arXiv:2606. 13835v1 Announce Type: cross Abstract: LLM-based generative agents are increasingly used in urban simulators, yet it remains unclear whether they reproduce empirically realistic human mobility patterns or merely generate plausible mobility narratives.
By Gustavo H. Santos, Aline Carneiro Viana, Thiago H. Silva
arXiv:2602. 16727v2 Announce Type: replace Abstract: Simulating large-scale human mobility is fundamental to understanding population movement patterns and supporting real-world geospatial applications such as urban planning, epidemic response, and transportation analysis.
By Hua Yan, Heng Tan, Yingxue Zhang, Yu Yang
arXiv:2601.13247v2 Announce Type: replace-cross
Abstract: Current Large Language Models (LLMs) exhibit a critical modal disconnect: they possess vast semantic knowledge but lack the procedural ground...
By Baochang Ren, Yunzhi Yao, Rui Sun, Shuofei Qiao, Ningyu Zhang, Huajun Chen
arXiv:2504. 09662v4 Announce Type: replace-cross Abstract: Multi-agent large language model simulations have the potential to model complex human behaviors and interactions.
By Jenny Ma, Riya Sahni, Karthik Sreedhar, Lydia B. Chilton
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
The paper introduces two minimal simulation foundations—SD-AgentFoundry-2D and SD-AgentFoundry-3D—for educational and rapid prototyping use with large language models (LLMs) and vision-language models (VLMs). SD-AgentFoundry-2D offers a 2D multi‑agent environment where LLM agents move, communicate, and react to local events such as fire, while SD-AgentFoundry-3D provides a 3D digital‑twin setting where a VLM interprets first‑person images to generate natural‑language movement instructions. Both frameworks run locally on macOS, Windows, and Linux, are intentionally lightweight, and are open for modification rather than being finished applications.
By Ryuki Hyodo