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今天的主题是：Search-o1: Agentic Search-Enhanced Large Reasoning Models

Summary

The paper introduces Search-o1, a framework enhancing large reasoning models (LRMs) by integrating an agentic search workflow. This allows the LRM to dynamically retrieve external knowledge when encountering uncertainties during complex reasoning tasks. A key component is the Reason-in-Documents module, which refines retrieved information to maintain coherent reasoning. Experiments across various domains demonstrate Search-o1's superior performance compared to existing methods, even rivaling human experts in certain areas. The framework addresses knowledge insufficiency, a major limitation of current LRMs, improving their reliability and versatility. The code is publicly available.

这篇论文介绍了Search-o1，一个通过集成代理式搜索工作流来增强大型推理模型（LRMs）的框架。该框架使LRM在遇到复杂推理任务中的不确定性时，能够动态地检索外部知识。一个关键组件是“文档中的推理”模块（Reason-in-Documents），该模块通过精炼检索到的信息，保持推理的一致性。跨多个领域的实验表明，Search-o1在性能上优于现有方法，甚至在某些领域能够与人类专家相媲美。该框架解决了当前LRM的知识不足问题，提升了模型的可靠性和多功能性。代码已公开。

原文链接：https://arxiv.org/abs/2501.05366