Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Tools
Junde Wu, Jiayuan Zhu, Yuyuan Liu, Min Xu, Yueming Jin

TL;DR
This paper presents Agentic Reasoning, a framework that improves LLM reasoning by integrating external tools like web search and structured memory, achieving state-of-the-art results on complex tasks.
Contribution
The paper introduces the Mind-Map agent and an advanced Web-Search agent, enhancing reasoning coherence and search effectiveness in LLMs, surpassing previous approaches.
Findings
Achieved SOTA performance on DeepSeek-R1
Validated effectiveness of Mind-Map in reasoning tasks
Demonstrated superior web search capabilities
Abstract
We introduce Agentic Reasoning, a framework that enhances large language model (LLM) reasoning by integrating external tool-using agents. Agentic Reasoning dynamically leverages web search, code execution, and structured memory to address complex problems requiring deep research. A key innovation in our framework is the Mind-Map agent, which constructs a structured knowledge graph to store reasoning context and track logical relationships, ensuring coherence in long reasoning chains with extensive tool usage. Additionally, we conduct a comprehensive exploration of the Web-Search agent, leading to a highly effective search mechanism that surpasses all prior approaches. When deployed on DeepSeek-R1, our method achieves a new state-of-the-art (SOTA) among public models and delivers performance comparable to OpenAI Deep Research, the leading proprietary model in this domain. Extensive…
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Taxonomy
TopicsArtificial Intelligence in Law · Law, AI, and Intellectual Property
