Update README.md

README.md

@@ -19,7 +19,7 @@ In this repository, we present GENERator, a generative genomic foundation model
 
 Beyond benchmark performance, the GENERator adheres to the central dogma of molecular biology, accurately generating protein-coding DNA sequences that produce proteins structurally analogous to known families. Moreover, the GENERator showcases significant promise in sequence optimization, particularly in the design of enhancer sequences that regulate gene expression during various biological stages, highlighting its potential for a series of biologically significant tasks. Our findings position the GENERator as a vital resource for genomic research and biotechnological advancement.
 
-For more technical details, please refer to our paper [GENERator: A Long-Context Generative Genomic Foundation Model](https://huggingface.co/GenerTeam). The code and implementation details are available on Github: [https://github.com/GenerTeam/GENERator](https://github.com/GenerTeam/GENERator).
+For more technical details, please refer to our paper [GENERator: A Long-Context Generative Genomic Foundation Model](https://arxiv.org/abs/2502.07272). The code and implementation details are available on Github: [https://github.com/GenerTeam/GENERator](https://github.com/GenerTeam/GENERator).
 
 ## How to use
 ### Simple example1:  generation