A fundamental tension exists between the demand for sophisticated AI assistance in web search and the need for user data privacy. Current centralized models require users to transmit sensitive browsing data to external services, which limits user control. In this paper, we present a browser extension that provides a viable in-browser alternative. We introduce a hybrid architecture that functions entirely on the client side, combining two components: (1) an adaptive probabilistic model that learns a user's behavioral policy from direct feedback, and (2) a Small Language Model (SLM), running in the browser, which is grounded by the probabilistic model to generate context-aware suggestions. To evaluate this approach, we conducted a three-week longitudinal user study with 18 participants. Our results show that this privacy-preserving approach is highly effective at adapting to individual user behavior, leading to measurably improved search efficiency. This work demonstrates that sophisticated AI assistance is achievable without compromising user privacy or data control.

翻译：网络搜索中对先进人工智能辅助的需求与用户数据隐私保护之间存在根本性矛盾。当前的中心化模型要求用户将敏感浏览数据传输至外部服务，这限制了用户对数据的控制权。本文提出一种浏览器扩展方案，提供可行的浏览器内替代方案。我们引入一种完全在客户端运行的混合架构，该架构结合两个核心组件：(1) 通过直接反馈学习用户行为策略的自适应概率模型；(2) 在浏览器中运行的小型语言模型，该模型以概率模型为基础生成上下文感知建议。为评估该方案，我们开展了为期三周、包含18名参与者的纵向用户研究。实验结果表明，这种隐私保护方法能高效适应用户个体行为模式，显著提升搜索效率。本工作证明，在不损害用户隐私或数据控制权的前提下，实现先进的人工智能辅助是完全可行的。