Text-to-SQL as Dual-State Reasoning: Integrating Adaptive Context and Progressive Generation

• URL: http://arxiv.org/abs/2511.21402v1
• Date: Wed, 26 Nov 2025 13:52:50 GMT
• Title: Text-to-SQL as Dual-State Reasoning: Integrating Adaptive Context and Progressive Generation
• Authors: Zhifeng Hao, Qibin Song, Ruichu Cai, Boyan Xu,
• Abstract summary: We introduce DSR-sourced, a textbfDual-textbfS textbfReasoning framework that models Text-to-context as an interaction between an adaptive context state and a progressive generation state.<n>Without any post-training or in-context examples, DSR-sourced achieves competitive performance, reaching 35.28% execution accuracy on Spider 2.0-Snow and 68.32% on BIRD development set.
• Score: 54.53145282349042
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent divide-and-conquer reasoning approaches, particularly those based on Chain-of-Thought (CoT), have substantially improved the Text-to-SQL capabilities of Large Language Models (LLMs). However, when applied to complex enterprise databases, such methods struggle to maintain coherent reasoning due to limited context capacity, unreliable schema linking, and weak grounding in database semantics. To overcome these issues, we introduce DSR-SQL, a \textbf{D}ual-\textbf{S}tate \textbf{R}easoning framework that models Text-to-SQL as an interaction between an adaptive context state and a progressive generation state. The first constructs a compact, semantically faithful environment by refining large schemas and selecting relevant structures, while the second formalizes SQL synthesis as feedback-guided state transitions, enabling the model to self-correct and align with user intent. Without any post-training or in-context examples, DSR-SQL achieves competitive performance, reaching 35.28\% execution accuracy on Spider 2.0-Snow and 68.32\% on BIRD development set. Our implementation will be open-sourced at: https://github.com/DMIRLAB-Group/DSR-SQL.

Related papers

• APEX-SQL: Talking to the data via Agentic Exploration for Text-to-SQL [39.76924093980244]
  APEX- verbalize is a framework that shifts the paradigm from passive translation to agentic exploration.<n>Our framework employs a hypothesis-verification loop to ground model reasoning in real data.
  arXiv  Detail & Related papers  (2026-02-11T07:50:47Z)
• Bridging Global Intent with Local Details: A Hierarchical Representation Approach for Semantic Validation in Text-to-SQL [30.78817492504152]
  HERO is a hierarchical representation approach that integrates global intent and local details.<n>We employ a Nested Message Passing Neural Network (NMPNN) to capture inherent information in relational schema-guided semantics.<n>Our approach outperforms existing state-of-the-art methods, achieving an average 9.40% improvement of AUPRC and 12.35% of AUROC in identifying semantic inconsistencies.<n>It excels at detecting fine-grained semantic errors, provides large language models with more granular feedback, and ultimately enhances the reliability and interpretability of data querying platforms.
  arXiv  Detail & Related papers  (2025-12-28T02:25:33Z)
• CRED-SQL: Enhancing Real-world Large Scale Database Text-to-SQL Parsing through Cluster Retrieval and Execution Description [15.080310729603466]
  CRED- is a framework designed for large-scale databases that integrates Cluster Retrieval and Execution Description.<n>It bridges the gap between natural language questions (NLQs) and their correspondingsql queries.<n>CRED- achieves new state-of-git-the-art (SOTA) performance, validating its effectiveness and scalability.
  arXiv  Detail & Related papers  (2025-08-18T09:43:07Z)
• ReFoRCE: A Text-to-SQL Agent with Self-Refinement, Consensus Enforcement, and Column Exploration [32.83579488224367]
  We present ReFoRCE, a Text-to-confidence agent that tops the Spider 2.0 leaderboard.<n>ReFoRCE achieves state-of-the-art results, with scores of 35.83 Spider 2.0-Snow and 36.56 on Spider 2.0-Lite.
  arXiv  Detail & Related papers  (2025-02-02T05:25:03Z)
• RSL-SQL: Robust Schema Linking in Text-to-SQL Generation [51.00761167842468]
  We propose a novel framework called RSL- that combines bidirectional schema linking, contextual information augmentation, binary selection strategy, and multi-turn self-correction. benchmarks demonstrate that our approach achieves SOTA execution accuracy among open-source solutions, with 67.2% on BIRD and 87.9% on GPT-4ocorrection. Our approach outperforms a series of GPT-4 based Text-to-Seek systems when adopting DeepSeek (much cheaper) with same intact prompts.
  arXiv  Detail & Related papers  (2024-10-31T16:22:26Z)
• SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended) [53.95151604061761]
  This paper introduces the framework for enhancing Text-to- filtering using large language models (LLMs) With few-shot prompting, we explore the effectiveness of consistency decoding with execution-based error analyses. With instruction fine-tuning, we delve deep in understanding the critical paradigms that influence the performance of tuned LLMs.
  arXiv  Detail & Related papers  (2023-05-26T21:39:05Z)
• UNITE: A Unified Benchmark for Text-to-SQL Evaluation [72.72040379293718]
  We introduce a UNIfied benchmark for Text-to-domain systems. It is composed of publicly available text-to-domain datasets and 29K databases. Compared to the widely used Spider benchmark, we introduce a threefold increase in SQL patterns.
  arXiv  Detail & Related papers  (2023-05-25T17:19:52Z)
• Wav2SQL: Direct Generalizable Speech-To-SQL Parsing [55.10009651476589]
  Speech-to-Spider (S2Spider) aims to convert spoken questions intosql queries given databases. We propose the first direct speech-to-speaker parsing model Wav2 which avoids error compounding across cascaded systems. Experimental results demonstrate that Wav2 avoids error compounding and achieves state-of-the-art results by up to 2.5% accuracy improvement over the baseline.
  arXiv  Detail & Related papers  (2023-05-21T19:26:46Z)
• Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing [66.55478402233399]
  We propose a framework to elicit relational structures via a probing procedure based on Poincar'e distance metric. Compared with commonly-used rule-based methods for schema linking, we found that probing relations can robustly capture semantic correspondences. Our framework sets new state-of-the-art performance on three benchmarks.
  arXiv  Detail & Related papers  (2022-06-28T14:05:25Z)
• RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL [37.173390754207766]
  We propose a Transformer seq2seq architecture augmented with relationaware self-attention. Our model is able to incorporate almost all types of existing relations in the literature.
  arXiv  Detail & Related papers  (2022-05-14T06:27:40Z)
• Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing [110.97778888305506]
  BRIDGE represents the question and DB schema in a tagged sequence where a subset of the fields are augmented with cell values mentioned in the question. BRIDGE attained state-of-the-art performance on popular cross-DB text-to- relational benchmarks. Our analysis shows that BRIDGE effectively captures the desired cross-modal dependencies and has the potential to generalize to more text-DB related tasks.
  arXiv  Detail & Related papers  (2020-12-23T12:33:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.