Multi-Agent Collaborative Reasoning with Tool-Augmented Evidence for Urban Region Profiling

• URL: http://arxiv.org/abs/2607.13558v1
• Date: Wed, 15 Jul 2026 08:01:42 GMT
• Title: Multi-Agent Collaborative Reasoning with Tool-Augmented Evidence for Urban Region Profiling
• Abstract summary: Urban region profiling supports applications such as population estimation, economic assessment, and environmental monitoring.<n>We propose UrbanAgent, an agentic framework that reframes urban region profiling as a reasoning-driven inference problem.<n>Experiments on global urban datasets for Carbon emissions, GDP, and Population estimation show that UrbanAgent consistently outperforms existing baselines.
• Score: 31.71830721319945
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• An Empirical Evaluation of Cross-City POI Recommendation on a Large-Scale Benchmark [55.73054722370559]
  Cross-city point-of-interest (POI) recommendation is crucial for navigating unfamiliar urban environments.<n>We re-examine whether conclusions drawn on small prior benchmarks still hold under worldwide coverage, low home-destination region overlap, and large, semantically rich POI inventories.
  arXiv  Detail & Related papers  (2026-08-28T02:16:33Z)
• What Makes Good Agentic Data? An ACE Lens on Data Generation for LLM Agents [81.23160457293714]
  LLM agents increasingly rely on generated interaction data to learn how to interact with external environments.<n>Existing work spans many agent domains, but domain-centered organization and heterogeneous evaluation often obscure common generation mechanisms.<n>This work develops a two-level framework for the field.
  arXiv  Detail & Related papers  (2026-08-27T15:43:50Z)
• UrbanAgent: A Tool-Augmented Agent for Cross-System Urban Tasks [12.579199084038969]
  Urban-Agent is a tool-augmented agent framework for cross-system urban tasks.<n>It couples the cognitive and reasoning capabilities of a large language model with a tool-set supporting code execution, API calls, and Model Context Protocol.<n>Experiments indicate that Urban-Agent reaches a 71% task success rate, 10 points above the strongest baseline.
  arXiv  Detail & Related papers  (2026-08-04T02:04:44Z)
• CITYREP: A Unified Benchmark for Urban Representations Across Cities, Tasks, and Modalities [0.8319452826580411]
  CityRep is a benchmark that evaluates urban representations across data modalities, cities, and tasks using spatially structured splits.<n>Results show that performance is highly sensitive to the split protocol, with random splits inflating scores and altering model rankings.<n>CityRep is released as a reproducible benchmark with datasets, evaluation pipelines, and diagnostic tools to facilitate fair comparison.
  arXiv  Detail & Related papers  (2026-05-25T17:03:46Z)
• UrbanMoE: A Sparse Multi-Modal Mixture-of-Experts Framework for Multi-Task Urban Region Profiling [47.568568425459716]
  We develop a benchmark for multi-task urban region profiling, featuring multi-modal features and a diverse set of strong baselines.<n>We then propose UrbanMoE, the first sparse multi-modal, multi-expert framework specifically architected to solve the multi-task challenge.<n>We conduct extensive experiments on three real-world datasets within our benchmark, where UrbanMoE consistently demonstrates superior performance over all baselines.
  arXiv  Detail & Related papers  (2026-01-30T09:25:05Z)
• Urban-MAS: Human-Centered Urban Prediction with LLM-Based Multi-Agent System [0.0]
  Urban Artificial Intelligence (Urban AI) has advanced human-centered urban tasks such as perception prediction and human dynamics.<n>Large Language Models (LLMs) can integrate multimodal inputs to address heterogeneous data in complex urban systems but often underperform on domain-specific tasks.<n>Urban-MAS, an LLM-based Multi-Agent System (MAS), is introduced for human-centered urban prediction under zero-shot settings.
  arXiv  Detail & Related papers  (2025-10-30T10:26:02Z)
• Urban-R1: Reinforced MLLMs Mitigate Geospatial Biases for Urban General Intelligence [64.36291202666212]
  Urban General Intelligence (UGI) refers to AI systems that can understand and reason about complex urban environments.<n>Recent studies have built urban foundation models using supervised fine-tuning (SFT) of LLMs and MLLMs.<n>We propose Urban-R1, a reinforcement learning-based post-training framework that aligns MLLMs with the objectives of UGI.
  arXiv  Detail & Related papers  (2025-10-18T15:59:09Z)
• Collaborative Imputation of Urban Time Series through Cross-city Meta-learning [54.438991949772145]
  We propose a novel collaborative imputation paradigm leveraging meta-learned implicit neural representations (INRs)<n>We then introduce a cross-city collaborative learning scheme through model-agnostic meta learning.<n>Experiments on a diverse urban dataset from 20 global cities demonstrate our model's superior imputation performance and generalizability.
  arXiv  Detail & Related papers  (2025-01-20T07:12:40Z)
• Explainable Hierarchical Urban Representation Learning for Commuting Flow Prediction [1.5156879440024378]
  Commuting flow prediction is an essential task for municipal operations in the real world. We develop a heterogeneous graph-based model to generate meaningful region embeddings for predicting different types of inter-level OD flows. Our proposed model outperforms existing models in terms of a uniform urban structure.
  arXiv  Detail & Related papers  (2024-08-27T03:30:01Z)
• Enhanced Urban Region Profiling with Adversarial Self-Supervised Learning for Robust Forecasting and Security [12.8405655328298]
  Existing methods often struggle with issues such as noise, data incompleteness, and security vulnerabilities.<n>This paper proposes a novel framework, Enhanced Urban Region Profiling with Adversarial Self-Supervised Learning (EUPAS)<n>EUPAS ensures robust performance across various forecasting tasks such as crime prediction, check-in prediction, and land use classification.
  arXiv  Detail & Related papers  (2024-02-02T06:06:45Z)
• Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks [82.82866901799565]
  We build a new set of multimodal remote sensing benchmark datasets (including hyperspectral, multispectral, SAR) for the study purpose of the cross-city semantic segmentation task. Beyond the single city, we propose a high-resolution domain adaptation network, HighDAN, to promote the AI model's generalization ability from the multi-city environments. HighDAN is capable of retaining the spatially topological structure of the studied urban scene well in a parallel high-to-low resolution fusion fashion.
  arXiv  Detail & Related papers  (2023-09-26T23:55:39Z)
• Dynamic Federated Learning [57.14673504239551]
  Federated learning has emerged as an umbrella term for centralized coordination strategies in multi-agent environments. We consider a federated learning model where at every iteration, a random subset of available agents perform local updates based on their data. Under a non-stationary random walk model on the true minimizer for the aggregate optimization problem, we establish that the performance of the architecture is determined by three factors, namely, the data variability at each agent, the model variability across all agents, and a tracking term that is inversely proportional to the learning rate of the algorithm.
  arXiv  Detail & Related papers  (2020-02-20T15:00:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.