docs: update UPM paper links and bioRxiv citation

README.md

@@ -46,7 +46,7 @@ tags:
 
 ## Overview
 
-Open-X-Cell is part of the [UPM (Unified Perturbation Modeling)](https://github.com/youlab2025/UPM) project, supporting the work *How Much Can AI Virtual Cells Gain from Unified Perturbational Modeling?* This repository provides the X-Cell implementation and checkpoint release; visit the UPM main repository for the overall project.
+Open-X-Cell is part of the [UPM (Unified Perturbation Modeling)](https://github.com/youlab2025/UPM) project, supporting the work *[How Much Can AI Virtual Cells Gain from Unified Perturbational Modeling?](https://www.biorxiv.org/content/10.64898/2026.10.25.761625v1)* This repository provides the X-Cell implementation and checkpoint release; visit the UPM main repository for the overall project.
 
 **Open-X-Cell predicts single-cell gene expression after a genetic perturbation, starting from control cells.** The Mini model combines control expression, a target gene and five fixed biological priors, then predicts a response through four diffusion steps.
 
@@ -244,14 +244,16 @@ Paper screenshot: first-page title and author crop from [Wang et al. (2026)](htt
 
 ## Citation
 
-If you use this code, please cite our paper.
+If you use this code, please cite our [paper](https://www.biorxiv.org/content/10.64898/2026.10.25.761625v1) (bioRxiv, 2026; DOI: [10.64898/2026.10.25.761625](https://doi.org/10.64898/2026.10.25.761625)).
 
 ```bibtex
-@misc{upm2026,
-  title={How Much Can AI Virtual Cells Gain from Unified Perturbational Modeling?},
+@article{upm2026,
+  title={How Much Can {AI} Virtual Cells Gain from Unified Perturbational Modeling?},
+  author={Wei, Zongting and Ye, Yuedian and Cui, Shuguang and Ma, Wenjing and You, Yuning},
+  journal={bioRxiv},
   year={2026},
-  note={Manuscript in preparation},
-  url={https://github.com/youlab2025/UPM}
+  doi={10.64898/2026.10.25.761625},
+  url={https://www.biorxiv.org/content/10.64898/2026.10.25.761625v1}
 }
 ```