AgenticCity-Bench is a longitudinal multimodal benchmark that unifies state modeling and agentic reasoning for urban dynamics. It covers 16 cities across 12 countries from 2000 to 2025, providing 18 modalities that capture urban function, activity, and environmental state. The benchmark aligns heterogeneous data around a common spatial reference and calendar index, enabling cross‑modal translation and diverse agentic city tasks such as forecasting, planning, and resource analysis.
By Xiaofei Yu, Kailai Sun, Yanyu Mao, Baoshen Guo, Heye Huang, Yongcai Wang, Baotong Mi, Alok Prakash, Shenhao Wang, Jinhua Zhao
arXiv:2608. 03018v1 Announce Type: new Abstract: Modern cities rely on an increasing number of digital services to operate, but residents' daily needs are still difficult to meet.
By Jiayu Cao, Xingyuan Zeng, feiyu Li, Zhijing Huang, Xujie Yuan, Rongxiang Chen, Shimin Di, Libin Zheng, Jian Yin
Urban diagnosis integrates heterogeneous observations to identify urban problems, localize affected areas, and investigate contributing factors, informing evidence-based urban planning and management....
Urban environments are shaped by design choices with long-term implications for health, safety, and quality of life, yet evaluating proposed interventions remains costly, time-consuming, and often imp...
arXiv:2608. 08045v1 Announce Type: new Abstract: Urban embodied intelligence requires coordination among heterogeneous agents (e.
By Xiaohe Li, Yiru Wang, Junhao Fan, Mingyuan Liu, Jie Huang, Kaixin Zhang, Jiahao Li, Chen Qian, Zide Fan
arXiv:2607. 26724v1 Announce Type: new Abstract: Large language model (LLM) agents have been widely applied in automating data science tasks.
By Zhilun Zhou, Jianghao Yu, Yuming Lin, yongjun yang, Sun Yongquan, Depeng Jin, Yong Li
arXiv:2610.01870v1 Announce Type: new
Abstract: Urban environments are shaped by design choices with long-term implications for health, safety, and quality of life, yet evaluating proposed interventi...
By Hosam Elgendy, Utkarsh Mall
arXiv:2607. 13558v1 Announce Type: new Abstract: Urban region profiling constitutes a core problem in urban computing, supporting applications such as population estimation, economic assessment, and environmental monitoring.
By Xixuan Hao, Yutian Jiang, Jiabo Liu, Yihang Yang, Guangyin Jin, Song Gao, Yuxuan Liang
The paper introduces ANASSA, an agentic AI orchestration framework designed for spatial intelligence in geographic information systems. It addresses gaps in current systems by integrating structured spatial reasoning, multi‑agent workflow orchestration, execution feedback, authoritative validation, provenance, uncertainty handling, and human decision authority. The architecture is detailed with eleven components across four layers, a six‑step Geospatial AI Cognitive Loop, cross‑component contracts, and governance mechanisms to ensure traceability, reproducibility, and accountability.
By Constantinos Papantoniou, Brian Hilton
arXiv:2604. 17456v2 Announce Type: replace Abstract: Large language model (LLM) agents have shown strong capabilities in long-horizon reasoning, tool use, and decision-making in digital environments, yet extending them to physically grounded systems remains challenging.
By Siqi Lai, Pan Zhang, Yuping Zhou, Jindong Han, Yansong Ning, Hao Liu
arXiv:2608. 11679v1 Announce Type: new Abstract: Digital twins are increasingly used to monitor and simulate the behavior of cyber-physical systems.
By Touseef Hasan, Mounika Ghanta, Souvika Sarkar, Ujjwal Guin
CityReal is a modular framework that uses large language model agents to simulate human-aligned urban behavior. It models agents as intention-driven decision makers who pursue coherent mobility and activity plans, learning habits and preferences over time. By training textual adapters to align agent decisions with observed population statistics, CityReal improves realism at both micro and macro levels and can scale to tens of thousands of agents for analyzing crowd density, place popularity, mobility flows, and well‑being under various urban scenarios.
By Nicolas Bougie, Xiaotong Ye, Narimasa Watanabe