Agentic Reasoning: Reasoning LLMs with Tools for the Deep Research

• URL: http://arxiv.org/abs/2502.04644v1
• Date: Fri, 07 Feb 2025 04:08:46 GMT
• Title: Agentic Reasoning: Reasoning LLMs with Tools for the Deep Research
• Authors: Junde Wu, Jiayuan Zhu, Yuyuan Liu,
• Abstract summary: We introduce Agentic Reasoning, a framework that enhances large language model (LLM) reasoning by integrating external tool-using agents.<n>Our framework introduces the Mind Map agent, which constructs a structured knowledge graph to track logical relationships.<n> Evaluations on PhD-level scientific reasoning (GPQA) and domain-specific deep research tasks demonstrate that our approach significantly outperforms existing models.
• Score: 7.4327380079414676
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce Agentic Reasoning, a framework that enhances large language model (LLM) reasoning by integrating external tool-using agents. Unlike conventional LLM-based reasoning approaches, which rely solely on internal inference, Agentic Reasoning dynamically engages web search, code execution, and structured reasoning-context memory to solve complex problems requiring deep research and multi-step logical deduction. Our framework introduces the Mind Map agent, which constructs a structured knowledge graph to track logical relationships, improving deductive reasoning. Additionally, the integration of web-search and coding agents enables real-time retrieval and computational analysis, enhancing reasoning accuracy and decision-making. Evaluations on PhD-level scientific reasoning (GPQA) and domain-specific deep research tasks demonstrate that our approach significantly outperforms existing models, including leading retrieval-augmented generation (RAG) systems and closed-source LLMs. Moreover, our results indicate that agentic reasoning improves expert-level knowledge synthesis, test-time scalability, and structured problem-solving. The code is at: https://github.com/theworldofagents/Agentic-Reasoning.

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