{"ID":2895208,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.09592","arxiv_id":"2507.09592","title":"THOR: Transformer Heuristics for On-Demand Retrieval","abstract":"We introduce the THOR (Transformer Heuristics for On-Demand Retrieval) Module, designed and implemented by eSapiens, a secure, scalable engine that transforms natural-language questions into verified, read-only SQL analytics for enterprise databases. The Text-to-SQL module follows a decoupled orchestration/execution architecture: a Supervisor Agent routes queries, Schema Retrieval dynamically injects table and column metadata, and a SQL Generation Agent emits single-statement SELECT queries protected by a read-only guardrail. An integrated Self-Correction \u0026 Rating loop captures empty results, execution errors, or low-quality outputs and triggers up to five LLM-driven regeneration attempts. Finally, a Result Interpretation Agent produces concise, human-readable insights and hands raw rows to the Insight \u0026 Intelligence engine for visualization or forecasting. Smoke tests across finance, sales, and operations scenarios demonstrate reliable ad-hoc querying and automated periodic reporting. By embedding schema awareness, fault-tolerant execution, and compliance guardrails, the THOR Module empowers non-technical users to access live data with zero-SQL simplicity and enterprise-grade safety.","short_abstract":"We introduce the THOR (Transformer Heuristics for On-Demand Retrieval) Module, designed and implemented by eSapiens, a secure, scalable engine that transforms natural-language questions into verified, read-only SQL analytics for enterprise databases. The Text-to-SQL module follows a decoupled orchestration/execution ar...","url_abs":"https://arxiv.org/abs/2507.09592","url_pdf":"https://arxiv.org/pdf/2507.09592v3","authors":"[\"Isaac Shi\",\"Zeyuan Li\",\"Fan Liu\",\"Wenli Wang\",\"Lewei He\",\"Yang Yang\",\"Tianyu Shi\"]","published":"2025-07-13T11:48:24Z","proceeding":"cs.DB","tasks":"[\"cs.DB\",\"cs.AI\"]","methods":"[\"Transformer\",\"Large Language Model\"]","has_code":false}