AI RESEARCH
Learning to Retrieve: Dual-Level Long-Term Memory for Text-to-SQL Agents
arXiv CS.CL
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ArXi:2606.00547v1 Announce Type: new 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 dynamic methods often learn from sparse final outcomes and retrieve memories at a single decision horizon.