用结构化布尔查询精准筛选网页内容，提升搜索效率与准确率
Search, Inspect, Fetch: Exploiting Structure-Aware Boolean Retrieval for Deep-Search Agents
- 通过布尔查询语言筛选网页字段，精准定位目标信息
- 在三个问答数据集上准确率更高，且上下文token减少20.7%-50.6%
- 适合需要高效检索与低资源消耗的智能搜索系统
现有深度搜索代理采用搜索-访问流程，检索整个网页而忽略标题、段落、章节和元数据等结构信息，导致上下文包含无关内容。本文提出Sieve策略：基于布尔查询语言（BQL），先搜索网页字段过滤候选，再用可替换排序器排序，以结构丰富的结果卡片供检查，并仅提取选定部分。在三个QA数据集上，Sieve比最强的常规搜索-访问配置更准确，同时减少20.7%至50.6%的上下文token。布尔过滤提升所有测试排序器表现，该精度与上下文优势在不同检索器和代理模型间均稳定。实现已开源于SkimSearchAgent库。
原文摘要 · Abstract (English)
Existing deep-search 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 https://github.com/ielab/skim-search-agent.
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