Recently, Large Language Model based Autonomous system(LLMAS) has gained
great popularity for its potential to simulate complicated behaviors of human
societies. One of its main challenges is to present and analyze the dynamic
events evolution of LLMAS. In this work, we present a visualization approach to
explore detailed statuses and agents' behavior within LLMAS. We propose a
general pipeline that establishes a behavior structure from raw LLMAS execution
events, leverages a behavior summarization algorithm to construct a
hierarchical summary of the entire structure in terms of time sequence, and a
cause trace method to mine the causal relationship between agent behaviors. We
then develop AgentLens, a visual analysis system that leverages a hierarchical
temporal visualization for illustrating the evolution of LLMAS, and supports
users to interactively investigate details and causes of agents' behaviors. Two
usage scenarios and a user study demonstrate the effectiveness and usability of
our AgentLens.

利用大型语言模型的自动化系统，提出了一种可视化方法，用于探索 LLMAS 中的详细状态和代理行为，在时间序列上构建了整个结构的分层摘要并挖掘了代理行为之间的因果关系，开发了 AgentLens 可视分析系统以揭示 LLMAS 的演化并支持用户交互式探索代理行为的细节和原因。