Motif：发现并自动化个人网络工作流程
Motif: Discovering and Automating Personal Web Workflows
- University of California, Irvine（加州大学伊文斯顿分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
Motif系统通过被动观察浏览器活动发现可编程交互模式，向用户推荐并生成程序，经多日研究评估，其发现的可自动化模式多于用户识别出的，多数模式有用，多数用户会继续使用Motif生成的程序。
AI中文摘要：
大语言模型（LLMs）的最新进展和现有的示范编程工作使终端用户能够通过向LLMs明确展示行为来创建自动化。然而，这些方法依赖于用户知道要自动化什么以及能够自动化什么的假设。此外，与程序相比，通过LLM代理进行自动化通常成本高昂。我们引入了Motif系统，它被动观察日常浏览器活动，发现可编程的重复交互模式，发现模式时向用户推荐并在用户确认后生成要安装的程序。用户可以使用自然语言审查和完善程序。我们在一项为期多天的研究中评估了Motif，将其环境发现与用户尝试通过“氛围编码”构建的自动化进行比较。有八名参与者，Motif发现的可自动化模式比用户识别出的更多。大多数模式与参与者的日常工作匹配且有用。后续调查显示大多数人会继续使用Motif生成的程序。
英文摘要：
Recent advances in LLMs and existing work on programming by demonstration have made it possible for end users to create automations by explicitly demonstrating their behavior to LLMs. However, these approaches rely on the assumption that users know what to automate and what is capable of being automated. Additionally, automation via LLM agents is often expensive compared with programs. We introduce Motif, a system that passively observes everyday browser activity to discover recurring interaction patterns that are programmable, makes recommendations to users whenever a pattern is discovered and generate a program to install after user confirmation. Users can review, and refine the program using natural language. We evaluated Motif in a multi-day study, comparing its ambient discoveries against automations users attempted to build via ``vibe coding.'' With eight participants, Motif discovered more automatable patterns than users recognized. Most of them matched participants' routines and were useful. Follow-up surveys showed most would continue using Motif-generated programs.
