AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A retrieval-reranking decomposition is introduced and a two-stage pipeline combining a multi-signal retriever with an LLM reranker that matches or exceeds the best baseline's top@1 accuracy on all six benchmark suites, with no causal graph or labeled data required.
Randomized experiments are often run in one population to guide decisions in another. Allocating by experimental proportions wastes budget on groups that rarely appear in deployment, whereas allocating by deployment proportions under-samples groups that are hard to measure precisely. We propose \textbf{TWNA} (Target-We...
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