Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Tools
Wu, Junde Zhu, Jiayuan Liu, Yuyuan Xu, Min Jin, Yueming
Wu, Junde
Zhu, Jiayuan
Liu, Yuyuan
Xu, Min
Jin, Yueming
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Department
Computer Vision
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Type
Conference proceeding
Date
2025
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Language
English
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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 ablation studies validate the optimal selection of agentic tools and confirm the effectiveness of our Mind-Map and Web-Search agents in enhancing LLM reasoning. Our code and data are publicly available.
Citation
J. Wu, J. Zhu, Y. Liu, M. Xu, and Y. Jin, “Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Tools,” 2025. Accessed: Jul. 29, 2025. [Online]. Available: https://aclanthology.org/2025.acl-long.1383
Source
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics
Conference
63rd Annual Meeting of the Association for Computational Linguistics, 2025
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Subjects
Source
63rd Annual Meeting of the Association for Computational Linguistics, 2025
Publisher
Association for Computational Linguistics