mlsanityData to 7 Oct 2026

OwlerLite: Scope- and Freshness-Aware Web Retrieval for LLM Assistants

arXiv 2601.17824 ↗First posted 25 Jan 2026cs.HC cs.IRcited by 3

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 14 of its 35 references that are in the map. First posted on arXiv 25 Jan 2026.

Builds on

14 of 35 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Support Evaluation for the TREC 2024 RAG Track: Comparing Human versus LLM Judges21 Apr
  2. 2024Ragnar"ok: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Track24 Jun

Other references · 12

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,722
  3. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,591
  4. 2024LightRAG: Simple and Fast Retrieval-Augmented Generationcited by 310
  5. 2023SemDeDup: Data-efficient learning at web-scale through semantic deduplicationcited by 295
  6. 2022RealTime QA: What's the Answer Right Now?cited by 239
  7. 2023WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferencescited by 100
  8. 2024Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answeringcited by 95
  9. 2025Knowledge Graph-Guided Retrieval Augmented Generationcited by 57
  10. 2025Explainable Knowledge Graph Retrieval-Augmented Generation (KG-RAG) with KG-SMILEcited by 4
  11. 2020Change Rate Estimation and Optimal Freshness in Web Page Crawlingcited by 3
  12. 2016iCrawl: Improving the Freshness of Web Collections by Integrating Social Web and Focused Web Crawlingcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026NuggetIndex: Governed Atomic Retrieval for Maintainable RAGcited by 1
  2. 2026Metadata, Structure, or Strategy? A Decomposition of RAG Context Enrichmentcited by 1
  3. 2026The Compaction Cliff in Long-Running AI Agent Memorycited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2026Risk-Constrained Freshness-Aware Semantic Caching for Open-Web Retrieval-Augmented LLMs
  2. 2025LevelRAG: Enhancing Retrieval-Augmented Generation with Multi-hop Logic Planning over Rewriting Augmented Searchers
  3. 2025Fast or Better? Balancing Accuracy and Cost in Retrieval-Augmented Generation with Flexible User Control
  4. 2026Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning
  5. 2026Beyond Pipelines: A Fundamental Study on the Rise of Generative-Retrieval Architectures in Web Research
  6. 2025WebRec: Enhancing LLM-based Recommendations with Attention-guided RAG from Web
  7. 2025DailyQA: A Benchmark to Evaluate Web Retrieval Augmented LLMs Based on Capturing Real-World Changes
  8. 2026RAG-Enhanced Large Language Models for Dynamic Content Expiration Prediction in Web Search