BAP-SQL: Budget-Aware Observation Planning for Agentic Text-to-SQL
A recent study presents BAP-SQL, a strategy designed for budget-aware observation planning within agentic text-to-SQL frameworks. This method views the formation of observations as a stage for budget management, which involves estimating query risks, rewriting SQL when beneficial, and assigning strict limits to a separate runtime shield. BAP-SQL enhances success rates under tight budgets across general 4B, specialized FINER-SQL 4B, and 7B models. In the primary BIRD-derived scenario, it achieves an improvement of 3.4/3.6 percentage points compared to matched SFT while utilizing 4.5/5.0% fewer tokens. The paper, which tackles the issue of how tool-using agents influence subsequent observations, is accessible on arXiv in the Computer Science > Artificial Intelligence section.
Key facts
- BAP-SQL is a method for budget-aware observation planning in agentic text-to-SQL.
- It estimates query risk, rewrites SQL when useful, and delegates hard limits to an independent runtime shield.
- Tested on general 4B, specialized FINER-SQL 4B, and 7B backbones.
- Improves tight-budget success on the BIRD-derived setting.
- Gains 3.4/3.6 percentage points over matched SFT while using 4.5/5.0% fewer tokens.
- Benefit attenuates as model capability and budget increase.
- Reverses at the loosest setting.
- Does not reduce database work.
Entities
Institutions
- arXiv