AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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HiLight, an Evidence Emphasis framework that decouples evidence selection from reasoning for frozen LLM solvers, is introduced, an Evidence Emphasis framework that decouples evidence selection from reasoning for frozen LLM solvers.
High-capacity Click-Through Rate (CTR) models for ads recommendation often exhibit pronounced one-epoch overfitting: performance peaks after a single training pass (epoch) over the data, while additional epochs degrade generalization as the model memorizes noise in high-variance click labels. To address this challenge,...
Large Language Models (LLMs) have emerged as powerful assets for recommender systems. However, deploying them as generative recommenders or zero-shot rankers at web-scale remains bottlenecked by prohibitive computational overhead and grounding challenges. In this paper, we revitalize the classic, highly efficient two-t...
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