arxiv
PublishedJune 2, 2026 at 4:00 AM
—neutral
Learning to Retrieve: Dual-Level Long-Term Memory for Text-to-SQL Agents
Publisher summary· verbatim
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 retrie
Stay posted· Newsletter
A 5-min weekly brief — top movers, price watch, story of the week.
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivSFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning13harxivOptical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning13harxivDynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models13harxivTemporal Order Matters for Agentic Memory: Segment Trees for Long-Horizon Agents13hThe Bubble Brief
WEEKLYRead AI insights every Tuesday — top movers, new releases, story of the week.
Originally published on arxiv ↗