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

CityReal框架使用LLM模拟真实的城市人类行为

研究人员开发了CityReal,一个使用大型语言模型(LLM)模拟城市动态和人类行为的新框架。与依赖有限提示的先前方法不同,CityReal将代理建模为意图驱动的决策者,他们会随着时间的推移学习习惯和偏好。这种方法将代理决策与观察到的人口统计数据相结合,从而实现更真实微观和宏观层面的人类行为模拟。该框架可以扩展到数万个代理,从而能够分析各种城市场景下的人群密度、流动性和福祉。 AI

影响 通过提供更真实的人类行为和城市动态模拟,增强城市规划和政策分析。

排序理由 该集群描述了一篇关于使用LLM进行城市模拟的新颖框架的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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CityReal框架使用LLM模拟真实的城市人类行为

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该集群描述了一篇关于使用LLM进行城市模拟的新颖框架的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

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

    CityReal:基于大规模LLM智能体的人类对齐城市行为与城市动态模拟

    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…