Update README.md

README.md

@@ -24,7 +24,7 @@ With the rapid proliferation of large language models (LLMs) -- each optimized f
 
 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. 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 to enable preference-based routing in multi-LLM systems in a seamless way. This model is described in the paper: https://arxiv.org/abs/2506.16655
+This model is described in the paper: https://arxiv.org/abs/2506.16655, and powers [Arch](https://github.com/katanemo/arch) the open-source AI-native proxy for agents to enable preference-based routing in multi-LLM systems in a seamless way.
 
 ### How It Works