Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs (2507.09477v1)

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 unified taxonomy that intertwines retrieval and reasoning through iterative, agentic frameworks.
• Methodologies include retrieval optimization, evidence synthesis, and dynamic agent orchestration to improve LLM reasoning and factual accuracy.
• The survey highlights open challenges such as latency, scalability, and multimodal integration, guiding future RAG-reasoning research.

Agentic RAG with Deep Reasoning: A Comprehensive Survey of RAG-Reasoning Systems in LLMs

This survey provides a systematic and detailed synthesis of Retrieval-Augmented Generation (RAG) and deep reasoning in LLMs, with a particular focus on the emerging paradigm of agentic, synergized RAG-reasoning systems. The work delineates the evolution from traditional, unidirectional RAG and reasoning enhancements to tightly coupled, iterative frameworks where retrieval and reasoning co-evolve, often orchestrated by agentic LLMs. The survey also offers a taxonomy of methods, benchmarks, and open challenges, and discusses practical implications for the development and deployment of advanced RAG-reasoning systems.

Motivation and Problem Setting

LLMs, despite their impressive generative and reasoning capabilities, are fundamentally limited by knowledge hallucinations and difficulties with complex, multi-step reasoning. RAG addresses the knowledge limitation by injecting external information, but standard RAG pipelines—typically retrieval followed by generation—are insufficient for tasks requiring deep, multi-hop inference or adaptive reasoning. Conversely, reasoning-centric approaches without external grounding are prone to hallucination and factual errors. The survey argues that these limitations are inherently intertwined and that their resolution requires a unified, iterative approach where retrieval and reasoning inform and refine each other.

Taxonomy of RAG-Reasoning Systems

The survey introduces a three-stage taxonomy:

1. Reasoning-Enhanced RAG: Reasoning is used to optimize retrieval, integration, and generation stages in the RAG pipeline.
   • Retrieval Optimization: Techniques such as query decomposition, reformulation, and expansion (e.g., Collab-RAG, DynQR) improve the relevance and coverage of retrieved content.
   • Integration Enhancement: Reasoning-based filtering and evidence synthesis (e.g., SEER, DualRAG) reduce noise and improve the coherence of the context.
   • Generation Enhancement: Context-aware and grounded generation strategies (e.g., Open-RAG, RARR, TRACE) ensure outputs are faithful to retrieved evidence and logically consistent.
2. RAG-Enhanced Reasoning: External or in-context retrieval is used to supply missing premises, bridge logical gaps, and ground the reasoning process.
   • External Knowledge Retrieval: Incorporates structured knowledge bases, web content, or tool outputs to support complex reasoning (e.g., Premise-Retrieval, ReaRAG, ALR²).
   • In-Context Retrieval: Leverages prior experiences, demonstrations, or training data to guide reasoning (e.g., RAP, UPRISE, MoD).
3. Synergized RAG-Reasoning: Retrieval and reasoning are interleaved in an iterative, agentic loop, enabling dynamic adaptation to evolving information needs.
   • Reasoning Workflows: Structured as chains, trees, or graphs, these workflows allow for multi-path exploration, verification, and aggregation of evidence (e.g., IRCoT, RATT, AirRAG, Think-on-Graph).
   • Agent Orchestration: Single-agent and multi-agent systems coordinate retrieval, reasoning, and tool use, often employing reinforcement learning or instruction tuning for adaptive behavior (e.g., ReAct, Toolformer, R1-Searcher, HM-RAG, Agentic Reasoning).

Benchmarks and Evaluation

The survey compiles a comprehensive set of benchmarks spanning single-hop and multi-hop QA, fact-checking, mathematics, code generation, web browsing, and multimodal tasks. These benchmarks are analyzed in terms of their retrieval and reasoning challenges, with particular attention to the need for multi-document synthesis, expert-level knowledge, symbolic reasoning, and agentic planning. The survey highlights the lack of standardized metrics for evaluating the full retrieval-reasoning trajectory, including intermediate query quality, logical consistency, and efficiency.

Implementation Considerations

System Design: The survey emphasizes the importance of modular architectures that allow for flexible integration of retrieval, reasoning, and tool use. Iterative, agentic frameworks require careful orchestration of retrieval calls, reasoning steps, and evidence aggregation, often under tight latency and resource constraints.

Retrieval Strategies: Advanced systems employ dynamic query planning, adaptive retrieval depth, and memory-aware caching to balance efficiency and coverage. Graph-based and knowledge-guided retrieval is particularly effective in domains with structured data.

Reasoning Workflows: Chain-based approaches are efficient but susceptible to error propagation; tree- and graph-based methods offer higher recall and robustness at the cost of increased computational overhead. The choice of workflow should be guided by task complexity, domain structure, and resource availability.

Agent Orchestration: Single-agent systems are simpler to implement but may struggle with specialization and scalability. Multi-agent systems enable modularity and robustness but introduce coordination and communication overhead. Reinforcement learning and instruction tuning are effective for aligning agent behavior with task objectives and user preferences.

Performance and Scaling: Synergized RAG-reasoning systems can incur significant latency due to iterative retrieval and reasoning. Techniques such as thought distillation, length-penalty, model compression, and budget-aware planning are necessary for practical deployment. The survey notes that executing a single deep research query can take over 10 minutes in some settings, underscoring the need for efficiency optimizations.

Open Challenges and Future Directions

The survey identifies several key challenges and research opportunities:

• Reasoning Efficiency: Reducing latency and computational cost through latent reasoning, strategic control of reasoning depth, and model compression.
• Human-Agent Collaboration: Developing interactive interfaces and adaptive agents that can incorporate user feedback and intent, especially in open-ended or high-stakes domains.
• Agentic Capabilities: Advancing frameworks for dynamic tool selection, retrieval planning, and adaptive orchestration across reasoning workflows.
• Multimodal Retrieval and Reasoning: Extending current systems to handle images, tables, and heterogeneous documents, with unified multimodal retrievers and cross-modal reasoning.
• Trustworthiness and Robustness: Ensuring the reliability of retrieved content through watermarking, uncertainty quantification, and robust generation, especially in adversarial or noisy environments.

Implications and Outlook

The integration of retrieval and reasoning in LLMs, particularly through agentic and synergized frameworks, represents a significant advance in the pursuit of robust, trustworthy, and adaptive AI systems. These developments have immediate practical implications for open-domain QA, scientific discovery, legal and medical reasoning, and interactive programming. The survey's taxonomy and benchmark analysis provide a foundation for systematic evaluation and comparison of RAG-reasoning systems, while its discussion of open challenges points to critical areas for future research.

The move toward agentic, multimodal, and human-centric RAG-reasoning systems is likely to drive further progress in both the theoretical understanding and practical deployment of LLMs in complex, real-world environments. The survey's comprehensive synthesis and practical focus make it a valuable resource for researchers and practitioners seeking to design, implement, and evaluate advanced RAG-reasoning architectures.