Agentic Reasoning for Large Language Models

Cheng Qian; Chi Wang; Dongqi Fu; Duo Zhou; Gaotang Li; Hanghang Tong; Heng Ji; Hui Liu; Jiaru Zou; Jiaxuan You

Agentic Reasoning for Large Language Models

T0 review · 0 major / 2 minor · reviewed 2026-05-17 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Agentic reasoning turns large language models into autonomous agents that plan, act, and adapt through interaction.

desk verdict This survey organizes agentic reasoning into three layers and splits in-context from post-training methods, giving a usable map of the literature but no new techniques or results. read the letter →

arxiv 2601.12538 v1 pith:UJUDHYFT submitted 2026-01-18 cs.AI cs.CL

Tianxin Wei , Ting-Wei Li , Zhining Liu , Xuying Ning , Ze Yang , Jiaru Zou , Zhichen Zeng , Ruizhong Qiu

show 21 more authors

Xiao Lin Dongqi Fu Zihao Li Mengting Ai Duo Zhou Wenxuan Bao Yunzhe Li Gaotang Li Cheng Qian Yu Wang Xiangru Tang Yin Xiao Liri Fang Hui Liu Xianfeng Tang Yuji Zhang Chi Wang Jiaxuan You Heng Ji Hanghang Tong Jingrui He

This is my paper · ORCID

classification cs.AIcs.CL

keywords agenticreasoninglargelanguagemodelsautonomousagentsplanningandtooluseself-evolvingmulti-agentsystemsin-contextreinforcementlearning

checked against Foundation.LogicAsFunctionalEquation Foundation.HierarchyEmergence

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 organizes methods that let large language models function as agents in open and changing environments instead of closed problems. It divides the approaches into three layers: foundational capabilities for planning and tool use in stable settings, self-evolving processes where agents improve through feedback and memory, and collective systems where multiple agents coordinate and share knowledge. The work also separates in-context orchestration used at test time from optimization through training, and it reviews applications across science, robotics, and healthcare. It ends by identifying open problems such as personalization and long-horizon interaction needed for practical use.

What carries the argument

The three complementary dimensions—foundational agentic reasoning for core single-agent capabilities, self-evolving agentic reasoning for refinement through feedback and adaptation, and collective multi-agent reasoning for coordination and shared goals—organize the field and bridge thought with action.

What would settle it

Discovery of a major agentic reasoning method or framework that requires a fourth distinct category or shows substantial overlap across the proposed dimensions would challenge the survey's organizational structure.

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Extended reading notes

Core claim

Agentic reasoning reframes large language models as autonomous agents that plan, act, and learn through continual interaction with their environments. The survey organizes this capability along three complementary dimensions: foundational agentic reasoning that establishes core single-agent skills including planning, tool use, and search in stable environments; self-evolving agentic reasoning that studies refinement through feedback, memory, and adaptation; and collective multi-agent reasoning that extends intelligence to collaborative coordination, knowledge sharing, and shared goals. These layers are further split into in-context reasoning that scales test-time interaction through structed

Load-bearing premise

The three dimensions of foundational, self-evolving, and collective agentic reasoning cover the entire field comprehensively without significant overlap or omission.

Editorial extensions

If this is right

Foundational methods support reliable planning and tool use by single agents in stable environments.
Self-evolving techniques enable agents to improve their own performance using memory and feedback over repeated interactions.
Collective reasoning allows multiple agents to coordinate actions and share knowledge toward common objectives.
Applications in robotics, healthcare, and autonomous research follow directly from applying the organized roadmap.
Open challenges in long-horizon interaction and scalable multi-agent training must be resolved for broader deployment.

Reading between the lines

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

The three-dimension roadmap could guide researchers in systematically identifying gaps for personalization of agent behaviors.
Integrating explicit world modeling may emerge naturally as an extension of the foundational and self-evolving layers.
Governance requirements for real-world agents might be derived from the coordination mechanisms in the collective dimension.
Testable extensions could involve applying the in-context versus post-training split to new benchmarks in mathematics or science.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

This survey organizes agentic reasoning for LLMs into three layers: foundational single-agent skills like planning and tool use, self-evolving adaptation through feedback and memory, and collective multi-agent coordination. It also separates in-context orchestration at test time from post-training optimization via RL or fine-tuning. The abstract and structure show an attempt to connect these pieces into a single roadmap that covers applications in robotics, science, healthcare, and math while listing practical open problems such as personalization, long-horizon tasks, world modeling, and governance. That synthesis is the main contribution and can help readers who are new to the area get oriented without reading dozens of separate papers. The review of representative frameworks and benchmarks is straightforward and covers the expected ground. The softer spots are in the taxonomy itself. The three dimensions are presented as complementary, yet real systems frequently combine self-evolution with multi-agent coordination, so the boundaries are not shown to be clean or exhaustive. As a survey the paper contains no new derivations, experiments, or falsifiable claims, which means its value depends on how accurately and completely it cites and groups the prior work. Readers looking for a high-level overview of LLM agents will find it useful. Those seeking original methods or fresh data will not. I would bring this to a reading group to discuss whether the layers hold up in practice. I would not cite it in my own work unless pointing to the listed challenges. It deserves peer review because the field moves quickly and a balanced synthesis can still be worth referee time to tighten the categories and check coverage.

Referee Report

0 major / 2 minor

Summary. This survey organizes agentic reasoning for LLMs along three complementary dimensions: foundational agentic reasoning establishing core single-agent capabilities (planning, tool use, search) in stable environments; self-evolving agentic reasoning focusing on refinement via feedback, memory, and adaptation; and collective multi-agent reasoning addressing coordination, knowledge sharing, and shared goals. It distinguishes in-context orchestration from post-training optimization, reviews frameworks in applications like science, robotics, healthcare, autonomous research, and mathematics, and outlines open challenges such as personalization, long-horizon interaction, world modeling, scalable multi-agent training, and governance.

Significance. If the taxonomy holds, this survey makes a useful contribution by synthesizing a rapidly growing literature into a unified roadmap that connects reasoning processes with agentic action. The explicit separation of in-context scaling from post-training optimization provides a practical lens for comparing approaches, and the enumeration of concrete open challenges (personalization, world modeling, governance) supplies clear signposts for future work. The review of domain-specific frameworks adds concrete grounding to the high-level structure.

minor comments (2)

[Introduction] Introduction: the positioning of the three dimensions as complementary and comprehensive would be clearer if the manuscript briefly noted selection criteria for the taxonomy and acknowledged possible boundary overlaps (e.g., adaptive multi-agent systems) rather than treating the partition as self-evident.
[Applications and benchmarks] Applications and benchmarks section: a compact summary table mapping representative frameworks to the three dimensions, listing primary techniques and benchmark results, would make the review more scannable and allow readers to assess coverage at a glance.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive assessment of our survey and for the recommendation of minor revision. The referee's summary accurately captures the three-layer taxonomy (foundational, self-evolving, and collective), the distinction between in-context orchestration and post-training optimization, and the enumerated open challenges. We appreciate the recognition that this structure provides a useful roadmap connecting reasoning processes with agentic action.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity in this literature survey

full rationale

This paper is a literature survey synthesizing existing agentic reasoning methods for LLMs into a high-level roadmap. It organizes the field along three complementary dimensions (foundational, self-evolving, and collective) and distinguishes in-context from post-training approaches, but presents this taxonomy explicitly as an editorial organizing lens drawn from external references rather than a derived result. No mathematical derivations, equations, fitted parameters, predictions, or uniqueness theorems appear in the manuscript. All claims are positioned as reviews of prior work, with no self-citation chains or self-definitional reductions that would make the central synthesis equivalent to its inputs by construction. The paper is therefore self-contained against external benchmarks and receives a score of zero.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

This is a literature survey containing no new free parameters, invented entities, or original axioms beyond standard assumptions in AI research. All substantive claims rest on the body of cited prior work.

assumptions (1)

domain assumption LLMs can be reframed as autonomous agents capable of planning, acting, and learning through interaction in dynamic environments
Presented in the abstract as the core paradigm shift underlying the entire survey.

how reviews work

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Cite this review

Pith. "Pith review of Agentic Reasoning for Large Language Models." pith.science (2026). https://pith.science/paper/UJUDHYFT

@misc{pith2026260112538,
  author       = {Pith},
  title        = {Pith review of: Agentic Reasoning for Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UJUDHYFT}},
  note         = {Machine review of arXiv:2601.12538}
}

read the original abstract

Reasoning is a fundamental cognitive process underlying inference, problem-solving, and decision-making. While large language models (LLMs) demonstrate strong reasoning capabilities in closed-world settings, they struggle in open-ended and dynamic environments. Agentic reasoning marks a paradigm shift by reframing LLMs as autonomous agents that plan, act, and learn through continual interaction. In this survey, we organize agentic reasoning along three complementary dimensions. First, we characterize environmental dynamics through three layers: foundational agentic reasoning, which establishes core single-agent capabilities including planning, tool use, and search in stable environments; self-evolving agentic reasoning, which studies how agents refine these capabilities through feedback, memory, and adaptation; and collective multi-agent reasoning, which extends intelligence to collaborative settings involving coordination, knowledge sharing, and shared goals. Across these layers, we distinguish in-context reasoning, which scales test-time interaction through structured orchestration, from post-training reasoning, which optimizes behaviors via reinforcement learning and supervised fine-tuning. We further review representative agentic reasoning frameworks across real-world applications and benchmarks, including science, robotics, healthcare, autonomous research, and mathematics. This survey synthesizes agentic reasoning methods into a unified roadmap bridging thought and action, and outlines open challenges and future directions, including personalization, long-horizon interaction, world modeling, scalable multi-agent training, and governance for real-world deployment.

Discussion (0). Continue with ORCID to comment.

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Reviewed May 17, 2026 · model on record in the stance chip above.

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