扫描还是索引？长文档 RAG 中的证据覆盖率、答案质量与成本对比

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Abstract:Scanning every passage in a long document seems thorough, but does it preserve more useful evidence or produce better answers? We compare 11 retrieval configurations spanning lexical and dense search, hybrid reranking, and full-document scanning with Jev, OpenAI Decisions, and Cloudflare Clef. Our evaluation uses 396 QASPER questions and 80 questions from eight complete textbooks, with common passages, a five-passage limit, and a fixed answer generator. On books, Hybrid + Jev achieves the highest observed evidence coverage (94.1%), compared with 88.7% for Jev full scan and 81.3\% for OpenAI dense retrieval. Full scans examine every passage initially, yet later reduction steps can discard relevant evidence. Higher evidence coverage also does not consistently produce better answers: Hybrid + Decisions achieves the highest observed book Answer Accuracy among retrieval methods (0.892), despite recovering less reference evidence. Full scanning incurs substantially higher latency on books under the tested deployments, while reusable dense retrieval has lower projected API cost than paid decision-based methods as queries accumulate. Most QASPER articles already fit within the hybrid candidate pool, limiting the benefit of scanning. These results highlight evidence retention, answer quality, and reuse cost as complementary criteria for choosing a long-document retrieval strategy.

Comments:	15 pages, 4 figures, 13 tables
Subjects:	Computation and Language (cs.CL)
ACM classes:	H.3.3; I.2.7
Cite as:	arXiv:2610.28957 [cs.CL]
	(or arXiv:2610.28957v1 [cs.CL] for this version)
	https://doi.org/10.48550/arXiv.2610.28957 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Juan Terven [view email]
[v1] Tue, 20 Oct 2026 22:11:07 UTC (69 KB)