Search, Inspect, Fetch: Exploiting Boolean Retrieval for Deep-Research Agents

2026-08-03

AI Summary

This work addresses the limitation of existing deep research agents that overlook structured web fields—such as titles, sections, and metadata—during the search-and-retrieve process, leading to redundant retrieval and excessive contextual noise. To overcome this, the authors propose SIEVE, a novel framework that introduces fielded Boolean queries (BQL) into the research agent paradigm for the first time. SIEVE implements a three-stage pipeline: search, inspect, and retrieve. It first filters candidate pages using document-level fields, then presents results as structured cards for selective inspection, and finally retrieves only the content of chosen sections. By leveraging web structure at a fine-grained level, SIEVE achieves higher accuracy than state-of-the-art baselines across three question-answering benchmarks while reducing context token consumption by 20.7%–50.6%, demonstrating both efficiency and broad applicability.

Abstract

Existing deep-research agents use a search-visit workflow that retrieves and reads whole pages, without considering the addressable structure that web sources expose through titles, headings, sections, and metadata. This prevents agents from directly constraining retrieval to document fields and often carries irrelevant page content into their context. We introduce SIEVE, a search-inspect-fetch interface driven by fielded Boolean retrieval (BQL). SIEVE filters candidates over document fields, ranks the admitted set, presents structure-rich result cards for inspection, and fetches only selected sections. Across three QA collections, SIEVE achieves higher accuracy than the most accurate conventional Search-Visit configuration on each collection while using 20.7-50.6% fewer tokens. Further analyses show that BQL filtering improves all tested rankers and that the accuracy-context advantage persists across retriever choices and agent backbones. Code and data are available at https://github.com/ielab/skim-search-agent.

Problem

• deep-research agents
• Boolean retrieval
• fielded retrieval
• document structure
• information retrieval

Innovation

• Boolean retrieval
• fielded search
• deep-research agents
• token efficiency
• structured web retrieval