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• Agent-issued queries in deep research typically follow web-search-style syntax (e.g., quoted exact matches), which favors lexical, learned sparse, and multi-vector retrievers.result 90%
• Passage-level retrieval units are more efficient than document-level units under limited context windows in deep research tasks.result 90%
• Passage-level retrieval avoids the difficulties of document length normalisation in lexical retrieval.technical 90%
• Re-ranking is highly effective in improving text ranking performance for deep research tasks.result 90%
• Translating agent-issued queries into natural-language questions significantly bridges the query mismatch problem in deep research.method 90%
• Black-box web search APIs hinder systematic analysis of search components in deep research, leaving established text ranking methods' behavior unclear.limitation 85%
• The research approach could replace or significantly enhance current search methodologies that depend on black-box web search APIs.market 80%
• Potential limitations include dependency on specific types of queries aligning with training data and challenges adapting approaches to different domains.limitation 85%

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