让用户自定义网页范围和更新时间，提升大模型问答的可控性与可信度。
OwlerLite: Scope- and Freshness-Aware Web Retrieval for LLM Assistants
- 用户可设定检索范围与更新时间，控制使用哪些网页
- 自动监测网页变化，仅重索引有语义更新的内容
- 适合需要实时、精准信息的科研或新闻工作者
基于浏览器的语言模型通常采用检索增强生成（RAG），但依赖固定且过时的索引，用户无法控制检索来源，可能导致答案混杂可信与不可信内容或使用陈旧信息。我们提出OwlerLite，一个以用户定义的检索范围和数据新鲜度为核心的浏览器端RAG系统。用户可创建可复用的范围（一组网页或来源），查询时选择相应范围。一个感知新鲜度的爬虫持续监控网页，通过语义变化检测器识别有意义的更新，并仅对变更内容进行选择性重索引。OwlerLite将文本相关性、范围选择与时效性整合到统一检索模型中。该系统作为浏览器扩展实现，推动更可控、更可信的网络助手发展。
原文摘要 · Abstract (English)
Browser-based language models often use retrieval-augmented generation (RAG) but typically rely on fixed, outdated indices that give users no control over which sources are consulted. This can lead to answers that mix trusted and untrusted content or draw on stale information. We present OwlerLite, a browser-based RAG system that makes user-defined scopes and data freshness central to retrieval. Users define reusable scopes-sets of web pages or sources-and select them when querying. A freshness-aware crawler monitors live pages, uses a semantic change detector to identify meaningful updates, and selectively re-indexes changed content. OwlerLite integrates text relevance, scope choice, and recency into a unified retrieval model. Implemented as a browser extension, it represents a step toward more controllable and trustworthy web assistants.
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