arXiv AI July 21, 2026 By Chen Xia, Zexi Kuang, Yuqing Hu

Empirical Grounding Improves the Realism of LLM Agents Simulating Human Behavior During Disruptions

arXiv:2607. 17437v1 Announce Type: new Abstract: Large language model (LLM) agents offer a generative approach to simulating human behavior under conditions that may have few or no direct historical analogues, a common challenge in disaster and infrastructure-disruption planning.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Aug 19

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

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

llms agents safety

arXiv AI

Jun 12

TrajGenAgent: A Hierarchical LLM Agent for Human Mobility Trajectory Generation

arXiv:2606. 12657v1 Announce Type: new Abstract: Human mobility data is important for transportation, urban planning, and epidemic control, but large-scale trajectory collection is often costly and privacy-constrained, motivating realistic synthetic trajectory generation.

By Siyu Li, Toan Tran, Lingyi Zhao, Khurram Shafique, Li Xiong

llms agents fine-tuning benchmarks safety

arXiv AI

Jul 1

LLM-Driven Personalities for Decision Making in Emergency Simulations

arXiv:2606. 31038v1 Announce Type: cross Abstract: For virtual humans to appear believable, they must exhibit agency and spatial awareness while interacting with their environment in ways that reflect competence and intelligence.

By Stefano Calzolari, Rubens Montanha, Gabriel Schneider, Gustavo Wide, Paulo Knob, Francesco Strada, Andrea Bottino, Soraia Raupp Musse

llms agents

arXiv AI

Jul 29

How Affect Propagates among LLM Agents: Emergent Emotional Contagion in Crowd Simulation

arXiv:2607. 25140v1 Announce Type: new Abstract: This paper studies the behavior of language models in a multi-agent crowd simulation, focusing on how affect propagates among agents that perceive and appraise one another.

By Funda Durupinar

llms agents

arXiv AI

Jun 15

When Plausible Is Not Realistic: Evaluating Human Mobility in LLM-Based Urban Simulation

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

llms agents

arXiv AI

Sep 10

From the Fluency Fallacy to the Micro-to-Macro Validity Gap: Opportunities and Pitfalls of LLMs in Social Simulation

arXiv:2507.19364v3 Announce Type: replace Abstract: The integration of Large Language Models (LLMs) into social simulation has generated considerable enthusiasm, but also raises substantial methodolo...

By Patrick Taillandier, Jean Daniel Zucker, Arnaud Grignard, Benoit Gaudou, Nghi Quang Huynh, Haojia Kong, Alexis Drogoul

llms agents safety

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Related stories

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

TrajGenAgent: A Hierarchical LLM Agent for Human Mobility Trajectory Generation

LLM-Driven Personalities for Decision Making in Emergency Simulations

How Affect Propagates among LLM Agents: Emergent Emotional Contagion in Crowd Simulation

When Plausible Is Not Realistic: Evaluating Human Mobility in LLM-Based Urban Simulation

From the Fluency Fallacy to the Micro-to-Macro Validity Gap: Opportunities and Pitfalls of LLMs in Social Simulation