Learning What to Remember: Utility-Guided Memory for Long-Horizon Text-to-SQL

Memory systems have proven beneficial in a variety of long-horizon settings. As text-to-SQL research increasingly extends to multi-turn interactions, these systems must retain user-specific knowledge across turns, creating a need for effective memory management. Text-to-SQL presents both opportunities and challenges for memory: each completed round yields a structured record comprising a request, SQL program, execution outcome, and possible correction, while interactions tend to revisit a bounded set of schema-grounded concepts. However, data reflecting real user workloads in long-horizon, multi-turn settings remains limited. Leveraging structured interactions, we represent completed rounds as standardized memory entries and formulate bounded text-to-SQL memory management as an eviction problem. We further exploit schema concentration through P-UTL, a lightweight, text-to-SQL-native policy that predicts the utility of stored information using execution-based supervision. To address the lack of workload simulators, we introduce a controlled trajectory generator that varies dormancy, recurrence, information density, and memory pressure across extended SParC- and CoSQL-derived streams. Experiments across workload configurations and baselines demonstrate the advantages of our method and show that predictive utility is particularly valuable when useful information competes for bounded memory.