CitySim: Modeling Urban Behaviors and City Dynamics with Large-Scale LLM-Driven Agent Simulation (2025.emnlp-industry)
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| Challenge: | Existing models rely on rigid, hand-crafted rules to model nuanced behavior in urban environments. |
| Approach: | They propose an urban simulator that generates realistic daily schedules using a recursive value-driven approach that balances mandatory activities, personal habits, and situational factors. |
| Outcome: | The proposed urban simulator exhibits closer alignment with real humans than previous work. |
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| Challenge: | Existing methods for simulating realistic urban behaviors rely on static profiles and synchronous inference pipelines that hinder scalability. |
| Approach: | They propose a lightweight generative agent framework for city-scale simulation powered by cognitively-grounded generative agents. |
| Outcome: | Experiments with 4,000 agents show that MobileCity generates more human-like urban dynamics than baselines while maintaining high computational efficiency. |
UrbanLLM: Autonomous Urban Activity Planning and Management with Large Language Models (2024.findings-emnlp)
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| Challenge: | UrbanLLM is a fine-tuned large language model designed to tackle diverse urban problems. |
| Approach: | They propose a fine-tuned large language model to tackle diverse urban problems . UrbanLLM decomposes urban-related queries into manageable sub-tasks . |
| Outcome: | The proposed model outperforms existing models in urban planning and management tasks. |
A Parallelized Framework for Simulating Large-Scale LLM Agents with Realistic Environments and Interactions (2025.acl-industry)
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| Challenge: | Existing work on large language models lacks a realistic environment and parallelized framework to support complex interactions between agents and environments. |
| Approach: | They propose a framework that integrates realistic societal environments and parallelized interactions to support simulations of large-scale agents. |
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Investigating and Extending Homans’ Social Exchange Theory with Large Language Model based Agents (2025.acl-long)
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| Challenge: | Social exchange theory (SET) is widely recognized as a basic framework for understanding human interactions and interactions. |
| Approach: | They propose to use large language models to study Homans’ social exchange theory (SET) by constructing a virtual society composed of three LLM agents and having them engage in a social exchange game to observe their behaviors. |
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LifeSim: Long-Horizon User Life Simulator for Personalized Assistant Evaluation (2026.findings-acl)
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| Challenge: | Existing benchmarks for personalized assistants fail to capture the complexity of external contexts and users’ cognitive states. |
| Approach: | They propose a user simulator that models user cognition through the Belief-Desire-Intention model within physical environments for coherent life trajectories generation and simulates intention-driven user interactive behaviors. |
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AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)
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Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang
| Challenge: | Large language models (LLMs) are promising foundations to build generally-capable agents . however, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents. |
| Approach: | They propose a framework that features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
| Outcome: | The proposed framework features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
Systematic Biases in LLM Simulations of Debates (2024.emnlp-main)
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| Challenge: | Current research suggests that LLM-based agents become increasingly human-like in their performance, sparking interest in using these AI agents as substitutes for human participants in behavioral studies. |
| Approach: | They propose to use LLMs to simulate political debates on topics that are important aspects of people’s day-to-day lives and decision-making processes. |
| Outcome: | The proposed model can simulate political debates on topics that are important aspects of people’s day-to-day lives and decision-making processes. |
Is this the real life? Is this just fantasy? The Misleading Success of Simulating Social Interactions With LLMs (2024.emnlp-main)
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| Challenge: | Recent advances in large language models have enabled richer social simulations . however, the role of information asymmetry in these simulations has been overlooked . |
| Approach: | They develop an evaluation framework to simulate social interactions with LLMs in different settings. |
| Outcome: | The proposed framework performs better in unrealistic, omniscient simulation settings but struggles in those with information asymmetry. |
Unveiling the Truth and Facilitating Change: Towards Agent-based Large-scale Social Movement Simulation (2024.findings-acl)
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| Challenge: | Existing methods for simulating social movements encounter challenges in capturing behavior of participants. |
| Approach: | They propose a hybrid framework for social media user simulation wherein users are categorized into two types: core and ordinary users. |
| Outcome: | The proposed framework is able to simulate the behavior of social media users across real-world datasets and demonstrate its effectiveness and flexibility. |
Simulating Opinion Dynamics with Networks of LLM-based Agents (2024.findings-naacl)
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Yun-Shiuan Chuang, Agam Goyal, Nikunj Harlalka, Siddharth Suresh, Robert Hawkins, Sijia Yang, Dhavan Shah, Junjie Hu, Timothy Rogers
| Challenge: | Existing approaches to simulating opinion dynamics often over-simplify human behavior . authors propose refining LLMs with real-world discourse to better simulate evolution of beliefs . |
| Approach: | They propose to use large language models to simulate opinion dynamics in groups of simulated agents . they found that LLM agents produce more accurate information than ABMs . |
| Outcome: | The proposed model can be used to better simulate opinion dynamics in real-world discourses. |