Link the arXiv paper and add its BibTeX entry

README.md

@@ -19,11 +19,12 @@ diffusion model, post-trained online for 390 updates with the six-objective
 MORAD reward. One shared policy designs nucleotide sequences for any target 3D
 backbone, with no per-target optimization.
 
+[Paper](https://arxiv.org/abs/2610.13305) ·
 [Project page](https://gabrile166.github.io/MORAD/) ·
 [Code](https://github.com/Gabrile166/MORAD) ·
 [Data](https://huggingface.co/datasets/MORAD-RNA/MORAD-Targets)
 
-## Results on the 153 test targets
+## Results
 
 | Model | GDT-TS ↑ | TM-score ↑ | RMSD (Å) ↓ | Pairing MCC ↑ | NED ↓ |
 |---|:---:|:---:|:---:|:---:|:---:|
@@ -66,8 +67,12 @@ Training this model from pretrained RIDE takes about 2 hours on 4 × RTX 3090
 
 ```bibtex
 @misc{tang2026morad,
-  title  = {{MORAD}: Reinforcement Learning with Multi-Objective Rewards for {RNA} Inverse Design},
-  author = {Tang, Jixin and Guo, Ji and Zhao, Jun},
-  year   = {2026}
+  title         = {{MORAD}: Reinforcement Learning with Multi-Objective Rewards for {RNA} Inverse Design},
+  author        = {Tang, Jixin and Guo, Ji and Zhao, Jun},
+  year          = {2026},
+  eprint        = {2610.13305},
+  archivePrefix = {arXiv},
+  primaryClass  = {cs.LG},
+  url           = {https://arxiv.org/abs/2610.13305}
 }
 ```