The paper introduces GROUND, a framework that limits large language model (LLM) analytics to a governed semantic layer for enterprise data warehouses. GROUND supplies approved metrics, dimensions, join paths, filters, and security rules, then validates generated SQL against these constraints before execution, retrying or abstaining on violations. In benchmarks, GROUND eliminates hallucinations across all evaluated categories and prevents row‑level security breaches, outperforming schema‑only, schema‑RAG, and semantic‑only approaches.
arXiv:2605. 21027v2 Announce Type: replace-cross Abstract: Enterprise analytics aims to make organizational data accessible for decision-making, yet non-technical users still face barriers when using traditional business intelligence tools or Text-to-SQL systems.
arXiv:2608. 06331v1 Announce Type: cross Abstract: From natural-language query interfaces to automated report generation, data analysis tools need a description of the data: the real-world entities it contains, which columns function as measures or identifiers, and how tables connect into units of analysis.
The paper introduces DRL, a deterministic relational middleware layer designed to enable transaction-safe natural‑language to SQL (NL2SQL) interfaces over large enterprise OLTP catalogs. DRL interposes between front‑ends and SQL back‑ends, employing dynamic context pruning, relational AST typing, and transactional safeguards (EXPLAIN gating and NULL guards) to keep context within LLM attention limits and detect silent divergence. Experiments on PostgreSQL and MySQL show significant context reductions (up to 92%) and high execution match rates (≈53%) for GPT‑4o, Claude Sonnet 4.5, and Gemini 2.5 Flash, while also revealing that evaluation code quality can materially affect reported performance gaps.
arXiv:2607. 08028v1 Announce Type: new Abstract: Enterprise large language model (LLM) applications often begin as prototypes whose behavior is carried by prompts and retrieval context.
arXiv:2606. 18021v1 Announce Type: new Abstract: AI systems deployed in legal workflows hallucinate at rates that aggregate metrics report at ~52%, but this average conceals where errors concentrate and in which direction they run, leaving compliance officers without an actionable signal for trustworthy deployment.
arXiv:2608. 14590v1 Announce Type: new Abstract: LLM agents increasingly perform irreversible real-world actions, including database updates, API calls, file operations, and autonomous use of tools.
arXiv:2608. 07946v1 Announce Type: cross Abstract: Text-to-SQL benchmarks ship schemas whose column names already say what the columns mean.
arXiv:2606. 28387v1 Announce Type: cross Abstract: Enterprise text-to-SQL systems often fail before SQL is generated: the model receives the wrong schema context.
ESQ‑Bench is an Oracle‑first NL2SQL benchmark that introduces systematic complexity tiers and silent‑divergence evaluation across three enterprise schema levels. It provides six populated schemas (465 tables, 164,682 rows) on Oracle, PostgreSQL, MySQL, and SQL Server, along with 550 gold‑validated question‑query pairs and a four‑metric evaluation harness. The benchmark reveals that state‑of‑the‑art models such as GPT‑4o and Claude Sonnet 4.6 experience significant drops in execution accuracy and high silent‑divergence rates as schema complexity increases, highlighting a gap between closed‑API models and open‑weight baselines on enterprise Oracle schemas.
arXiv:2608. 16663v1 Announce Type: cross Abstract: Direct text-to-SQL asks a language model to do two jobs: interpret the business question and construct the complete relational query.
The paper argues that answer accuracy alone is insufficient for evaluating large language model (LLM) data agents, especially in structured-data tasks where a correct answer can be produced by an invalid trace. It introduces Trace Integrity as a reliability criterion that ensures the computation behind an answer is explicit, executable, schema-valid, operator-faithful, replayable, answer-consistent, and auditable. The authors operationalize this concept with execution contracts and present the CAIT (Correct Answer / Invalid Trace) Rate to quantify how often answer-only evaluations mistakenly reward unsupported outputs, demonstrating that accuracy, trace validity, and silent-failure risk are distinct signals.
arXiv:2606. 04990v1 Announce Type: cross Abstract: Large language model (LLM)-based agents increasingly solve complex tasks by interacting with external tools, retrieval systems, memory modules, environments, and other agents.