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arXiv · 2606.27650

GenWorld: Empirically Grounded Urban Simulation Infrastructure for Scalable LLM-Agent Studies

Gen Li·Jieyuan Lan·Pengcheng Xu·Zongyuan Wu·Masaki Ogura·Tao Feng
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Abstract

LLM-agent simulation faces a joint grounding and scaling problem: agents should act in environments that reflect real urban constraints, yet direct online LLM calls for city-scale populations are computationally prohibitive. We present GenWorld, an empirically grounded urban simulation infrastructure that combines a building-level synthetic city, a structured agent-environment interface, and offline compilation of LLM-derived decision signals into lookup policies for scalable rollout. In a reference instantiation for Higashihiroshima, Japan, GenWorld grounds 196,608 synthetic residents in census and geospatial data, validates demographic consistency against census tabulations, and uses YJMob100K mobile-phone data as a commuting-distance diagnostic. We demonstrate the infrastructure through three reproducible cases: a full-city weekday rollout, a weekday-weekend behavioral contrast, and a warning-response perturbation with auditable replanning traces. These cases support GenWorld as a reproducible platform for grounded and scalable LLM-agent studies, while calibrated forecasting for traffic, evacuation, or policy outcomes remains future work.

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Gen Li, Jieyuan Lan, Pengcheng Xu, Zongyuan Wu, Masaki Ogura, Tao Feng. 2026-06-26. GenWorld: Empirically Grounded Urban Simulation Infrastructure for Scalable LLM-Agent Studies. https://arxiv.org/abs/2606.27650

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