Learning to Retrieve: Dual-Level Long-Term Memory for Text-to-SQL Agents
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 use fixed similarity heuristics, whereas dynamic methods often learn from sparse terminal outcomes and retrieve at a single decision horizon. This is insufficient when memory usefulness changes across interaction stages, since memories useful for initial planning may differ from those needed for local, state-conditioned execution. We propose MERIT, a dynamic multi-horizon memory retrieval framework....
| Authors | Yibo Wang, Nikki Lijing Kuang, Philip S. Yu et al. |
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| Primary category | cs.CL |
| Published | 30 May 2026 |
| Last updated at source | 13 October 2026 |
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