Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Tools (arXiv 2502.04644)

Forum topic · 小凯 · 2026-07-05

Summary

Agentic Reasoning is a February 2025 arXiv paper (arXiv:2502.04644) by Junde Wu, Jiayuan Zhu, Yuyuan Liu, Min Xu, and Yueming Jin that proposes a framework for improving large language model (LLM) reasoning, particularly on complex problems such as expert-level questions and mathematical computation. Instead of relying on a single model to solve tasks end-to-end, the framework casts the LLM as an agent that dynamically invokes external tools during its reasoning process. According to the paper's abstract, the agent leverages a mind-map tool to structure and decompose the problem, a web-search tool to retrieve knowledge mid-reasoning when gaps are encountered, and a coding tool to perform precise calculations. The authors report that this agentic approach outperforms existing reasoning techniques and deep-research systems, including OpenAI's Deep Research and Deep Research (DeepSeek R1), on benchmarks containing complex questions requiring both scientific expertise and precise computation. This forum post is a structured Chinese-language reading note summarizing the paper's metadata, motivation, method architecture, and evaluation design, with cross-references to related surveys on deep research and LLM-based search agents.

Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Tools

Source: arXiv:2502.04644, February 2025 Authors: Junde Wu, Jiayuan Zhu, Yuyuan Liu, Min Xu, Yueming Jin

Overview

This paper introduces Agentic Reasoning, a framework designed to enhance the reasoning capability of large language models (LLMs) by integrating agentic tool use directly into the reasoning process, rather than treating reasoning as a purely internal chain-of-thought exercise.

Key Ideas

The LLM is cast as an agent that can invoke external tools while it reasons, addressing cases where the model alone lacks knowledge or computational precision.
The framework employs:
A mind-map tool to structure and decompose the problem into an explicit reasoning graph;
A web-search tool to retrieve knowledge from external sources mid-process whenever the agent identifies a gap in its own understanding;
A coding tool to handle calculations and precise numerical computation.
The target scenarios are complex tasks that require both domain expertise and exact computation, such as scientific expert questions and mathematical problems.

Reported Results

The authors state that Agentic Reasoning outperforms existing reasoning approaches and deep-research systems, including OpenAI's Deep Research and Deep Research based on DeepSeek R1, on benchmarks of complex questions requiring expertise and precise calculation.

Notes from the Forum Post

The source post is a structured reading note from a Chinese tech forum's "Deep Research" collection. Beyond the paper summary, it adds editorial context:

The paper sits at the intersection of agentic search and large-scale search/recommendation systems, addressing how to redistribute responsibility among retrieval, ranking, generation, and tool calling in the LLM era.
The editors suggest viewing retrieval-augmented pipelines as a funnel (recall → re-rank → generate), where the new LLM-era variables are the reasoning budget and action space (whether to retrieve, how many times, and which tools to call).
Practical engineering considerations highlighted include latency budgets (cascaded retrieval with early stopping, caching, asynchronous re-ranking), data safety (PII handling, versioned embeddings), evaluation gaps between offline metrics and online satisfaction, and per-query token/GPU cost.
Recommended related reading includes surveys on deep research systems, LLM-based deep search agents, and scientific LLM agents.

> Quantitative results should be verified against the original PDF tables; the forum note is based on the abstract and public metadata.