JudgeSQL: Reasoning over SQL Candidates with Weighted Consensus Tournament

• URL: http://arxiv.org/abs/2510.15560v1
• Date: Fri, 17 Oct 2025 11:46:38 GMT
• Title: JudgeSQL: Reasoning over SQL Candidates with Weighted Consensus Tournament
• Abstract summary: Judge is a principled framework that redefines candidate selection through structured reasoning and weighted consensus tournament mechanism.<n>We show that Judge exhibits superior judgment capabilities and good cross-scale generalization and to generator capacity.
• Score: 26.476304887882133
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text-to-SQL is a pivotal task that bridges natural language understanding and structured data access, yet it remains fundamentally challenging due to semantic ambiguity and complex compositional reasoning. While large language models (LLMs) have greatly advanced SQL generation though prompting, supervised finetuning and reinforced tuning, the shift toward test-time scaling exposes a new bottleneck: selecting the correct query from a diverse candidate pool. Existing selection approaches, such as self-consistency or best-of-$N$ decoding, provide only shallow signals, making them prone to inconsistent scoring, fragile reasoning chains, and a failure to capture fine-grained semantic distinctions between closely related SQL candidates. To this end, we introduce JudgeSQL, a principled framework that redefines SQL candidate selection through structured reasoning and weighted consensus tournament mechanism. JudgeSQL develops a reasoning-based SQL judge model that distills reasoning traces with reinforcement learning guided by verifiable rewards, enabling accurate and interpretable judgments. Building on this, a weighted consensus tournament integrates explicit reasoning preferences with implicit generator confidence, yielding selections that are both more reliable and more efficient. Extensive experiments on the BIRD benchmark demonstrate that JudgeSQL exhibits superior SQL judgment capabilities and good cross-scale generalization and robustness to generator capacity.

Related papers

• ProSPy: A Profiling-Driven SQL-Python Agentic Framework for Enterprise Text-to-SQL [51.98164069124653]
  ProSPy is a Profiling-driven framework for enterprise-scale Text-to-gnostic analysis.<n>ProSPy structures the reasoning process into four stages.<n>It first extracts fine-grained data evidence through automatic profiling.<n>It prunes large schemas into task-relevant contexts.
  arXiv  Detail & Related papers  (2026-06-04T08:13:05Z)
• LEAF-SQL: Level-wise Exploration with Adaptive Fine-graining for Text-to-SQL Skeleton Prediction [4.626100354811136]
  LEAF- is a novel framework that reframes skeleton prediction as a coarse-to-fine tree search process.<n>Our method achieves 71.6 execution accuracy, which outperforms leading search-based and skeleton-based methods.
  arXiv  Detail & Related papers  (2026-05-10T03:43:29Z)
• R$^3$-SQL: Ranking Reward and Resampling for Text-to-SQL [55.967050404665606]
  R$3$- achieves 75.03 execution accuracy on BIRD-dev, a new state of the art among methods using models with disclosed sizes, with consistent gains across five benchmarks.
  arXiv  Detail & Related papers  (2026-04-28T07:40:50Z)
• SQL-Commenter: Aligning Large Language Models for SQL Comment Generation with Direct Preference Optimization [19.179416473507633]
  We propose an advanced method for comment generation based on LLaMA-3.1-8B.<n>We first construct a comprehensive dataset of complexsql queries with expert-verified comments.<n>Next, we perform continual pre-training on a largesql corpus to enhance the LLM's syntax and semantic understanding.<n>Finally, we introduce Direct Preference Optimization (DPO) using human feedback.
  arXiv  Detail & Related papers  (2026-03-19T08:23:40Z)
• SQL-Trail: Multi-Turn Reinforcement Learning with Interleaved Feedback for Text-to-SQL [20.49395306069103]
  We introduce a multi-turn reinforcement learning (RL) agentic framework for Text-to-one generation.<n>Rather than producing a query in one shot, SQL-Trail interacts with the database environment and uses execution feedback to iteratively refine its predictions.<n>Our approach centers on two key ideas: (i) an adaptive turn-budget allocation mechanism that scales the agent's interaction depth to match question difficulty, and (ii) a composite reward panel that jointly incentivizessql correctness and efficient exploration.
  arXiv  Detail & Related papers  (2026-01-25T05:16:52Z)
• Text-to-SQL as Dual-State Reasoning: Integrating Adaptive Context and Progressive Generation [54.53145282349042]
  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.
  arXiv  Detail & Related papers  (2025-11-26T13:52:50Z)
• Chain-of-Query: Unleashing the Power of LLMs in SQL-Aided Table Understanding via Multi-Agent Collaboration [22.351384833450567]
  Chain-of-Query (CoQ) is a novel multi-agent framework for table understanding.<n>CoQ adopts natural-language-style representations of table schemas to abstract away structural noise and enhance understanding.<n>Experiments across four models and five widely used benchmarks demonstrate that CoQ achieves substantial accuracy improvements.
  arXiv  Detail & Related papers  (2025-08-14T19:46:46Z)
• Arctic-Text2SQL-R1: Simple Rewards, Strong Reasoning in Text-to-SQL [35.21185734929167]
  We present Arctic-Text2-R1, a reinforcement learning (RL) framework and model family designed to generate accurate, executablesql.<n>Our approach avoids curated intermediate supervision and complex reward shaping, promoting stable training and alignment with the end task.<n> Notably, our 7B model outperforms prior 70B-class systems, highlighting the framework's scalability and efficiency.
  arXiv  Detail & Related papers  (2025-05-22T23:33:47Z)
• SQLCritic: Correcting Text-to-SQL Generation via Clause-wise Critic [8.680252929322684]
  We introduce a clause-wise critique generation task along with a benchmark,sqlCriticBench, which performs fine-grained error localization.<n>We also propose an automatically training dataset curation pipeline which annotates clause-wise critique at scale.
  arXiv  Detail & Related papers  (2025-03-11T02:52:39Z)
• STaR-SQL: Self-Taught Reasoner for Text-to-SQL [20.719165038519744]
  "chain-of-thought" rationales have proven effective for improving the performance of large language models on complex reasoning tasks.<n>Applying such techniques to structured tasks, such as text-to-driven, remains largely unexplored.<n>In this paper, we introduce Self-Taughter for text-to-driven (STaR-), a novel approach that reframes query generation as a reasoning process.<n> Experimental results on the challenging Spider benchmark demonstrate that STaR- significantly improves text-to-performance, achieving an execution accuracy of 86.6%.<n>These findings underscore the potential of reasoning-augmented training for
  arXiv  Detail & Related papers  (2025-02-19T08:58:44Z)
• A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL [20.010431872384714]
  We introduce XiYan, an innovative framework that employs a multi-context ensemble strategy to improve candidate generation.<n>We introduce M- schema, a semi-structured representation method designed to enhance the understanding of database structures.<n>Overall, our proposed XiYan-the-art framework achieves the state-of-the-art execution accuracy of 75.63% on Bird datasets benchmark.
  arXiv  Detail & Related papers  (2024-11-13T13:30:21Z)
• PTD-SQL: Partitioning and Targeted Drilling with LLMs in Text-to-SQL [54.304872649870575]
  Large Language Models (LLMs) have emerged as powerful tools for Text-to-sense tasks. In this study, we propose that employing query group partitioning allows LLMs to focus on learning the thought processes specific to a single problem type.
  arXiv  Detail & Related papers  (2024-09-21T09:33:14Z)
• 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)
• 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)
• SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers [61.48159785138462]
  This paper aims to improve the performance of text-to-dependence by exploring the intrinsic uncertainties in the neural network based approaches (called SUN) Extensive experiments on five benchmark datasets demonstrate that our method significantly outperforms competitors and achieves new state-of-the-art results.
  arXiv  Detail & Related papers  (2022-09-14T06:27:51Z)
• Weakly Supervised Text-to-SQL Parsing through Question Decomposition [53.22128541030441]
  We take advantage of the recently proposed question meaning representation called QDMR. Given questions, their QDMR structures (annotated by non-experts or automatically predicted) and the answers, we are able to automatically synthesizesql queries. Our results show that the weakly supervised models perform competitively with those trained on NL- benchmark data.
  arXiv  Detail & Related papers  (2021-12-12T20:02:42Z)
• Photon: A Robust Cross-Domain Text-to-SQL System [189.1405317853752]
  We present Photon, a robust, modular, cross-domain NLIDB that can flag natural language input to which a mapping cannot be immediately determined. The proposed method effectively improves the robustness of text-to-native system against untranslatable user input.
  arXiv  Detail & Related papers  (2020-07-30T07:44:48Z)

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.