Pyramid of Thought: Enhancing the Chain of Thought with the Fibonacci Sequence and the Inner Thought Journal

Abstract

Recent advances in large language models (LLMs) have demonstrated their remarkable abilities in complex language tasks. However, achieving the depth and flexibility of human reasoning remains a challenge. Inspired by the power of step-by-step reasoning highlighted in "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models," this paper introduces the "Pyramid of Thought" framework. This framework leverages principles of Euclidean geometry, the Fibonacci sequence, and psychological engagement to create structured, adaptable AI responses while promoting transparent and intuitive communication.

The "Pyramid of Thought" uses a dynamic layer mechanism, adjustable via linear, logarithmic, or Fibonacci-based scaling. This aligns the AI's thought process with natural mathematical patterns. Mirroring psychological principles, this approach aims to promote user engagement. Each layer builds on the previous one, starting with foundational facts ("Five Ws") and progressing through reasoning steps ("How," "Then"), culminating in an "Apex" of analysis or insight. An inner thought journal maintains contextual richness.

The flexible and iterative nature of the "Pyramid of Thought" reflects the emphasis on refinement and evolution found in conceptual modeling. Experiments will evaluate its effectiveness against traditional AI response methods. Metrics will include user engagement, response coherence, satisfaction, and the model's ability to communicate its reasoning process transparently. We anticipate that the "Pyramid of Thought" will produce more intuitive, meaningful, and thought-provoking interactions, aligning with the goals of conceptual modeling in simulation. This work contributes to the development of AI systems that better emulate human reasoning patterns and foster clearer communication.