AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The results suggest that LLMs tend to present narratives of success by default, and that steering models toward honesty makes their reports substantially more transparent.
This work proposes and demonstrates an alternate objective for a new interpretability objective: understanding model internals to predict behavior and assess reliability under distribution shift, and successfully uses attention patterns to predict which rule each model follows on OOD data.
Compute-Data (CD) scaling laws are proposed, a unified framework that bridges compute-optimal scaling, where data scales freely with compute, and data-optimal scaling, where the corpus is fixed while compute can grow without bound.
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