Analyzing creative activity traces requires capturing activity at appropriate granularity and interpreting it in ways that reflect the structure of creative practice. However, existing approaches record state changes without preserving the intent or relationships that define higher-level creative moves. This decoupling manifests differently across domains: GenAI tools lose non-linear exploration structure, visualization authoring obscures representational intent, and programmatic environments flatten interaction boundaries. We present three complementary approaches: a node-based interface for stateful GenAI artifact management, a vocabulary of visual cues as higher-level creative moves in visualization authoring, and a programming model that embeds semantic histories directly into interaction state.

翻译：分析创作活动痕迹需要在适当的粒度上捕捉活动，并以反映创作实践结构的方式进行解释。然而，现有方法仅记录状态变化，却未能保留定义高层级创作行为的意图或关联关系。这种解耦在不同领域表现出不同形态：生成式人工智能工具丢失了非线性探索结构，可视化创作工具掩盖了表征意图，而编程环境则压平了交互边界。我们提出了三种互补的方法：一种用于有状态生成式人工智能制品管理的节点式界面，一套作为可视化创作中高层级创作行为的视觉提示词汇，以及一种将语义历史直接嵌入交互状态的编程模型。