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CityReal framework uses LLMs to simulate realistic urban human behavior

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CityReal, a new framework for simulating urban dynamics and human behavior using large language models (LLMs). Unlike previous methods that relied on limited prompting, CityReal models agents as intention-driven decision-makers who learn habits and preferences over time. This approach aligns agent decisions with observed population statistics, leading to more realistic micro and macro-level human behavior simulations. The framework can scale to tens of thousands of agents, enabling analysis of crowd density, mobility, and well-being under various urban scenarios. AI

IMPACT Enhances urban planning and policy analysis by providing a more realistic simulation of human behavior and city dynamics.

RANK_REASON The cluster describes a new research paper detailing a novel framework for urban simulation using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

CityReal framework uses LLMs to simulate realistic urban human behavior

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The cluster describes a new research paper detailing a novel framework for urban simulation using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Nicolas Bougie, Xiaotong Ye, Narimasa Watanabe ·

    CityReal: Human-Aligned Urban Behavior and City Dynamics Simulation with Large-Scale LLM Agents

    arXiv:2608.16897v1 Announce Type: cross Abstract: Large-scale urban simulation plays a pivotal role in social science, traffic safety, and transportation policy. Recent work has shown that large language models, when prompted as agents, can generate lifelike daily routines at cit…

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