Text2Dashboard is a prototype that converts natural‑language analytic requests into inspectable dashboards for DataBrain. It uses a Codex plugin and an Agent Runtime that enforce schema constraints, read‑only SQL, and deterministic hooks for approval, audit, and failure handling. The system was evaluated on real DataBrain tasks, achieving high success rates on metadata, SQL, and dashboard composition while handling fault scenarios without unintended side effects.
By Yiou Wu, Zezhi Tang, Ningwei Bai, Liuhaichen Yang
arXiv:2607. 22642v1 Announce Type: new Abstract: Enterprise coding agents translate natural-language analytical requests into executable code over proprietary APIs, schemas, and metric definitions.
By Aakash Kolekar, Sahika Genc, Shahriar Shariat, Bunyamin Sisman, Tibor Mezi, Barbara Poblete, Shree Vandana Kachroo, Calvin Chi, Parth Parmar, Ari Singer, Prayaas Jain, Cindy Barker, Benoit Dumoulin
arXiv:2608. 16181v1 Announce Type: cross Abstract: Recent advances in large language models have enabled a new class of agentic data science systems that allow users to complete complex data science workflows through natural language.
By Wei-Hao Chen, Weixi Tong, Yuan Tian, Chenglong Wang, Tianyi Zhang
arXiv:2610.14777v1 Announce Type: new
Abstract: LLM-powered data analytics aims to translate natural language instructions into executable analytical programs over underlying data. However, existing...
By Ruichen Xia, Xiao Han, Dingyu Yang, Luwei Yang, Sai Wu, Dongxiang Zhang, Binhui Yao, Gang Chen
AdaLens is an interactive system designed to monitor and steer long-running, autonomous data analysis workflows powered by large language models. It provides a storyline-based interface that unifies analytical plans, execution progress, intermediate findings, and data-column involvement, enabling analysts to observe evolving reasoning and evidence. The system also offers steering interactions that allow users to redirect low-value directions or deepen promising ones during execution.
By Yangtian Liu, Yan Miao, Shuhan Liu, Yunfan Zhou, Dae Hyun Kim, Di Weng, Yingcai Wu
Spreadsheets and tables are widely used representations for structured data analysis, but effective analysis still requires substantial manual effort and domain expertise. Recent large language model (LLM) agents can automate parts of this process, but they often provide limited transparency into intermediate decisions, rely on implicit assumptions, struggle with multi-table comparison, and repeat similar workflows without adapting to a user's preferences.