Add arXiv paper link and citation (2605.13059)

README.md

@@ -9,6 +9,7 @@ python_version: '3.10'
 app_file: app.py
 pinned: false
 license: mit
+arxiv: 2605.13059
 models:
   - Simmonstt/BrainAnytime
 ---
@@ -17,6 +18,13 @@ models:
 
 Official implementation of **BrainAnytime: Anatomy-Aware Cross-Modal Pretraining for Brain Image Analysis with Arbitrary Modality Availability**.
 
+## Paper
+
+**BrainAnytime: Anatomy-Aware Cross-Modal Pretraining for Brain Image Analysis with Arbitrary Modality Availability**
+
+- arXiv: [2605.13059](https://arxiv.org/abs/2605.13059)
+- PDF: [https://arxiv.org/pdf/2605.13059](https://arxiv.org/pdf/2605.13059)
+
 ## Congrats: This paper has been early accepted (top 9%) by MICCAI 2026.
 
 ## Pretrained Weights
@@ -148,4 +156,18 @@ This project is released for academic research purposes only.
 
 ## Citation
 
-Citation information will be provided upon paper acceptance.
+If you use BrainAnytime in your research, please cite:
+
+```bibtex
+@misc{yang2026brainanytimeanatomyawarecrossmodalpretraining,
+      title={BrainAnytime: Anatomy-Aware Cross-Modal Pretraining for Brain Image Analysis with Arbitrary Modality Availability},
+      author={Guangqian Yang and Tong Ding and Wenlong Hou and Yue Xun and Ye Du and Qian Niu and Shujun Wang},
+      year={2026},
+      eprint={2605.13059},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2605.13059},
+}
+```
+
+Paper page: https://arxiv.org/abs/2605.13059