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.

翻译：近年来，基于大语言模型的自主系统（LLMAS）因其模拟人类社会复杂行为的潜力而备受关注。其主要挑战之一在于呈现和分析LLMAS中动态事件的演化过程。本文提出一种可视化方法，用于探索LLMAS内部的详细状态与智能体行为。我们构建了一条通用处理流程：从原始LLMAS执行事件中提取行为结构，利用行为摘要算法生成基于时间序列的整个结构的分层摘要，并采用因果追踪方法挖掘智能体行为之间的因果关系。进而开发了AgentLens——一个通过分层时间可视化呈现LLMAS演化的视觉分析系统，支持用户交互式探究智能体行为的细节与成因。通过两个使用场景与一项用户研究，验证了AgentLens的有效性与实用性。