Update README.md

README.md

@@ -24,6 +24,8 @@ base_model:
 Unlike traditional guardian models that screen for a fixed set of harms (e.g., violence or self-harm) , DynaGuard can enforce bespoke, application-specific rules. This includes scenarios like preventing a customer service bot from mistakenly issuing refunds or ensuring a medical bot avoids giving unauthorized advice.
 The DynaGuard series achieves state-of-the-art performance across a wide range of safety and compliance benchmarks, with the flagship **DynaGuard-8B** model outperforming other guardian models and even strong generalist models like GPT-4o-mini.
 
+[https://arxiv.org/abs/2509.02563](https://arxiv.org/abs/2509.02563)
+
 ## Model Details
 
 * **Developed by:** University of Maryland, Capital One
@@ -121,6 +123,10 @@ DynaGuard-8B achieves state-of-the-art performance, outperforming other dedicate
 If you use DynaGuard or the DynaBench dataset in your research, please cite our work:
 ```
 @article{hoover2025dynaguard,
-  title={DynaGuard: A Dynamic Guardrail Model With User-Defined Policies},
+    title={DynaGuard: A Dynamic Guardrail Model With User-Defined Policies}, 
+    author={Monte Hoover and Vatsal Baherwani and Neel Jain and Khalid Saifullah and Joseph Vincent and Chirag Jain and Melissa Kazemi Rad and C. Bayan Bruss and Ashwinee Panda and Tom Goldstein},
+    journal={arXiv preprint},
+    year={2025},
+    url={https://arxiv.org/abs/2509.02563}, 
 }
 ```