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README.md

@@ -15,7 +15,7 @@ base_model:
 
 > **GitHub repository** for exploring the source code and additional resources: https://github.com/wangkevin02/USP
 
-The **AI Detect Model** is a binary classification model designed to determine whether a given text is AI-generated (label=1) or written by a human (label=0). This model plays a crucial role in providing AI detection rewards, helping to prevent reward hacking during Reinforcement Learning with Cycle Consistency (RLCC). For more details, please refer to [our paper](https://tongyi.aliyun.com/qianwen/?sessionId=ea3bbcf36a2346a0a7819b06fcb36a1c#).
+The **AI Detect Model** is a binary classification model designed to determine whether a given text is AI-generated (label=1) or written by a human (label=0). This model plays a crucial role in providing AI detection rewards, helping to prevent reward hacking during Reinforcement Learning with Cycle Consistency (RLCC). For more details, please refer to [our paper](https://arxiv.org/pdf/2502.18968).
 
 This model is built upon the [Longformer](https://huggingface.co/allenai/longformer-base-4096) architecture and trained using our proprietary [LMSYS-USP](https://huggingface.co/datasets/wangkevin02/LMSYS-USP) dataset. Specifically, in a dialogue context, texts generated by the assistant are labeled as AI-generated (label=1), while user-generated texts are assigned the opposite label (label=0).
 
@@ -78,5 +78,13 @@ if __name__ == "__main__":
 If you find this model useful, please cite:
 
 ```plaintext
-[Authors], "[Paper Title]," [Venue], [Year], [URL or DOI].
+@misc{wang2025knowbettermodelinghumanlike,
+      title={Know You First and Be You Better: Modeling Human-Like User Simulators via Implicit Profiles}, 
+      author={Kuang Wang and Xianfei Li and Shenghao Yang and Li Zhou and Feng Jiang and Haizhou Li},
+      year={2025},
+      eprint={2502.18968},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2502.18968}, 
+}
 ```