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今天的主题是：Agentic Reasoning: Reasoning LLMs with Tools for the Deep Research

Summary

This technical report introduces Agentic Reasoning, a novel framework designed to enhance the reasoning capabilities of large language models (LLMs). Unlike traditional methods relying solely on internal knowledge, Agentic Reasoning equips LLMs with external tools accessed through specialized agents, such as a web search agent, a code execution agent, and a "Mind Map" agent for structured memory. The framework enables LLMs to tackle complex problems requiring in-depth research and multi-step logical deduction by dynamically retrieving information, performing computations, and organizing knowledge. Evaluations on challenging tasks demonstrate that Agentic Reasoning significantly outperforms existing models, highlighting the benefits of integrating external tools and agentic capabilities for advanced reasoning.

本技术报告介绍了“代理性推理”（Agentic Reasoning），这是一种旨在增强大型语言模型（LLMs）推理能力的新型框架。与传统方法仅依赖内部知识不同，代理性推理为LLMs配备了可通过专用代理访问的外部工具，例如网页搜索代理、代码执行代理以及用于结构化记忆的“思维导图”代理。该框架使LLMs能够动态获取信息、执行计算并组织知识，从而应对需要深入研究和多步逻辑推理的复杂问题。在对具有挑战性的任务进行评估后显示，代理性推理在性能上显著优于现有模型，突显了整合外部工具与代理能力在提升高级推理方面的优势。

原文链接：https://arxiv.org/abs/2502.04644