Understanding Chain-of-Thought in Large Language Models via Topological Data Analysis

TL;DR

This paper introduces a novel approach using Topological Data Analysis to analyze the structural properties of reasoning chains in large language models, revealing correlations between topology and reasoning accuracy.

Contribution

It is the first to apply persistent homology to study the structural mechanisms of reasoning chains in LLMs, linking topological features to reasoning performance.

Findings

Topological complexity correlates positively with accuracy.

Successful reasoning exhibits simpler topologies with less redundancy.

Topological features can identify logical gaps and improve reasoning efficiency.

Abstract

With the development of large language models (LLMs), particularly with the introduction of the long reasoning chain technique, the reasoning ability of LLMs in complex problem-solving has been significantly enhanced. While acknowledging the power of long reasoning chains, we cannot help but wonder: Why do different reasoning chains perform differently in reasoning? What components of the reasoning chains play a key role? Existing studies mainly focus on evaluating reasoning chains from a functional perspective, with little attention paid to their structural mechanisms. To address this gap, this work is the first to analyze and evaluate the quality of the reasoning chain from a structural perspective. We apply persistent homology from Topological Data Analysis (TDA) to map reasoning steps into semantic space, extract topological features, and analyze structural changes. These changes reveal semantic coherence, logical redundancy, and identify logical breaks and gaps. By calculating homology groups, we assess connectivity and redundancy at various scales, using barcode and persistence diagrams to quantify stability and consistency. Our results show that the topological structural complexity of reasoning chains correlates positively with accuracy. More complex chains identify correct answers sooner, while successful reasoning exhibits simpler topologies, reducing redundancy and cycles, enhancing efficiency and interpretability. This work provides a new perspective on reasoning chain quality assessment and offers guidance for future optimization.