Mapping the RAG landscape: a four-axis taxonomy of efficiency, defense, interactivity, and reasoning

Abstract

Large Language Models (LLMs) have demonstrated remarkable fluency across many tasks but remain limited by their static, parameter-bound knowledge and their susceptibility to hallucinating information. Retrieval-Augmented Generation (RAG) addresses these issues by incorporating external retrieval into the generation process, grounding model outputs in verifiable and up-to-date sources. While prior surveys primarily focus on core RAG architectures and standard pipelines, recent research explores broader challenges and capabilities that extend beyond these foundational designs. This survey provides a consolidated and structured examination of contemporary RAG developments, organizing the field into a four-axis taxonomy: improving retrieval efficiency, strengthening robustness and security, supporting user-driven and interactive workflows, and enabling multi-step or complex reasoning. We formalize key components of the RAG framework and review methods spanning dense and sparse retrieval, fusion strategies, embedding optimizations, and reinforcement-learning–based retrieval policies, highlighting how these advances influence practical deployment and system design. We also synthesize evaluation practices, domain-specific applications, and architectural variants such as Naive, Advanced, and Modular RAG. Finally, we outline persistent challenges related to retrieval quality, reliability, domain adaptation, scalability, and explainability, and identify opportunities for building RAG systems that are more reliable, adaptable, and transparent.