license: apache-2.0
task_categories:
- text-classification
language:
- en
tags:
- aigc-detection
- text-classification
- qwen
base_model: Qwen/Qwen3-8B
REVEAL_think_3class
REVEAL-think-3class is a reasoning-driven AI-Generated Content (AIGC) detection model based on Qwen3-8B. It uses a Think-then-Answer paradigm, generating a transparent reasoning chain (<think>...</think>) before outputting the final fine-grained classification (<answer>...</answer>).
This model is introduced in the paper: Reasoning-Aware AIGC Detection via Alignment and Reinforcement.
🔗 Project Homepage & Code: https://aka.ms/reveal
📚 Associated Dataset: AIGC-text-bank
🌟 Model Overview
This model performs fine-grained detection, discriminating between three categories:
- Human: Authentic human-authored text.
- AI-Native: Fully machine-generated text.
- AI-Polish: Human-authored text refined by AI to improve fluency and style while preserving original semantics.
🚀 How to Use
To run inference, simply use the think.py script provided in our GitHub repository. It handles prompt formatting, vLLM acceleration, and automatically extracts the final prediction along with fine-grained confidence scores.
python think.py \
--model_path "bmbgsj/REVEAL_think_3class" \
--text "The rapid advancement of Large Language Models has ushered in an era where AI-generated content is increasingly pervasive..."
📖 Citation
If you use this model in your research, please cite:
@misc{wang2026reasoningawareaigcdetectionalignment,
title={Reasoning-Aware AIGC Detection via Alignment and Reinforcement},
author={Zhao Wang and Max Xiong and Jianxun Lian and Zhicheng Dou},
year={2026},
eprint={2604.19172},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2604.19172},
}