@inproceedings{xu-etal-2025-mekb,
title = "{M}e{KB}-Sim: Personal Knowledge Base-Powered Multi-Agent Simulation",
author = "Xu, Zhenran and
Wang, Jifang and
Hu, Baotian and
Wang, Longyue and
Zhang, Min",
editor = "Dziri, Nouha and
Ren, Sean (Xiang) and
Diao, Shizhe",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-demo.33/",
doi = "10.18653/v1/2025.naacl-demo.33",
pages = "393--403",
ISBN = "979-8-89176-191-9",
abstract = "Language agents have demonstrated remarkable emergent social behaviors within simulated sandbox environments. However, the characterization of these agents has been constrained by static prompts that outline their profiles, highlighting a gap in achieving simulations that closely mimic real-life interactions. To close this gap, we introduce MeKB-Sim, a multi-agent simulation platform based on a dynamic personal knowledge base, termed MeKB. Each agent{'}s MeKB contains both fixed and variable attributes{---}such as linguistic style, personality, and memory{---}crucial for theory-of-mind modeling. These attributes are updated when necessary, in response to events that the agent experiences. Comparisons with human annotators show that the LLM-based attribute updates are reliable. Based on the dynamic nature of MeKB, experiments and case study show that MeKB-Sim enables agents to adapt their planned activities and interactions with other agents effectively. Our platform includes a Unity WebGL game interface for visualization and an interactive monitoring panel that presents the agents' planning, actions, and evolving MeKBs over time. For more information, including open-source code, a live demo website, and videos, please visit our project page at https://mekb-sim.github.io/."
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<abstract>Language agents have demonstrated remarkable emergent social behaviors within simulated sandbox environments. However, the characterization of these agents has been constrained by static prompts that outline their profiles, highlighting a gap in achieving simulations that closely mimic real-life interactions. To close this gap, we introduce MeKB-Sim, a multi-agent simulation platform based on a dynamic personal knowledge base, termed MeKB. Each agent’s MeKB contains both fixed and variable attributes—such as linguistic style, personality, and memory—crucial for theory-of-mind modeling. These attributes are updated when necessary, in response to events that the agent experiences. Comparisons with human annotators show that the LLM-based attribute updates are reliable. Based on the dynamic nature of MeKB, experiments and case study show that MeKB-Sim enables agents to adapt their planned activities and interactions with other agents effectively. Our platform includes a Unity WebGL game interface for visualization and an interactive monitoring panel that presents the agents’ planning, actions, and evolving MeKBs over time. For more information, including open-source code, a live demo website, and videos, please visit our project page at https://mekb-sim.github.io/.</abstract>
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%0 Conference Proceedings
%T MeKB-Sim: Personal Knowledge Base-Powered Multi-Agent Simulation
%A Xu, Zhenran
%A Wang, Jifang
%A Hu, Baotian
%A Wang, Longyue
%A Zhang, Min
%Y Dziri, Nouha
%Y Ren, Sean (Xiang)
%Y Diao, Shizhe
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-191-9
%F xu-etal-2025-mekb
%X Language agents have demonstrated remarkable emergent social behaviors within simulated sandbox environments. However, the characterization of these agents has been constrained by static prompts that outline their profiles, highlighting a gap in achieving simulations that closely mimic real-life interactions. To close this gap, we introduce MeKB-Sim, a multi-agent simulation platform based on a dynamic personal knowledge base, termed MeKB. Each agent’s MeKB contains both fixed and variable attributes—such as linguistic style, personality, and memory—crucial for theory-of-mind modeling. These attributes are updated when necessary, in response to events that the agent experiences. Comparisons with human annotators show that the LLM-based attribute updates are reliable. Based on the dynamic nature of MeKB, experiments and case study show that MeKB-Sim enables agents to adapt their planned activities and interactions with other agents effectively. Our platform includes a Unity WebGL game interface for visualization and an interactive monitoring panel that presents the agents’ planning, actions, and evolving MeKBs over time. For more information, including open-source code, a live demo website, and videos, please visit our project page at https://mekb-sim.github.io/.
%R 10.18653/v1/2025.naacl-demo.33
%U https://aclanthology.org/2025.naacl-demo.33/
%U https://doi.org/10.18653/v1/2025.naacl-demo.33
%P 393-403
Markdown (Informal)
[MeKB-Sim: Personal Knowledge Base-Powered Multi-Agent Simulation](https://aclanthology.org/2025.naacl-demo.33/) (Xu et al., NAACL 2025)
ACL
- Zhenran Xu, Jifang Wang, Baotian Hu, Longyue Wang, and Min Zhang. 2025. MeKB-Sim: Personal Knowledge Base-Powered Multi-Agent Simulation. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations), pages 393–403, Albuquerque, New Mexico. Association for Computational Linguistics.