• RAG — Retrieval-augmented generation — fetching relevant documents first and pasting them into the model's context so it answers from your data instead of memory.
• LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.

Graph-based RAG is expensive and unnecessary for most queries. A routing adapter that predicts which queries actually need multi-hop graph reasoning lets teams get graph-RAG's accuracy only where it's worth the latency cost.

• articleUniversal Pathologies, Conditional Consequences: A Triple-Robustness Analysis of RAG for Multi-Hop TraceabilityMeftun Akarsu, Burak Ozdemir
• articleNot All Tokens Are Equal: Inflation-Aware Routing for Agentic LLM SystemsHeming Fu, Shan Lin, Qianqian Xie, Guojun Xiong
• articleNexus: Depth-Adaptive KV-Cache Splicing and Retrieval-Decoupled Tool Routing for Agentic LLMs on Unified MemoryMustafa Arslan

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