UniGame: A Unified Foundation Model for Board and Card Games via Shared–Specific Fusion of Game Experts

Large language models (LLMs) for board and card games offer a promising foundation for developing general-purpose agents capable of strategic reasoning, decision-making and interaction across diverse game environments. However, despite recent progress, game-playing LLMs still underperform specialized conventional methods, and a single model is typically optimized for only one game. Here we introduce UniGame, a unified board-and-card-game model that consolidates multiple game experts through shared–specific fusion while preserving their specialized policies. UniGame comprises three complementary designs: Cross-Game Subspace Sharing, which extracts reusable adaptation directions from independently trained LoRA experts; Game-Conditioned Effect Projection, which maps these shared directions to distinct game-specific updates; and Private Residual Adaptation, which preserves specialized knowledge not captured by the shared subspace. This post-hoc decomposition enables cross-game knowledge sharing without forcing heterogeneous games to adopt identical parameter updates, while using game identity to select the appropriate expert components during inference. Experiments across five board and card games demonstrate that UniGame substantially reduces adapter storage while retaining near-specialist gameplay performance and outperforming static merging approaches.