mlsanityData to 14 Oct 2026

AGENTIQL: An Agent-Inspired Multi-Expert Framework for Text-to-SQL Generation

arXiv 2510.10661 ↗First posted 12 Oct 2025cs.CL cs.AIcited by 2

On the map: LLM agents and tool use → Web, search, and research agents

Abstract (arXiv)

LLMs have advanced text-to-SQL generation, yet monolithic architectures struggle with complex reasoning and schema diversity. We propose AGENTIQL, an agent-inspired multi-expert framework that combines a reasoning agent for question decomposition, a coding agent for sub-query generation, and a refinement step for column selection. An adaptive router further balances efficiency and accuracy by selecting between our modular pipeline and a baseline parser. Several steps in the pipeline can be executed in parallel, making the framework scalable to larger workloads. Evaluated on the Spider benchmark, AGENTIQL improves execution accuracy and interpretability and achieves up to 86.07% EX with 14B models using the Planner&Executor merging strategy. The attained performance is contingent upon the efficacy of the routing mechanism, thereby narrowing the gap to GPT-4-based SOTA (89.65% EX) while using much smaller open-source LLMs. Beyond accuracy, AGENTIQL enhances transparency by exposing intermediate reasoning steps, offering a robust, scalable, and interpretable approach to semantic parsing.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 19 of its 28 references that are in the map. First posted on arXiv 12 Oct 2025.

Builds on

19 of 28 references.

References · 19

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  3. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
  4. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 688
  5. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 477
  6. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 424
  7. 2023Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluationcited by 385
  8. 2024CodeS: Towards Building Open-source Language Models for Text-to-SQLcited by 187
  9. 2023C3: Zero-shot Text-to-SQL with ChatGPTcited by 164
  10. 2024CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQLcited by 138
  11. 2023Exploring Chain-of-Thought Style Prompting for Text-to-SQLcited by 69
  12. 2024A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?cited by 56
7 more
  1. 2024A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQLcited by 46
  2. 2024DTS-SQL: Decomposed Text-to-SQL with Small Large Language Modelscited by 45
  3. 2023SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended)cited by 42
  4. 2023Improving Generalization in Language Model-Based Text-to-SQL Semantic Parsing: Two Simple Semantic Boundary-Based Techniquescited by 24
  5. 2022Semantic Enhanced Text-to-SQL Parsing via Iteratively Learning Schema Linking Graphcited by 22
  6. 2023Text-to-SQL Error Correction with Language Models of Codecited by 21
  7. 2024SQL-GEN: Bridging the Dialect Gap for Text-to-SQL Via Synthetic Data And Model Mergingcited by 16

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Natural Language Interfaces for Spatial and Temporal Databases: A Comprehensive Overview of Methods, Taxonomy, and Future Directionscited by 0
  2. 2026The Nuts and Bolts of Natural Language to SQL Translation: A Systematic Analysis of Model Pipeline Optimisation Approaches and their Interactionscited by 0

Similar, not cited

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  1. 2025BAPPA: Benchmarking Agents, Plans, and Pipelines for Automated Text-to-SQL Generation
  2. 2023MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL
  3. 2026AgentSM: Semantic Memory for Agentic Text-to-SQL
  4. 2025RAISE: Reasoning Agent for Interactive SQL Exploration
  5. 2023On Evaluating the Integration of Reasoning and Action in LLM Agents with Database Question Answering
  6. 2026AV-SQL: Decomposing Complex Text-to-SQL Queries with Agentic Views
  7. 2024Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents
  8. 2026Agentic-SQL Revisited: Autonomy-Based Taxonomy and Empirical Benchmark Analysis for LLM Text-to-SQL