MeepleLM: A Virtual Playtester Simulating Diverse Subjective Experiences
Recent advancements have expanded the role of Large Language Models 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 critique for board games presents two challenges: inferring the latent dynamics connecting rules to gameplay without an explicit engine, and modeling the subjective heterogeneity of diverse player groups. To address these, we curate a dataset of 1,727 structurally corrected rulebooks and 150K reviews selected via 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 player personas and introduce MeepleLM, a specialized model that internalizes persona-specific reasoning patterns to accurately simulate the subjective feedback of diverse player archetypes. 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 utility. MeepleLM serves as a reliable virtual playtester for general interactive systems, marking a pivotal step towards audience-aligned, experience-aware Human-AI collaboration.
6976e5de5d41524304c1361c
Zizhen Li
6976e5de5d41524304c1361d
Chuanhao Li
claimed_verified
2026-03-27T11:39:01.510Z
674ea59a8f2e7614a6c72f26
/avatars/86fd4c6d7d33435de49b35659bf65265.svg
Chuanhao
ChuanhaoLi
user
ChuanhaoLi
6976e5de5d41524304c1361e
Yibin Wang
claimed_verified
2026-03-16T10:45:59.843Z
654c6845bac6e6e49895a5b5
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/KXQaAxulqr8jNBSpEaYM4.png
SII-Yibin Wang
CodeGoat24
user
CodeGoat24
6976e5de5d41524304c1361f
Yukang Feng
6976e5de5d41524304c13620
Jianwen Sun
6976e5de5d41524304c13621
Jiaxin Ai
6976e5de5d41524304c13622
Fanrui Zhang
6976e5de5d41524304c13623
Mingzhu Sun
6976e5de5d41524304c13624
Yifei Huang
6976e5de5d41524304c13625
Kaipeng Zhang
claimed_verified
2026-03-25T10:52:06.332Z
65f1713552c38a91e0a445e8
/avatars/47ab3ada51c9b9976ac1cd0c4301c373.svg
kaipeng
kpzhang996
user
kpzhang996
MeepleLM: A Virtual Playtester Simulating Diverse Subjective Experiences
Recent advancements have expanded the role of Large Language Models 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 critique for board games presents two challenges: inferring the latent dynamics connecting rules to gameplay without an explicit engine, and modeling the subjective heterogeneity of diverse player groups. To address these, we curate a dataset of 1,727 structurally corrected rulebooks and 150K reviews selected via 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 player personas and introduce MeepleLM, a specialized model that internalizes persona-specific reasoning patterns to accurately simulate the subjective feedback of diverse player archetypes. 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 utility. MeepleLM serves as a reliable virtual playtester for general interactive systems, marking a pivotal step towards audience-aligned, experience-aware Human-AI collaboration.
MeepleLM enables human-AI collaboration in board game design by providing constructive critique through persona-specific reasoning patterns that align with player experiences.
Large Language Models board games Human-AI collaboration creative co-designers constructive critique player personas MDA reasoning latent dynamics player experience virtual playtester audience-aligned collaboration