OpenBlock：自适应拼图匹配游戏的建设性与验证内容生成
OpenBlock: Constructive and Verified Content Generation for Adaptive Tile-Matching Games
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中文总结 AI 辅助
本文提出OpenBlock，一个双轨内容生成的自适应拼图匹配平台，通过验证门控保证可放置性，并利用自对弈强化学习诊断失败模式，实验表明其神经轨显著提升玩家留存与时长。
中文摘要 AI 辅助
拼图匹配益智游戏服务着数亿玩家，然而决定每回合呈现哪些拼图块的内容生成算法仍是专有的，且不存在用于研究该类型自适应难度的开放平台。我们提出一个自适应拼图匹配平台，其核心算法贡献是双轨内容生成架构：一个始终可用的确定性基于规则的生成器，以及一个可选的学习生成器，两者均受共同验证门控约束，该门控通过穷举顺序放置搜索确立每个交付的拼图块集合完全可放置，从而学习轨永远不会降低规则轨的建设性可行性保证。一个由辅助任务监督的自对弈强化学习放置智能体，这些辅助任务将每种形状的可放置性暴露给共享表示，用于诊断游戏的主要失败模式：在高棋盘填充率下，长条块失去了其大部分合法放置位置。在超过234,000次自对弈回合中，智能体达到35.6%的胜率（中位分数4,200），且受控模拟显示，在70–75%的棋盘填充率下，33–56%的长条块没有合法放置位置，而生成难度分布在胜利与失败游戏之间在统计上无显著差异——这表明棋盘状态退化（而非内容难度）驱动了后期游戏失败。头对头消融实验显示，每种形状的可放置性监督（而非聚合难度特征）驱动了表示增益，且为期14天的在线灰度发布（48,000名玩家；样本比例已验证，CUPED调整）将首日留存率提升了1.8个百分点，会话时长比仅规则轨提升了7%，量化了神经轨在实时游戏中的不对称上行空间。
英文摘要
Tile-matching puzzle games serve hundreds of millions of players, yet the content-generation algorithms that decide which pieces to present at each turn remain proprietary, and no open platform exists for studying adaptive difficulty in this genre. We present an adaptive tile-matching platform whose central algorithmic contribution is a dual-track content-generation architecture: a deterministic rule-based generator that is always available, and an optional learned generator, both subject to a common verification gate that establishes, by exhaustive sequential-placement search, that every delivered piece set is fully placeable so the learned track can never degrade the constructive-feasibility guarantee of the rule track. A self-play reinforcement-learning placement agent, supervised by auxiliary tasks that expose per-shape placeability to shared representations, is used to diagnose the game's dominant failure mode: at high board fill, long-bar pieces lose the majority of their legal placements. Across 234,000+ self-play episodes the agent reaches a 35.6\% win rate (median score 4,200), and controlled simulation shows that at board fill rates of 70--75\%, 33--56\% of long-bar pieces have no legal placement, while spawn difficulty distributions are statistically indistinguishable between won and lost games---evidence that board-state degeneration, not content difficulty, drives late-game failure. Head-to-head ablations show that per-shape placeability supervision---not aggregate difficulty features---drives the representation gain, and a 14-day online gray rollout (48,000 players; sample-ratio verified, CUPED-adjusted) lifts day-1 retention by 1.8 percentage points and session duration by 7\% over the rule track alone, quantifying the neural track's asymmetric upside in live play.
