arXiv AI July 21, 2026 By Jiaming Tian, Liyao Li, Wentao Ye, Haobo Wang, Lihua Yu, Zujie Ren, Gang Chen, Junbo Zhao

Semantically Similar, Logically Distinct: Diagnosing the Semantic-Answerability Gap in Table RAG

arXiv:2607. 17742v1 Announce Type: new Abstract: Tables are a critical knowledge source in retrieval-augmented generation (RAG), but a retrieved table may lack sufficient evidence to answer a query, a property we call answerability.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Computation and Language

Aug 25

Same Agent, Different Answers: A Repeat-Aware Audit of Corpus-Induced Answer Churn in Retrieval-Augmented QA

arXiv:2608.22856v1 Announce Type: cross Abstract: A retrieval-augmented QA system can return different answers after an index expansion even when its requested model identifier, prompt, retrieval pol...

By Jingjie Ning, Xueqi Li

rag agents

arXiv AI

Jun 4

QO-Bench: Diagnosing Query-Operator-Preserving Retrieval over Typed Event Tuples

arXiv:2606. 04646v1 Announce Type: cross Abstract: Many real-world questions over business, legal, and scientific corpora are natural-language versions of database-style queries over records latent in text.

By Mengao Zhang, Xiang Yang, Chang Liu, Tianhui Tan, Ke-wei Huang

llms rag nlp benchmarks data-engineering

arXiv AI

2d ago

BudgetSchemaBench: A Budget-Swept Diagnostic for Schema Context in Text-to-SQL

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

llms agents data-engineering

arXiv AI

Sep 1

CORE-T: COherent REtrieval of Tables for Text-to-SQL

arXiv:2601.13111v3 Announce Type: replace-cross Abstract: Realistic text-to-SQL workflows often require joining multiple tables. As a result, accurately retrieving the relevant set of tables becomes...

By Hassan Soliman, Vivek Gupta, Dan Roth, Iryna Gurevych

llms data-engineering

arXiv AI

Aug 24

Clarify-Then-Search: A Clarification Benchmark for Deep Search with End-to-End Nugget Restoration

Clarify-Then-Search is a benchmark that tests whether large language models can ask clarification questions to improve the usefulness of deep search results. It uses 518 real-world query pairs from Baidu, where each intent query is paired with an underspecified version. The evaluation involves a clarifier asking up to three questions, a user answerer providing only explicit information, and a rewriter generating a new query that is then searched; performance is measured by a weighted nugget-recall score.

By Deqiang Huang, Jingbo Zhou, Xinjiang Lu, Tong Xu, Hua Wu, Enhong Chen

llms benchmarks

arXiv AI

Jul 8

Modality Relevance is not Modality Utility: Post-hoc Selective Modality Escalation for Cost-Aware Multimodal RAG

arXiv:2607. 05438v1 Announce Type: cross Abstract: Multimodal retrieval-augmented generation (RAG) grounds a generator in evidence drawn from heterogeneous modalities -- text, tables, and images.

By Xue Li, Yiming Gai

llms rag multimodal

One email a morning, machine-written

Related stories

Same Agent, Different Answers: A Repeat-Aware Audit of Corpus-Induced Answer Churn in Retrieval-Augmented QA

QO-Bench: Diagnosing Query-Operator-Preserving Retrieval over Typed Event Tuples

BudgetSchemaBench: A Budget-Swept Diagnostic for Schema Context in Text-to-SQL

CORE-T: COherent REtrieval of Tables for Text-to-SQL

Clarify-Then-Search: A Clarification Benchmark for Deep Search with End-to-End Nugget Restoration

Modality Relevance is not Modality Utility: Post-hoc Selective Modality Escalation for Cost-Aware Multimodal RAG