Multi-Agent Collaborative Reasoning with Tool-Augmented Evidence for Urban Region Profiling
Quick Answer
UrbanAgent introduces a novel multi-agent framework for urban region profiling, outperforming existing methods by 8.1% in R2 on global datasets for carbon emissions, GDP, and population estimation.
Quick Take
By employing structured collaborative reasoning and tool-augmented evidence retrieval, it addresses cross-modal inconsistencies effectively, enhancing robustness in diverse urban environments.
Key Points
- UrbanAgent utilizes independent agents for each data modality in urban profiling.
- The framework achieves an 8.1% average improvement in R2 over existing baselines.
- It employs active evidence acquisition and iterative reasoning for enhanced predictions.
- UrbanAgent demonstrates strong generalization in unseen urban settings.
- The approach addresses limitations of correlation-driven methods in urban computing.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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.
| Comments: | Accepted by KDD 2026 |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.13558 [cs.AI] |
| (or arXiv:2607.13558v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.13558 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Xixuan Hao [view email]
[v1]
Wed, 15 Jul 2026 08:01:42 UTC (7,362 KB)
— Originally published at arxiv.org
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