations! Learn more at the [Qwen2-VL blog](https://qwenlm.github.io/blog/qwen2-vl/).
+- **[2024-07-19]** [open-compass/VLMEvalKit](https://github.com/open-compass/VLMEvalKit) now supports **MATH-V**, utilizing LLMs for more accurate answer extraction!🔥
+- **[2024-05-19]** OpenAI's **GPT-4o** scores **30.39%** on **MATH-V**, considerable advancement in short time! 💥
+- **[2024-03-01]** **InternVL-Chat-V1-2-Plus** achieves **16.97%**, establishing itself as the new best-performing open-sourced model. 🎉 Congratulations!
+- **[2024-02-23]** Our dataset is now accessible at [huggingface](https://huggingface.co/datasets/MathLLMs/MathVision).
+- **[2024-02-22]** The top-performing model, **GPT-4V** only scores **23.98%** on **MATH-V**, while human performance is around **70%**.
+- **[2024-02-22]** Our paper is now accessible at [ArXiv Paper](https://arxiv.org/abs/2402.14804).
+
+## 👀 Introduction
+
+Recent advancements in Large Multimodal Models (LMMs) have shown promising results in mathematical reasoning within visual contexts, with models approaching human-level performance on existing benchmarks such as MathVista. However, we observe significant limitations in the diversity of questions and breadth of subjects covered by these benchmarks. To address this issue, we present the MATH-Vision (MATH-V) dataset, a meticulously curated collection of 3,040 high-quality mathematical problems with visual contexts sourced from real math competitions. Spanning 16 distinct mathematical disciplines and graded across 5 levels of difficulty, our dataset provides a comprehensive and diverse set of challenges for evaluating the mathematical reasoning abilities of LMMs.
+
+
+<p align="center">
+    <img src="https://raw.githubusercontent.com/mathvision-cuhk/MathVision/main/assets/figures/figure1_new.png" width="66%">  The accuracies of four prominent Large Multimodal Models (LMMs), random chance, and human <br>
+performance are evaluated on our proposed <b>MATH-Vision (MATH-V)</b> across 16 subjects.
+</p>
+<br>
+Through extensive experimentation, we unveil a notable performance gap between current LMMs and human performance on MATH-V, underscoring the imperative for further advancements in LMMs.
+
+You can refer to the [project homepage](https://mathvision-cuhk.github.io/) for more details.
+
+## 🏆 Leaderboard
+
+The leaderboard is available [here](https://mathvision-cuhk.github.io/#leaderboard).
+
+We are commmitted to maintain this dataset and leaderboard in the long run to ensure its quality!
+🔔 If you find any mistakes, please paste the question_id to the issue page, we will modify it accordingly.
+
+## 📐 Dataset Examples
+
+Some examples of MATH-V on three subjects: analytic geometry, topology, and graph theory.
+
+<details>
+<summary>Analytic geometry</summary><p align="center">
+    <img src="https://raw.githubusercontent.com/mathvision-cuhk/MathVision/main/assets/examples/exam_analytic_geo.png" width="60%"> <br>
+</p></details>
+
+<details>
+<summary>Topology</summary><p align="center">
+    <img src="https://raw.githubusercontent.com/mathvision-cuhk/MathVision/main/assets/examples/exam_topology.png" width="60%"> <br>
+</p></details>
+
+<details>
+<summary>Graph Geometry</summary><p align="center">
+    <img src="https://raw.githubusercontent.com/mathvision-cuhk/MathVision/main/assets/examples/exam_graph.png" width="60%"> <br>
+</p></details>
+
+## 📑 Citation
+
+If you find this benchmark useful in your research, please consider citing this BibTex:
+
+```
+@inproceedings{
+wang2024measuring,
+title={Measuring Multimodal Mathematical Reasoning with MATH-Vision Dataset},
+author={Ke Wang and Junting Pan and Weikang Shi and Zimu Lu and Houxing Ren and Aojun Zhou and Mingjie Zhan and Hongsheng Li},
+booktitle={The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
+year={2024},
+url={https://openreview.net/forum?id=QWTCcxMpPA}
+}
+
+@inproceedings{
+wang2025mathcodervl,
+title={MathCoder-{VL}: Bridging Vision and Code for Enhanced Multimodal Mathematical Reasoning},
+author={Ke Wang and Junting Pan and Linda Wei and Aojun Zhou and Weikang Shi and Zimu Lu and Han Xiao and Yunqiao Yang and Houxing Ren and Mingjie Zhan and Hongsheng Li},
+booktitle={The 63rd Annual Meeting of the Association for Computational Linguistics},
+year={2025},
+url={https://openreview.net/forum?id=nuvtX1imAb}
+}
+```

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eval.yaml

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+name: MATH-Vision
+description: >
+  MATH-Vision (MATH-V) is a meticulously curated collection of 3,040
+  high-quality mathematical problems with visual contexts sourced from real
+  math competitions. Spanning 16 distinct mathematical disciplines and graded
+  across 5 levels of difficulty, it evaluates the multimodal mathematical
+  reasoning abilities of Large Multimodal Models (LMMs).
+evaluation_framework: "inspect-ai"
+
+tasks:
+  - id: mathvision
+    config: default
+    split: test
+
+    field_spec:
+      input: question
+      input_image: decoded_image
+      target: answer
+      choices: options
+
+    solvers:
+      - name: system_message
+        args:
+          template: |
+            Solve the following mathematics problem, analyzing the accompanying
+            image. If answer options are provided, choose the single best one.
+            Reason step by step, then on the final line output ONLY your answer
+            in the form:
+            ANSWER: <your final answer>
+      - name: generate
+
+    scorers:
+      - name: match
+        args:
+          location: end
+
+  - id: mathvision_testmini
+    config: default
+    split: testmini
+
+    field_spec:
+      input: question
+      input_image: decoded_image
+      target: answer
+      choices: options
+
+    solvers:
+      - name: system_message
+        args:
+          template: |
+            Solve the following mathematics problem, analyzing the accompanying
+            image. If answer options are provided, choose the single best one.
+            Reason step by step, then on the final line output ONLY your answer
+            in the form:
+            ANSWER: <your final answer>
+      - name: generate
+
+    scorers:
+      - name: match
+        args:
+          location: end

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