AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Feedback from automated testing, human red teaming, and incident response can strengthen an AI system's defenses when discovered failures lead to effective repairs. We study when this feedback process provides sufficient protection and when investing in it is economically worthwhile. We begin by showing that an attack...
Localized LoRA-MoE is introduced, a unified framework that fuses localized spatial blocking with dynamic, context-conditioned routing, and decentralized cell-level gating achieves complete statistical parity with an omniscient global coordinator, providing a robust"gradient firewall" that protects surviving pathways fr...
This work reframe trajectory evaluation as a distance between the agent's execution graph and a set of valid solution graphs, and instantiates OTAP via an unbalanced fused Gromov-Wasserstein transport problem over attributed dependency graphs, which is a pseudo-metric that is provably invariant to dependency-preserving...
A consistency theory for robust neural classifiers based on the S-divergence family that requires no identifiability assumption is developed and it is proved that empirical S-divergence minimizers converge to the population-optimal equivalence class under mild regularity conditions.
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