@inproceedings{li-etal-2026-meeplelm,
title = "{M}eeple{LM}: A Virtual Playtester Simulating Diverse Subjective Experiences",
author = "Li, Zizhen and
Li, Chuanhao and
Wang, Yibin and
Sun, Jianwen and
Feng, Yukang and
Ai, Jiaxin and
Zhang, Fanrui and
Sun, Mingzhu and
Huang, Yifei and
Zhang, Kaipeng",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.850/",
pages = "18678--18722",
ISBN = "979-8-89176-390-6",
abstract = "Recent advancements have expanded the role of Large Language Models (LLMs) in board games from playing agents to creative co-designers. However, a critical gap remains: current systems lack the capacity to offer constructive critique grounded in the emergent user experience. Bridging this gap is fundamental for harmonizing Human-AI collaboration, as it empowers designers to refine their creations via external perspectives while steering models away from biased or unpredictable outcomes. Automating this evaluation presents two challenges: inferring the latent dynamics connecting static rules to gameplay without an explicit engine, and modeling the subjective heterogeneity of diverse player groups. To address these, we curate a comprehensive dataset of 1,727 structurally corrected rulebooks and 150K reviews selected via rigorous quality scoring and facet-aware sampling. We augment this data with Mechanics-Dynamics-Aesthetics (MDA) reasoning to explicitly bridge the causal gap between written rules and player experience. We further distill distinct player personas and introduce MeepleLM, a specialized model that internalizes persona-specific reasoning patterns to accurately simulate the subjective feedback of diverse player archetypes. Extensive experiments demonstrate that MeepleLM significantly outperforms latest commercial models (e.g., GPT-5.1, Gemini3-Pro) in community alignment and critique quality, achieving a 70{\%} preference rate in user studies assessing practical utility. MeepleLM serves as a reliable virtual playtester that provides experience-grounded feedback, offering a practical step towards audience-aligned Human-AI collaboration."
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<abstract>Recent advancements have expanded the role of Large Language Models (LLMs) in board games from playing agents to creative co-designers. However, a critical gap remains: current systems lack the capacity to offer constructive critique grounded in the emergent user experience. Bridging this gap is fundamental for harmonizing Human-AI collaboration, as it empowers designers to refine their creations via external perspectives while steering models away from biased or unpredictable outcomes. Automating this evaluation presents two challenges: inferring the latent dynamics connecting static rules to gameplay without an explicit engine, and modeling the subjective heterogeneity of diverse player groups. To address these, we curate a comprehensive dataset of 1,727 structurally corrected rulebooks and 150K reviews selected via rigorous quality scoring and facet-aware sampling. We augment this data with Mechanics-Dynamics-Aesthetics (MDA) reasoning to explicitly bridge the causal gap between written rules and player experience. We further distill distinct player personas and introduce MeepleLM, a specialized model that internalizes persona-specific reasoning patterns to accurately simulate the subjective feedback of diverse player archetypes. Extensive experiments demonstrate that MeepleLM significantly outperforms latest commercial models (e.g., GPT-5.1, Gemini3-Pro) in community alignment and critique quality, achieving a 70% preference rate in user studies assessing practical utility. MeepleLM serves as a reliable virtual playtester that provides experience-grounded feedback, offering a practical step towards audience-aligned Human-AI collaboration.</abstract>
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%0 Conference Proceedings
%T MeepleLM: A Virtual Playtester Simulating Diverse Subjective Experiences
%A Li, Zizhen
%A Li, Chuanhao
%A Wang, Yibin
%A Sun, Jianwen
%A Feng, Yukang
%A Ai, Jiaxin
%A Zhang, Fanrui
%A Sun, Mingzhu
%A Huang, Yifei
%A Zhang, Kaipeng
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F li-etal-2026-meeplelm
%X Recent advancements have expanded the role of Large Language Models (LLMs) in board games from playing agents to creative co-designers. However, a critical gap remains: current systems lack the capacity to offer constructive critique grounded in the emergent user experience. Bridging this gap is fundamental for harmonizing Human-AI collaboration, as it empowers designers to refine their creations via external perspectives while steering models away from biased or unpredictable outcomes. Automating this evaluation presents two challenges: inferring the latent dynamics connecting static rules to gameplay without an explicit engine, and modeling the subjective heterogeneity of diverse player groups. To address these, we curate a comprehensive dataset of 1,727 structurally corrected rulebooks and 150K reviews selected via rigorous quality scoring and facet-aware sampling. We augment this data with Mechanics-Dynamics-Aesthetics (MDA) reasoning to explicitly bridge the causal gap between written rules and player experience. We further distill distinct player personas and introduce MeepleLM, a specialized model that internalizes persona-specific reasoning patterns to accurately simulate the subjective feedback of diverse player archetypes. Extensive experiments demonstrate that MeepleLM significantly outperforms latest commercial models (e.g., GPT-5.1, Gemini3-Pro) in community alignment and critique quality, achieving a 70% preference rate in user studies assessing practical utility. MeepleLM serves as a reliable virtual playtester that provides experience-grounded feedback, offering a practical step towards audience-aligned Human-AI collaboration.
%U https://aclanthology.org/2026.acl-long.850/
%P 18678-18722
Markdown (Informal)
[MeepleLM: A Virtual Playtester Simulating Diverse Subjective Experiences](https://aclanthology.org/2026.acl-long.850/) (Li et al., ACL 2026)
ACL
- Zizhen Li, Chuanhao Li, Yibin Wang, Jianwen Sun, Yukang Feng, Jiaxin Ai, Fanrui Zhang, Mingzhu Sun, Yifei Huang, and Kaipeng Zhang. 2026. MeepleLM: A Virtual Playtester Simulating Diverse Subjective Experiences. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 18678–18722, San Diego, California, United States. Association for Computational Linguistics.