A Survey of (Deep RAG) Deep Retrieval Augmented Generation and Reasoning in Large Language Models

[TechRxiv] Lihui Liu released a review on March 5 focusing on an advanced form of retrieval-augmented generation (RAG), "deep RAG," which integrates reasoning, memory, and multi-step planning into an end-to-end research workflow. The paper states that the review systematically organizes the key components of deep RAG systems, including query planning, information retrieval, memory management, and answer generation, and covers optimization techniques such as supervised fine-tuning, iterative self-evolution, and agentic reinforcement learning. The author also analyzes different planning strategies, adaptive retrieval mechanisms, conflict resolution methods, and multimodal synthesis, points out their advantages and limitations, classifies methods by workflow type, reasoning strategy, and optimization paradigm, outlines the current landscape, and raises open challenges in evaluation, scalability, and multimodal reasoning. The review states that its goal is to provide a roadmap for designing flexible, reliable, and intelligent deep RAG systems that can complete complex real-world research tasks with minimal human supervision.