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.

通过采用 Flower 和 Hayes 的认知过程理论，我们提出了 CoCo Matrix，这是一个基于熵和信息增益的二维分类法，用以描述新的人工智能写作系统与人类合作写作的模型。我们将三十四个已发布的系统位于四个象限中，发现低熵高信息增益系统是未被充分探索但具有潜在发展方向的，可以在写作任务中充分发挥代理人的发散规划和人类的聚焦翻译。CoCo Matrix 不仅对不同的写作系统进行分类，还加深了我们对人工智能写作中认知过程的理解。通过分析写作过程中的微小变化，CoCo Matrix 可以作为写作者心智模型的代理，使写作者能够反思自己的贡献。信息增益和熵这两个测量指标提供了洞见，不受所使用的写作系统的影响。