Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges (2506.10408v1)

Abstract: Retrieval-Augmented Generation (RAG) has emerged as a powerful framework to overcome the knowledge limitations of 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.

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

The paper "Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges" explores the evolution and methodologies of Retrieval-Augmented Generation (RAG) systems as they adapt to complex real-world tasks. With LLMs reporting limitations in retrieving accurate information in dynamic environments, RAG serves as a promising remedy by integrating external retrieval processes into the language generation frameworks. This survey systematically explores the landscape of reasoning agentic RAG methods, categorizing them into predefined and agentic reasoning systems, highlighting architectural designs, reasoning strategies, and tool coordination.

Traditional RAG systems, noted for their utility in structured environments, demonstrate deficiencies in nuanced and complex data scenarios. The focus of the research shifts towards Reasoning Agentic RAG, which embeds decision-making abilities and adaptive tool utilization into retrieval processes. This paradigm incorporates two main variants: predefined reasoning and agentic reasoning. Predefined reasoning relies on static, modular pipelines, optimizing task execution through fixed strategies. In contrast, agentic reasoning empowers models to autonomously orchestrate tool interaction, dynamically adjusting their retrieval strategies during inference.

The paper provides a comprehensive review of methodologies falling under both paradigms. Predefined reasoning systems—characterized by route-based, loop-based, tree-based, and hybrid approaches—emphasize structured interactions with external data resources to bolster information retrieval and synthesis. For instance, Self-RAG envisions iterative refinement through a feedback loop, showcasing the potential in enhancing accuracy and coherence in generated outputs. On the other hand, agentic reasoning strategies, including prompt-based and training-based methods, emphasize adaptive learning and strategic tool interaction. Techniques such as ReAct and Search-o1 showcase dynamic reasoning capabilities, allowing LLMs to interleave reasoning with real-time information retrieval.

Essentially, the paper discusses the implications of embedding reasoning within RAG systems, marking a shift towards more autonomous, context-aware, and dynamic information processing. Theoretical insights into the cognitive alignment of these methodologies suggest parallels with human cognitive processes, particularly System 1 and System 2 thinking. While predefined reasoning reflects fast, heuristic approaches akin to System 1, agentic reasoning embodies more deliberative, conscious problem-solving akin to System 2.

In terms of future developments, the paper speculates on several paths to further enhance the utility of reasoning agentic RAG systems. These include refining the sophistication of reward mechanisms in training frameworks, improving tool interaction interfaces, automating data synthesis processes, and enhancing robustness to perform effectively across diverse, dynamic environments.

By focusing on industry challenges, the paper positions reasoning agentic RAG systems as vital players in broadening the applicability and effectiveness of LLMs in handling complex, knowledge-intensive tasks. The work encourages continued exploration in embedding adaptive reasoning into RAG processes, ultimately fostering advancements in AI that closely mimic human-like cognitive capacities, proving crucial for elevating real-world decision-making systems.