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New frameworks boost enterprise Text-to-SQL with LLMs

By PulseAugur Editorial · [4 sources] ·

Researchers have developed two new frameworks, ProSPy and APEX-SQL, designed to improve the accuracy and efficiency of Text-to-SQL systems in enterprise environments. These systems leverage large language models but struggle with complex databases, incomplete metadata, and varied SQL dialects. ProSPy uses a four-stage process involving data profiling, schema pruning, and a hybrid SQL-Python analysis, achieving over 60% execution accuracy with Claude-4.5-Opus on benchmark datasets. APEX-SQL introduces an agentic exploration approach with a hypothesis-verification loop, demonstrating strong performance on BIRD and Spider 2.0-Snow datasets while reducing token consumption. AI

IMPACT These agentic frameworks enhance LLM capabilities for complex database interactions, potentially accelerating enterprise adoption of AI for data analysis.

RANK_REASON The cluster contains two academic papers detailing new research frameworks for Text-to-SQL systems.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

New frameworks boost enterprise Text-to-SQL with LLMs

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The cluster contains two academic papers detailing new research frameworks for Text-to-SQL systems.
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COVERAGE [4]

  1. arXiv cs.AI TIER_1 English(EN) · Wei Liu, Yang Gu, Xi Yan, Zihan Nan, Beicheng Xu, Keyao Ding, Bin Cui, Wentao Zhang ·

    ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows

    arXiv:2605.12376v2 Announce Type: replace Abstract: Table processing-including cleaning, transformation, augmentation, and matching-is a foundational yet error-prone stage in real-world data pipelines. While recent LLM-based approaches show promise for automating such tasks, they…

  2. arXiv cs.CL TIER_1 English(EN) · Zhaorui Yang, Huawei Zheng, Sen Yang, Yuhui Zhang, Haoxuan Li, Zhizhen Yu, Xuan Yi, Chen Hou, Defeng Xie, Chao Hu, Minfeng Zhu, Dazhen Deng, Haozhe Feng, Danqing Huang, Yingcai Wu, Peng Chen, Wei Chen ·

    ProSPy: A Profiling-Driven SQL-Python Agentic Framework for Enterprise Text-to-SQL

    arXiv:2606.05836v1 Announce Type: new Abstract: Large language models have substantially advanced Text-to-SQL systems, yet applying them to enterprise-scale databases remains challenging. Real-world databases often contain large and heterogeneous schemas, incomplete metadata, dia…

  3. arXiv cs.CL TIER_1 English(EN) · Wei Chen ·

    ProSPy: A Profiling-Driven SQL-Python Agentic Framework for Enterprise Text-to-SQL

    Large language models have substantially advanced Text-to-SQL systems, yet applying them to enterprise-scale databases remains challenging. Real-world databases often contain large and heterogeneous schemas, incomplete metadata, dialect-specific SQL syntax, and complex analytical…

  4. arXiv cs.AI TIER_1 English(EN) · Bowen Cao, Weibin Liao, Yushi Sun, Dong Fang, Haitao Li, Wai Lam ·

    APEX-SQL: Talking to the data via Agentic Exploration for Text-to-SQL

    arXiv:2602.16720v2 Announce Type: replace-cross Abstract: Text-to-SQL systems powered by Large Language Models have excelled on academic benchmarks but struggle in complex enterprise environments. The primary limitation lies in their reliance on static schema representations, whi…

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