DataSTORM: Deep Research on Large-Scale Databases using Exploratory Data Analysis and Data Storytelling

arXiv cs.CL / 4/9/2026

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Key Points

DataSTORM is an LLM-based agentic system designed for deep research across large-scale structured databases in addition to internet sources, addressing gaps left by web-focused methods.
The approach frames deep structured-data research as a thesis-driven process using Exploratory Data Analysis and Data Storytelling: generating candidate theses, validating them via iterative cross-source investigation, and converging on a coherent narrative.
Evaluations on InsightBench show DataSTORM achieving new state-of-the-art performance, improving insight-level recall by 19.4% and summary-level scores by 7.2% relative to prior methods.
The paper also introduces an ACLED-derived dataset and reports that DataSTORM outperforms proprietary systems such as ChatGPT Deep Research on both automated metrics and human evaluations.

Abstract

Deep research with Large Language Model (LLM) agents is emerging as a powerful paradigm for multi-step information discovery, synthesis, and analysis. However, existing approaches primarily focus on unstructured web data, while the challenges of conducting deep research over large-scale structured databases remain relatively underexplored. Unlike web-based research, effective data-centric research requires more than retrieval and summarization and demands iterative hypothesis generation, quantitative reasoning over structured schemas, and convergence toward a coherent analytical narrative. In this paper, we present DataSTORM, an LLM-based agentic system capable of autonomously conducting research across both large-scale structured databases and internet sources. Grounded in principles from Exploratory Data Analysis and Data Storytelling, DataSTORM reframes deep research over structured data as a thesis-driven analytical process: discovering candidate theses from data, validating them through iterative cross-source investigation, and developing them into coherent analytical narratives. We evaluate DataSTORM on InsightBench, where it achieves a new state-of-the-art result with a 19.4% relative improvement in insight-level recall and 7.2% in summary-level score. We further introduce a new dataset built on ACLED, a real-world complex database, and demonstrate that DataSTORM outperforms proprietary systems such as ChatGPT Deep Research across both automated metrics and human evaluations.