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.

翻译：近期研究进展已将大语言模型在棋盘游戏中的角色从游戏代理扩展至创意协同设计者。然而，当前系统仍存在一个关键缺陷：缺乏基于涌现用户体验提供建设性批评的能力。弥补这一缺陷对于协调人机协作至关重要，因为它使设计者能够通过外部视角完善创作，同时引导模型避免产生偏见或不可预测的结果。为棋盘游戏实现自动化批评面临两大挑战：一是在缺乏显式游戏引擎的情况下，推断连接规则与游戏过程的潜在动态；二是建模多样化玩家群体的主观异质性。为此，我们构建了一个包含1,727份结构校正规则手册和15万条评论的数据集，这些数据通过质量评分与多维度感知采样筛选获得。我们进一步运用机制-动态-美学（MDA）推理框架增强数据，以显式弥合书面规则与玩家体验之间的因果鸿沟。通过提炼玩家角色原型，我们提出了MeepleLM——一个能够内化角色特定推理模式、精准模拟多样化玩家原型主观反馈的专用模型。实验表明，MeepleLM在社区契合度与批评质量上显著优于最新商用模型（如GPT-5.1、Gemini3-Pro），在评估实用性的用户研究中获得70%的偏好率。MeepleLM可作为通用交互系统的可靠虚拟测试者，标志着向受众对齐、体验感知的人机协作迈出了关键一步。