AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Industrial recommendation systems rely on multi-stage cascades whose retrieval, ranking, and serving components are difficult to replace jointly. We present GRP, a generative recommendation framework that combines retrieval, ranking, and reward modeling in a single encoder-decoder model, and evaluate a progressive path...
We demonstrate GraphAgent, a user-friendly and effective system for knowledge-guided selection of Graph Neural Network (GNN) models for new graph data. Existing methods, such as Neural Architecture Search, are computationally expensive, while pretrained graph foundation models often fail to generalize across diverse...
PolyUQuest is presented, a verifiable, structure-aware web RAG framework built on a heterogeneous graph that unifies hyperlink topology between pages, DOM hierarchy within pages, and entity-relation knowledge across pages.
The design and launch of SnapLGR, an LLM-based generative retrieval system for short-video recommendation at Snapchat shows that successful production SnapLGR requires joint design across representation learning, vocabulary grounding, and efficient training and serving.
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