3 papers
cs.AI2026
Fail Loudly: An Auditable Runtime for Agentic Data Analysis
Hanxu Yan, Langxuan Deng, Zhengle Wang +2
Large language models (LLMs) have enabled data-science agents to automate multi-step analyses over heterogeneous files. However, incorrect choices regarding data sources, scope, or…
cs.DB2025
AGRO-SQL: Agentic Group-Relative Optimization with High-Fidelity Data Synthesis
Cehua Yang, Dongyu Xiao, Junming Lin +7
The advancement of Text-to-SQL systems is currently hindered by the scarcity of high-quality training data and the limited reasoning capabilities of models in complex scenarios. In…
cs.DB2025
QUITE: A Query Rewrite System Beyond Rules with LLM Agents
Yuyang Song, Hanxu Yan, Jiale Lao +5
Query rewrite transforms SQL queries into semantically equivalent forms that run more efficiently. Existing approaches mainly rely on predefined rewrite rules, but they handle a li…