DataSTORM is an LLM‑based agentic system designed to conduct deep research over large‑scale structured databases and internet sources. It applies principles of Exploratory Data Analysis and Data Storytelling to frame research as a thesis‑driven analytical process, iteratively generating hypotheses, performing quantitative reasoning, and crafting coherent narratives. Evaluations on InsightBench and a new ACLED‑based dataset show that DataSTORM surpasses existing systems, achieving significant improvements in insight‑level recall and summary‑level scores.
By Shicheng Liu, Yucheng Jiang, Sajid Farook, Camila Nicollier Sanchez, David Fernando Castro Pena, Monica S. Lam
The paper introduces DAG-EDA, a system that pairs an intent graph and a multi-layered knowledge graph to help analysts navigate exploratory data analysis. The intent graph, driven by a grammar of analytical intent, breaks down ambiguous natural-language questions into concrete analysis tasks while preserving alternative paths for branching and comparison. The knowledge graph externalizes domain knowledge from a large language model, linking concepts to dataset variables so analysts can inspect and challenge how their questions map to data, with results displayed as interactive dashboards.
By Junran Yang, Shruti Badrish, Teanna Barrett, Leilani Battle
arXiv:2607. 06482v1 Announce Type: cross Abstract: Current benchmarks for evaluating Large Language Models (LLMs) in data analysis often fail to reflect real-world settings.
By So Hasegawa, Shailaja Keyur Sampat, Lei Liu, Wei-Peng Chen
arXiv:2608. 16045v1 Announce Type: cross Abstract: LLM-based data-analysis tools are increasingly used to help users analyze messy spreadsheets and workbooks, from answering questions over uploaded files to generating code, summaries, and visualizations.
By Yike Yuan, Virum Ranka, Tina Lasisi, Lin Ma
The paper introduces Data Journalist Agent (Data2Story), a multi‑agent framework that orchestrates specialized roles into a single virtual newsroom to produce evidence‑grounded, multimodal news stories. It ensures every claim is traceable to data, code, or external references via an Inspector, and generates interactive visualizations such as maps and audio to match reader interests. Evaluations on 18 articles show competitive performance in angle coverage, rubric scores, and verifiability, while human writers still lead in editorial angle and creative design.
By Kevin Qinghong Lin, Batu EI, Yuhong Shi, Pan Lu, Juil Sock, Djordje Padejski, Philip Torr, James Zou
arXiv:2607. 18029v1 Announce Type: cross Abstract: Researchers need to answer ad-hoc questions about the contents of domain-specific archives but often lack the expertise to write structured queries on the metadata.
By Blake G. Fitch, Cato Elia Kurtz
arXiv:2608. 03451v1 Announce Type: new Abstract: Data agents enable natural-language analytics over organizational workspaces, where relevant evidence may be scattered across databases, structured files, long documents, and multimedia.
By Boyan Li, Zhuowen Liang, Yupeng Xie, Xiaotian Lin, Tianqi Luo, Xinyu Liu, Yizhang Zhu, Zhangyang Peng, Yuan Li, Zhengxuan Zhang, Jiayi Zhang, Nan Tang, Guoliang Li, Yuyu Luo
The paper introduces EXYGEN, a framework that enables conversational access to large knowledge graphs by combining VoID descriptions, ShEx schemas, retrieved triples, and example question‑query pairs in a retrieval‑augmented generation pipeline. On the SciQA benchmark, this approach achieves an exact‑match score of 0.419 without fine‑tuning any large language model, and shows that larger general‑purpose LLMs can outperform smaller code‑specialized ones when provided sufficient context. To scale metadata generation for very large KGs, the authors propose a predicate‑coverage‑aware parallel graph sampling strategy that preserves structural diversity, reduces runtime by over 80× on OpenCitations Meta and GESIS, and is the only tractable method for obtaining complete metadata on ORKG.
By Harshdeep Singh, Yurui Zhu, Giovanni Colavizza, Matteo Romanello
arXiv:2607. 01647v1 Announce Type: cross Abstract: Data science aims to derive actionable insights from heterogeneous raw data, unlocking the value of the massive amounts of data generated in modern society.
By Zhaoyan Sun, Shan Zhong, Daizhou Wen, Jiaxing Han, Guoliang Li, Ying Yan, Peng Zhang, Yu Su, Xiang Qi, Baolin Sun, Chengyuan Yang, Tao Fang, Huaiyu Ruan
The paper introduces data stories as narrative documents that combine explanatory text, images, and executable SPARQL queries with visualized results to make cultural‑heritage knowledge graphs more accessible. It describes how these stories guide users through unfamiliar graphs, create reproducible narratives, and uncover hidden data‑quality issues. The authors present LODEON, an authoring platform with Sparnatural and AI‑assisted tools, and report early positive feedback from seminars and workshops.
By Tabea Tietz, Torsten Schrade, Etienne Posthumus, Linnaea S\"ohn, Jonatan Jalle Steller, J\"org Waitelonis, Harald Sack
arXiv:2606. 28601v1 Announce Type: cross Abstract: Recent progress in Text-to-SQL has been driven by stronger language models and prompting strategies, yet performance on real enterprise benchmarks such as Spider 2.
By Jingwen Liu, Weibin Liao, Xin Gao, Junfeng Zhao, Yasha Wang
arXiv:2609.22209v1 Announce Type: cross
Abstract: Empirical legal research often relies on turning research questions into structured data extracted from large collections of rulings and judgments. D...
By Albert Sawczyn, Jakub Binkowski, Kamil Tagowski, {\L}ukasz Augustyniak, Berenika Kaczmarek-Templin, Tomasz Kajdanowicz