AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Decoupled Contrastive Decoding is introduced, which drafts with an expert-aligned lightweight proposer and applies the amateur only in unchanged CD verification and achieves average greedy speedups of 1.65 to 1.95x over vanilla CD and reduces MMLU proposal-path latency by about 5 to 12x relative to amateur-coupled prop...
Scaling model-generated data is usually viewed as improving distillation: more examples should increase coverage, reduce noise, and produce stronger students. We show a second effect: larger datasets can make subtle teacher-specific signals easier to detect in the trained student, even when examples are off-task and ne...
Trait-direction drift is proposed and validated as a mechanism for subliminal learning: biased generation creates measurable preference gaps in teacher data, and student-recognizable gaps induce trait-aligned updates during supervised fine-tuning that accumulate into behavioral transfer.
A semantic-entropy-based method, using task uncertainty to guide prompt optimization, which requires no training, works with black-box models, and integrates easily into existing prompt optimizers.
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