Update README.md

README.md

@@ -8,7 +8,7 @@ library_name: transformers
 ---
 ##  MingTok: A Unified Tokenizer for Visual Understanding and Generation without Vector Quantization
 
-<p align="center">📑 <a href="https://inclusionai.github.io/blog/mingtok/">Technical Report</a> ｜ 📖 <a href="https://inclusionai.github.io/blog/mingtok/">Project Page</a> ｜ 🤗 <a href="https://huggingface.co/inclusionAI/MingTok-Vision">Hugging Face</a> ｜ 🤖 <a href="https://modelscope.cn/models/inclusionAI/MingTok-Vision">ModelScope</a> ｜ 💾 <a href="https://github.com/inclusionAI/Ming-UniVision">GitHub</a></p>
+<p align="center">📑 <a href="https://arxiv.org/pdf/2510.06590">Technical Report</a> ｜ 📖 <a href="https://inclusionai.github.io/blog/mingtok/">Project Page</a> ｜ 🤗 <a href="https://huggingface.co/inclusionAI/MingTok-Vision">Hugging Face</a> ｜ 🤖 <a href="https://modelscope.cn/models/inclusionAI/MingTok-Vision">ModelScope</a> ｜ 💾 <a href="https://github.com/inclusionAI/Ming-UniVision">GitHub</a></p>
 
 ## Key Features
 - 🖼️ **First Continuous Unified Vision Tokenizer:** MingTok enables unified vision understanding and generation via a continuous latent space, eliminating quantization while preserving semantic and perceptual fidelity.
@@ -223,4 +223,11 @@ output_image.save(save_path)
 </div>
 
 ## Reference
-TBD.
+```
+@article{huang2025mingunivision,
+  title={Ming-UniVision: Joint Image Understanding and Generation with a Unified Continuous Tokenizer},
+  author={Huang, Ziyuan and Zheng, DanDan and Zou, Cheng and Liu, Rui and Wang, Xiaolong and Ji, Kaixiang and Chai, Weilong and Sun, Jianxin and Wang, Libin and Lv, Yongjie and Huang, Taozhi and Liu, Jiajia and Guo, Qingpei and Yang, Ming and Chen, Jingdong and Zhou, Jun},
+  journal={arXiv preprint arXiv:2510.06590},
+  year={2025}
+}
+```