In the human-AI collaboration area, the context formed naturally through multi-turn interactions is typically flattened into a chronological sequence and treated as a fixed whole in subsequent reasoning, with no mechanism for dynamic organization and management along the collaboration workflow. Yet these contexts differ substantially in lifecycle, structural hierarchy, and relevance. For instance, temporary or abandoned exchanges and parallel topic threads persist in the limited context window, causing interference and even conflict. Meanwhile, users are largely limited to influencing context indirectly through input modifications (e.g., corrections, references, or ignoring), leaving their control neither explicit nor verifiable. To address this, we propose Mixed-Initiative Context, which reconceptualizes the context formed across multi-turn interactions as an explicit, structured, and manipulable interactive object. Under this concept, the structure, scope, and content of context can be dynamically organized and adjusted according to task needs, enabling both humans and AI to actively participate in context construction and regulation. To explore this concept, we implement Contextify as a probe system and conduct a user study examining users' context management behaviors, attitudes toward AI initiative, and overall collaboration experience. We conclude by discussing the implications of this concept for the HCI community.

翻译：在人机协作领域，通过多轮交互自然形成的上下文通常被压缩为时序序列，并在后续推理中作为固定整体处理，缺乏沿协作工作流进行动态组织与管理的机制。然而，这些上下文在生命周期、结构层级与相关性方面存在显著差异。例如，临时或废弃的交互记录、并行的主题线程会持续占据有限的上下文窗口，引发干扰甚至冲突。与此同时，用户主要局限于通过修改输入（如修正、引用或忽略）间接影响上下文，其控制既非显式也非可验证。为解决这一问题，我们提出"混合主动上下文"概念，将多轮交互形成的上下文重新定义为一种显式、结构化且可操控的交互对象。在该概念框架下，上下文的结构、范围与内容可根据任务需求动态组织与调整，使人机双方均能主动参与上下文的构建与调控。为探索这一概念，我们实现了原型系统Contextify，并通过用户研究考察了用户的上下文管理行为、对AI主动性的态度及整体协作体验。最后，我们讨论了该概念对人机交互研究社区的意义。