# Beyond Text-to-SQL: Autonomous Research-Driven Database Exploration with DAR

arXiv: https://arxiv.org/abs/2512.14622 · HTML: https://mlsanity.com/paper/2512.14622/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2025.

## Built on by (most cited first, 2 of 2)

- [Hunt Instead of Wait: Evaluating Deep Data Research on Large Language Models](https://mlsanity.com/paper/2602.02039/) (2026)
- [AgileLog: A Forkable Shared Log for Agents on Data Streams](https://mlsanity.com/paper/2604.14590/) (2026)

## Abstract (arXiv)

Large language models can already query databases, yet most existing systems remain reactive: they rely on explicit user prompts and do not actively explore data. We introduce DAR (Data Agnostic Researcher), a multi-agent system that performs end-to-end database research without human-initiated queries. DAR orchestrates specialized AI agents across three layers: initialization (intent inference and metadata extraction), execution (SQL and AI-based query synthesis with iterative validation), and synthesis (report generation with built-in quality control). All reasoning is executed directly inside BigQuery using native generative AI functions, eliminating data movement and preserving data governance. On a realistic asset-incident dataset, DAR completes the full analytical task in 16 minutes, compared to 8.5 hours for a professional analyst (approximately 32x times faster), while producing useful pattern-based insights and evidence-grounded recommendations. Although human experts continue to offer deeper contextual interpretation, DAR excels at rapid exploratory analysis. Overall, this work shifts database interaction from query-driven assistance toward autonomous, research-driven exploration within cloud data warehouses.