发表机构
The Hong Kong University of Science and Technology (Guangzhou); University of Washington; Chang’an University; University of Wisconsin - Madison（香港科技大学（广州）; 华盛顿大学; 长安大学; 威斯康星大学麦迪逊分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对城市区域剖析问题，提出UrbanAgent框架，通过多智能体协作推理解决跨模态不一致，将指标预测扩展为闭环过程，经实验验证其性能优于现有基线，在未见城市设置中有强泛化性。
AI 中文摘要
城市区域剖析是城市计算中的核心问题，支持人口估计、经济评估和环境监测等应用。现有方法通常将此任务表述为多模态表示学习，融合卫星图像、兴趣点、文本描述和3D建筑信息等异构城市数据到潜在嵌入中进行预测。但这些方法大多由相关性驱动，假设跨模态一致性，依赖静态管道，限制了其在异构或未见城市区域的鲁棒性。我们提出UrbanAgent，一个将城市区域剖析重新构建为推理驱动推理问题的智能体框架。UrbanAgent为每个数据模态实例化一个独立智能体，进行结构化多智能体协作推理以明确解决跨模态不一致性，而非将其吸收到单一表示中。此外，UrbanAgent将指标预测扩展为主动证据获取和迭代推理的闭环过程，使智能体能够通过经强化学习优化的外部知识的工具增强检索来验证不确定推理。在全球城市数据集上进行的碳排放、GDP和人口估计的广泛实验表明，UrbanAgent始终优于现有基线，R2平均提高8.1%，并在未见城市设置中表现出强大的泛化性能。
英文摘要
Urban region profiling constitutes a core problem in urban computing, supporting applications such as population estimation, economic assessment, and environmental monitoring. Existing methods typically formulate this task as multimodal representation learning, fusing heterogeneous urban data, e.g., satellite imagery, points of interest, textual descriptions, and 3D building information, into latent embeddings for prediction. However, these approaches are largely correlation-driven, assume cross-modal consistency, and rely on static pipelines, which limit their robustness in heterogeneous or unseen urban regions. We propose UrbanAgent, an agentic framework that reframes urban region profiling as a reasoning-driven inference problem. UrbanAgent instantiates an independent agent for each data modality and performs structured multi-agent collaborative reasoning to explicitly address cross-modal inconsistencies rather than absorbing them into a single representation. In addition, UrbanAgent extends indicator prediction as a closed-loop process of active evidence acquisition and iterative reasoning, enabling agents to verify uncertain inferences through tool-augmented retrieval of external knowledge optimized via reinforcement learning. Extensive experiments on global urban datasets for Carbon emissions, GDP, and Population estimation show that UrbanAgent consistently outperforms existing baselines, achieving an average improvement of 8.1% in R2, and exhibiting strong generalization performance in unseen-city settings.
CommentsAccepted by KDD 2026
