{"slug": "urbands-a-graph-guided-llm-multi-agent-system-for-data-intensive-urban-tasks", "title": "UrbanDS: A Graph-Guided LLM Multi-Agent System for Data-Intensive Urban Tasks", "summary": "Researchers propose UrbanDS, a graph-guided LLM multi-agent system for data-intensive urban tasks, which constructs a unified dataset graph to organize reusable dataset skills and relationships. UrbanDS outperforms existing data science agents on data-intensive tasks in experiments and has been deployed on the urban operations platform of Dongxihu District, Wuhan.", "body_md": "arXiv:2607.26724v1 Announce Type: new\nAbstract: Large language model (LLM) agents have been widely applied in automating data science tasks. However, existing methods typically rely on a limited set of provided datasets, and they face challenges in data-intensive scenarios that require discovering and leveraging relevant information from large-scale and heterogeneous data repositories. Urban tasks are representative examples of such scenarios, as urban data are not only large-scale and multi-sourced, but also exhibit complex spatial, temporal, and semantic relationships. To address these challenges, we propose UrbanDS, a graph-guided LLM multi-agent system for data-intensive urban tasks. We first construct a unified dataset graph to organize reusable dataset skills and the relationships among datasets. Specifically, we develop a Data Profiling Agent that constructs a skill for each dataset. Moreover, a Relation Agent identifies relationships among datasets and integrates these relationships into the dataset graph. At runtime, a Planner Agent retrieves task-relevant datasets from the graph and generates execution plans. Multiple Execution Agents then perform data processing and analysis, while their execution progress and intermediate results are shared through a common memory. Finally, a Report Agent synthesizes the experimental logs into a report, which can be further refined based on user feedback. To systematically evaluate the capability of agents in handling data-intensive urban scenarios, we further construct UrbanDS-Bench, an urban data science benchmark covering representative data analysis and modeling tasks. Experiments on both general and urban benchmarks demonstrate that UrbanDS consistently outperforms existing data science agents on data-intensive tasks. Furthermore, UrbanDS has been deployed on the urban operations platform of Dongxihu District, Wuhan, demonstrating its effectiveness in real-world urban applications.", "url": "https://wpnews.pro/news/urbands-a-graph-guided-llm-multi-agent-system-for-data-intensive-urban-tasks", "canonical_source": "https://arxiv.org/abs/2607.26724", "published_at": "2026-07-31 04:00:00+00:00", "updated_at": "2026-07-31 04:29:45.678412+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-agents", "ai-research"], "entities": ["UrbanDS", "UrbanDS-Bench", "Dongxihu District", "Wuhan"], "alternates": {"html": "https://wpnews.pro/news/urbands-a-graph-guided-llm-multi-agent-system-for-data-intensive-urban-tasks", "markdown": "https://wpnews.pro/news/urbands-a-graph-guided-llm-multi-agent-system-for-data-intensive-urban-tasks.md", "text": "https://wpnews.pro/news/urbands-a-graph-guided-llm-multi-agent-system-for-data-intensive-urban-tasks.txt", "jsonld": "https://wpnews.pro/news/urbands-a-graph-guided-llm-multi-agent-system-for-data-intensive-urban-tasks.jsonld"}}