A Visualized Framework for Event Cooperation with Generative Agents
This work addresses the problem of limited realism and evaluation in agent simulations for researchers, though it is incremental as it builds on existing LLM-based agent frameworks.
The authors tackled the lack of systematic evaluation and visualization for event organization in agent societies by developing MiniAgentPro, a platform with a map editor and simulation player, and introduced a test set of event scenarios; evaluations with GPT-4o showed strong performance in basic settings but coordination issues in hard variants.
Large Language Models (LLMs) have revolutionized the simulation of agent societies, enabling autonomous planning, memory formation, and social interactions. However, existing frameworks often overlook systematic evaluations for event organization and lack visualized integration with physically grounded environments, limiting agents' ability to navigate spaces and interact with items realistically. We develop MiniAgentPro, a visualization platform featuring an intuitive map editor for customizing environments and a simulation player with smooth animations. Based on this tool, we introduce a comprehensive test set comprising eight diverse event scenarios with basic and hard variants to assess agents' ability. Evaluations using GPT-4o demonstrate strong performance in basic settings but highlight coordination challenges in hard variants.