The proliferation of Large Language Models (LLMs) raises a critical question about what it means to be human when we share an increasingly symbiotic relationship with persuasive and creative machines. This paper examines patterns of human-AI coevolution in creative writing, investigating how human craft and agency are adapting alongside machine capabilities. We challenge the prevailing notion of stylistic homogenization by examining diverse patterns in longitudinal writing data. Using a large-scale corpus spanning the pre- and post-LLM era, we observe patterns suggestive of a "Dual-Track Evolution": thematic convergence around AI-related topics, coupled with structured stylistic differentiation. Our analysis reveals three emergent adaptation patterns: authors showing increased similarity to AI style, those exhibiting decreased similarity, and those maintaining stylistic stability while engaging with AI-related themes. This Creative Archetype Map illuminates how authorship is coevolving with AI, contributing to discussions about human-AI collaboration, detection challenges, and the preservation of creative diversity.

翻译：大型语言模型（LLM）的激增提出了一个关键问题：当我们与具有说服力和创造力的机器建立起日益共生的关系时，何以为人？本文考察了创意写作中人类与人工智能的协同演化模式，探究人类技艺与能动性如何与机器能力共同适应。通过分析纵向写作数据中的多样化模式，我们挑战了风格同质化的主流观点。利用一个横跨前LLM时代与后LLM时代的大规模语料库，我们观察到暗示着"双轨演化"的模式：围绕人工智能相关主题的主题趋同，与结构化的风格分化并存。我们的分析揭示了三种新兴的适应模式：作者风格与人工智能风格相似度增加、相似度降低，以及在探讨人工智能主题的同时保持风格稳定性。这幅"创造原型图谱"阐明了作者身份如何与人工智能协同演化，为关于人机协作、检测挑战以及创造性多样性保护的讨论提供了贡献。