Update README.md

README.md

@@ -18,8 +18,8 @@ base_model: loubb/aria-medium
 Applications include score-conditioned generation across expertise levels, performance quality assessment, competition ranking, technique classification, mistake detection, and expressive-style analysis.
 
 📖 Paper: *MAJEPPA: Morphing and Assessing in a Unified Piano Performance Space* (ISMIR 2026)  
-💻 Code (loading, inference, generation): [github.com/anusfoil/majeppa](https://github.com/anusfoil/majeppa)  
-📊 Benchmark: [github.com/anusfoil/evpmr](https://github.com/anusfoil/evpmr)
+💻 Code (loading, inference, generation): [github.com/kkwsts/majeppa](https://github.com/kkwsts/majeppa)  
+📊 Benchmark: [github.com/kkwsts/evpmr](https://github.com/kkwsts/evpmr)
 
 ## Model Details
 
@@ -36,7 +36,7 @@ pip install torch safetensors transformers
 pip install git+https://github.com/EleutherAI/aria-utils.git
 ```
 
-Load via the reference implementation in the [MAJEPPA repository](https://github.com/anusfoil/majeppa):
+Load via the reference implementation in the [MAJEPPA repository](https://github.com/kkwsts/majeppa):
 
 ### Understanding — global embedding
 
@@ -68,7 +68,7 @@ performance = model.generate(
 tokenizer.decode_to_file(performance, "generated.mid")
 ```
 
-See the [repository](https://github.com/anusfoil/majeppa) for the full list of condition tags and advanced usage.
+See the [repository](https://github.com/kkwsts/majeppa) for the full list of condition tags and advanced usage.
 
 ## Intended Use