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

T0 review · 2 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This survey organizes agentic RAG systems into two reasoning paradigms—predefined, fixed pipelines and autonomous, tool-orchestrating agents—mapped to System 1 and System 2 cognition.

desk verdict A useful survey of recent agentic RAG work, but its central predefined/agentic taxonomy is internally inconsistent and needs a clearer criterion before it can be trusted. read the letter →

arxiv 2506.10408 v1 pith:NYDZ7KQQ submitted 2025-06-12 cs.AI cs.IR

Jintao Liang , Gang Su , Huifeng Lin , You Wu , Rui Zhao , Ziyue Li This is my paper · ORCID
classification cs.AIcs.IR
keywords retrieval-augmentedgenerationagenticRAGreasoningSystem1/2reinforcementlearningtooluseLLMagentssurvey
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This survey argues that the current landscape of reasoning agentic retrieval-augmented generation (RAG) is organized by two design families. Predefined reasoning uses fixed modular pipelines—routing, looping, tree-structured exploration, and hybrid modules—to control when and how retrieval happens. Agentic reasoning lets the language model itself decide when to retrieve and which tools to call, either through prompting or through reinforcement-learning training. The paper maps these families onto dual-process cognition: predefined reasoning as System 1 (fast, rule-based) and agentic reasoning as System 2 (slow, deliberative). A sympathetic reader comes away with a structured map of the field and a clear statement of the open problems—reward design, tool control, retrieval efficiency, and generalization—that will decide whether these systems work in industrial settings.

What carries the argument

The organizing device is a two-branch taxonomy of Reasoning Agentic RAG, carried by the System 1/System 2 analogy from cognitive science. Predefined reasoning is defined as structured, rule-based execution with fixed modular pipelines, subdivided into route-, loop-, tree-, and hybrid-modular designs; agentic reasoning is defined as autonomous, model-driven decision-making about tool interaction, subdivided into prompt-based and training-based implementations. The taxonomy does the analytical work: it groups representative systems, aligns each group with a cognitive mode, and frames the central efficiency-adaptability trade-off.

What would settle it

A reader could test the taxonomy's completeness by coding recent agentic RAG papers on two independent rubrics: the paper's binary (predefined vs. agentic) and a set of orthogonal features (routing, looping, retraining, tool interface). If a substantial fraction of systems mix both branches—for example, an RL-trained retriever wrapped inside a fixed route-based workflow—or if the binary explains little variance in efficiency and adaptability across an evaluation benchmark, the central claim loses force.

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Extended reading notes

Core claim

The paper's central claim is that Reasoning Agentic RAG methods are not a random assortment of tricks but fall into two coherent families distinguished by who controls the retrieval process. In predefined reasoning, control sits in a fixed workflow: route-based methods (RAGate, Self-Route) turn retrieval on conditionally; loop-based methods (Self-RAG, CRAG) iterate retrieval-feedback cycles; tree-based methods (RAPTOR, MCTS-RAG) organize hierarchical search; hybrid-modular systems (Adaptive-RAG, Modular-RAG) compose specialized modules under an orchestrator. In agentic reasoning, control sits in the model: prompt-based approaches (ReAct, Self-Ask, function calling, Search-o1) let instruction-following capabilities drive tool use, while training-based approaches (Search-R1, R1-Searcher, ReZero, DeepRetrieval, DeepResearcher) use reinforcement learning to teach the model when and how to search. The claimed payoff is that this two-branch taxonomy gives practitioners a principled way to trade efficiency against adaptability, and gives researchers a shared vocabulary for comparing systems.

Load-bearing premise

The survey's organizing claim depends on the assumption that the binary split between predefined and agentic reasoning is a complete and meaningful partition of existing Reasoning Agentic RAG designs, and that the System 1/System 2 analogy is a valid analytical lens for them.

Editorial extensions

If this is right

  • For well-structured queries with available evidence, predefined reasoning gives predictable, efficient answers; for open-ended, multi-step tasks, agentic reasoning becomes necessary.
  • Prompt-based agentic methods provide lightweight adaptability on frozen LLMs, while RL-trained methods trade training cost for more reliable search policies.
  • The efficiency-adaptability trade-off is the main axis for system choice: predefined systems are cheaper but rigid, agentic systems are flexible but require careful reward design and risk unproductive searches.
  • Progress in the field will come from finer-grained reward functions, more advanced tool configuration, retrieval-efficiency constraints, and training in realistic environments rather than static corpora.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The System 1/System 2 mapping is an analogy, not a proven architectural law; a learned router inside a predefined pipeline or a routinized agentic policy would blur the boundary in a way the taxonomy does not address.
  • An independent benchmark could test whether the predefined/agentic split predicts performance on multi-hop, multi-source tasks better than orthogonal dimensions such as degree of tool access, ability to retrain, or number of reasoning steps.
  • The field may converge on hybrid controllers—predefined at the top, agentic at the leaves—so the two categories may be endpoints of a spectrum rather than a true dichotomy.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 5 minor

Summary. This manuscript surveys Reasoning Agentic Retrieval-Augmented Generation (RAG), proposing a two-branch taxonomy: predefined reasoning (route-based, loop-based, tree-based, and hybrid-modular pipelines) versus agentic reasoning (prompt-based and training-based methods). The taxonomy is mapped onto the cognitive-science distinction between System 1 and System 2. The survey reviews representative methods in each branch, provides a summary table with code links, and concludes with future directions on reward design, tool control, retrieval efficiency, and robustness.

Significance. The survey addresses a timely topic with strong industrial relevance. Its strengths include a curated GitHub repository, a compact table of methods with code availability, and a detailed treatment of recent RL-based training approaches such as Search-R1, R1-Searcher, ReZero, DeepRetrieval, and DeepResearcher. If the taxonomy were consistently applied, the paper would be a useful organizing reference for practitioners. However, the central classification claim is currently not sound as stated: the paper's own definitions and Table 2 put the same methods into both predefined and agentic categories, and the System 1/System 2 framing is asserted rather than argued. The main contribution therefore needs substantial clarification before the survey can be relied upon.

major comments (2)
  1. [§3, §4 (especially Table 2)] The central predefined/agentic distinction is not mutually exclusive under the paper's own definitions. Section 4 defines agentic reasoning as the LLM acting as an autonomous agent that "identifies knowledge gaps, determines when and what to retrieve, and interacts with external tools". Section 3 describes Self-RAG as a loop-based method in which "the model can decide mid-generation to fetch additional information or to critique its own draft answer", which satisfies the agentic definition, and Table 2 lists Self-RAG's control type as "Agentic" while placing it in the Predefined Reasoning table. The same issue affects MCTS-RAG (tree-based predefined reasoning but control type "Agentic"), and the hybrid-modular category is described as containing "an agent that coordinates modular components". This overlap means the two categories do not form a sound partition, undermining the paper's central claim. A fix would be to choose a single unambiguous criterion, such as fixed control graph versus free-form model-generated action sequence, or trained versus prompted retrieval decisions, and then reclassify all methods consistently.
  2. [§1, Table 1] The System 1/System 2 alignment is asserted without operational grounding. The manuscript does not define what makes a workflow "fast" or "deliberative" in a way that applies to the surveyed systems; for example, a route-based system that invokes a confidence estimator before retrieval is not obviously System 1, and a training-based agent using a learned policy is not obviously System 2. Because Table 1 merely restates the problematic predefined/agentic split, the cognitive-science framing inherits the same weakness. I recommend presenting this as a heuristic analogy and supporting it with explicit behavioral or architectural criteria, rather than as a categorical alignment.
minor comments (5)
  1. [§3 (opening paragraph)] The sentence "This synergy supports a predefined reasoning, where the agent iteratively queries external sources (e.g., a local database or web search) and refines its reasoning based on the retrieved evidence" contradicts the intended meaning of predefined reasoning, since iterative querying and refinement are exactly what the paper later defines as agentic; this should be reworded.
  2. [Table 2 and §4] There are several typographical errors: "Funciton calling" (Table 2) should be "Function calling", "apporaches" (§4 introduction) should be "approaches", and "predefine reasoning" (§3 final paragraph) should be "predefined reasoning". The Search-o1 paragraph also contains a line-broken "dist ill" that should be "distill".
  3. [Figure 4] The legend uses "Router-based" while the text and Figure 3 use "Route-based"; the terminology should be made consistent.
  4. [References] The bibliography entry [Zhang et al., ] has no year; it should be completed (the citation appears to refer to an ICLR 2024 workshop paper).
  5. [Abstract and §1] The paper calls itself a comprehensive review, but the coverage is necessarily selective; I suggest explicitly stating the selection criteria or qualifying the scope as representative rather than comprehensive.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the survey's predefined/agentic taxonomy is an organizational framing, not a result derived from fitted inputs or a self-citation chain.

full rationale

This paper does not fit parameters, derive predictions, or invoke a uniqueness theorem; it organizes existing RAG methods into two author-chosen categories and explicitly frames the cognitive-science alignment as an analogy: 'we can draw an analogy between these RAG paradigms and human cognitive modes.' The central claim that Reasoning Agentic RAG divides into predefined and agentic reasoning is a literature-organizing statement, not a result forced by definition from its own inputs. I also checked Table 2's control-type labels, which mark Self-RAG, MCTS-RAG, and R2R as 'Agentic' inside the Predefined Reasoning table; this is a classification-consistency issue, not a circular reduction, because no empirical outcome is being predicted from a fitted value. No load-bearing self-citation appears: the cited works are external papers and method descriptions, and the paper's own System 1/System 2 framing is presented as an interpretive lens rather than as evidence that validates the classification. Therefore the appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

This survey introduces no free parameters or invented entities. The taxonomy depends on the background assumptions listed above, two of which are the paper's own framing choices.

assumptions (3)
  • domain assumption LLMs have static knowledge and hallucinate, so external retrieval is beneficial.
    Stated in the Introduction as the motivation for RAG; it is a widely accepted premise in the field.
  • ad hoc to paper Agentic RAG systems can be meaningfully divided into predefined and agentic reasoning based on control structure.
    This is the paper's own taxonomy, introduced in Section 1 and Figure 3. It is an organizing choice, not an empirically derived fact.
  • ad hoc to paper Dual-process theory (System 1 and System 2) transfers to AI system design.
    The mapping is an analogy presented in Section 1; it is not derived from cognitive science evidence and may be questioned as a guiding principle.

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Cite this review

Pith. "Pith review of Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges." pith.science (2026). https://pith.science/paper/NYDZ7KQQ

@misc{pith2026250610408,
  author       = {Pith},
  title        = {Pith review of: Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NYDZ7KQQ}},
  note         = {Machine review of arXiv:2506.10408}
}
read the original 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.

Figures

Figures reproduced from arXiv: 2506.10408 by the authors.

Figure 1
Figure 1. Overview of two major types of reasoning Agentic Systems. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Distributed Works of Reasoning Agentic RAG. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. A taxonomy of Reasoning Agentic RAG. 2022 2023 2024 2025 Agentic Reasoning Predefined Reasoning Self￾RAG RAPTOR CRAG RAGate Self￾Route MCTS￾RAG Adaptive￾RAG Modular￾RAG Self￾ASK ReAct Search-O1 DeepRetrieval ReZero Function Calling R1- Searcher Search-R1 Loop-based Router-based Tree-based Hybrid-modular Prompt-based Training-based [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Illustration of the evolution of Reasoning Agentic RAG. [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: A demonstration of Predefined Reasoning. [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: A demonstration of Agentic Reasoning. RL approach for enhancing search capabilities. ReZero (Retry-Zero) [Dao and Le, 2025] introduces another dimension to RL-based agentic search by specifically focusing on incentivizing persistence. It addresses the common sce￾nario …

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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    A turn-level faithfulness reward improves a Search-R1-style agent's Information-Think and Think-Answer faithfulness as judged by the same reward model used for training, while task accuracy is roughly unchanged.

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