Verifying Text-to-SQL Queries under Changing Business Semantics

Authors: Genliang Zhu , Xiaobo Chen , Ziyuan Wang , Chu Wang

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Abstract: In text-to-SQL and AI-agent analytics, successful SQL execution does not establish semantic correctness under changing business definitions. We study revalidation of fixed queries after changes to refund attribution, tax treatment, eligible populations and exchange-rate dates. An executable verification profile binds the requested revision and knowledge cutoff to legal counterexample data and exact monetary comparison. A preregistered confirmation on 32 newly authored revision families and two local producer bundles retains 192 query-definition cells: 144 nonconforming, 36 finite-suite agreeing and 12 unresolved. Of the nonconforming cells, 63 agree on the current snapshot. At an eight-ledger budget, obligation-guided checking detects 137 discrepancies versus 127 for generic ordering, a paired difference of 6.9 percentage points (family bootstrap 95% interval [2.1, 13.6]; exact family sign-flip p=0.0156). Every ledger arm rejects zero of the 36 agreeing cells. Current-obligation and equal-weight scoring match the guided result, identifying coverage rather than revision-specific priority as the observed source of benefit. Controlled assays, a natural-query pilot and repeated samples provide separate mechanism and robustness evidence. A legal discrepancy refutes conformance; agreement remains restricted to the recorded suite. The study establishes an auditable business-semantic revalidation profile within its authored schema and local-model scope.

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22 pages

Subjects:

Artificial Intelligence (cs.AI)

Cite as:

arXiv:2610.41090 [cs.AI]

(or

arXiv:2610.41090v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.41090

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arXiv-issued DOI via DataCite (pending registration)

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From: Xiaobo Chen [ view email ]

[v1]

Fri, 30 Oct 2026 00:13:39 UTC (34 KB)

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