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

2026-04-07

AI Summary

This work addresses the limitations of existing large language model (LLM) agents in conducting deep research that requires multi-step analysis, hypothesis generation, and quantitative reasoning over structured databases. The authors propose a novel LLM agent framework that uniquely integrates principles of exploratory data analysis and data storytelling into a hypothesis-driven research pipeline, enabling iterative validation across both structured databases and web-based information while generating coherent narratives. Evaluated on InsightBench, the system achieves a 19.4% relative improvement in insight recall and a 7.2% increase in summary scores. On a complex real-world dataset constructed from ACLED, it significantly outperforms existing systems—including ChatGPT Deep Research—according to both automated metrics and human evaluations.

Technology Category

Machine Learning: Large Multimodal Models (LMMs)Data Mining & Knowledge Management: Conversational Systems for Recommendation & RetrievalNatural Language Processing: (Large) Language Models

Application Category

Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsSearch and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved informationUser Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• deep research
• structured databases
• large language models
• exploratory data analysis
• data storytelling

Innovation

• DataSTORM
• LLM agents
• Exploratory Data Analysis
• Data Storytelling
• structured databases