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From State Changes to Creative Decisions: Documenting and Interpreting Traces Across Creative Domains

Xiaohan Peng, Sotiris Piliouras, Carl Abou Saada Nujaim

TL;DR

Three complementary approaches are presented: 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.

Abstract

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.

From State Changes to Creative Decisions: Documenting and Interpreting Traces Across Creative Domains

TL;DR

Three complementary approaches are presented: 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.

Abstract

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.
Paper Structure (7 sections, 2 figures)

This paper contains 7 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Documenting GenAI Creative Traces: Tracking Interactive Generative Visual Artifacts across Scales
  3. Stateful Nodes for Dynamic Visual Interaction and Documentation
  4. Multi-Scale GenAI Traces Combine Commands, Artifact States, and Exploration Paths
  5. Visualizing Creative Traces: Representation and Framing Choices in Expressive 3D Visualization Authoring
  6. Programming Creative Traces: Embedding Semantic Histories in Interaction State
  7. Conclusion

Figures (2)

  • Figure 1: DesignTrace: a GenAI design tool for exploring alternatives and tracking design progress. Designers can extract or create semantic attributes then apply them to visuals, branch multiple alternatives under shared semantic categories, make localized edits inside individual canvas node while navigating editing history and different versions.
  • Figure 2: (1) A reactive signal records value changes in a persistent history. (2) A semantic action groups a segment of history changes recorded between the start and end of an interaction, such as a drag.