研究人员提出CAD方法，从进化程序中自动提取可重用原语，显著提升程序化内容生成质量。
研究团队在Sokoban、Zelda、Dangerous Dave和Lode Runner四个游戏领域，通过语言模型的变异和交叉操作进化完整的Python生成器。他们引入了持续抽象发现(CAD)方法，从高适应度程序中提取可重用的原语到运行特定辅助模块，并在2x2实验中与固定手写领域API结合。数据集包含160次完整运行，每个单元格至少有10次50代运行，CAD在所有八个领域和API比较中提高了平均最终最佳适应度。
Procedural Content Metageneration via Program Search and Continual Abstraction Discovery
Large language models can generate executable programs, which makes it possible to search directly over procedural content generators rather than individual levels. We study this approach in Sokoban, Zelda, Dangerous Dave, and Lode Runner. Each run evolves complete Python generators through language-model mutation and crossover. We introduce Continual Abstraction Discovery, or CAD, which extracts reusable primitives from high-fitness programs into a run-specific helper module. A 2x2 experiment crosses CAD with access to a fixed hand-written domain API. The completed data set contains 160 complete runs, with at least ten 50-generation runs in every cell. CAD raises mean final best fitness in all eight domain and API comparisons. Across all CAD runs, learned libraries are adopted by most later programs and repeatedly rediscover validation, reachability, and structural utilities. These results support that discovering reusable primitives improves evolutionary program search for content generators.