@inproceedings{zhang-etal-2025-simagents,
title = "{S}im{A}gents: Bridging Literature and the Universe Via A Multi-Agent Large Language Model System",
author = "Zhang, Xiaowen and
Bi, Zhenyu and
LaChance, Patrick and
Wang, Xuan and
Di Matteo, Tiziana and
Croft, Rupert",
editor = "Liu, Xuebo and
Purwarianti, Ayu",
booktitle = "Proceedings of The 14th International Joint Conference on Natural Language Processing and The 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: System Demonstrations",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.ijcnlp-demo.7/",
pages = "55--66",
ISBN = "979-8-89176-301-2",
abstract = "As cosmological simulations and their associated software become increasingly complex, physicists face the challenge of searching through vast amounts of literature and user manuals to extract simulation parameters from dense academic papers, each using different models, and formats. Translating these parameters into executable scripts remains a time-consuming and error-prone process. To improve efficiency in physics research and accelerate the cosmological simulation process, we introduce SimAgents, a multi-agent system designed to automate both parameter configuration from the literature and preliminary analysis for cosmology research. SimAgents is powered by specialized LLM agents capable of physics reasoning, simulation software validation, and tool execution. These agents collaborate through structured communication, ensuring that extracted parameters are physically meaningful, internally consistent, and software-compliant. We also construct a cosmological parameter extraction evaluation dataset by collecting over 40 simulations in published papers from Arxiv and leading journals that cover diverse simulation types. Experiments on the dataset demonstrate a strong performance of SimAgents, highlighting its effectiveness and potential to accelerate scientific research for physicists. Our demonstration video is available at: https://youtu.be/w1zLpm{\_}CaWA. The complete system and dataset are publicly available at https://github.com/xwzhang98/SimAgents."
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<abstract>As cosmological simulations and their associated software become increasingly complex, physicists face the challenge of searching through vast amounts of literature and user manuals to extract simulation parameters from dense academic papers, each using different models, and formats. Translating these parameters into executable scripts remains a time-consuming and error-prone process. To improve efficiency in physics research and accelerate the cosmological simulation process, we introduce SimAgents, a multi-agent system designed to automate both parameter configuration from the literature and preliminary analysis for cosmology research. SimAgents is powered by specialized LLM agents capable of physics reasoning, simulation software validation, and tool execution. These agents collaborate through structured communication, ensuring that extracted parameters are physically meaningful, internally consistent, and software-compliant. We also construct a cosmological parameter extraction evaluation dataset by collecting over 40 simulations in published papers from Arxiv and leading journals that cover diverse simulation types. Experiments on the dataset demonstrate a strong performance of SimAgents, highlighting its effectiveness and potential to accelerate scientific research for physicists. Our demonstration video is available at: https://youtu.be/w1zLpm_CaWA. The complete system and dataset are publicly available at https://github.com/xwzhang98/SimAgents.</abstract>
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%0 Conference Proceedings
%T SimAgents: Bridging Literature and the Universe Via A Multi-Agent Large Language Model System
%A Zhang, Xiaowen
%A Bi, Zhenyu
%A LaChance, Patrick
%A Wang, Xuan
%A Di Matteo, Tiziana
%A Croft, Rupert
%Y Liu, Xuebo
%Y Purwarianti, Ayu
%S Proceedings of The 14th International Joint Conference on Natural Language Processing and The 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: System Demonstrations
%D 2025
%8 December
%I Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-301-2
%F zhang-etal-2025-simagents
%X As cosmological simulations and their associated software become increasingly complex, physicists face the challenge of searching through vast amounts of literature and user manuals to extract simulation parameters from dense academic papers, each using different models, and formats. Translating these parameters into executable scripts remains a time-consuming and error-prone process. To improve efficiency in physics research and accelerate the cosmological simulation process, we introduce SimAgents, a multi-agent system designed to automate both parameter configuration from the literature and preliminary analysis for cosmology research. SimAgents is powered by specialized LLM agents capable of physics reasoning, simulation software validation, and tool execution. These agents collaborate through structured communication, ensuring that extracted parameters are physically meaningful, internally consistent, and software-compliant. We also construct a cosmological parameter extraction evaluation dataset by collecting over 40 simulations in published papers from Arxiv and leading journals that cover diverse simulation types. Experiments on the dataset demonstrate a strong performance of SimAgents, highlighting its effectiveness and potential to accelerate scientific research for physicists. Our demonstration video is available at: https://youtu.be/w1zLpm_CaWA. The complete system and dataset are publicly available at https://github.com/xwzhang98/SimAgents.
%U https://aclanthology.org/2025.ijcnlp-demo.7/
%P 55-66
Markdown (Informal)
[SimAgents: Bridging Literature and the Universe Via A Multi-Agent Large Language Model System](https://aclanthology.org/2025.ijcnlp-demo.7/) (Zhang et al., IJCNLP 2025)
ACL
- Xiaowen Zhang, Zhenyu Bi, Patrick LaChance, Xuan Wang, Tiziana Di Matteo, and Rupert Croft. 2025. SimAgents: Bridging Literature and the Universe Via A Multi-Agent Large Language Model System. In Proceedings of The 14th International Joint Conference on Natural Language Processing and The 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: System Demonstrations, pages 55–66, Mumbai, India. Association for Computational Linguistics.