{"ID":2856828,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.12831","arxiv_id":"2510.12831","title":"MTSQL-R1: Towards Long-Horizon Multi-Turn Text-to-SQL via Agentic Training","abstract":"Multi-turn Text-to-SQL aims to translate a user's conversational utterances into executable SQL while preserving dialogue coherence and grounding to the target schema. However, most existing systems only regard this task as a simple text translation task and follow a short-horizon paradigm, generating a query per turn without execution, explicit verification, and refinement, which leads to non-executable or incoherent outputs. We present MTSQL-R1, an agentic training framework for long-horizon multi-turn Text-to-SQL. We cast the task as a Markov Decision Process (MDP) in which an agent interacts with (i) a database for execution feedback and (ii) a persistent dialogue memory for coherence verification, performing an iterative propose to execute -\u003e verify -\u003e refine cycle until all checks pass. Experiments on COSQL and SPARC demonstrate that MTSQL-R1 consistently outperforms strong baselines, highlighting the importance of environment-driven verification and memory-guided refinement for conversational semantic parsing. Full recipes (including code, trained models, logs, reasoning trajectories, etc.) will be released after the internal review to contribute to community research.","short_abstract":"Multi-turn Text-to-SQL aims to translate a user's conversational utterances into executable SQL while preserving dialogue coherence and grounding to the target schema. However, most existing systems only regard this task as a simple text translation task and follow a short-horizon paradigm, generating a query per turn...","url_abs":"https://arxiv.org/abs/2510.12831","url_pdf":"https://arxiv.org/pdf/2510.12831v3","authors":"[\"Taicheng Guo\",\"Hai Wang\",\"ChaoChun Liu\",\"Mohsen Golalikhani\",\"Xin Chen\",\"Xiangliang Zhang\",\"Chandan K. Reddy\"]","published":"2025-10-12T16:12:05Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\",\"cs.DB\",\"cs.LG\"]","methods":"[]","has_code":false}