In recent years, there has been a growing interest in employing intelligent agents in writing. Previous work emphasizes the evaluation of the quality of end product-whether it was coherent and polished, overlooking the journey that led to the product, which is an invaluable dimension of the creative process. To understand how to recognize human efforts in co-writing with intelligent writing systems, we adapt Flower and Hayes' cognitive process theory of writing and propose CoCo Matrix, a two-dimensional taxonomy of entropy and information gain, to depict the new human-agent co-writing model. We define four quadrants and situate thirty-four published systems within the taxonomy. Our research found that low entropy and high information gain systems are under-explored, yet offer promising future directions in writing tasks that benefit from the agent's divergent planning and the human's focused translation. CoCo Matrix, not only categorizes different writing systems but also deepens our understanding of the cognitive processes in human-agent co-writing. By analyzing minimal changes in the writing process, CoCo Matrix serves as a proxy for the writer's mental model, allowing writers to reflect on their contributions. This reflection is facilitated through the measured metrics of information gain and entropy, which provide insights irrespective of the writing system used.

翻译：近年来，利用智能体进行写作日益受到关注。以往研究侧重于评估最终产物的质量（即文本是否连贯、精炼），却忽视了创作过程本身——而这正是创意过程中极具价值的维度。为理解如何识别人类在与智能写作系统协同写作中的贡献，我们借鉴Flower与Hayes的写作认知过程理论，提出CoCo矩阵——一个基于熵与信息增益的二维分类法，用以刻画新型人机协作写作模式。我们定义了四个象限，并将三十四个已发表系统纳入该分类框架。研究发现，低熵高信息增益的系统尚未得到充分探索，但为受益于智能体发散性规划与人类聚焦性转换的写作任务提供了有前景的未来发展方向。CoCo矩阵不仅对不同写作系统进行归类，更深化了我们对人机协同写作中认知过程的理解。通过分析写作过程中的细微变化，CoCo矩阵可作为作者心智模型的代理表征，使写作者得以反思自身贡献。这种反思借助信息增益与熵的量化指标得以实现，且其洞察不受具体写作系统的影响。