READ beats dense retrieval by 43.1 points

READ, an embedding-free document-search agent, answered 58.8% of 51 verified questions on a 780-page government financial report, versus 15.7% for dense retrieval—a 43.1-point gap. The retrieval study says 86.8% of the report’s lines were table rows and units sat a median 13 lines above figures; deterministic lexical search, structural navigation, and bounded reads preserved that context, though BM25 was statistically indistinguishable from READ. For teams applying Onton’s warning about benchmark-shaped search wins, the practical takeaway is narrow: audit table-heavy financial RAG against lexical and structure-aware baselines before paying for embeddings, and keep replayable search traces for every numeric answer.