AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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GIOF first transforms geometry-derived interaction channels into reusable generator bases, then combines them through a context-dependent selector and adaptive propagation scale, and finally realizes the selected operator through stable continuous-time propagation and a bottleneck residual layer.
Network Feasibility Geometry Reinforcement Learning (NFG-RL) is proposed, which models coupled constraints via transport theory and the residual inclusion of $\bphi_{\mathfrak{N}}(x,a)\in\cK_{\mathfrak{N}}$, defining the executed policy as the pushforward of a proto-policy through a feasibility-transport map.
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