BAP-SQL: Budget-Aware Observation Planning for Agentic Text-to-SQL

Computer Science > Artificial Intelligence [Submitted on 3 Aug 2026] BAP-SQL: Budget-Aware Observation Planning for Agentic Text-to-SQL Chong Peng, Pin Qian, Su Wang, Yihang Chen, Varun Sah Tool-using agents do not merely consume observations: their actions determine what arrives next. In agentic text-to-SQL, a broad query can spend context and database work before useful evidence appears, while post-hoc compression cannot recover omitted rows or expended work. We present BAP-SQL, which treats observation formation as a budget-control stage: it estimates query risk, rewrites SQL when useful, and delegates hard limits to an independent runtime shield. Across general 4B, specialized FINER-SQL 4B, and 7B backbones, BAP-SQL improves tight-budget success. On the primary BIRD-derived setting, it gains 3.4/3.6 percentage points over matched SFT while using 4.5/5.0% fewer tokens. Matched retraining and task-level transfer associate the gain with policy-visible planning and budget-sensitive rescue. The benefit attenuates as model capability and budget increase, reverses at the loosest setting, and does not reduce database work. Comments: 10 pages, 3 figures Subjects: Artificial Intelligence (cs.AI) ACM classes: I.2.7; H.2.3 Cite as: arXiv:2608.02876 [cs.AI]   (or arXiv:2608.02876v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2608.02876 Focus to learn more Submission history From: Chong Peng [view email] [v1] Mon, 3 Aug 2026 20:55:58 UTC (1,451 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)