Update README.md

README.md

@@ -21,8 +21,7 @@ However, existing LLM routing approaches are limited in two key ways: they evalu
 We introduce a preference-aligned routing framework that guides model selection by matching queries to user-defined domains (e.g., travel) or action types (e.g., image editing) -- offering a practical mechanism to encode preferences in routing decisions. 
 Specifically, we introduce Arch-Router, a compact 1.5B model that learns to map queries to domain-action preferences for model routing decisions. 
 
-Our approach also supports seamlessly adding new models for routing without requiring retraining or architectural modifications. Experiments on conversational datasets demonstrate that our approach achieves state-of-the-art (SOTA) results in matching queries with human preferences, outperforming top proprietary models. 
-Our approach captures subjective evaluation criteria and makes routing decisions more transparent and flexible.
+Experiments on conversational datasets demonstrate that our approach achieves state-of-the-art (SOTA) results in matching queries with human preferences, outperforming top proprietary models. 
 
 Arch-Router powers [Arch](https://github.com/katanemo/arch) the open-source AI-native proxy for agents and enables seamless, preference-based routing in multi-LLM systems.