@inproceedings{zhang-etal-2025-parallelized,
title = "A Parallelized Framework for Simulating Large-Scale {LLM} Agents with Realistic Environments and Interactions",
author = "Zhang, Jun and
Yan, Yuwei and
Yan, Junbo and
Zheng, Zhiheng and
Piao, Jinghua and
Jin, Depeng and
Li, Yong",
editor = "Rehm, Georg and
Li, Yunyao",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-industry.94/",
doi = "10.18653/v1/2025.acl-industry.94",
pages = "1339--1349",
ISBN = "979-8-89176-288-6",
abstract = "The development of large language models (LLMs) offers a feasible approach to simulating complex behavioral patterns of individuals, enabling the reconstruction of microscopic and realistic human societal dynamics. However, this approach demands a realistic environment to provide feedback for the evolving of agents, as well as a parallelized framework to support the massive and uncertain interactions among agents and environments. To address the gaps in existing works, which lack real-world environments and struggle with complex interactions, we design a scalable framework named **AgentSociety**, which integrates realistic societal environments and parallelized interactions to support simulations of large-scale agents. Experiments demonstrate that the framework can support simulations of 30,000 agents that are faster than the wall-clock time with 24 NVIDIA A800 GPUs and the performance grows linearly with the increase of LLM computational resources. We also show that the integration of realistic environments significantly enhances the authenticity of the agents' behaviors. Through the framework and experimental results, we are confident that deploying large-scale LLM Agents to simulate human societies becomes feasible. This will help practitioners in fields such as social sciences and management sciences to obtain new scientific discoveries via language generation technologies, and even improve planning and decision-making in the real world. The code is available at https://github.com/tsinghua-fib-lab/agentsociety/."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhang-etal-2025-parallelized">
<titleInfo>
<title>A Parallelized Framework for Simulating Large-Scale LLM Agents with Realistic Environments and Interactions</title>
</titleInfo>
<name type="personal">
<namePart type="given">Jun</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yuwei</namePart>
<namePart type="family">Yan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Junbo</namePart>
<namePart type="family">Yan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhiheng</namePart>
<namePart type="family">Zheng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jinghua</namePart>
<namePart type="family">Piao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Depeng</namePart>
<namePart type="family">Jin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yong</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Georg</namePart>
<namePart type="family">Rehm</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yunyao</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Vienna, Austria</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-288-6</identifier>
</relatedItem>
<abstract>The development of large language models (LLMs) offers a feasible approach to simulating complex behavioral patterns of individuals, enabling the reconstruction of microscopic and realistic human societal dynamics. However, this approach demands a realistic environment to provide feedback for the evolving of agents, as well as a parallelized framework to support the massive and uncertain interactions among agents and environments. To address the gaps in existing works, which lack real-world environments and struggle with complex interactions, we design a scalable framework named **AgentSociety**, which integrates realistic societal environments and parallelized interactions to support simulations of large-scale agents. Experiments demonstrate that the framework can support simulations of 30,000 agents that are faster than the wall-clock time with 24 NVIDIA A800 GPUs and the performance grows linearly with the increase of LLM computational resources. We also show that the integration of realistic environments significantly enhances the authenticity of the agents’ behaviors. Through the framework and experimental results, we are confident that deploying large-scale LLM Agents to simulate human societies becomes feasible. This will help practitioners in fields such as social sciences and management sciences to obtain new scientific discoveries via language generation technologies, and even improve planning and decision-making in the real world. The code is available at https://github.com/tsinghua-fib-lab/agentsociety/.</abstract>
<identifier type="citekey">zhang-etal-2025-parallelized</identifier>
<identifier type="doi">10.18653/v1/2025.acl-industry.94</identifier>
<location>
<url>https://aclanthology.org/2025.acl-industry.94/</url>
</location>
<part>
<date>2025-07</date>
<extent unit="page">
<start>1339</start>
<end>1349</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T A Parallelized Framework for Simulating Large-Scale LLM Agents with Realistic Environments and Interactions
%A Zhang, Jun
%A Yan, Yuwei
%A Yan, Junbo
%A Zheng, Zhiheng
%A Piao, Jinghua
%A Jin, Depeng
%A Li, Yong
%Y Rehm, Georg
%Y Li, Yunyao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-288-6
%F zhang-etal-2025-parallelized
%X The development of large language models (LLMs) offers a feasible approach to simulating complex behavioral patterns of individuals, enabling the reconstruction of microscopic and realistic human societal dynamics. However, this approach demands a realistic environment to provide feedback for the evolving of agents, as well as a parallelized framework to support the massive and uncertain interactions among agents and environments. To address the gaps in existing works, which lack real-world environments and struggle with complex interactions, we design a scalable framework named **AgentSociety**, which integrates realistic societal environments and parallelized interactions to support simulations of large-scale agents. Experiments demonstrate that the framework can support simulations of 30,000 agents that are faster than the wall-clock time with 24 NVIDIA A800 GPUs and the performance grows linearly with the increase of LLM computational resources. We also show that the integration of realistic environments significantly enhances the authenticity of the agents’ behaviors. Through the framework and experimental results, we are confident that deploying large-scale LLM Agents to simulate human societies becomes feasible. This will help practitioners in fields such as social sciences and management sciences to obtain new scientific discoveries via language generation technologies, and even improve planning and decision-making in the real world. The code is available at https://github.com/tsinghua-fib-lab/agentsociety/.
%R 10.18653/v1/2025.acl-industry.94
%U https://aclanthology.org/2025.acl-industry.94/
%U https://doi.org/10.18653/v1/2025.acl-industry.94
%P 1339-1349
Markdown (Informal)
[A Parallelized Framework for Simulating Large-Scale LLM Agents with Realistic Environments and Interactions](https://aclanthology.org/2025.acl-industry.94/) (Zhang et al., ACL 2025)
ACL