AI RESEARCH
LatentRAG: Latent Reasoning and Retrieval for Efficient Agentic RAG
arXiv CS.LG
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ArXi:2605.06285v1 Announce Type: cross Single-step retrieval-augmented generation (RAG) provides an efficient way to incorporate external information for simple question answering tasks but struggles with complex questions. Agentic RAG extends this paradigm by replacing single-step retrieval with a multi-step process, in which the large language model (LLM) acts as a search agent that generates intermediate thoughts and subqueries to iteratively interact with the retrieval system.