OwlerLite: Scope- and Freshness-Aware Web Retrieval for LLM Assistants
Saber Zerhoudi, Michael Dinzinger, Michael Granitzer, Jelena Mitrovic

TL;DR
OwlerLite is a browser extension that enhances web retrieval for language models by allowing user-defined scopes and freshness monitoring, leading to more controlled and trustworthy information retrieval.
Contribution
It introduces a scope- and freshness-aware retrieval system that enables user control and dynamic updating of web content for language model assistants.
Findings
Supports user-defined web source scopes.
Uses semantic change detection for live page updates.
Integrates relevance, scope, and recency into retrieval.
Abstract
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsWeb Data Mining and Analysis · Personal Information Management and User Behavior · Information Retrieval and Search Behavior
