MERIT: training-free repair memory helps Text-to-SQL agents modestly — and the paper says plainly where it doesn't

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MERIT (arXiv 2608.05906, submitted 2026-08-06) keeps an online dual-polarity memory of oracle-verified corrections and observed dead ends, classifies each failure into a coarse type, and conditions a hybrid lexical-dense retriever before a frozen Qwen2.5-7B-Instruct generates each revision. Execution accuracy rises from 66.34% to 69.79% on Spider and 47.35% to 48.44% on BIRD versus stateless iterative repair. The honesty is the value: paired analysis supports the Spider gain but is weak on BIRD, MERIT is not reliably separated from untyped dynamic retrieval on either benchmark, and Reflexion-style memory reaches 51.24% on BIRD at substantially higher inference cost. Ablations find negative memory contributes only modestly and schema-local experience is the most consistent benefit — a useful counterweight to the current wave of agent-memory claims.