Update README.md

README.md

@@ -14,11 +14,17 @@ library_name: transformers
 # katanemo/Arch-Router-1.5B
 
 ## Overview
-With the rapid proliferation of large language models (LLM)—each optimized for different strengths, style, or latency/cost profile—routing has become an essential technique to operationalize the use of different models.
 
-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.
+With the rapid proliferation of large language models (LLMs) -- each optimized for different strengths, style, or latency/cost profile -- routing has become an essential technique to operationalize the use of different models. 
 
-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.
+However, existing LLM routing approaches are limited in two key ways: they evaluate performance using benchmarks that often fail to capture human preferences driven by subjective evaluation criteria, and they typically select from a limited pool of models. 
+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.
+
+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.
 
 ### How It Works
 
@@ -36,7 +42,7 @@ Both domain and action configs are associated with preferred models or model var
 - **Production-Ready Performance**: Optimized for low-latency, high-throughput applications in multi-model environments.
 
 
-Arch-Router powers the open-source [Arch Gateway](https://github.com/katanemo/arch), enabling seamless, preference-based prompt routing in multi-LLM systems.
+Arch-Router powers [Arch](https://github.com/katanemo/arch) the open-source AI-native proxy for AI agents and enables seamless, preference-based routing in multi-LLM systems.
 
 
 # Requirements