MTSQL-R1: Towards Long-Horizon Multi-Turn Text-to-SQL via Agentic Training (2026.acl-long)
Copied to clipboard
Taicheng Guo, Hai Wang, Chaochun Liu, Mohsen Golalikhani, Xin Chen, Xiangliang Zhang, Chandan K. Reddy
| Challenge: | Existing systems for multi-turn Text-to-SQL are limited to a short-horizon paradigm, generating a query per turn without execution, explicit verification, and refinement, which leads to non-executable or incoherent outputs. |
| Approach: | They propose to train an agentic training framework for long-horizon multi-turn Text-to-SQL that uses a Markov Decision Process to generate a query per turn without execution, explicit verification, and refinement. |
| Outcome: | Experiments on CoSQL and SParC show that MTSQL-R1 consistently outperforms strong baselines, highlighting the importance of environment-driven verification and memory-guided refinement for conversational semantic parsing. |
Similar Papers
SQL-Trail: Multi-Turn Reinforcement Learning with Interleaved Feedback for Text-to-SQL (2026.acl-long)
Copied to clipboard
Harper Hua, Zhen Han, Zhengyuan Shen, Meng-Chieh Lee, Sheng Guan, Qi Zhu, Sullam Jeoung, Yueyan Chen, Yunfei Bai, Shuai Wang, Vassilis N. Ioannidis, Huzefa Rangwala
| Challenge: | Recent large language models (LLMs) have significantly improved Text-to-SQL generation, but a gap remains between AI systems and human experts on challenging benchmarks such as BIRD-Sql. |
| Approach: | They propose a multi-turn reinforcement learning agentic framework for Text-to-SQL that uses execution feedback to iteratively refine its predictions. |
| Outcome: | The proposed framework outperforms proprietary systems on 7B and 14B models by **5% on average, underscoring the effectiveness of interactive, agentic workflows for robust Text-to-SQL generation. |
Rethinking Text-to-SQL: Dynamic Multi-turn SQL Interaction for Real-world Database Exploration (2026.findings-acl)
Copied to clipboard
Linzhuang Sun, Tianyu Guo, Hao Liang, Ruitong Liu, Yuying Li, Qifeng Cai, Jingxuan Wei, Yuchen Wu, Bihui Yu, Xiangxiang Zhang, Wentao Zhang, Bin Cui
| Challenge: | Structured Query Language (SQL) is the cornerstone for data-driven decision-making. |
| Approach: | They propose a benchmark to rigorously evaluate Large Language Models within a dynamic interaction framework. |
| Outcome: | The proposed benchmark aims to rigorously evaluate LLMs within a dynamic interaction framework. |
MT-OSC: Path for LLMs that Get Lost in Multi-Turn Conversation (2026.findings-acl)
Copied to clipboard
Jyotika Singh, Fang Tu, Miguel Ballesteros, Weiyi Sun, Sandip Ghoshal, Michelle Yuan, Yassine Benajiba, Sujith Ravi, Dan Roth
| Challenge: | Large language models suffer performance degradation when user instructions and context are distributed over multiple conversational turns. |
| Approach: | They propose a framework that condenses chat history in the background without disrupting the user experience. |
| Outcome: | The proposed framework reduces token counts by up to 72% in 10-turn dialogues while remaining robust to distractors and irrelevant turns. |
MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models (2024.emnlp-main)
Copied to clipboard
Wai-Chung Kwan, Xingshan Zeng, Yuxin Jiang, Yufei Wang, Liangyou Li, Lifeng Shang, Xin Jiang, Qun Liu, Kam-Fai Wong
| Challenge: | Existing evaluation frameworks focus on single-turn evaluations, overlooking the models’ capabilities in multi-turn interactions. |
| Approach: | They propose a benchmark to evaluate the multi-turn conversational abilities of large language models (LLMs) by analyzing human-LLM conversations and constructing multi-turned queries for each category using GPT-4. |
| Outcome: | The proposed model outperforms open-source models in multi-turn tasks while retaining and recalling historical information. |
On the Multi-turn Instruction Following for Conversational Web Agents (2024.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable abilities in planning and executing multi-step interactions within web-based environments. |
| Approach: | They propose a framework for conversational web navigation that uses multi-turn interactions with both the user and the environment. |
| Outcome: | The proposed framework is based on a multi-turn Mind2Web (MT-Mind2Web) it is designed to perform multi-step interactions with web-based environments . |
LUMINA: Long-horizon Understanding for Multi-turn Interactive Agents (2026.findings-acl)
Copied to clipboard
Amin Rakhsha, Thomas Hehn, Pietro Mazzaglia, Fabio Valerio Massoli, Arash Behboodi, Tribhuvanesh Orekondy
| Challenge: | Large language models struggle on multi-turn, long-horizon agentic problems that require skills such as planning, state tracking, and long context processing. |
| Approach: | They propose an oracle counterfactual framework for multi-turn problems that asks: how would an agent perform if it could leverage an or acle to execute a specific skill? |
| Outcome: | The proposed framework allows for precise oracle interventions without confounding effects present in real-world benchmarks. |
Arctic-Text2SQL-R1: Simple Rewards, Strong Reasoning in Text-to-SQL (2026.findings-acl)
Copied to clipboard
Zhewei Yao, Guoheng Sun, Łukasz Borchmann, Zheyu Shen, Minghang Deng, Bohan Zhai, Hao Zhang, Ang Li, Yuxiong He
| Challenge: | Translating natural language questions into SQL is a core challenge in natural language understanding and human-computer interaction. |
| Approach: | They propose a reinforcement learning framework and model family to generate accurate, executable SQL using a lightweight reward signal based solely on execution correctness. |
| Outcome: | The proposed framework outperforms previous versions of 70B-class systems and achieves state-of-the-art execution accuracy across six diverse Text2SQL benchmarks. |
Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning (2026.findings-acl)
Copied to clipboard
Fengran Mo, Yifan Gao, Sha Li, Hansi Zeng, Xin Liu, Zhaoxuan Tan, Xian Li, Jianshu Chen, Dakuo Wang, Meng Jiang
| Challenge: | Existing studies focus on single-turn scenarios, which might lack the ability to handle multi-turn interactions. |
| Approach: | They propose a conversational agent that interleaves search and reasoning across turns and provides tailored rewards towards evolving user goals. |
| Outcome: | The proposed agent interleaves search and reasoning across turns, enabling exploratory and adaptive behaviors learned through reinforcement learning (RL) training with tailored rewards towards evolving user goals. |
Can a Single Model Master Both Multi-turn Conversations and Tool Use? CoALM: A Unified Conversational Agentic Language Model (2025.acl-long)
Copied to clipboard
Emre Can Acikgoz, Jeremiah Greer, Akul Datta, Ze Yang, William Zeng, Oussama Elachqar, Emmanouil Koukoumidis, Dilek Hakkani-Tür, Gokhan Tur
| Challenge: | Large Language Models (LLMs) with API-calling capabilities enabled building effective Language Agents (LA) current approaches excel in one domain but underperform in the other. |
| Approach: | They propose a unified approach that integrates both conversational and agentic capabilities. |
| Outcome: | The proposed model outperforms top domain-specific models across three benchmarks. |
MTO: A Multi-turn Conversational Text-to-OverpassQL Dataset for Enhanced OpenStreetMap Query Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | a framework for constructing multi-turn Text-to-OverpassQL dialogue datasets is proposed . a dataset of over 7,800 dialogues contains more than 20,000 individual utterances . |
| Approach: | They propose a framework for constructing multi-turn Text-to-OverpassQL dialogue datasets . they convert Overpass queries into syntax trees using a custom parser based on OverpassQl . |
| Outcome: | The proposed dataset includes over 7,800 dialogues, each containing 2 to 4 user utterances . it is the first multi-turn Text-to-OverpassQL dataset built upon the OverpassNL corpus . |