AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Experiments across diverse upstream models show that PILA consistently improves ad effectiveness while preserving response quality, highlighting its promise as a practical solution for LLM-native advertising.
This work constructs the missing supervision through a psychologically grounded agent simulation framework, and distil it into a parameter-efficient evaluator that predicts click-through intent, together with the three companion dimensions of ad quality, as smooth, differentiable estimates.
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