AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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ID-TAP is presented, a scalable, IDS-inspired neural dueling-bandit framework for large discrete persona spaces that combines a query-conditioned neural utility model with a lightweight Bradley-Terry explorer, and transfers comparison evidence across related queries through similarity-weighted warm starts.
COBRA-Skills is introduced, an efficient framework that formulates skill optimization as budgeted sequential optimization over a dynamically evolving candidate space and remains robust to changes in the agent harness and performs effectively when the target model itself is used for skill generation and refinement.
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