Update README.md

README.md

@@ -18,12 +18,12 @@ tags:
 library_name: transformers
 paper:
   title: "Closing the Distribution Gap in Adversarial Training for LLMs"
-  url: "https://arxiv.org/pdf/2602.15238"
+  url: "https://arxiv.org/abs/2602.15238"
 ---
 
 # DAT - Distributional Adversarial Training
 
-[![arXiv](https://img.shields.io/badge/arXiv-2511.04316-b31b1b.svg)](...)
+[![arXiv](https://img.shields.io/badge/arXiv-2602.15238-b31b1b.svg)](https://arxiv.org/abs/2602.15238)
 [![GitHub](https://img.shields.io/badge/GitHub-DAT-181717?logo=github&logoColor=white)](https://github.com/ASSELab/DAT)
 
 DAT utilizes [continuous adversarial training](https://arxiv.org/abs/2405.15589) on [diffusion-based](https://arxiv.org/abs/2511.00203v1) adversarial examples to close the gap between empirical and population-robust risk.
@@ -36,12 +36,13 @@ For further information, consult our paper []() or repository [https://github.co
 ## Citation
 
 ```tex
-@misc{,
-      title={}, 
-      author={},
+@misc{hu2026closingdistributiongapadversarial,
+      title={Closing the Distribution Gap in Adversarial Training for LLMs}, 
+      author={Chengzhi Hu and Jonas Dornbusch and David Lüdke and Stephan Günnemann and Leo Schwinn},
       year={2026},
-      eprint={},
+      eprint={2602.15238},
       archivePrefix={arXiv},
-      primaryClass={cs.LG}
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2602.15238}, 
 }
 ```