Update README.md

README.md

@@ -19,9 +19,8 @@ With the rapid proliferation of large language models (LLM)—each optimized for
 Existing work on LLM routing typically focuses on learning an optimal policy to route between a limited pool of models, where optimal is measured via well-defined performance benchmarks. This framework, however, is misaligned with real-world scenarios. 
 Benchmark performance does not capture subjective evaluation and testing criteria in the real world. 
 
-Arch-Router is a **preference-based routing model** designed to intelligently guide model selection by matching queries to user-defined domains (e.g., finance and healthcare) and action types (e.g., code generation, image editing, etc.). 
-Experiments on conversational datasets demonstrate that our approach achieves state-of-the-art (SOTA) results in matching queries with human preferences, outperforming top proprietary routing systems. 
-Our preference-aligned approach matches practical definitions of performance in the real world and makes routing decisions more transparent and adaptable.
+Arch-Router is a **preference-aligned routing model** designed to intelligently guide model selection by matching queries to user-defined domains (e.g., finance and healthcare) and action types (e.g., code generation, image editing, etc.). 
+Experiments on conversational datasets demonstrate that our approach achieves state-of-the-art (SOTA) results in matching queries with human preferences, outperforming top proprietary routing systems. Our preference-aligned approach matches practical definitions of performance in the real world and makes routing decisions more transparent and adaptable.
 
 ### How It Works