AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A measurement protocol is contributed for action-conditioned latent predictors with a physical readout that separately tests retention, physical-neighborhood organization, forecasting, and local response to command perturbations, and the protocol provides the basis for its closed-loop evaluation.
Results indicate that the parameter efficiency reported for KANs does not transfer to deployment cost on scalar embedded cores, and that an MLP residual branch is the more dependable default for embedded HRPINN deployment unless specific quantization co-design is used.
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