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

Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for combining large language models with external knowledge sources to produce accurate, context-aware, and verifiable outputs. In this survey, we focus on an advanced instantiation of this paradigm, referred to as Deep RAG, which integrates reasoning, memory, and multi-step planning into end-to-end research workflows. We provide a structured overview of Deep RAG systems, covering key components such as query planning, information retrieval, memory management, and answer generation, as well as practical optimization techniques including supervised fine-tuning, iterative self-evolving, and agentic reinforcement learning. We analyze different planning strategies, adaptive retrieval mechanisms, conflict resolution methods, and multimodal synthesis, highlighting both their strengths and limitations. By categorizing approaches according to workflow types, reasoning strategies, and optimization paradigms, we clarify the current landscape and identify open challenges for evaluation, scalability, and multimodal reasoning. This survey aims to provide a comprehensive roadmap for designing flexible, reliable, and intelligent Deep RAG systems capable of performing complex, real-world research tasks with minimal human supervision.