AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work introduces S IEVE, a search–inspect–fetch strategy built around a Boolean Query Language (BQL), which improves accuracy with every tested ranker, and the accuracy–context advantage persists across retriever choices and agent backbones.
It is shown that duplicate redundancy and LLM paraphrasing does not significantly improve answer correctness, however, providing diverse documents is highly beneficial, improving answer correctness by 17%-47%.
Overall, LightMem offers a context-efficiency trade-off rather than a general advantage over Naive RAG, whose value depends on the retriever and available token budget, motivating future work on retrieval, reranking, query formulation, and their interaction with raw and constructed memory representations.
This work introduces Sieve, a search-inspect-fetch strategy driven by a Boolean Query Language (BQL): it searches webpage fields to filter candidates, uses an interchangeable ranker to order them, presents structure-rich result cards for inspection, and fetches only selected sections.
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