AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Certo is a small non-generative decision model (Qwen3-4B): it scores candidate actions from their text and returns a probability, instead of generating an answer, and how much rule-sensitivity survives that move, and whether it can be trained back.
A narrow, practical question is studied: for already-trained standard LoRA adapters -- not adapters retrained for cache compatibility -- how much task quality is preserved if the backbone's prefill KV cache is computed once and reused across specialists, and what does that buy in serving cost?
The theory that explains the blindness, the theory that explains the blindness, the two-population conservation law, under which every in-loop metric improves while true quality does not, and a synthetic study that validates the mechanism are given.
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