{"ID":2885904,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.04634","arxiv_id":"2508.04634","title":"VirtLab: An AI-Powered System for Flexible, Customizable, and Large-scale Team Simulations","abstract":"Simulating how team members collaborate within complex environments using Agentic AI is a promising approach to explore hypotheses grounded in social science theories and study team behaviors. We introduce VirtLab, a user-friendly, customizable, multi-agent, and scalable team simulation system that enables testing teams with LLM-based agents in spatial and temporal settings. This system addresses the current frameworks' design and technical limitations that do not consider flexible simulation scenarios and spatial settings. VirtLab contains a simulation engine and a web interface that enables both technical and non-technical users to formulate, run, and analyze team simulations without programming. We demonstrate the system's utility by comparing ground truth data with simulated scenarios.","short_abstract":"Simulating how team members collaborate within complex environments using Agentic AI is a promising approach to explore hypotheses grounded in social science theories and study team behaviors. We introduce VirtLab, a user-friendly, customizable, multi-agent, and scalable team simulation system that enables testing team...","url_abs":"https://arxiv.org/abs/2508.04634","url_pdf":"https://arxiv.org/pdf/2508.04634v1","authors":"[\"Mohammed Almutairi\",\"Charles Chiang\",\"Haoze Guo\",\"Matthew Belcher\",\"Nandini Banerjee\",\"Maria Milkowski\",\"Svitlana Volkova\",\"Daniel Nguyen\",\"Tim Weninger\",\"Michael Yankoski\",\"Trenton W. Ford\",\"Diego Gomez-Zara\"]","published":"2025-08-06T17:02:01Z","proceeding":"cs.HC","tasks":"[\"cs.HC\"]","methods":"[\"Large Language Model\"]","has_code":false}