---
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id: "314f23b30b1f6e9b1af9191bf81c2956a80cd0fe140b4f9ca086937121dc569b"
canonical_url: "https://aidr.today/314f23b3?lang=en"
title: "Reimagining Text-to-SQL: A Survey of Large Language Model Architectures and Strategies"
lang: "en"
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available_langs: ["en","vi"]
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published_at: "2026-11-03T05:00:00.000Z"
category: "Research"
topics: ["llm"]
source_urls: ["https://arxiv.org/abs/2611.00171"]
summary: "Text-to-SQL systems translate natural language questions into executable SQL queries. Large language models like ReFoRCE, Agentar-Scale-SQL, Snowflake's Arctic-Text2SQL-R1, and XiYan-SQL have advanced the field. Modular frameworks decompose the task into sub-problems while agentic systems coordinate specialized agents."
---

# Reimagining Text\-to\-SQL: A Survey of Large Language Model Architectures and Strategies

> [Open the canonical story](<https://aidr.today/314f23b3?lang=en>)

**Published:** 2026-11-03T05:00:00.000Z
**Category:** Research
**Topics:** llm

## Summary

Text\-to\-SQL systems translate natural language questions into executable SQL queries\. Large language models like ReFoRCE, Agentar\-Scale\-SQL, Snowflake's Arctic\-Text2SQL\-R1, and XiYan\-SQL have advanced the field\. Modular frameworks decompose the task into sub\-problems while agentic systems coordinate specialized agents\.

## Sources

- [Story source](<https://arxiv.org/abs/2611.00171>)