AI开国元勋：多智能体管道中GIS搜索的案例研究
AI Founding Fathers: A Case Study of GIS Search in Multi-Agent Pipelines
AI总结：
本文提出通过构建多智能体管道以渐进、增量和顺序（GIS）方式遍历搜索空间来增强LLM推理的框架，经实验验证含递归细化层的复杂管道在推理性能上显著优于简单管道。
摘要：
尽管大型语言模型（LLMs）表现出卓越的流畅性，但人们仍在持续努力从中提取更强的推理能力。本文借鉴基于搜索的LLM计算解释，提出一个理解LLM推理和优化的系统框架，即增强推理的最佳方式是构建多智能体管道，确保以渐进、增量和顺序（GIS）的方式遍历搜索空间。简言之，高质量推理是受控的增量搜索。为测试该框架，我们研究了递归细化（RR）的有效性——这是一个包含自我批评、对抗性压力测试和整合关键反馈的迭代过程，作为实现GIS搜索的实用方法。我们设计了一项实验，将简单线性管道与利用递归细化层的复杂显式结构管道进行比较。多智能体模型利用RAG驱动的语料库构建，以反映三位美国开国元勋（汉密尔顿、杰斐逊和麦迪逊）的历史角色，并被提示对三个当代政治问题生成回应。模型性能通过两层方法评估：LLM仲裁智能体的定量评分和定性人类判断。结果显示，复杂模型在所有九个测试案例中持续优于简单模型，仲裁智能体输出的平均分为88.3分对71.7分。复杂模型的论点在分析深度、结构细微差别和战略框架方面更优。我们得出结论，递归细化是通过GIS搜索增强LLM推理的稳健架构特征。
英文摘要：
Although Large Language Models (LLMs) show exceptional fluency, efforts persist to extract stronger reasoning capabilities from them. Drawing on search-based interpretations of LLM computation, this paper advances a systematic framework for understanding LLM reasoning and optimization. Namely, that enhancing reasoning is best achieved by structuring a multi-agent pipeline to ensure a traversal of the search space in a gradual, incremental, and sequential (GIS) manner. Stated succinctly, high-quality reasoning is a controlled, incremental search. To test this framework, we investigate the efficacy of recursive refinement (RR)--an iterative process of self-criticism, adversarial stress-testing, and integrating critical feedback--as a practical method for implementing GIS search. We designed an experiment comparing a simple, linear pipeline against a complex, explicitly structured pipeline leveraging a recursive refinement layer. The multi-agent models were constructed to reflect the historical personas of three US Founding Fathers (Hamilton, Jefferson, and Madison) using RAG-powered corpora and were prompted to generate responses to three contemporary political issues. Model performance was evaluated using a two-tiered approach: a quantitative score from an LLM arbiter agent and qualitative human judgment. Our results revealed that the complex model consistently outperformed the simple model across all nine test cases with an average arbiter-outputted score of 88.3 versus 71.7. The complex model's arguments were superior in analytical depth, structural nuance, and strategic framing. We conclude that recursive refinement is a robust architectural feature for enhancing LLM reasoning via GIS search.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
