A Survey of RAG -Reasoning Systems in Large Language Models (2025)

Abstract: 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-search 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). Finally, we spotlight emerging Synergized RAG-Reasoning frameworks, where (agentic) LLMs iteratively interleave search and thought to achieve state-of-the-art performance across knowledge-intensive benchmarks. We categorize methods, datasets, and open challenges, and outline research avenues toward deeper RAG-Reasoning systems that are more effective, multimodally-adaptive, trustworthy, and human-centric.

## introduction the remarkable progress in large language models (llms) has transformed a wide array of fields, showcasing unprecedented capabilities across diverse tasks @xcite . despite these advancements, the effectiveness of llms remains hindered by two fundamental limitations: knowledge hallucinations, due to the static and parametric manner of their knowledge storage @xcite ; and struggles with complex reasoning, especially when tackling real-world problems @xcite . these limitations have driven the development of two major directions: retrieval-augmented generation (rag) @xcite , which provides llms with external knowledge; and various methods aimed at enhancing their inherent reasoning abilities @xcite . the two limitations are inherently intertwined: missing knowledge can impede reasoning, and flawed reasoning hinders knowledge utilization @xcite . naturally, researchers have increasingly explored combining retrieval with reasoning, though early work followed two separate, one-way enhancements. the first, reasoningenhanced rag @xcite (@xmath0, leverages reasoning to improve specific stages of the rag pipeline. the second path, ragenhanced reasoning @xcite (@xmath1, supplies external factual grounding or contextual cues to bolster llm reasoning. while beneficial, the above methods remain bound to a static retrieval-then-reasoning (rtr) framework, offering only localized improvements to individual components. several inherent limitations persist: (1) retrieval adequacy and accuracy cannot be guaranteed; pre-retrieved knowledge may fail to align with the actual knowledge needs that emerge during reasoning, especially in complex tasks @xcite . as shown in figure 1 , these shortcomings have catalyzed a paradigm shift toward synergized retrieval and reasoning within llms (rag ⇔ reasoning). these methods support a dynamic, iterative interplay where reasoning actively guides retrieval, and newly retrieved knowledge, in turn, continuously refines the reasoning process. this trend is further exemplified by recent "deep research" products from openai foot_0 , gemini foot_1 , perplexity foot_2 , and others, which emphasize tightly coupled retrieval and reasoning @xcite . these systems employ agentic capabilities to orchestrate multi-step web search and leverage reasoning to comprehensively interpret retrieved content, solving problems demanding in-depth investigation. this survey charts the shift from isolated enhancements to cutting-edge synergized frameworks where retrieval and reasoning are deeply interwoven and co-evolve. while surveys on rag @xcite and llm reasoning @xcite ) exist, a dedicated synthesis focusing on their integration remains lacking. our goal is to provide a comprehensive overview of how the symbiosis between retrieval and reasoning is advancing llm capabilities, with particular emphasis on the move towards a synergized rag and reasoning framework. the survey is structured as follows: section 2 introduces the background; section 3 and 4 review two one-way enhancements, respectively. section 5 unifies both lines into synergized rag-reasoning frameworks. section 6 lists benchmarks, and section 7 outlines open challenges. ## background and preliminary rag mitigates knowledge cut-off of llms through three sequential stages: (i) retrieval, fetching taskrelevant content from external knowledge stores; ## reasoning-enhanced rag traditional rag methods first retrieve relevant documents, then concatenate the retrieved knowledge with the original query to generate the final answer. these methods often fail to capture the deeper context or intricate relationships necessary for complex reasoning tasks. by integrating reasoning capabilities across retrieval, integration, and generation stages of the rag pipeline, the system can identify and fetch the most relevant information, reducing hallucinations and improving response accuracy. 4 ## rag-enhanced reasoning integrating external knowledge or in-context knowledge during reasoning can help llms reduce hallucinations and bridge logical gaps. external retrieval leverages structured sources like databases or web content, providing factual grounding, like iag @xcite . in-context retrieval utilizes internal contexts like prior interactions or training examples, enhancing contextual coherence, like ra-dt @xcite . both strategies collectively improve factual accuracy, interpretability, and logical consistency of reasoning processes. ## synergized rag-reasoning many real-world problems, such as open-domain question answering @xcite and scientific discovery @xcite @xcite , require an iterative approach where new evidence continuously informs better reasoning and vice versa. a single retrieval step may not provide sufficient information, and a single round of reasoning may overlook key insights @xcite . by tightly integrating retrieval and reasoning in a multi-step, interactive manner, these systems can progressively refine both the search relevance of retrieved information and the reasoningbased understanding of the original query. we focus on two complementary perspectives within existing approaches: reasoning workflows, which emphasize structured, often pre-defined inference formats for multi-step reasoning; and agent orchestration, which focus on how agents interact with environment and coordinate with each others. ## benchmarks and datasets benchmarks and datasets for simultaneously evaluating knowledge (rag) and reasoning capability cover a wide range of complexities, from basic fact retrieval to intricate multi-step reasoning in general or specific domains. we categorize notable benchmarks in several tasks and list them in ## future work future research directions for synergized rag-reasoning systems center around enhancing both reasoning and retrieval capabilities to meet realworld demands for accuracy, efficiency, trust, and user alignment. we outline several key challenges and opportunities below. ## conclusion this survey charts the rapid convergence of retrieval and llm reasoning. we reviewed three evo-