AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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On surrogate benchmarks ScAn-Bench-LLM and ScAn-Bench-VLM, the first systematic evaluation of both data acquisition and extrapolation methodology for scaling analysis across different data modalities is performed.
A framework for efficient scaling law construction that formulates data collection as a Bayesian optimization problem is proposed, and metrics for comparing scaling law fitting methods under constrained compute budgets are introduced.
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