ResearchResearch paperRetrieval, RAG & Search · Large Language Models1 source · Oct 20, 2026

Scan or Index? large Evidence Coverage, Answer Quality, and Cost in Long-Document RAG

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.
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.
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.
These results highlight evidence retention, answer quality, and reuse cost as complementary criteria for choosing a long-document retrieval strategy.

[1]Scan or Index? large Evidence Coverage, Answer Quality, and Cost in Long-Document RAG
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 10:11 PM
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.

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