Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs (2507.09477v2)

Abstract: Retrieval-Augmented Generation (RAG) lifts the factuality of 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). Finally, we spotlight emerging Synergized RAG-Reasoning frameworks, where (agentic) LLMs iteratively interleave search and reasoning 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. The collection is available at https://github.com/DavidZWZ/Awesome-RAG-Reasoning.

• The paper introduces a taxonomy that categorizes three paradigms of RAG systems synergizing retrieval with deep reasoning to overcome LLM limitations.
• It details methodologies such as reasoning-aware query reformulation, chain-based planning, and agent orchestration to optimize both knowledge retrieval and synthesis.
• The survey outlines benchmarks and future directions, emphasizing multimodal retrieval, human-agent collaboration, and efficient reasoning workflows for trustworthy information synthesis.

Surveying Agentic RAG with Deep Reasoning: Taxonomy, Methodologies, and Future Directions

This survey provides a comprehensive synthesis of Retrieval-Augmented Generation (RAG) systems with a focus on their integration with deep reasoning in LLMs. The authors systematically categorize the landscape into three principal paradigms: Reasoning-Enhanced RAG, RAG-Enhanced Reasoning, and Synergized RAG-Reasoning, culminating in a detailed taxonomy of methods, benchmarks, and open challenges.

Motivation and Problem Formulation

The paper identifies two persistent limitations in LLMs: (1) knowledge hallucinations due to static, parametric knowledge storage, and (2) limited capacity for complex, multi-step reasoning. RAG addresses the first by injecting external knowledge, while reasoning-oriented approaches target the second. However, these two lines are deeply intertwined: insufficient knowledge impedes reasoning, and weak reasoning hinders effective knowledge utilization. The survey argues that only a synergistic integration of retrieval and reasoning can address both limitations in a unified manner.

Taxonomy and Methodological Landscape

The survey introduces a hierarchical taxonomy, mapping the evolution from one-way enhancements to fully synergized frameworks:

1. Reasoning-Enhanced RAG

This paradigm leverages reasoning to optimize each stage of the RAG pipeline:

• Retrieval Optimization: Incorporates reasoning-aware query reformulation (e.g., query decomposition, expansion), retrieval strategy and planning (e.g., CoT-based multi-step planning, adaptive retrieval), and retrieval model enhancement (e.g., GNN-based retrievers, symbolic rule integration).
• Integration Enhancement: Applies reasoning for relevance assessment (e.g., NLI-based filtering, expert assessors) and information synthesis (e.g., probabilistic aggregation, reasoning graphs).
• Generation Enhancement: Utilizes context-aware synthesis (e.g., selective context utilization, explicit reasoning chains) and grounded generation control (e.g., fact verification, citation generation, knowledge-grounded reasoning).

2. RAG-Enhanced Reasoning

Here, external or in-context retrieval augments the reasoning process:

• External Knowledge Retrieval: Integrates knowledge bases, web retrieval, and tool use to provide factual grounding and fill knowledge gaps in reasoning.
• In-Context Retrieval: Leverages prior experience or retrieved examples from demonstrations/training data to guide reasoning, supporting tasks such as planning, decision-making, and code generation.

3. Synergized RAG-Reasoning

This paradigm supports dynamic, iterative interplay between retrieval and reasoning, often realized through agentic architectures:

• Reasoning Workflows: Structured as chain-based (e.g., IRCoT, CoV-RAG), tree-based (e.g., ToT, MCTS-RAG), or graph-based (e.g., QA-GNN, Think-on-Graph) approaches, each with distinct trade-offs in recall, transparency, and computational cost.
• Agent Orchestration: Encompasses single-agent (prompting, SFT, RL) and multi-agent (decentralized, centralized/hierarchical) systems, enabling dynamic tool selection, retrieval planning, and collaborative reasoning.

The survey provides a detailed comparison of these workflows and orchestration strategies, highlighting their strengths, limitations, and suitable application scenarios.

Benchmarks and Evaluation

A significant contribution is the systematic cataloging of benchmarks for RAG-reasoning, spanning single-hop and multi-hop QA, fact-checking, mathematics, code generation, web browsing, and multimodal tasks. The survey analyzes the primary retrieval and reasoning challenges for each benchmark, noting that most current datasets focus on deductive reasoning and text-based retrieval, with limited coverage of multimodal, domain-specific, or adversarial scenarios.

Open Challenges and Future Directions

The authors identify several open challenges and research directions:

• Reasoning Efficiency: Iterative retrieval and multi-step reasoning introduce significant latency. The survey calls for research into latent reasoning, thought distillation, and adaptive retrieval control to improve efficiency.
• Human-Agent Collaboration: Future systems should support interactive, user-aligned reasoning, modeling user intent and enabling iterative clarification.
• Agentic Capabilities: There is a need for agent frameworks capable of dynamic tool selection, retrieval planning, and adaptive orchestration.
• Multimodal Retrieval: Most systems remain text-centric; advancing multimodal retrieval and reasoning is essential for real-world applicability.
• Retrieval Trustworthiness: Ensuring the reliability of retrieved content, especially in the presence of adversarial or noisy sources, remains an open problem. The survey advocates for integrating uncertainty quantification, robust generation, and dynamic trust metrics.

Implications and Prospects

The survey’s synthesis of RAG and reasoning highlights the necessity of tightly coupled, agentic systems for knowledge-intensive tasks. The move toward synergized RAG-Reasoning frameworks, exemplified by recent "Deep Research" platforms, demonstrates improved factual grounding, logical coherence, and adaptability. The taxonomy and benchmark analysis provide a foundation for systematic evaluation and development of future systems.

Theoretically, the survey underscores the importance of iterative, feedback-driven architectures that blur the boundary between retrieval and reasoning. Practically, it points toward the emergence of autonomous research agents capable of complex, multi-modal, and trustworthy information synthesis.

Future developments are likely to focus on scalable, efficient, and robust agentic RAG systems, with enhanced support for multimodal content, human-in-the-loop collaboration, and real-world deployment in high-stakes domains. The survey’s comprehensive taxonomy and benchmark suite will serve as a reference point for both methodological innovation and rigorous evaluation in this rapidly evolving field.