The paper introduces ingest‑time fact compilation, an architecture that preprocesses and compiles corpus data into self‑contained facts with resolved revisions, deletions, and source trust. By storing this compiled state, query‑time models can retrieve answers directly, avoiding costly reconstruction from raw passages. Experiments show that this approach reduces read cost per question by 12.89× and token usage by 21.6× while maintaining accuracy.
By Kyle Wild, Yusuke Takahashi, Asako Uraki
arXiv:2608. 16621v1 Announce Type: new Abstract: Retrieval-augmented and agentic question-answering systems increasingly re-derive the meaning of a corpus at query time.
By Yusuke Takahashi, Kyle Wild, Asako Uraki
Retrieval-augmented and agentic question-answering systems increasingly re-derive the meaning of a corpus at query time. Put plainly, instead of re-deriving what a corpus means on every question, the work is done once when a document arrives and is thereafter merely consulted -- a compiler, not an interpreter, of meaning.
arXiv:2610.14428v1 Announce Type: new
Abstract: Enterprise workflow automation typically requires retrieving a complete and accurate set of relevant information from large operational document corpor...
By Istok Bako, Bonan Min, Tiffany Shao, Claire Cheng, Yashar Mehdad
BudgetSchemaBench is a diagnostic tool for evaluating how different schema‑context budgets affect text‑to‑SQL systems. It automatically derives relevance labels from gold SQL, tests four budgets across 80 databases, and compares three schema representations while keeping table rankings fixed. The study shows that increasing the budget improves execution accuracy, especially for lexical retrieval, and that dense retrieval already captures most needed tables at low budgets.
By Chen Shen
arXiv:2606. 30473v1 Announce Type: cross Abstract: We study retrieval over catalogs of structured metadata, where each record is a small schema whose fields answer different kinds of query.
By Aivin V. Solatorio, Olivier Dupriez, Rafael Macalaba