Exploring Decentralized User Profiles with Large Language Models: A Case Study on Uniswap V4

Abstract

To our knowledge, this paper is the first to introduce an iterative semantic-clustering framework driven by Large Language Models (LLMs) for refined user profiling on Uniswap V4. Traditional numerical techniques such as K-Means falter in high-dimensional feature spaces and identify only three coarse user groups. In contrast, our LLM-based pipeline repeatedly refines clustering criteria through natural-language reasoning, producing 21 semantically interpretable sub-clusters. These fine-grained clusters can be hierarchically aggregated into three macro categories that align with the K-Means result, thereby preserving global consistency while revealing nuanced behavioral motivations and patterns. The proposed framework offers both a theoretical perspective and practical toolkit for decentralized-finance user analysis, opening new avenues for understanding on-chain behavior.