BAP-SQL: Budget-Aware Observation Planning for Agentic Text-to-SQL
arXiv:2608.02876v1 Announce Type: new Abstract: 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.
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[Submitted on 3 Aug 2026]
Title:BAP-SQL: Budget-Aware Observation Planning for Agentic Text-to-SQL
View a PDF of the paper titled BAP-SQL: Budget-Aware Observation Planning for Agentic Text-to-SQL, by Chong Peng and 4 other authors
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Abstract: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
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Chong Peng [view email] [v1] Mon, 3 Aug 2026 20:55:58 UTC (1,451 KB)
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