A core challenge in program synthesis is online library learning: the incremental acquisition of reusable abstractions under uncertainty about future task demands. Existing algorithms treat library learning as retrospective compression over a static task distribution, where the learned library is determined by the corpus of past tasks. However, real-world learning domains are often non-stationary, with tasks arising from a generative process that evolves over time. We propose and test the hypothesis that in non-stationary domains human library learning selects abstractions prospectively: targeting compression of future tasks. We study this question using the Pattern Builder Task, a visual program synthesis paradigm in which participants construct increasingly complex geometric patterns from a small set of primitives, transformations, and custom helpers that carry forward across trials. Using this task, we conduct two experiments with complementary latent curricula, designed to dissociate between behaviors consistent with prospective compression, and alternative library learning accounts. Using six computational models spanning online library learning strategies, we show that human abstraction behavior reflects sensitivity to latent, non-stationary structure in the task-generating process. This behavior is consistent with prospective compression, and cannot be captured by existing retrospective compression-based algorithms, or inductive biases modeled by LLM-based program synthesis.

翻译：程序合成中的一个核心挑战是在线库学习：在对未来任务需求存在不确定性的情况下，逐步获取可复用的抽象概念。现有算法将库学习视为对静态任务分布的事后压缩，其中学习到的库由过去任务的语料库决定。然而，现实世界的学习领域往往是非平稳的，任务产生于随时间演化的生成过程。我们提出并检验了以下假设：在非平稳领域中，人类库学习会前瞻性地选择抽象概念——旨在压缩未来任务。我们通过模式构建任务（Pattern Builder Task）研究该问题，这是一种视觉程序合成范式，参与者需使用一组有限的基元、变换以及跨试验延续的自定义辅助函数，逐步构建复杂度递增的几何图案。利用该任务，我们设计了两个具有互补潜在课程设置的实验，旨在区分与前瞻性压缩一致的行为及其他替代性库学习解释。通过六个涵盖在线库学习策略的计算模型，我们证明人类抽象行为反映了对任务生成过程中潜在的、非平稳结构的敏感性。这种行为与前瞻性压缩一致，无法被现有基于事后压缩的算法或基于大语言模型的程序合成所建模的归纳偏置所捕捉。