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MeepleLM: A Virtual Playtester Simulating Diverse Subjective Experiences
Zizhen Li, Chuanhao Li, Yibin Wang, Yukang Feng, Jianwen Sun, Jiaxin Ai, Fanrui Zhang, Mingzhu Sun, Yifei Huang, Kaipeng Zhang
TL;DR
The paper addresses the lack of constructive, experience-grounded critique for interactive systems, where evaluation must connect static rules with emergent gameplay and heterogeneous player preferences. It builds a rulebook-review dataset, adds MDA-guided reasoning and persona modeling, and introduces MeepleLM as a virtual playtester; experiments report stronger community sentiment and actionable-insight capture than general LLMs. The authors also identify limits in multimodal understanding and persona granularity.
Problem
Current systems lack constructive critique grounded in emergent user experience, although such feedback is important for refining designs and supporting human-AI collaboration.
Method
MeepleLM combines 1,727 structured rulebooks, filtered reviews, MDA-guided reasoning, and data-driven player personas to simulate experience-specific feedback.
Results
MeepleLM significantly outperforms state-of-the-art LLMs in capturing authentic user experiences, community sentiment, and actionable design insights.
Takeaways & Limitations
The framework supports virtual playtesting and experience-aware Human-AI collaboration by connecting static rules with subjective audience feedback.
Takeaways & Limitations
MeepleLM currently processes rules only as text and uses five aggregated personas, limiting coverage of visual design and individual player idiosyncrasies.
Abstract
from arXiv · showhide
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.
1 Introduction
MeepleLM addresses the lack of constructive, experience-grounded critique in automated board-game development by connecting written rules to emergent gameplay and diverse player preferences. It combines MDA reasoning, curated rulebook-review data, and persona-specific modeling, then evaluates the resulting virtual playtester across alignment, fidelity, and utility.
- Research gap: Current automated systems lack constructive critique grounded in emergent user experience, limiting feedback available for human-AI co-creation.Designers need external perspectives to refine creations, while absent user feedback can contribute to biased or unpredictable experiences.
- Research challenges: Automated evaluation must infer latent gameplay dynamics from static rules and model subjective preferences that differ across player groups.Board-game experience emerges through interacting mechanics, player responses, and emotions rather than residing directly in rulebooks.
- Approach: MeepleLM combines a curated dataset of structurally corrected rulebooks and filtered reviews with MDA-based reasoning that makes rule-to-experience paths explicit.The approach uses Mechanics-Dynamics-Aesthetics reasoning to bridge written rules and gameplay experience.
- Approach: The model distills five data-driven player personas and simulates persona-specific reasoning to predict experiences reflecting diverse player preferences.Persona-specific modeling is intended to represent authentic gameplay experiences across real-world player archetypes.
- Evaluation: MeepleLM is evaluated on 207 games across macro-level alignment, micro-level fidelity, and practical utility, where it significantly outperforms state-of-the-art LLM baselines.The evaluation frames the system as a reliable virtual playtester.
- Implications: The framework is presented as a virtual playtesting paradigm that accelerates design iteration and supports personalized game selection through anticipated audience feedback.The authors also position its reasoning framework as applicable to a broader class of interactive systems.
2 Related Work
Prior LLM feedback and design-assistance systems evaluate static text or produce executable artifacts, but they often miss the logical and subjective dynamics of interactive experiences. MeepleLM instead targets experience-centered simulation directly from rules without requiring per-game executable engines.
- LLM feedback and assistance: Existing LLM feedback methods address open-ended text evaluation, while design tools generate levels or rule code but often prioritize syntactic correctness over logical coherence.Prior work covers writing feedback, peer review, narrative consistency, structural integrity, and subjective enjoyment.
- LLM feedback and assistance: Prior design assistance can yield functional yet meaningless mechanics, described as introns, when logical coherence is not preserved.The related-work discussion contrasts this failure mode with an experience-centered approach.
- Interactive-system evaluation: Interactive-system tools may require per-game forward models and primarily optimize strategic performance rather than subjective player experience.The paper positions MeepleLM as complementary by simulating dynamic interactions from rules without executable engines.
3 Data Construction
The paper constructs a multi-layered dataset linking structured board-game rules with high-quality, facet-diverse reviews, then derives interpretable player personas from review behavior. Sampling preserves rating sentiment while improving MDA quality and semantic coverage, revealing distinct preference patterns across personas.
- Dataset overview: The dataset maps objective game rulebooks to subjective player feedback across diverse personas through a multi-layered construction pipeline.The overall process is illustrated in Figure 2.
- Game selection: 1,727 board games are selected with stratification across market reception, cognitive complexity, release period, mechanics, and themes.The collection includes elite and long-tail titles, the full BGG Weight range of 1.0–5.0, 192 mechanics, and 81 themes.
- Review processing: The review corpus begins with 1.8 million rating-comment pairs and is evaluated using hard filtering, MDA scoring, and facet identification.These dimensions remove noise, assess Mechanics-to-Dynamics-to-Aesthetics links, and capture topics such as rule clarity and balance.
- Review filtering: ∼150k entries are retained at approximately 8% retention, with 50–100 reviews per game, while preserving rating sentiment and improving MDA and semantic coverage.Rating preservation achieved Pearson’s r = 0.920; quality filtering used scores > 4.
- Persona discovery: Player personas are derived by clustering review embeddings, refining 15 behavioral clusters with experts and GPT-5.1, and annotating the dataset using majority voting.The finalized taxonomy contains five distinct personas.
- Persona preferences: Persona analysis shows that party and adventure elements alienate System Purists, while heavy campaign games frustrate Efficiency Essentialists despite thematic appeal.These contrasting patterns illustrate the taxonomy’s intended aesthetic segregation.
- Annotation limitation: A DeBERTa-v3-large classifier was insufficient for subtle preference patterns, misclassifying reviews that combine house rules and balance with Thrill Seeker volatility.The paper uses this example to motivate LLM-based annotation.
4 Methodology
MeepleLM formulates critique generation as persona-conditioned feedback grounded in MDA reasoning, then trains a model to produce both the reasoning chain and final critique. Its pipeline reconstructs and verifies latent Mechanics-to-Dynamics reasoning before fine-tuning.
- MeepleLM generates ratings and reviews from rulebook context R and player persona P rather than mapping static rules directly to feedback.The formulation treats the task as conditional generation and introduces an intermediate reasoning sequence to bridge rules and emergent experience.
- MDA-guided reasoning traces Mechanics to Dynamics to Aesthetics before producing the critique Y, grounding feedback in simulated runtime experience.The latent sequence Z explicitly connects written rules, gameplay dynamics, and subjective aesthetics.
- Qwen3-235B reconstructs latent reasoning from high-quality review-rule pairs through a three-step flow covering Mechanics, Dynamics, and Aesthetics.The teacher prompt first extracts explicit rule components, then infers interaction dynamics and continues the reasoning chain toward experience.
- GPT-5.1 verifies synthesized reasoning against ground-truth ratings, removing contradictory or hallucinated chains and triggering regeneration.A post-hoc audit found all 200 reviewed MDA chains passed the same criteria used by GPT-5.1.
- The model fine-tunes Qwen3-8B to jointly predict the MDA chain Z and critique Y while encoding the full persona profile as a contextual prior.The target sequence is S = [Z; Y], optimized with standard cross-entropy loss and implemented with LoRA on all linear layers.
5 Experiments and Analysis
MeepleLM is evaluated as a virtual playtester across community alignment, review fidelity and diversity, and practical utility. Results show strong distributional and persona alignment, diverse grounded critiques, and higher user preference, with a limited-sample caveat.
- Evaluation setup: The evaluation tests macro-level alignment, micro-level fidelity, and practical utility across a disjoint, stratified test set of 207 games.The games span BGG complexity and rating tiers, including 34 newly released titles, with N = 100 simulation runs per game.
- RQ1: Macro-level Community Alignment: MeepleLM is assessed with MAE, Wasserstein Distance, and Kendall’s Rank Correlation to measure rating precision, distributional fidelity, and quality ranking.These complementary metrics target different aspects of alignment with collective community judgments.
- RQ1: Macro-level Community Alignment: WD = 0.82 shows MeepleLM recovers the high variance of human consensus in polarized cases where baselines collapse predictions toward scores 7–9.The result indicates improved representation of negative and positive player sentiment rather than safe-score clustering.
- RQ2: Content Fidelity and Diversity: MeepleLM matches state-of-the-art factual accuracy while producing greater lexical and perspective diversity across generated reviews.Perspective diversity is evaluated over batches of k = 5 reviews using topic coverage spanning mechanics, social interaction, and art style.
- RQ3: Practical Utility: MeepleLM achieves the highest Opinion Recovery Rate and wins blind A/B comparisons, with 78.3% average win rate for familiar games and 74.2% for unfamiliar games.Participants associated familiar-game wins with authenticity and unfamiliar-game wins with critical honesty; all reported dimensions had p < 0.001 and h = 0.69–1.03.
- Ablation and further analysis: Removing rule context reduces factual accuracy from 98.9 to 59.9, while replacing personas lowers ranking alignment to τ = 0.13.The ablation associates explicit rule grounding with factual accuracy and persona modeling with ranking alignment.
- Ablation and further analysis: Performance shifts are negligible when newly released titles are excluded, and robustness is strongest for high-variance personas such as The Social Lubricator and The Thrill Seeker.The analysis also reports that the framework captures social dynamics and subjective “vibes” that pure logical deduction cannot fully represent.
6 Conclusion
MeepleLM bridges static rulebooks and subjective player experiences by combining rule-critique data, MDA-based reasoning, and data-driven player personas. The resulting virtual testing paradigm targets experience-aware Human-AI collaboration across interactive systems.
- MeepleLM bridges static rulebooks and subjective player experiences.
- The model combines rule-critique pairs, MDA-based reasoning, and data-driven player personas to make gameplay dynamics explicit.
- Experiments show stronger capture of community sentiment and actionable design insights than general LLMs.
- The work establishes automated virtual testing for interactive systems attuned to diverse audience sensibilities.
Limitations
The authors identify two limitations of MeepleLM: text-only rule processing and personas aggregated into five broad player archetypes. They propose multimodal encoders and individual-level player modeling as future directions.
- MeepleLM currently processes game rules exclusively as text, omitting visual cues such as card art, board iconography, and component design.These cues contribute to immersion and usability, motivating future integration of visual encoders.
- Five aggregated personas capture broad player archetypes but may overlook individual idiosyncrasies.The proposed next step is individual-level modeling based on detailed historical data from specific players.
Ethics Statement
The paper describes privacy, consent, anonymization, and secure handling for publicly sourced data and human evaluation participants. The dataset removes personally identifiable information and restricts access to evaluation data.
- The dataset uses publicly available community content and anonymizes user IDs and review identifiers.The authors state that personally identifiable information is removed from the raw data.
- Human participants receive informed-consent information and may withdraw without negative consequences.
- Collected evaluation data, including interaction logs and questionnaires, is anonymized and securely access-restricted.
- The selected-game statistics are presented as a comprehensive breakdown of sampling coverage.
A.1 Distribution Analysis
The distribution analysis describes a broad, structurally diverse corpus spanning difficulty, quality, publication era, rankings, mechanics, themes, and review characteristics. Filtering preserves rating alignment while emphasizing detailed and varied feedback.
- Metadata coverage: Heavy/Very Heavy games comprise 28.5%, while Light games comprise 24.5% of the complexity distribution.The distribution has mean 2.57 and skewness 0.29, supporting a balanced complexity spectrum.
- Metadata coverage: 66% of games are rated above 7.0, with a mean rating of 7.22 and standard deviation 0.51.
- Metadata coverage: The dataset includes 159 pre-2000 games, 755 games published from 2015 onward, and 34 titles released in 2024–2025.
- Metadata coverage: More than half the dataset consists of long-tail games, with 909 games ranked above 1000, while 83% of the Top 100 is included.
- Content diversity: The corpus contains 192 mechanics and 81 themes, including Hand Management at 38.4% and Economic themes at 20.9%.Its mechanics and themes span systemic, abstract-strategy, and immersive-narrative categories, requiring contextual review vocabulary.
- Metadata coverage: 1,717 selected games span difficulty, quality, publication era, and market rank.The corpus includes both elite and long-tail games and emphasizes modern designs while retaining historical coverage.
- Review quality and diversity: Filtering preserves rating alignment with the original data, achieving Pearson r = 0.92 and Spearman ρ = 0.91.The filtered reviews also show a −0.20 mean shift and a 1.24x polarization ratio, with extreme-rating reviews averaging 195.7 words versus 158.2 midrange words.
- Review quality and diversity: Explicit Sentiment and Focus tags separate reviewers by evaluation criteria, while Figures 15–16 summarize group ratings and persona definitions.
D.5 Semantic Ambiguity Analysis
The analysis shows that keyword-based classifiers miss the intent behind semantically ambiguous reviews, motivating LLM-based persona interpretation and structured reasoning validation.
- ∼50% accuracy was achieved by DeBERTa-v3-large trained on cluster seeds, demonstrating the limitations of standard supervised classifiers.
- Standard models over-index on terms such as “balance” and “rules,” missing when reviewers use them to express opposing motivations.
- Persona profiles distinguish preferences across mechanics, with examples including cooperative games, dice rolling, auctions, area influence, and end-game bonuses.
- MDA chains were generated from rulebooks, reviews, and personas, then filtered for causal entailment and audited by three experienced players across 200 chains.
F.5 Opinion Recovery Evaluation
The opinion-recovery evaluation mines distinct human viewpoints and tests whether generated reviews semantically cover them, alongside a blinded human comparison of model outputs.
- The evaluation first extracts distinct, non-redundant viewpoints from human reviews and then checks their semantic presence in generated reviews.
- Opinion Recovery Rate is defined as the ratio of matched ground-truth viewpoints to all ground-truth viewpoints.
- The human study used a within-subject design in which 10 participants evaluated six games, split between familiar and unfamiliar titles.
- For each game, participants blindly compared randomized reviews from MeepleLM and GPT-5.1 on authenticity, resonance, diversity, shareability, and purchase-related criteria.
G.4 Full Evaluation Results
The full evaluation reports pairwise human-study results and ablations, while historical-subset analysis finds that recent titles minimally affect model comparisons.
- Table 7 reports pairwise win rates for MeepleLM against GPT-5.1, and Table 8 summarizes participants’ qualitative feedback.
- The ablation study isolates the effects of rulebook context, persona information, and MDA reasoning by comparing the full model with three direct-generation variants.
- The historical-subset evaluation excludes 35 newly released titles to test whether temporal novelty changes the RQ1 conclusions.
- Minimal deviation across metrics indicates that recent games do not significantly alter the benchmark’s relative rankings or performance conclusions.
H.3 Persona-wise Performance Analysis
Persona-wise results reveal a gap between rule-derivable strategic preferences and interaction-driven social experience, with MeepleLM showing more balanced alignment across personas.
- System Purist: General-purpose models perform competitively for System Purists, whose preferences for strategic depth and rule complexity are analytically derivable from rulebooks.
- Social Lubricator and Thrill Seeker: Baseline performance drops sharply for Social Lubricators and Thrill Seekers, whose preferences involve table talk, bluffing, and emotional highs.
- Social Lubricator: Qwen3-235B records τ < 0 for Social, illustrating poor alignment for an interaction-driven persona.
- MeepleLM: MeepleLM maintains strong positive correlations in high-variance social categories where baseline models fail, producing the most balanced persona-wise performance.