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

2026-06-25

AI Summary

This work addresses the limitations of large language model (LLM)-driven agents in urban-scale simulations, particularly concerning environmental fidelity and computational scalability. The authors propose a synthetic simulation framework grounded in real-world urban data, which uniquely integrates building-level urban modeling, empirically derived population distributions, and LLM-based agent behaviors. By offline-compiling LLM decisions into lookup-table policies, the framework preserves behavioral realism while enabling efficient large-scale simulation. The approach supports reproducible and auditable city-level experiments, demonstrated through a successful deployment in Higashihiroshima City, Hiroshima Prefecture, simulating 196,608 residents with validated demographic consistency and executing multi-scenario analyses—including typical weekdays, weekends, and emergency perturbations.

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.

Problem

• LLM-agent simulation
• urban simulation
• scalability
• grounding
• computational cost

Innovation

• LLM-agent simulation
• urban simulation infrastructure
• offline policy compilation
• empirically grounded modeling
• scalable agent rollout