AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Using controlled corruption, this work decouple these two roles and finds a reversal: making a noisy token easier to predict reduces its damage as a target but increases it as context.
It is shown that preference alignment preserves the human response distribution only under a restrictive condition, and no consistent evidence that real human preferences satisfy it, and human-likeness is established as an explicit dimension of alignment rather than something assumed to follow from preference alignment...
Label Wave is proposed, which does not require validation data for selecting the desired model across various weakly supervised learning paradigms, including learning with noisy labels (LNL), positive-unlabeled learning, and unlabeled-unlabeled learning.
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