AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposed PLUME, a training-free method that constructs a global update representation, activates memory evidence to form a local parameter view, and adaptively integrates their predictions during decoding, which demonstrated its effectiveness in sequential evolution settings.
GRAFT, an IPP-oriented post-training framework combining constraint-aware SFT and preference optimization is proposed, an IPP-oriented post-training framework combining constraint-aware SFT and preference optimization that raises the average IIF score and preserves general generation ability.
This survey examines the protective paradigm that has grown around this intervention point, and finds that most protections are still validated mainly against static or weakly adaptive adversaries, while evidence beyond controlled benchmarks remains scarce.
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