AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Wide tables offer tabular foundation models more evidence, but accessing it can exhaust their memory: full-width pairwise mixing grows quadratically with the number of columns, while feature selection makes inputs affordable by discarding evidence. We ask whether using more features requires interacting over all of the...
In-situ representation refinement is developed: support labels guide changes to the episode's representations, improving the information available to later queries, in a competitive, memory-efficient model.
A lightweight orchestration layer makes exploration explicit and programmable while leaving the underlying base agent unchanged, and a key insight is that accumulated discovery history can act as a replay simulator over the realized search space.
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