AI 中文总结
该研究提出以信息架构（IA）为共享语言的Maru框架，将用户交互捕获为IA偏好并持久化，实现生成式用户界面与用户需求的持续一致，经用户研究验证了其有效性。
AI 中文摘要
生成式用户界面（GenUI）承诺提供按需定制的组件以满足用户需求。在用户迭代信息任务的过程中，他们会为遇到的信息构建个人结构——包括如何对项目进行分组、如何确定优先级，以及在特定语境下术语的含义。然而，当前系统在每次生成时都将这些结构决策交由模型处理，忽略了用户已建立的结构逻辑。若用户与系统之间不存在持久的表征结构，GenUI就没有基础保持与用户已建立的内容一致。我们借鉴信息架构（IA，一种用于组织和结构化信息的设计实践）作为共享语言，以弥合用户构建的结构与系统生成之间的差距。我们提出一个框架，确定了四个IA要素——分区、层级、顺序和词汇，并描述了每个要素如何映射到具体的UI生成决策。我们将该框架实例化为Maru，这是一个会话系统，它将用户的提示和交互捕获为IA偏好，并以规则的形式持久化，供用户和系统在多次生成中使用。一项用户研究表明，IA的持久性使生成的UI在会话进行过程中保持一致，而没有IA持久性的情况下一致性会下降，且不同用户和语境会出现不同模式，这表明IA持久性在使GenUI与个人需求保持一致方面具有重要价值。
英文摘要
Generative user interfaces (GenUIs) promise on-demand components tailored to users' needs. As users iterate on information tasks, they construct personal structures over information they encounter---how items are grouped, what gets prioritized, and what terms mean in their context. Yet, current systems leave these structural decisions to the model at each generation, ignoring the structural logic users have established. Without a persistent representational structure shared between user and system, GenUIs have no basis to remain aligned with what users have established. We draw on Information Architecture (IA), a design practice for organizing and structuring information, as a shared language to bridge user-constructed structure and system generation. We present a framework identifying four IA elements---partition, hierarchy, order, and vocabulary---and characterize how each maps to concrete UI generation decisions. We instantiate this framework in Maru, a conversational system that captures user prompts and interactions as IA preferences, persisting as rules both user and system draw on across generations. A user study revealed that IA persistence kept generated UIs aligned as sessions progressed, while alignment without it degraded, with diverse patterns emerging across users and contexts, pointing to the value of IA persistence in aligning GenUI to individual needs.
