mlsanityData to 7 Oct 2026

Urban Generative Intelligence (UGI): A Foundational Platform for Agents in Embodied City Environment

arXiv 2312.11813 ↗First posted 19 Dec 2023cs.AI cs.CYcited by 37

On the map: LLM agents and tool use → Multimodal and embodied agents

Abstract (arXiv)

Urban environments, characterized by their complex, multi-layered networks encompassing physical, social, economic, and environmental dimensions, face significant challenges in the face of rapid urbanization. These challenges, ranging from traffic congestion and pollution to social inequality, call for advanced technological interventions. Recent developments in big data, artificial intelligence, urban computing, and digital twins have laid the groundwork for sophisticated city modeling and simulation. However, a gap persists between these technological capabilities and their practical implementation in addressing urban challenges in an systemic-intelligent way. This paper proposes Urban Generative Intelligence (UGI), a novel foundational platform integrating Large Language Models (LLMs) into urban systems to foster a new paradigm of urban intelligence. UGI leverages CityGPT, a foundation model trained on city-specific multi-source data, to create embodied agents for various urban tasks. These agents, operating within a textual urban environment emulated by city simulator and urban knowledge graph, interact through a natural language interface, offering an open platform for diverse intelligent and embodied agent development. This platform not only addresses specific urban issues but also simulates complex urban systems, providing a multidisciplinary approach to understand and manage urban complexity. This work signifies a transformative step in city science and urban intelligence, harnessing the power of LLMs to unravel and address the intricate dynamics of urban systems. The code repository with demonstrations will soon be released here https://github.com/tsinghua-fib-lab/UGI.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 61 of its 187 references that are in the map. First posted on arXiv 19 Dec 2023.

Builds on

61 of 187 references.

References · 61

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 18; 2025: 16; 2026: 3.

37 papers.

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  2. 2024LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Modelscited by 97
  3. 2024UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Predictioncited by 86
  4. 2024A Survey on LLM-based Multi-Agent System: Recent Advances and New Frontiers in Applicationcited by 54
  5. 2024Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Surveycited by 50
  6. 2024Y Social: an LLM-powered Social Media Digital Twincited by 43
  7. 2024CharacterBox: Evaluating the Role-Playing Capabilities of LLMs in Text-Based Virtual Worldscited by 38
  8. 2024OpenCity: A Scalable Platform to Simulate Urban Activities with Massive LLM Agentscited by 35
  9. 2024Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlookcited by 33
  10. 2025UrbanVideo-Bench: Benchmarking Vision-Language Models on Embodied Intelligence with Video Data in Urban Spacescited by 33
  11. 2025LLM-based Human Simulations Have Not Yet Been Reliablecited by 30
  12. 2024Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Modelscited by 23
25 more
  1. 2024UrbanWorld: An Urban World Model for 3D City Generationcited by 23
  2. 2025Foundation Models for Spatio-Temporal Data Science: A Tutorial and Surveycited by 23
  3. 2024From Text to Transformation: A Comprehensive Review of Large Language Models' Versatilitycited by 19
  4. 2024BIGCity: A Universal Spatiotemporal Model for Unified Trajectory and Traffic State Data Analysiscited by 16
  5. 2025UrbanLLaVA: A Multi-modal Large Language Model for Urban Intelligence with Spatial Reasoning and Understandingcited by 15
  6. 2025A Survey on Agent Workflow -- Status and Futurecited by 14
  7. 2025YuLan-OneSim: Towards the Next Generation of Social Simulator with Large Language Modelscited by 11
  8. 2024SmartAgent: Chain-of-User-Thought for Embodied Personalized Agent in Cyber Worldcited by 10
  9. 2025GATSim: Urban Mobility Simulation with Generative Agentscited by 10
  10. 2024Autonomous Workflow for Multimodal Fine-Grained Training Assistants Towards Mixed Realitycited by 8
  11. 2024Urban Region Pre-training and Prompting: A Graph-based Approachcited by 8
  12. 2025Modeling Earth-Scale Human-Like Societies with One Billion Agentscited by 8
  13. 2024UniFlow: A Foundation Model for Unified Urban Spatio-Temporal Flow Predictioncited by 7
  14. 2025UrbanPlanBench: A Comprehensive Urban Planning Benchmark for Evaluating Large Language Modelscited by 5
  15. 2025LatticeWorld: A Multimodal Large Language Model-Empowered Framework for Interactive Complex World Generationcited by 5
  16. 2025Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applicationscited by 4
  17. 2025Carbon and Silicon, Coexist or Compete? A Survey on Human-AI Interactions in Agent-based Modeling and Simulationcited by 3
  18. 2024Enhancing Traffic Prediction with Textual Data Using Large Language Modelscited by 2
  19. 2025Boundary Prompting: Elastic Urban Region Representation via Graph-based Spatial Tokenizationcited by 2
  20. 2025Urban Computing in the Era of Large Language Modelscited by 2
  21. 2025A Survey of Physics-Informed AI for Complex Urban Systemscited by 2
  22. 2025What Makes LLM Agent Simulations Useful for Policy Practice? An Iterative Design Study in Emergency Preparednesscited by 2
  23. 2026Can LLM Agents Sustain Long-Horizon Organizational Dynamics?cited by 1
  24. 2026How Far Are Large Multimodal Models from Human-Level Spatial Action? A Benchmark for Goal-Oriented Embodied Navigation in Urban Airspacecited by 0
  25. 2026CityLLM: A framework for natural-language querying of semantic 3D city modelscited by 0

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