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task_categories:
- question-answering
license: mit
language:
- en
tags:
- physics
---
# PhysReason: A Comprehensive Benchmark towards Physics-Based Reasoning
[](https://arxiv.org/abs/2502.12054)
[](https://huggingface.co/datasets/zhibei1204/PhysReason)
[](https://dxzxy12138.github.io/PhysReason/)
> **PhysReason is accepted by ACL-2025-main**
## 📋 Overview
PhysReason is a comprehensive physics-based reasoning benchmark consisting of **1,200 physics problems** spanning multiple domains, with a focus on both knowledge-based (25%) and reasoning-based (75%) questions. This benchmark addresses the critical gap in evaluating large language models' capabilities in physics-based reasoning, which requires applying physics theorems and constraints in complex problem-solving scenarios.
## ✨ Key Features
- **📊 Dataset Size**: 1,200 carefully curated physics problems
- **🎯 Problem Types**: Strategic mix of knowledge-based (25%) and reasoning-based (75%) questions
- **📚 Theorem Coverage**: Comprehensive coverage of 147 physics theorems
- **🎨 Visual Content**: 81% of problems include diagrams and visual elements
- **📈 Difficulty Levels**: Four distinct levels - Knowledge, Easy, Medium, Hard
- **🔄 Step-by-step Solutions**: Average of 8.1 solution steps per problem (15.6 for hard problems)
- **🌍 Multi-modal**: Supports both text and image inputs
## 🔧 Data Collection
Our rigorous data collection process ensures high-quality, challenging problems:
- **📖 Sources**: Global college entrance exams and international physics competitions
- **⚙️ Process**: Standardized using MinerU framework for consistent formatting
- **✅ Quality Control**: Two-phase translation process with expert verification
- **🔍 Filtering**: Systematically excluded easily searchable problems to prevent data leakage
- **📊 Classification**: Difficulty levels based on solving time and theorem complexity analysis
## 📊 Benchmark Comparison
| Benchmark | Multi-modal | Size | Knowledge | Question Type | Avg. T | Step-by-step | Avg. T | Avg. S |
|----------------|-------------|------|-----------|---------------|--------|--------------|--------|--------|
| JEEBench | ❌ | 123 | CEE | OE,MC | 169.7 | - | - | - |
| MMLU-Pro | ❌ | 1299 | COL | MC | 52.1 | - | - | - |
| GPQA | ❌ | 227 | PH.D. | OE | 111.4 | ❌ | 197.2 | 3.6 |
| SciEval | ❌ | 1657 | - | OE,MC | 154.5 | - | - | - |
| SciBench | ✅ | 295 | COL | OE | 80.5 | ❌ | 315.9 | 2.8 |
| MMMU | ✅ | 443 | COL | OE,MC | 53.8 | - | - | - |
| ScienceQA | ✅ | 617 | K1-K12 | MC | 13.3 | ❌ | 63.0 | 2.4 |
| OlympiadBench | ✅ | 2334 | COMP | OE | 222.0 | ❌ | 199.8 | 3.7 |
| EMMA | ✅ | 156 | - | MC | 109.5 | - | - | - |
| **Ours-Knowledge** | ✅ | 300 | CEE+COMP | OE | 163.7 | ✅ | 196.5 | 3.3 |
| **Ours-Easy** | ✅ | 300 | CEE+COMP | OE | 171.2 | ✅ | 241.5 | 5.0 |
| **Ours-Medium** | ✅ | 300 | CEE+COMP | OE | 229.2 | ✅ | 391.3 | 8.4 |
| **Ours-Hard** | ✅ | 300 | CEE+COMP | OE | 340.9 | ✅ | 936.1 | 15.6 |
| **Ours-Full** | ✅ | 1200 | CEE+COMP | OE | 226.3 | ✅ | 441.3 | 8.1 |
## 🔍 Evaluation Framework
We introduce the **Physics Solution Auto Scoring (PSAS)** framework with two complementary evaluation approaches:
### PSAS-A (Answer Level Evaluation)
- **Sub-question Assessment**: Evaluates answers for each sub-question independently
- **LLM-based Extraction**: Uses advanced language models for answer extraction
- **Semantic Verification**: Ensures semantic consistency between extracted and ground truth answers
- **Weighted Scoring**: Considers solution step lengths as weights for different sub-questions
### PSAS-S (Step Level Evaluation)
Provides detailed step-by-step assessment through four phases:
1. **Data Extraction**: Parses model responses and reference solutions
2. **Scoring**: Evaluates correctness of each reasoning step
3. **First Error Detection**: Identifies where models first deviate from correct reasoning
4. **Error Analysis**: Classifies error types into four key bottlenecks:
- Physics Theorem Application
- Physics Process Understanding
- Calculation
- Physics Condition Analysis
## 🚀 Usage
### Core Evaluation Files
- `answer_evaluation_with_ds_ch_prompt.py`: Answer-level evaluation using Chinese prompts
- `answer_evaluation_with_ds_en_prompt.py`: Answer-level evaluation using English prompts
- `format_result_ds.py`: Optimizes unstable outputs into stable, consistent formats
- `step_evaluation_with_ds_ch_prompt.py`: Step-level evaluation using Chinese prompts
- `step_evaluation_with_ds_en_prompt.py`: Step-level evaluation using English prompts
## 📈 Experimental Results
### Non-O-like Models Performance
| Model | Input | Knowledge | Easy | Medium | Hard | Avg. |
|-------------------|-------|-------------|-------------|-------------|-------------|-------------|
| Qwen2VL-72B | Q, I | 41.92/62.47 | 24.04/45.26 | 15.97/36.13 | 4.83/24.23 | 16.96/42.88 |
| InternVL2.5-78B | Q, I | 28.34/64.71 | 24.16/50.69 | 17.72/38.56 | 9.71/25.95 | 19.98/45.89 |
| GPT-4o | Q, I | 50.71/65.82 | 33.87/51.98 | 22.73/42.36 | 11.03/24.71 | 29.58/47.23 |
| Deepseek-V3-671B | Q, IC | 55.86/66.14 | 40.06/52.77 | 26.63/44.02 | 13.73/26.87 | 34.07/48.42 |
| Claude-3.5-Sonnet | Q, I | 54.14/66.45 | 41.35/55.85 | 28.14/44.86 | 15.11/28.51 | 34.69/49.88 |
| Gemini-2.0-Flash | Q, I | 65.08/75.04 | 54.84/68.60 | 39.79/55.67 | 21.99/38.39 | 45.20/60.40 |
| Gemini-2.0-Pro | Q, I | 67.99/79.01 | 55.43/71.47 | 44.29/57.74 | 23.81/42.66 | 47.88/62.74 |
### O-like Models Performance
| Model | Input | Knowledge | Easy | Medium | Hard | Avg. |
|------------------------------------|-------|-------------|-------------|-------------|-------------|-------------|
| o1-mini | Q, IC | 53.90/65.74 | 35.21/52.26 | 22.24/40.19 | 10.61/26.80 | 30.49/47.18 |
| QvQ-72B | Q, I | 62.44/70.92 | 53.74/64.65 | 28.18/54.88 | 14.30/36.47 | 32.67/57.66 |
| Gemini-2.0-Flash-Thinking-1206 | Q, I | 65.35/77.20 | 51.89/67.49 | 44.43/58.95 | 27.14/45.48 | 47.20/63.07 |
| QwQ-32B | Q, IC | 62.03/76.28 | 54.92/71.08 | 43.64/62.14 | 22.99/42.19 | 45.89/63.87 |
| GLM-Zero | Q, IC | 64.95/80.36 | 54.11/71.54 | 41.32/63.67 | 23.04/47.46 | 46.52/65.76 |
| o3-mini-high | Q, IC | 70.67/83.61 | 67.20/81.95 | 45.31/64.57 | 30.12/47.23 | 53.32/69.34 |
| Gemini-2.0-Flash-Thinking-0121 | Q, I | 73.44/84.15 | 63.17/75.94 | 50.41/66.60 | 31.90/48.47 | 54.73/69.73 |
| **Deepseek-R1** | Q, IC | **75.11/85.91** | **65.08/79.81** | **54.84/72.02** | **31.95/51.50** | **56.75/73.26** |
### PhysReason-mini Results
| Model | K. | E. | M. | H. | Avg. |
|------------------------------------|-------|-------|-------|-------|-------|
| o1-mini | 54.80 | 30.33 | 15.41 | 7.92 | 27.11 |
| QvQ-72B | 51.17 | 37.10 | 29.83 | 22.13 | 35.06 |
| QwQ-32B | 64.40 | 50.07 | 38.88 | 27.45 | 45.20 |
| Gemini-2.0-Flash-Thinking-1206 | 71.47 | 49.97 | 36.83 | 22.97 | 45.42 |
| GLM-Zero | 72.70 | 50.17 | 43.42 | 24.70 | 47.75 |
| o1 | 72.47 | 53.37 | 49.31 | 25.32 | 50.12 |
| o3-mini-high | 71.10 | 63.20 | 47.02 | 31.93 | 53.31 |
| Gemini-2.0-Flash-Thinking-0121 | 76.33 | 56.87 | 51.85 | 32.61 | 54.42 |
| **Deepseek-R1** | **85.17** | **60.77** | **47.24** | **33.23** | **56.60** |
## 🔑 Key Findings
- **Performance Gap**: Even top-performing models achieve less than 60% on answer-level evaluation
- **Difficulty Scaling**: Performance drops significantly from knowledge questions (75.11%) to hard problems (31.95%)
- **O-like Model Advantage**: Models with enhanced reasoning capabilities show superior performance
- **Multi-modal Benefits**: Visual content significantly enhances model understanding and performance
- **Four Critical Bottlenecks** identified through step-level evaluation:
1. **Physics Theorem Application**
2. **Physics Process Understanding**
3. **Calculation Accuracy**
4. **Physics Condition Analysis**
## 📝 Citation
If you find PhysReason useful in your research, please cite our paper:
```bibtex
@article{zhang2025physreason,
title={Physreason: A comprehensive benchmark towards physics-based reasoning},
author={Zhang, Xinyu and Dong, Yuxuan and Wu, Yanrui and Huang, Jiaxing and Jia, Chengyou and Fernando, Basura and Shou, Mike Zheng and Zhang, Lingling and Liu, Jun},
journal={arXiv preprint arXiv:2502.12054},
year={2025}
}
```
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 📧 Contact
We welcome contributions to PhysReason! Please contact us for more details.
---
**🔗 Quick Links:**
- [📄 Paper](https://arxiv.org/abs/2502.12054)
- [🤗 Dataset](https://huggingface.co/datasets/zhibei1204/PhysReason)
- [🌐 Project Page](https://dxzxy12138.github.io/PhysReason/)
- [💻 GitHub Repository](https://github.com/dxzxy12138/PhysReason)
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