The paper presents a DataBrain‑specific prototype that converts natural‑language analytic requests into inspectable dashboards. It combines a Codex plugin and an Agent Runtime that use schema‑constrained model decisions, typed tools, persistent state, and deterministic Hooks for approval, audit, checkpointing, recovery, and failure handling. The pipeline resolves entities, discovers metadata, enforces read‑only SQL, composes dashboards, and applies static checks, dynamic preflight, and browser inspection, with the model proposing actions while deterministic software controls execution and records state transitions.
By Yiou Wu, Zezhi Tang, Ningwei Bai, Liuhaichen Yang
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
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.
arXiv:2609.37233v1 Announce Type: cross
Abstract: Datalog underpins reasoning tasks such as program analysis, but its programs are hard to write. Existing synthesizers automate this task but require...
By Yuan Li, Hanyun Jiang, Guowei Tian, Chengpeng Wang, Peisen Yao
arXiv:2605. 12376v2 Announce Type: replace Abstract: Table processing-including cleaning, transformation, augmentation, and matching-is a foundational yet error-prone stage in real-world data pipelines.
By Wei Liu, Yang Gu, Xi Yan, Zihan Nan, Beicheng Xu, Keyao Ding, Bin Cui, Wentao Zhang