Add model card for REVEAL

README.md

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+---
+library_name: transformers
+pipeline_tag: text-classification
+---
+
+# REVEAL: Reasoning-Aware AIGC Detection
+
+REVEAL (Reasoning-Aware AIGC Detection via Alignment and Reinforcement) is a detection framework designed to identify AI-generated content (AIGC) by generating interpretable reasoning chains before final classification. 
+
+This model was introduced in the paper [Reasoning-Aware AIGC Detection via Alignment and Reinforcement](https://huggingface.co/papers/2604.19172).
+
+## Resources
+- **Paper:** [Reasoning-Aware AIGC Detection via Alignment and Reinforcement](https://huggingface.co/papers/2604.19172)
+- **Project Page:** [https://aka.ms/reveal](https://aka.ms/reveal)
+- **GitHub Repository:** [https://github.com/microsoft/AnthropomorphicIntelligence](https://github.com/microsoft/AnthropomorphicIntelligence)
+
+## Introduction
+The rapid advancement of Large Language Models (LLMs) has increased the need for reliable AIGC detection. REVEAL addresses this by proposing a detection framework that generates interpretable reasoning chains before classification. The approach utilizes a two-stage training strategy: 
+1. **Supervised Fine-Tuning (SFT):** Used to establish initial reasoning capabilities.
+2. **Reinforcement Learning (RL):** Used to improve detection accuracy, logical consistency, and reduce hallucinations in the reasoning process.
+
+The model is trained on **AIGC-text-bank**, a comprehensive multi-domain dataset featuring diverse LLM sources and authorship scenarios to ensure robust performance across various generative models.
+
+## Citation
+If you find this work useful, please cite:
+```bibtex
+@article{reveal2026,
+  title={Reasoning-Aware AIGC Detection via Alignment and Reinforcement},
+  author={Authors of the paper},
+  journal={arXiv preprint arXiv:2604.19172},
+  year={2026}
+}
+```