DataSTORM: Deep Research on Large-Scale Databases using Exploratory Data Analysis and Data Storytelling
Researchers have developed DataSTORM, a system that uses large language models to conduct deep research on both structured databases and internet sources. Unlike previous approaches focused on unstructured web data, DataSTORM is designed for iterative hypothesis generation, quantitative reasoning over structured schemas, and convergence toward a coherent analytical narrative. The system achieves state-of-the-art results in insight-level recall and summary-level score on the I
Researchers have developed DataSTORM, a system that uses large language models to conduct deep research on both structured databases and internet sources. Unlike previous approaches focused on unstructured web data, DataSTORM is designed for iterative hypothesis generation, quantitative reasoning over structured schemas, and convergence toward a coherent analytical narrative. The system achieves state-of-the-art results in insight-level recall and summary-level score on the InsightBench dataset, outperforming proprietary systems like ChatGPT Deep Research.
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Why it matters: This matters to AI researchers because it addresses the challenges of conducting deep research over large-scale structured databases, a previously underexplored area. DataSTORM's ability to autonomously conduct research and generate coherent analytical narratives has significant implications for the development of more effective data-centric research systems.
Source: https://arxiv.org/abs/2604.06474
This article was originally published at: https://arxiv.org/abs/2604.06474