AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Knowledge distillation can transfer reasoning from stronger teachers to frozen students through reusable prompts, but avoiding weight updates does not eliminate supervision. Without ground-truth answers, teacher solutions are unverified, and agreement with the teacher can reward shared mistakes. We introduce Knowledge-...
This review presents a unified Bayesian formulation of KD that formulates teacher predictions as prior information and establishes a rigorous connection to uncertainty quantification, providing a principled interpretation of how teacher information is incorporated into student learning.
This framework organizes existing methods around five components: explicit state representation, transition structuring, validation and constraint enforcement, search and rollback, and uncertainty management, and yields a diagnostic hypothesis: interventions should be most effective when they target the error or uncert...
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