Katanemo Labs introduces Arch-Router, a 1.5B parameter model that intelligently maps user queries to the most suitable LLM, achieving 93% accuracy without the need for costly retraining. It uses a preference-aligned routing framework based on a Domain-Action Taxonomy, allowing for flexible adaptation to evolving models and use cases.
This paper introduces Arch-Router, a preference-aligned routing framework for large language models (LLMs). It addresses limitations in existing routing approaches by focusing on matching queries to user-defined preferences (domain and action types) rather than solely relying on benchmark performance. The framework includes a 1.5B parameter model, Arch-Router, and a data creation pipeline. Experiments demonstrate state-of-the-art results in matching queries with human preferences and improved adaptability.
This paper proposes a preference-aligned routing framework for LLMs that guides model selection by matching queries to user-defined domains or action types. It introduces Arch-Router, a compact 1.5B model that learns to map queries to domain-action preferences for model routing decisions, outperforming proprietary models in subjective evaluation criteria.
The article discusses the use of AI agents for automating and optimizing tasks in the networking industry, including network deployment, configuration, and monitoring. It outlines a workflow with four agents that collectively achieve the setup and verification of network connectivity within a Linux and SR Linux container environment.
The author demonstrates a workflow involving four AI agents designed to deploy, configure, and monitor a network:
Document Specialist Agent: This agent extracts installation, topology deployment, and node connection instructions from a specified website.
- Linux Configuration Agent: Executes the installation and configuration commands on a Debian 12 UTM VM, checks the health of the VM, and verifies the successful deployment of network containers.
- Network Configuration Specialist Agent: Configures network IP allocation, interfaces, and routing based on the network topology, including detailed BGP configurations for different network nodes.
- Senior Network Administrator Agent: Applies the generated configurations to the network nodes, checks BGP peering, and verifies end-to-end connectivity through ping tests.
The author demonstrates a workflow involving four AI agents designed to deploy, configure, and monitor a network:
Document Specialist Agent: This agent extracts installation, topology deployment, and node connection instructions from a specified website.
- Linux Configuration Agent: Executes the installation and configuration commands on a Debian 12 UTM VM, checks the health of the VM, and verifies the successful deployment of network containers.
- Network Configuration Specialist Agent: Configures network IP allocation, interfaces, and routing based on the network topology, including detailed BGP configurations for different network nodes.
- Senior Network Administrator Agent: Applies the generated configurations to the network nodes, checks BGP peering, and verifies end-to-end connectivity through ping tests.
WilmerAI is a sophisticated middleware system designed to handle incoming prompts and route them to appropriate categories and workflows. It supports multiple Large Language Models (LLMs) and can handle a single incoming connection to many backend LLMs.
