Learning to Retrieve: Dual-Level Long-Term Memory for Text-to-SQL Agents 事件

Learning to Retrieve: Dual-Level Long-Term Memory for Text-to-SQL Agents arXiv:2606.00547v1 Announce Type: new Abstract: Interactive text-to-SQL agents solve database tasks through multi-turn interactions involving schema exploration, query execution, feedback interpretation, and decision revision. Long-term memory helps agents reuse past experiences, but existing retrieval methods remain limited. Static methods rely on fixed similarity heuristics that do not optimize downstream utility, while