AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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An information-theoretic lower bound is derived that captures the minimum cost of achieving prescribed testing errors and characterizes the value of AI information and human verification through a report-dependent information frontier.
This work studies a portfolio-and-selector paradigm in which a firm runs multiple workflow executions and selects the final answer after observing their outputs, and derives a finite-dimensional dual and an ellipsoid method using a pricing oracle to identify workflows with high weighted accuracy net of recurring comput...
The results show that data-driven parameter optimization can guide LLM-based search over a broad space of inventory policy classes and identify high-performing, interpretable, and transferable decision rules.
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