BANANA: Bounded Adaptive Navigation Architecture for Nested Archives

Abstract

Retrieval-augmented question answering (QA) over enterprise PDF archives frequently produces confident near-miss answers because dense embeddings discard entity identity, numerical units, and document provenance. Plain-text indices preserve the entity, unit, and provenance signals that dense embeddings discard. Banana (Bounded Adaptive Navigation Architecture for Nested Archives) replaces opaque vectors with plain-text Markdown indices built via one-time VLM page transcription, eliminating both a separate Optical Character Recognition (OCR) engine and a vector store. At query time it applies Progressive Retrieval with Overlap (PRO), an iterative selector with a deterministic contraction bound, followed by a K-adaptive extraction gate that escalates retrieval depth only upon evidence insufficiency. Across three benchmarks, Banana consistently outperforms the strongest baseline: 71.4~EM on TAT-DQA (+15.2 over PageIndex; p <0.001), within 5.2 of oracle TAT-LLM; 79.3% accuracy on FinanceBench-150 across 53K~pages (+17.3 over PageIndex; p <0.01); and 86.2% Completeness@10 on UniDoc-Bench across 8~domains (+20.8 over the strongest fusion baseline). All gains hold across multiple backbones, including a local model at zero API cost.