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Area of Science:

Artificial Intelligence
Human-Computer Interaction
Complex Systems Simulation

Background:

Large Language Model based Autonomous Systems (LLMAS) are increasingly used to simulate complex societal behaviors.
Analyzing the dynamic evolution of events and agent interactions within LLMAS presents significant challenges.
Existing methods lack effective tools for visualizing and understanding LLMAS behavior over time.

Purpose of the Study:

To introduce a novel visualization approach for exploring LLMAS event evolution and agent behavior.
To develop a system that facilitates the analysis of detailed statuses and causal relationships within LLMAS.
To enhance the understanding and interpretability of complex LLMAS simulations.

Main Methods:

A general pipeline to structure raw LLMAS execution events into a behavior model.
A behavior summarization algorithm for creating hierarchical, time-ordered summaries of agent actions.
A cause trace method to identify causal links between agent behaviors.
Development of AgentLens, a visual analysis system with hierarchical temporal visualization.

Main Results:

AgentLens effectively visualizes the temporal evolution of LLMAS.
The system enables interactive investigation of agent behaviors and their underlying causes.
Two usage scenarios and a user study confirmed the system's effectiveness and usability.

Conclusions:

AgentLens provides a powerful tool for understanding and analyzing complex LLMAS.
The visualization approach addresses key challenges in LLMAS dynamic event analysis.
This work contributes to the interpretability and trustworthiness of LLMAS in simulating societal dynamics.