Urban Artificial Intelligence (Urban AI) has advanced human-centered urban tasks such as perception prediction and human dynamics. Large Language Models (LLMs) can integrate multimodal inputs to address heterogeneous data in complex urban systems but often underperform on domain-specific tasks. Urban-MAS, an LLM-based Multi-Agent System (MAS) framework, is introduced for human-centered urban prediction under zero-shot settings. It includes three agent types: Predictive Factor Guidance Agents, which prioritize key predictive factors to guide knowledge extraction and enhance the effectiveness of compressed urban knowledge in LLMs; Reliable UrbanInfo Extraction Agents, which improve robustness by comparing multiple outputs, validating consistency, and re-extracting when conflicts occur; and Multi-UrbanInfo Inference Agents, which integrate extracted multi-source information across dimensions for prediction. Experiments on running-amount prediction and urban perception across Tokyo, Milan, and Seattle demonstrate that Urban-MAS substantially reduces errors compared to single-LLM baselines. Ablation studies indicate that Predictive Factor Guidance Agents are most critical for enhancing predictive performance, positioning Urban-MAS as a scalable paradigm for human-centered urban AI prediction. Code is available on the project website:https://github.com/THETUREHOOHA/UrbanMAS

翻译：城市人工智能（Urban AI）在感知预测与人类动态等以人为中心的城市任务中取得了进展。大语言模型（LLMs）能够整合多模态输入以处理复杂城市系统中的异构数据，但在特定领域任务中往往表现欠佳。本文提出了Urban-MAS，一种基于LLM的多智能体系统（MAS）框架，用于零样本场景下的以人为中心的城市预测。该框架包含三类智能体：预测因子引导智能体，其优先确定关键预测因子以指导知识提取，并增强LLM中压缩城市知识的有效性；可靠城市信息提取智能体，通过比较多个输出、验证一致性并在出现冲突时重新提取，以提高系统的鲁棒性；以及多源城市信息推理智能体，其整合跨维度提取的多源信息进行预测。在东京、米兰和西雅图进行的运行量预测与城市感知实验表明，与单一LLM基线相比，Urban-MAS显著降低了预测误差。消融研究表明，预测因子引导智能体对于提升预测性能最为关键，这使Urban-MAS成为以人为中心的城市AI预测的可扩展范式。代码已在项目网站发布：https://github.com/THETUREHOOHA/UrbanMAS