mlsanityData to 14 Oct 2026

SteinerSQL: Graph-Guided Mathematical Reasoning for Text-to-SQL Generation

arXiv 2509.19623 ↗First posted 23 Sep 2025cs.AIcited by 1

On the map: LLM reasoning → Knowledge- and tool-augmented reasoning

Abstract (arXiv)

Large Language Models (LLMs) struggle with complex Text-to-SQL queries that demand both sophisticated mathematical reasoning and intricate schema navigation. Existing methods often tackle these challenges in isolation, creating a fractured reasoning process that compromises logical and structural correctness. To resolve this, we introduce SteinerSQL, a framework that unifies these dual challenges into a single, graph-centric optimization problem. SteinerSQL operates in three stages: mathematical decomposition to identify required tables (terminals), optimal reasoning scaffold construction via a Steiner tree problem, and multi-level validation to ensure correctness. On the challenging LogicCat and Spider2.0-Lite benchmarks, SteinerSQL establishes a new state-of-the-art with 36.10% and 40.04% execution accuracy, respectively, using Gemini-2.5-Pro. Beyond accuracy, SteinerSQL presents a new, unified paradigm for Text-to-SQL, paving the way for more robust and principled solutions to complex reasoning tasks.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 20 of its 48 references that are in the map. First posted on arXiv 23 Sep 2025.

Builds on

20 of 48 references.

Closest ancestors · 4

Same direction, newest first

  1. 2025LogicCat: A Chain-of-Thought Text-to-SQL Benchmark for Complex Reasoning24 May
  2. 2025Sparks of Tabular Reasoning via Text2SQL Reinforcement Learning23 Apr
  3. 2025ExCoT: Optimizing Reasoning for Text-to-SQL with Execution Feedback25 Mar
  4. 2025STaR-SQL: Self-Taught Reasoner for Text-to-SQL19 Feb

Other references · 16

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  3. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
  4. 2024CHESS: Contextual Harnessing for Efficient SQL Synthesiscited by 169
  5. 2024Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQLcited by 120
  6. 2025OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scalecited by 91
  7. 2024A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQLcited by 46
  8. 2023SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended)cited by 42
  9. 2025Is Long Context All You Need? Leveraging LLM's Extended Context for NL2SQLcited by 30
  10. 2025LinkAlign: Scalable Schema Linking for Real-World Large-Scale Multi-Database Text-to-SQLcited by 29
  11. 2025Automatic Metadata Extraction for Text-to-SQLcited by 27
  12. 2025Understanding, Detecting, and Repairing Real-World In-Context-Learning-Based Text-to-SQL Errorscited by 14
4 more
  1. 2025Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise Rewardcited by 7
  2. 2025SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generationcited by 4
  3. 2024Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Typescited by 3
  4. 2025Text-to-SQL based on Large Language Models and Database Keyword Searchcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026IESR:Efficient MCTS-Based Modular Reasoning for Text-to-SQL with Large Language Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026DecoSearch: Complexity-Aware Routing and Plan-Level Repair for Text-to-SQL
  2. 2025Chain-of-Query: Unleashing the Power of LLMs in SQL-Aided Table Understanding via Multi-Agent Collaboration
  3. 2025Text-to-SQL as Dual-State Reasoning: Integrating Adaptive Context and Progressive Generation
  4. 2026Toward Multi-Database Query Reasoning for Text2Cypher
  5. 2025OraPlan-SQL: A Planning-Centric Framework for Complex Bilingual NL2SQL Reasoning
  6. 2025BAPPA: Benchmarking Agents, Plans, and Pipelines for Automated Text-to-SQL Generation
  7. 2025SATBench: Benchmarking LLMs' Logical Reasoning via Automated Puzzle Generation from SAT Formulas
  8. 2024Lucy: Think and Reason to Solve Text-to-SQL