mlsanityData to 14 Oct 2026

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

arXiv 2608.16897 ↗First posted 8 Jul 2026physics.soc-ph cs.AI cs.MAcited by 0

On the map: LLM agents and tool use → Multimodal and embodied agents

Abstract (arXiv)

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 city scale. Yet these methods typically rely on few-shot prompting, causing agents to reproduce the LLM's behavioral priors rather than the target population. We introduce CityReal, a modular framework for human-aligned urban simulation. CityReal models agents as intention-driven decision makers that pursue coherent mobility and activity plans rather than isolated step-by-step choices. They adapt over time by learning habits and preferences based on experience and constraints. To improve population-level realism, we learn textual adapters for behavior modules that align agent decisions with observed population statistics. Experiments show that CityReal improves alignment with real-world human behavior at both micro and macro levels. Scaling to tens of thousands of agents, it supports analysis of crowd density, place popularity, mobility flows, and well-being under different urban scenarios, offering a scalable testbed for urban simulation and forecasting.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 12 of its 29 references that are in the map. First posted on arXiv 8 Jul 2026.

Builds on

12 of 29 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025CitySim: Modeling Urban Behaviors and City Dynamics with Large-Scale LLM-Driven Agent Simulation26 Jun
  2. 2025MobileCity: An Efficient Framework for Large-Scale Urban Behavior Simulation18 Apr
  3. 2024Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation22 Feb

Other references · 9

  1. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,395
  2. 2023Can Large Language Models Be an Alternative to Human Evaluations?cited by 734
  3. 2023Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectivescited by 257
  4. 2023Humanoid Agents: Platform for Simulating Human-like Generative Agentscited by 73
  5. 2025SimUSER: Simulating User Behavior with Large Language Models for Recommender System Evaluationcited by 33
  6. 2024Generative Adversarial Reviews: When LLMs Become the Criticcited by 12
  7. 2026AlignUSER: Human-Aligned LLM Agents via World Models for Recommender System Evaluationcited by 6
  8. 2026Beyond Offline A/B Testing: Context-Aware Agent Simulation for Recommender System Evaluationcited by 3
  9. 2026PerceptUI: LLM Agents as Human-Aligned Synthetic Users for UI/UX Evaluationcited by 1

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Close in wording, no citation either way

  1. 2026CityBehavEx: A Scalable and Empirically Validated LLM-Assisted Urban Simulation Platform
  2. 2026When Plausible Is Not Realistic: Evaluating Human Mobility in LLM-Based Urban Simulation
  3. 2026Evaluating Large Language Models for Decision-Making in Agent-Based Urban Mobility Simulations
  4. 2026GenWorld: Empirically Grounded Urban Simulation Infrastructure for Scalable LLM-Agent Studies
  5. 2025Graph RAG as Human Choice Model: Building a Data-Driven Mobility Agent with Preference Chain
  6. 2024OpenCity: A Scalable Platform to Simulate Urban Activities with Massive LLM Agents
  7. 2025Modeling realistic human behavior using generative agents in a multimodal transport system: Software architecture and Application to Toulouse
  8. 2026Empirical Grounding Improves the Realism of LLM Agents Simulating Human Behavior During Disruptions