# StepMathAgent

**Repository Path**: yifraternity/StepMathAgent

## Basic Information

- **Project Name**: StepMathAgent
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-03-28
- **Last Updated**: 2025-03-28

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# StepMathAgent

**StepMathAgent** is a novel mathematical process evaluation agent based on Tree-of-Error, which incorporates four internal core operations: logical step segmentation, step scoring, score aggregation and error tree generation, along with four external extension modules: difficulty calibration, simplicity evaluation, completeness validation and format assessment.

![Architecture](./assets/model.png)

We also introduce **StepMathBench**, a benchmark comprising 1,000 step-divided process evaluation instances, derived from 200 high-quality math problems grouped by problem type, subject category and difficulty level.

## StepMathBench

Mathematical Problem: `data/question.jsonl`

StepMath: `data/stepmath.jsonl`

## Quick Start

### Use StepMathAgent

```python
python agent.py
```

### Use Four External Extension Modules in StepMathAgent

```python
# Modify model_name in agent.py
python agent.py
```

### Run Baseline

```python
# Modify api_name and model_name in agent.py
python agent.py
```

### Evaluation

```python
python evaluator.py
```

## Case Study

![Case](./assets/case.png)

## Citation

If you find this work useful in your research, please consider citing:

```
@misc{yang2025stepmathagentstepwiseagentevaluating,
      title={StepMathAgent: A Step-Wise Agent for Evaluating Mathematical Processes through Tree-of-Error}, 
      author={Shu-Xun Yang and Cunxiang Wang and Yidong Wang and Xiaotao Gu and Minlie Huang and Jie Tang},
      year={2025},
      eprint={2503.10105},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2503.10105}, 
}
```