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This paper proposes MERIT, a dynamic multi-horizon memory retrieval framework for interactive text-to-SQL agents that uses episode-level and turn-level memory with learned retrieval policies optimized via reinforcement learning and a process reward model for dense rewards. Experiments on BIRD-Interact and Spider2-Snow show that MERIT outperforms static and single-horizon dynamic baselines in success rate while requiring fewer interaction turns.
DisasterLex introduces a knowledge-graph-mediated framework that improves text-to-SQL for disaster analytics by using an expert knowledge graph with causal edges to constrain schema and guide query planning, outperforming state-of-the-art baselines.
This paper introduces EnterpriseMem-Bench, a multi-turn Text-to-SQL benchmark, and evaluates five frontier models across memory architectures, finding that stateless models collapse by the third turn and that working memory yields the largest gains.
This paper proposes a knowledge-aware Text-to-SQL framework that uses knowledge distillation to improve performance in low-resource settings by constructing task-specific knowledge bases and generating synthetic training data. Experiments on seven benchmarks show substantial improvements, especially for open-source models.
DivSkill-SQL is a residual skill optimization framework that builds complementary agentic Text-to-SQL ensembles without model fine-tuning, improving selected accuracy by up to +11.1 points on Spider2-Lite by targeting examples that current ensembles fail on.
AgentSwarms launches a new SQL & BI Agent workspace that allows users to upload CSVs and ask natural language questions, automatically converting them to SQL queries and generating visualizations.