@inproceedings{liu-etal-2026-iesr,
title = "{IESR}: Efficient {MCTS}-Based Modular Reasoning for Text-to-{SQL} with Large Language Models",
author = "Liu, Tao and
Lu, Jiafan and
Yu, Bohan and
Wu, Pengcheng and
Haixin, Liu and
Xu, Guoyu and
Li, Xiangheng and
Lixiaoli and
Hou, Jiaming and
Zhaoshijun and
Lyu, Xinglin and
Zhang, Kunli and
Jia, Yuxiang and
Zan, Hongying",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1958/",
pages = "44576--44604",
ISBN = "979-8-89176-459-0",
abstract = "Text-to-SQL is a key natural language processing task that maps natural language questions to SQL queries, enabling intuitive interaction with web-based databases. Although current methods perform well on benchmarks like BIRD and Spider, they struggle with complex reasoning, domain knowledge, and hypothetical queries, and remain costly in enterprise deployment. To address these issues, we propose a framework named IESR(Information Enhanced Structured Reasoning) for Lightweight Large Language Models: (i) leverages Large Language Models (LLMs) for key information understanding and schema linking, and decoupling mathematical computation and SQL generation, (ii) integrates a multi-path reasoning mechanism based on Monte Carlo Tree Search (MCTS) with majority voting, and (iii) introduces a trajectory consistency verification module with a discriminator model to ensure accuracy and consistency. Experimental results demonstrate that IESR achieves state-of-the-art performance on the complex reasoning benchmark LogicCat (24.28 EX) and the Archer dataset (37.28 EX), using only compact 7B and 8B models without fine-tuning. Furthermore, our analysis reveals that current coder models exhibit notable biases and deficiencies in physical knowledge, mathematical computation, and common-sense reasoning, highlighting important directions for future research. We released code at \url{https://github.com/Ffunkytao/IESR-SLM.}"
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<abstract>Text-to-SQL is a key natural language processing task that maps natural language questions to SQL queries, enabling intuitive interaction with web-based databases. Although current methods perform well on benchmarks like BIRD and Spider, they struggle with complex reasoning, domain knowledge, and hypothetical queries, and remain costly in enterprise deployment. To address these issues, we propose a framework named IESR(Information Enhanced Structured Reasoning) for Lightweight Large Language Models: (i) leverages Large Language Models (LLMs) for key information understanding and schema linking, and decoupling mathematical computation and SQL generation, (ii) integrates a multi-path reasoning mechanism based on Monte Carlo Tree Search (MCTS) with majority voting, and (iii) introduces a trajectory consistency verification module with a discriminator model to ensure accuracy and consistency. Experimental results demonstrate that IESR achieves state-of-the-art performance on the complex reasoning benchmark LogicCat (24.28 EX) and the Archer dataset (37.28 EX), using only compact 7B and 8B models without fine-tuning. Furthermore, our analysis reveals that current coder models exhibit notable biases and deficiencies in physical knowledge, mathematical computation, and common-sense reasoning, highlighting important directions for future research. We released code at https://github.com/Ffunkytao/IESR-SLM.</abstract>
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%0 Conference Proceedings
%T IESR: Efficient MCTS-Based Modular Reasoning for Text-to-SQL with Large Language Models
%A Liu, Tao
%A Lu, Jiafan
%A Yu, Bohan
%A Wu, Pengcheng
%A Haixin, Liu
%A Xu, Guoyu
%A Li, Xiangheng
%A Hou, Jiaming
%A Lyu, Xinglin
%A Zhang, Kunli
%A Jia, Yuxiang
%A Zan, Hongying
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%A Lixiaoli
%A Zhaoshijun
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F liu-etal-2026-iesr
%X Text-to-SQL is a key natural language processing task that maps natural language questions to SQL queries, enabling intuitive interaction with web-based databases. Although current methods perform well on benchmarks like BIRD and Spider, they struggle with complex reasoning, domain knowledge, and hypothetical queries, and remain costly in enterprise deployment. To address these issues, we propose a framework named IESR(Information Enhanced Structured Reasoning) for Lightweight Large Language Models: (i) leverages Large Language Models (LLMs) for key information understanding and schema linking, and decoupling mathematical computation and SQL generation, (ii) integrates a multi-path reasoning mechanism based on Monte Carlo Tree Search (MCTS) with majority voting, and (iii) introduces a trajectory consistency verification module with a discriminator model to ensure accuracy and consistency. Experimental results demonstrate that IESR achieves state-of-the-art performance on the complex reasoning benchmark LogicCat (24.28 EX) and the Archer dataset (37.28 EX), using only compact 7B and 8B models without fine-tuning. Furthermore, our analysis reveals that current coder models exhibit notable biases and deficiencies in physical knowledge, mathematical computation, and common-sense reasoning, highlighting important directions for future research. We released code at https://github.com/Ffunkytao/IESR-SLM.
%U https://aclanthology.org/2026.emnlp-main.1958/
%P 44576-44604
Markdown (Informal)
[IESR: Efficient MCTS-Based Modular Reasoning for Text-to-SQL with Large Language Models](https://aclanthology.org/2026.emnlp-main.1958/) (Liu et al., EMNLP 2026)
ACL
- Tao Liu, Jiafan Lu, Bohan Yu, Pengcheng Wu, Liu Haixin, Guoyu Xu, Xiangheng Li, Lixiaoli, Jiaming Hou, Zhaoshijun, Xinglin Lyu, Kunli Zhang, Yuxiang Jia, and Hongying Zan. 2026. IESR: Efficient MCTS-Based Modular Reasoning for Text-to-SQL with Large Language Models. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 44576–44604, Budapest, Hungary. Association for Computational Linguistics.