AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes a EA foundation model driven by a parallel encoding strategy that facilitates anchor-conditioned message passing and significantly shortens the inference trajectory by leveraging local structural proximity instead of global search.
The LLM–KGFR collaborative framework, where an LLM works with a structured retriever, the Knowledge Graph Foundation Retriever (KGFR), achieves strong performance while maintaining scalability and generalization, providing a practical solution for KG-augmented reasoning.
OaK is presented, an ontology-as-a-kernel framework that dynamically constructs and refines task-oriented ontologies for LLM agents and shows that OaK improves standard LLM agents, strengthens evidence grounding, and boosts the reliability of multi-step reasoning.
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