Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs

Understand

Retrieval-Augmented Generation (RAG) lifts the factuality of Large Language Models (LLMs) by injecting external knowledge, yet it falls short on problems that demand multi-step inference; conversely, purely reasoning-oriented approaches often hallucinate or mis-ground facts.

• This survey synthesizes both strands under a unified reasoning-retrieval perspective.
• We first map how advanced reasoning optimizes each stage of RAG (Reasoning-Enhanced RAG).
• Then, we show how retrieved knowledge of different type supply missing premises and expand context for complex inference (RAG-Enhanced Reasoning).