AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work introduces a retain-aware localization method that considers parameter importance to both forgotten and retained data, and introduces a retain-similar evaluation set, constructed using cosine similarity in the model embedding space, to directly measure collateral damage.
Empirical validation shows that GMHF substantially reduces deployment loss as expert reliability increases, and that the divergence between generated and target data falls under reliable feedback, directly corroborating the divergence-minimisation mechanism predicted by the theory.
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