大语言模型（LLM）智能体仿真面临联合的接地性（grounding）与可扩展性问题：智能体需在反映真实城市约束的环境中行动，但对城市规模人口直接调用在线LLM在计算上不可行。本文提出GenWorld——一种经验驱动的城市仿真基础设施，整合了建筑级合成城市、结构化的智能体-环境交互接口，以及将LLM生成的决策信号离线编译为查找式策略（lookup policies）以实现可扩展仿真的机制。在以日本东广岛市为参考的实例化中，GenWorld基于人口普查与地理空间数据构建了196,608名合成居民，通过人口普查汇总数据验证其人口统计一致性，并利用YJMob100K手机移动数据作为通勤距离诊断依据。我们通过三个可复现案例展示该基础设施：全城工作日仿真、工作日与周末行为对比、以及含可审计重规划轨迹的预警响应扰动实验。这些案例表明GenWorld可作为支持接地性与可扩展性兼具的LLM智能体研究的可复现平台；而针对交通、疏散或政策效果的校准化预测仍属未来工作。

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.