标题： 通过多路径协作反应和反思代理增强大语言模型推理能力 显示英文标题

标题： Enhancing LLM Reasoning with Multi-Path Collaborative Reactive and Reflection agents

摘要： 代理通过大型语言模型在科学推理任务中展示了它们的潜力。 然而，它们在处理复杂推理任务时常常面临准确性不足和思维退化等挑战，这阻碍了它们的表现。 为了克服这些问题，我们提出了具有多路径推理的反应与反思代理（RR-MP）框架，旨在增强大型语言模型的推理能力。 我们的方法通过采用多路径推理机制来提高科学推理的准确性，其中每个路径由一个反应代理和一个反思代理组成，它们协作以防止单一代理依赖所固有的思维退化。 此外，RR-MP框架不需要额外训练；它为每个推理路径使用多个对话实例，并使用一个独立的总结器来整合所有路径的见解。 这种设计整合了多样化的视角，并增强了每条路径的推理能力。 我们在涉及道德情景、大学水平物理和数学的任务上进行了零样本和少样本评估。 实验结果表明，我们的方法优于基线方法，突显了RR-MP框架在管理复杂科学推理任务中的有效性和优势。 显示英文摘要

摘要： Agents have demonstrated their potential in scientific reasoning tasks through large language models. However, they often face challenges such as insufficient accuracy and degeneration of thought when handling complex reasoning tasks, which impede their performance. To overcome these issues, we propose the Reactive and Reflection agents with Multi-Path Reasoning (RR-MP) Framework, aimed at enhancing the reasoning capabilities of LLMs. Our approach improves scientific reasoning accuracy by employing a multi-path reasoning mechanism where each path consists of a reactive agent and a reflection agent that collaborate to prevent degeneration of thought inherent in single-agent reliance. Additionally, the RR-MP framework does not require additional training; it utilizes multiple dialogue instances for each reasoning path and a separate summarizer to consolidate insights from all paths. This design integrates diverse perspectives and strengthens reasoning across each path. We conducted zero-shot and few-shot evaluations on tasks involving moral scenarios, college-level physics, and mathematics. Experimental results demonstrate that our method outperforms baseline approaches, highlighting the effectiveness and advantages of the RR-MP framework in managing complex scientific reasoning tasks.