AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work casts OL as an explicit prompt optimization problem over LLM modules, and proposes two ontology learner architectures: a greedy and an autoregressive learner, and optimize both using GEPA, a greedy evolutionary prompt optimizer built on DSPy.
This dataset defines a new basis for reproducible and data-driven AI design, advancing the emerging paradigm of LLM-driven AutoML and architectural generalization across modalities and hardware.
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