VisibilityOS
notableReviewed Sat Oct 31

SIEVE demonstrates structure-aware search, inspection and section-level fetching for research agents

Wang and colleagues introduced SIEVE, a deep-research agent workflow combining fielded Boolean document filtering, ranked structure-rich result cards and selective section fetching.

Context

Wang and colleagues introduced SIEVE, a deep-research agent workflow combining fielded Boolean document filtering, ranked structure-rich result cards and selective section fetching.

What changed

SIEVE separates document eligibility from ranking and exposes titles, headings, sections and metadata as actionable retrieval fields. Its experiments compared this search–inspect–fetch approach with conventional whole-page Search–Visit systems.

Why it matters

Agentic discovery may depend on which parts of a source can be addressed and fetched, not just whether the page appears in a search index. Structured document access affects retrieval precision and context efficiency in the tested system.

Visibility OS interpretation

Visibility OS interprets SIEVE as controlled evidence for structure-aware agent retrieval, not evidence that a particular schema.org property or markup change increases live AI-search citations.

What remains uncertain

Will section-level retrieval gains reproduce on live, changing web corpora and consumer agents? How robust are Boolean constraints and fallback behavior across heterogeneous sites?

Evidence claims

Emerging Evidencesupports

In three controlled QA collections, SIEVE's fielded Boolean search–inspect–fetch workflow achieved higher accuracy than the strongest conventional Search–Visit configuration on each collection while using 20.7–50.6% fewer distinct tokens.