MeepleLM: A Virtual Playtester Simulating Diverse Subjective Experiences

ACL ARR 2026 January Submission653 Authors

Keywords: Human-AI collaboration, virtual playtesting, persona alignment, subjective experience simulation, board games

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. Ultimately, MeepleLM serves as a reliable virtual playtester for general interactive systems, marking a pivotal step towards audience-aligned, experience-aware Human-AI collaboration.

Paper Type: Long

Research Area: Human-AI Interaction/Cooperation and Human-Centric NLP

Research Area Keywords: human-AI interaction/cooperation,user-centered design,participatory/community-based NLP

Contribution Types: NLP engineering experiment, Data resources, Data analysis

Languages Studied: English

Submission Number: 653