TabQueryBench: A Query-Centric Benchmark for Synthetic Tabular Data

Automating End-to-End Hybrid Query Processing: Benchmark, Solution, and Insights

Hybrid queries—natural language questions over structured data that require both database capabilities and LLM reasoning—have recently emerged as a prominent research topic. However, existing solutions remain overly dependent on manual workflows, and current benchmarks are limited in scale and diversity. To bridge this gap, we present (1) HyQBench \xspace, a large-scale benchmark with 60\sim 90× more queries than prior work, built on 3× more databases; (2) AutoHyQ \xspace, an automated pipeline that can execute existing methods without manual intervention; (3) multi-dimensional, fine-grained evaluation metrics for comprehensive assessment. Through extensive experiments across multiple hybrid query approaches on diverse LLM backbones, we reveal their strengths and limitations, and identify research opportunities for advancing this emerging field. Our code and data are available at https://github.com/XMUDM/HyQBench.

DBRepro: Automated Database Synthesis via a Hybrid Constraint-Solving Approach for Reproducing Slow Queries

Slow queries frequently cause severe performance bottlenecks in database management systems. Diagnosing their root causes online risks exacerbating resource contention, while data privacy regulations often prohibit copying production data to test environments. Synthesizing a proxy database from non-intrusive metadata that induces the query optimizer to generate the same physical execution plans is therefore critical for offline diagnosis. High-fidelity reproduction requires preserving global statistical distributions while enforcing exact local cardinalities. Existing data-driven and workload-aware approaches cannot satisfy both requirements simultaneously. We present DBRepro, an automated end-to-end framework that formulates database generation as a constrained distribution synthesis problem. DBRepro initializes a global distribution from lightweight column statistics, extracts execution constraints from target queries, and progressively adjusts the distribution to satisfy these constraints while preserving the global distribution. Experiments on TPC-H and SSB show that DBRepro reduces cardinality error by up to 20.3% over a data-driven baseline while maintaining identical plan consistency. Compared with a workload-aware baseline, it reproduces 15% more consistent execution plans and reduces latency proportion error by 21.5%. We further validate DBRepro on a nearly 1 TB real-world dataset managed by KingbaseES, where it reproduces the execution performance of complex slow queries with high fidelity.

Finding the Right Tables and Columns: A Benchmark and Corpus-Adaptive Embeddings for SQL Schema Retrieval

This work recast five text-to-SQL datasets as retrieval tasks at both table and column granularity, covering realistic and enterprise-scale schemas under two document representations, and proposes corpus-adaptive fine-tuning, establishing schema linking as a standalone retrieval task and lightweight, label-free corpus adaptation as a practical route to deploying it at enterprise scale.

TABERTA: A Language Model for Dataset Discovery

ExpeSQL: An Efficient, Experience-Guided Decompositional Search Framework for Text-to-SQL

This work introduces ExpeSQL, a zero-shot, open-source–compatible, and efficient framework that combines divide-and-conquer reasoning, Best-of-N candidate selection, and self-critique with experience-guided refinement that establishes a new paradigm for deployable, self-improving Text-to-SQL systems in dynamic, real-world environments.

LLM-augmented query optimization: a hybrid framework for intelligent SQL performance tuning

LLM-QOpt++ is presented, a novel hybrid, confidence-aware query optimization framework that unifies traditional CBO estimation, machine learning–based cost prediction, and large language model (LLM) reasoning within a single adaptive pipeline.