AI Founding Fathers: A Case Study of GIS Search in Multi-Agent Pipelines
Alvin Chauhan

TL;DR
This paper proposes a structured multi-agent pipeline framework for improving LLM reasoning by guiding search in a gradual, incremental manner, demonstrated through a recursive refinement method with historical persona-based experiments.
Contribution
It introduces a systematic GIS search framework for LLM reasoning and demonstrates the effectiveness of recursive refinement in multi-agent pipelines.
Findings
Recursive refinement improves reasoning quality.
Complex structured pipelines outperform simple linear ones.
Model responses show greater depth and nuance.
Abstract
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…
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Taxonomy
TopicsComputational and Text Analysis Methods · Topic Modeling · Natural Language Processing Techniques
