---
license: mit
---

# MoSEs SAR Models: Stylistics-Aware Router

This repository contains the trained Stylistics-Aware Router (SAR) models for the MoSEs framework, an uncertainty-aware AI-generated text detection system. The SAR models are used to route input texts to relevant reference samples based on stylistic features.

## Model Overview

Two SAR models are provided, trained on different datasets:

### main_1000.pt (Main SAR Model)

- **Training Dataset**: Main dataset with 8,000 samples across 8 domains
- **Domains**: CMV, SciXGen, WP, Xsum (with human and AI-generated continuations)

### tiny_200.pt (Tiny SAR Model)

- **Training Dataset**: Tiny dataset with 1,600 samples across 4 domains
- **Domains**: CNN, DialogSum, IMDB, PubMed (with human and GPT-4 generated variants)

## Citation

If you use these models in your research, please cite the MoSEs paper:

```
@inproceedings{wu2025moses,
  title={MoSEs: Uncertainty-Aware AI-Generated Text Detection via Mixture of Stylistics Experts with Conditional Thresholds},
  author={Wu, Junxi and Wang, Jinpeng and Liu, Zheng and Chen, Bin and Hu, Dongjian and Wu, Hao and Xia, Shu-Tao},
  booktitle={Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
  year={2025},
  publisher={Association for Computational Linguistics}
}
```

For the specific SAR models:

```
@model{moses_sar_models,
  title={MoSEs Stylistics-Aware Router},
  author={Wu, Junxi and Wang, Jinpeng and Liu, Zheng and Chen, Bin and Hu, Dongjian and Wu, Hao and Xia, Shu-Tao},
  year={2025},
  url={https://huggingface.co/zhengliu8/Stylistics_Aware_Router}
}
```

## Related Resources

- **MoSEs Paper**: [arXiv:2509.02499](https://arxiv.org/abs/2509.02499)
- **MoSEs Code**: [GitHub Repository](https://github.com/creator-xi/MoSEs)
- **Stylistics Reference Repository**: [HuggingFace Dataset](https://huggingface.co/datasets/zhengliu8/Stylistics_Reference_Repository)

## License

This model is licensed under MIT Licence.