The dominant paradigm for LLM interaction in AI co-writing uses disposable prompts that vanish after use. This may lead to imprecise results, cumbersome workflows, and diminished author agency and ownership. We propose LLM-based story archeology, where prompts serve as a hierarchical story instrument refined over time to extract the writer's intended story. Drawing on the fossil theory of story- telling, where stories exist as latent structures that writers excavate through their craft, this approach supports agency and ownership through high involvement and control. Writers work at the level of story beats rather than prose. They generate character actions in scenes to discover emergent possibilities, simulated by the LLM or directly nudged, then edit resulting beats to refine scenes iteratively. Prose is generated from beats based on style and genre, separating structure from style. We developed TombWriter, a web-based tool that visualizes stories as navigable cards -- characters, scenes, and beats -- through a five-stage narrative pipeline. We conducted a qual- itative study with five experienced writers who used the system over three days. Through semi-structured interviews, we found that writers framed AI as a generation engine rather than collabo- rator, claimed ownership while reporting voice loss, and valued the system for structural discovery rather than prose production. We contribute the story archeology approach, the TombWriter system, and qualitative findings on beat-level human-AI co-writing.

翻译：人工智能协同写作中与大语言模型交互的主流范式使用一次性提示，这些提示在使用后即消失。这可能导致结果不精确、工作流程繁琐，并削弱作者的自主权和所有权。我们提出基于大语言模型的故事考古学，其中提示作为一种分层的故事工具，随时间推移不断优化以提取作者意图表达的故事。借鉴故事的化石理论——该理论认为故事作为潜在结构存在，作者通过创作技艺将其发掘出来——这种方法通过高参与度和高控制性支持自主权和所有权。作者在故事节拍层面而非散文层面工作。他们生成场景中的角色动作以发现涌现的可能性（由大语言模型模拟或直接引导），然后编辑生成的节拍以迭代优化场景。散文从节拍生成，并基于风格和体裁，将结构与风格分离。我们开发了TombWriter，一个基于网页的工具，通过五阶段叙事流水线将故事可视化为可导航的卡片——角色、场景和节拍。我们进行了一项定性研究，邀请五位经验丰富的作者使用该系统三天。通过半结构化访谈，我们发现作者将人工智能视为生成引擎而非协作者，在声称所有权的同时报告了声音缺失，并更重视系统在结构发现方面的价值而非散文生成。我们贡献了故事考古学方法、TombWriter系统以及关于节拍级人机协同写作的定性发现。