近期基于大语言模型（LLM）的多智能体城市仿真器虽能生成语义丰富的城市日常行为，但其扩展成本高昂，且常缺乏对真实世界出行模式的实证验证。本文提出 CityBehavEx——一个交互式 LLM 辅助城市仿真平台，支持城市规模人口量级的仿真，开放智能体行为以供检查，支持实证验证，并生成在空间、时间及语义分布上更贴近真实世界出行模式的结果。CityBehavEx 并非为每个智能体动作调用大语言模型，而是将成熟的人类移动性模型与微调后的跨编码器（cross-encoder）相结合，用于评估智能体画像、日程安排及活动转换之间的语义一致性。该设计实现了大规模仿真能力，如案例研究所示：在单块消费级 GPU 上，仅用不到一小时即完成覆盖 100,000 名智能体、持续 75 天的仿真。该平台支持用户定义仿真区域、启动实验、检查轨迹与活动序列、调试不合理的智能体行为，并依据真实世界的出行、时间使用及语义指标验证所生成日常行为的合理性。

Recent LLM-based multi-agent urban simulators can generate semantically rich city routines, but they remain costly to scale and are often weakly validated against empirical mobility patterns. We present CityBehavEx, an interactive LLM-assisted urban simulation platform that scales to city-size populations, exposes agent behavior for inspection, supports empirical validation, and generates mobility patterns that better match real-world spatial, temporal, and semantic distributions. Instead of invoking large language models for every agent action, CityBehavEx combines established human mobility models with fine-tuned cross-encoders that estimate semantic alignment between agent profiles, schedules, and activity transitions. This design enables large-scale simulations, as demonstrated in a case study of 100,000 agents over 75 days in under one hour on a single consumer GPU. The platform allows users to define simulation regions, launch experiments, inspect trajectories and activity traces, debug unrealistic behaviors, and validate generated routines against real-world mobility, time-use, and semantic metrics.