捕捉创作痕迹中的意图与关系，提升跨领域的创意分析精度。
From State Changes to Creative Decisions: Documenting and Interpreting Traces Across Creative Domains
- 用节点接口管理生成式AI的创作状态
- 为可视化创作定义视觉线索作为高层次创意动作
- 将语义历史嵌入交互状态，保留创作过程细节
分析创意活动痕迹需要在适当粒度下记录，并以反映创意实践结构的方式进行解读。然而，现有方法仅记录状态变化，未能保留定义高层级创意行为的意图或关系。这种脱节在不同领域表现各异：生成式AI工具丢失非线性探索结构，可视化创作掩盖表征意图，编程环境则模糊交互边界。本文提出三种互补方法：基于节点的界面用于状态化生成式AI成果管理；为可视化创作定义视觉线索作为更高层次的创意动作词汇表；以及一种将语义历史直接嵌入交互状态的编程模型。
原文摘要 · Abstract (English)
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.
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