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README.md

@@ -30,7 +30,7 @@ Ziyong Feng,</span>
 Glint Lab
 
 [![GitHub](https://img.shields.io/badge/⭐-GitHub-black?logo=github)](https://github.com/deepglint/UniME-R1)
-[![Paper](https://img.shields.io/badge/📄-Paper-b31b1b.svg)]()
+[![Paper](https://img.shields.io/badge/📄-Paper-b31b1b.svg)](https://arxiv.org/abs/2608.06060)
 [![Model](https://img.shields.io/badge/🤗-UniME--R1_Models-yellow)](https://huggingface.co/DeepGlint-AI)
 </div>
 
@@ -147,11 +147,14 @@ UniME-R1 achieves the best overall performance at both model scales. Notably, th
 If you find this repository useful, please use the following BibTeX entry for citation.
 
 ```bibtex
-@misc{unime-r1,
-      title  = {Learning from Failures: Retrieval-Centric CoT via Hard Negatives for Unified Multimodal Retrieval},
-      author = {Sun, Zelong and Wang, Jun and Yang, Kaicheng and Gu, Tiancheng and Feng, Ziyong and Lu, Zhiwu},
-      year   = {2025},
-      url    = {https://github.com/deepglint/UniME-R1}
+@misc{unimer1,
+      title={Learning from Failures: Retrieval-Centric CoT via Hard Negatives for Unified Multimodal Retrieval}, 
+      author={Zelong Sun and Jun Wang and Kaicheng Yang and Tiancheng Gu and Ziyong Feng and Zhiwu Lu},
+      year={2026},
+      eprint={2608.06060},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2608.06060}, 
 }
 ```