PAUSE: Editable Strategy Artifacts for Long-Form Cultural Story Adaptation

Generative AI systems increasingly mediate cul- tural adaptation, but their cultural decisions are of- ten hidden inside prompts, transient model plans, or final prose. We study PAUSE (Pause-And- Update Strategy Editing), an intervention that exposes an editable adaptation strategy as a hu- man control surface for cultural decisions in long- form story adaptation. The strategy is a structured artifact that can be inspected, edited, and then projected through downstream character, entity, and chapter-localization stages. In two Chinese- source serialized novels, we test whether human edits to this strategy propagate into chapter-level prose. Across 9 edited-vs-control chapter com- parisons, judges select the edited-strategy output in all 9; a marker audit shows target markers in 8/9 edited outputs and 0/9 controls, with for- bidden markers absent from edited outputs and present in all controls. We frame these results as a smoke-scale edit-adherence study, not a claim that the outputs are culturally authoritative or literary- quality improvements. PAUSE offers one practi- cal way to make AI-mediated cultural adaptation more inspectable and contestable before decisions propagate through long-form generation.