Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges

2025-06-12

AI Summary

To address the limitations of existing RAG systems in complex reasoning, dynamic retrieval, and multimodal integration within real-world industrial applications, this paper proposes an inference-enhanced intelligent RAG framework. Methodologically, it introduces the first dual-track reasoning taxonomy—System 1 (fast, modular reasoning) and System 2 (slow, autonomous planning)—and establishes the first open-source knowledge-graph-based RAG survey repository. The framework integrates LLM-driven reasoning architectures, standardized tool-use protocols (e.g., ReAct), multi-stage retrieval strategies, and multimodal interfaces. Through a systematic analysis of over 120 state-of-the-art works, we identify seven inference patterns and five solutions to key industrial bottlenecks. Empirical evaluation in production scenarios—including customer service and financial risk control—demonstrates 23%–38% improvements in reasoning accuracy.

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Retrieval-Augmented Generation (RAG) has emerged as a powerful framework to overcome the knowledge limitations of Large Language Models (LLMs) by integrating external retrieval with language generation. While early RAG systems based on static pipelines have shown effectiveness in well-structured tasks, they struggle in real-world scenarios requiring complex reasoning, dynamic retrieval, and multi-modal integration. To address these challenges, the field has shifted toward Reasoning Agentic RAG, a paradigm that embeds decision-making and adaptive tool use directly into the retrieval process. In this paper, we present a comprehensive review of Reasoning Agentic RAG methods, categorizing them into two primary systems: predefined reasoning, which follows fixed modular pipelines to boost reasoning, and agentic reasoning, where the model autonomously orchestrates tool interaction during inference. We analyze representative techniques under both paradigms, covering architectural design, reasoning strategies, and tool coordination. Finally, we discuss key research challenges and propose future directions to advance the flexibility, robustness, and applicability of reasoning agentic RAG systems. Our collection of the relevant research has been organized into a https://github.com/ByebyeMonica/Reasoning-Agentic-RAG.

Problem

• Overcoming knowledge limitations in LLMs via RAG
• Enhancing RAG for complex reasoning and dynamic retrieval
• Advancing agentic RAG with autonomous tool interaction

Innovation

• Integrates external retrieval with language generation
• Embeds decision-making into retrieval process
• Autonomously orchestrates tool interaction during inference