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：近年来，大型语言模型在桌游中的角色已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。弥合这一差距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，同时引导模型避免偏见或不可预测的结果。为桌游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；二是对多样化玩家群体的主观异质性进行建模。为解决这些问题，我们构建了一个包含1,727份结构校正规则书和15万条评论的数据集，这些数据通过质量评分和面向多维度的采样方法筛选获得。我们运用机制-动态-美学推理框架对该数据进行增强，以显式弥合书面规则与玩家体验之间的因果鸿沟。进一步地，我们提炼了玩家角色原型，并提出了MeepleLM——一个能够内化角色特定推理模式的专业模型，可精准模拟多样化玩家原型的个性化反馈。实验表明，MeepleLM在社区契合度与批评质量上显著优于最新的商业模型（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游戏测试者，标志着向受众对齐、体验感知的人机协作迈出了关键一步。