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

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 spa

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ethics.ai (6 August 2026), “Beyond Top-K: Replacing Black-Box Retrieval with Interpretable Agentic Operations,” evidence record 17065, https://ethics.ai/record/17065 (originally published by arXiv).

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