ArXiv

Beyond Top-K: Replacing Black-Box Retrieval with Interpretable Agentic Operations

2026-08-06 · 17:23 UTC ·Sagar Tamang, Ayush Vyas, Tabarakul Hazarika ·1 min read
Authors
Sagar Tamang, Ayush Vyas, Tabarakul Hazarika
Categories
cs.AI, cs.CL, cs.IR
arXiv
https://arxiv.org/abs/2608.06305v1
PDF
https://arxiv.org/pdf/2608.06305v1

Brief

Financial and regulatory documents with dense tabular content break the chunk-and-embed retrieval paradigm: in a 780-page report most lines are table rows and numeric units sit many lines above values, causing large errors. The authors introduce READ, an embedding-free agentic interface combining lexical search, structural navigation, and bounded span reads, producing replayable audit trails and markedly higher QA accuracy (58.8% on 51 questions) versus embedding-based retrieval. Full text was not provided here; summary is based on the abstract.

Why it matters

On a 780-page government financial report 86.8% of content lines are table rows and numeric values inherit units from a header a median of 13 lines above, so chunk boundaries commonly separate a number from its unit (errors up to two orders of magnitude).

Key details

  • A table-aware chunker fixes many unit errors but still leaves 27–30% of numeric chunks without a fiscal-year header at every chunk size tried.
  • READ (Reliable Embedding-free Agentic Document-search) exposes three deterministic operations — normalized lexical search, structural navigation, bounded span reads — and on 51 verified questions achieves 58.8% accuracy versus dense (embedding) retrieval's 15.7% (p_Holm = 2×10^-5); tuned dense reaches 35.3% (READ leads by 23.5 points, p_Holm = 0.017). BM25 was statistically indistinguishable from READ; an agent using the same loop but a top-k tool reached only 27.5%.
Source evidence

Abstract

Retrieval-augmented generation over long documents is dominated by one design: chunk the text, embed the chunks, and surface the top-k nearest neighbours of the query. We argue that for an important class of documents -- financial statements, audit reports, regulatory returns -- this design is structurally unsound, and we make the argument measurable. On a 780-page government financial report, 86.8% of content lines are table rows, thousands of near-identical figures compete in one embedding space, and a figure inherits its unit from a header a median of 13 lines above it -- so a chunk boundary routinely separates a number from whether it is in lakh or crore, an error of two orders of magnitude. A table-aware chunker built as a steelman fixes the unit problem but leaves 27-30% of numeric chunks with no fiscal-year header at every chunk size we tried. We propose READ (Reliable Embedding-free Agentic Document-search), in which an agent reads the raw document through three deterministic operations -- normalized lexical search, structural navigation, and bounded span reads -- exposed over the Model Context Protocol, so a trajectory is a replayable audit trail, not an opaque similarity score. On 51 verified questions READ answers 58.8% against dense retrieval's 15.7% (pHolm = 2 x 10^-5) -- or 35.3% tuned, which READ still leads by 23.5 points (pHolm = 0.017). An agent given the same loop but a top-k tool reaches only 27.5%, locating the gain in the interface rather than in iteration. We also report what the evidence does not support: BM25 is statistically indistinguishable from READ, so our result separates embedding-based from embedding-free retrieval, not agentic from lexical search.