Trusted Kernel Pattern Proposed for LLM Text-to-SQL Systems

A new arXiv paper (2608.13926) proposes an architectural pattern for large language model (LLM) text-to-SQL systems, addressing the critical issue of hallucinated outputs in natural language interfaces to databases (NLIDB). The paper argues that the primary problem is reliability rather than accuracy: when a system produces a fluent but wrong answer, users cannot distinguish it from a correct one, especially in enterprise deployments and operational dashboards where the generated query is not inspectable. The proposed solution is a 'trusted kernel with a generative shell' architecture, which maintains the invariant that a component capable of fabrication may influence which question the system answers, but never which value it returns. This design separates the generative aspects (interpreting input and phrasing replies) from a deterministic core that ensures the factual accuracy of returned values. The paper is authored by researchers and was announced on arXiv with the identifier 2608.13926. It highlights the growing concern of AI reliability in data-driven applications and offers a concrete pattern for mitigating risks in LLM-based database interfaces.