What Drives Interaction Budget Scaling in Text-to-SQL?

Interactive Text-to-SQL can resolve underspecified requirements through user clarification, yet larger interaction budgets do not consistently yield better SQL solutions. We study what makes additional interaction useful by comparing five representative methods and conducting controlled experiments on clarification evidence in Bird-Interact-Full. Context-only questioning is competitive initially, while guiding questions with differences between as few as two SQL candidates yields larger gains as the interaction budget increases. Wider pools bring earlier gains, but their advantage narrows with further interaction. Building on these findings, we introduce $\text{mbox}\text{DuetSQL}$, which combines context-guided requirement elicitation with iterative SQL-contrast-guided clarification and uses execution agreement to control further sampling and questioning. Across three benchmarks, DuetSQL outperforms the strongest compared methods by 5-12 percentage points at matched interaction allowances. DuetSQL configurations also lie on the empirical Pareto frontiers in our comparisons under shared resource limits, offering favorable trade-offs between task success and resource use.