Search, Inspect, Fetch: Exploiting Structure-Aware Boolean Retrieval for Deep-Search Agents
cs.IR, cs.AI, cs.CL
Submitted: 2026-08-03
Updated: 2026-08-28
Comments: added statistical test, restructure appendix etc
Code: https://github.com/ielab/skim-search-agent
License: http://creativecommons.org/licenses/by/4.0/
The gist: Existing deep-research agents use a Search--Visit workflow that retrieves whole webpages without considering the structure they expose through titles, headings, sections, and metadata.
Terminology
Abstract
Existing deep-research agents use a Search--Visit workflow that retrieves whole webpages without considering the structure they expose through titles, headings, sections, and metadata. This prevents agents from directly constraining retrieval to parts of a webpage and often carries irrelevant content into their context. We introduce Sieve, a search--inspect--fetch strategy driven by a Boolean Query Language (BQL): it searches webpage fields to filter candidates, uses an interchangeable ranker to order them, presents structure-rich result cards for inspection, and fetches only selected sections. Across three QA collections, Sieve is more accurate than the strongest conventional Search--Visit configuration on each collection while using 20.7 -- 50.6% fewer tokens. Boolean filtering improves every tested ranker, and the accuracy--context advantage persists across retriever choices and agent backbones. Our implementation is included in the SkimSearchAgent library at https://github.com/ielab/skim-search-agent.
Sources
- From RAG to Memory: Non-Parametric Continual Learning for Large Language Models
- Keyword search is all you need: Achieving RAG-Level Performance without vector databases using agentic tool use
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