Multi-Agent Collaborative Reasoning with Tool-Augmented Evidence for Urban Region Profiling
For urban computing researchers, this work introduces a reasoning-based paradigm that improves robustness and generalization in heterogeneous urban region profiling, though it is an incremental step over existing representation learning methods.
UrbanAgent reframes urban region profiling as a reasoning-driven inference problem, using multi-agent collaborative reasoning and tool-augmented evidence retrieval to address cross-modal inconsistencies. It achieves an average 8.1% improvement in R2 over baselines on global datasets for carbon emissions, GDP, and population estimation.
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.