AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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GoldiMask is introduced, which selects tokens to reveal as context by approximately maximizing a submodular objective and weights the remaining targets according to how they benefit from the selected context and their remaining learning potential.
It is found that configuration shift consistently erodes CP validity, often driving empirical coverage below the target, and coverage lower bounds are derived that attribute this loss to a discrepancy between calibration and test score distributions.
Evolutionary Soups, a mixture-of-experts framework for fine-grained generation control, with gating networks trained via an evolutionary algorithm, achieves the best hypervolume, linear utility, and Tchebyshev utility among controllable methods on all tasks.
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