Large language models (LLMs) are increasingly used to generate queries, invoke tools, and construct analytical workflows. Although recent advances have substantially improved workflow generation and execution, the semantic information required to operationalize analytical concepts often lies beyond what is explicitly represented in database schemas and data values.
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:2605. 21027v2 Announce Type: replace-cross Abstract: Enterprise analytics aims to make organizational data accessible for decision-making, yet non-technical users still face barriers when using traditional business intelligence tools or Text-to-SQL systems.
By Gundeep Singh, Parsa Kavehzadeh, Jing Xia, Xue-Yong Fu, Julien Bouvier Tremblay, Md Tahmid Rahman Laskar, Vincent Lum, Shashi Bhushan TN
arXiv:2606. 29116v1 Announce Type: new Abstract: Large Language Models (LLMs) are rapidly being adopted in low-code and no-code automation platforms, where non-expert users design workflows that combine natural language understanding with external services and APIs.
By Yutian Tang, Yuming Zhou, Huaming Chen
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. Although these s...
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
arXiv:2602.02475v2 Announce Type: replace
Abstract: AI agents often fail in ways that are difficult to localize because executions are probabilistic, long-horizon, multi-agent, and mediated by noisy...
By Shraddha Barke, Arnav Goyal, Alind Khare, Avaljot Singh, Suman Nath, Chetan Bansal
arXiv:2607. 11019v1 Announce Type: new Abstract: Enterprise data analysis is emerging as a distinct frontier for autonomous agents.
By Tianjing Zeng, Yuntao Hong, Zhongjun Ding, Dandan Liu, Yinan Mei, Yunxiang Su, Yiming Wang, Xiaojian Zhang, Jingyu Zhu, Junhao Zhu, Zhuowen Liang, Jiazhen Peng, Lianggui Weng, Zhihao Ding, Kerui Yi, Qifeng Wang, Rong Zhu, Bolin Ding, Liyu Mou, Jingren Zhou
arXiv:2606. 08661v1 Announce Type: cross Abstract: Data agents integrate LLM-driven reasoning with relational data access, executable analytical tools, and multi-step workflow orchestration, making them increasingly central to enterprise analytics.
By Kuncan Wang, Ziting Wang, Peizhuo Lv, Haoyang Li, Guoliang Li, Gao Cong, Wei Dong
arXiv:2607. 14456v1 Announce Type: cross Abstract: Large Language Models (LLMs) have accelerated the adoption of software development agents, now widely available as Integrated Development Environment (IDE) extensions and standalone applications.
By Harris Borman, Herman Wandabwa, Fusun Yu, Sandeepa Kannangara, Justin Liu, Anna Leontjeva, Ritchie Ng
Large language model (LLM) agents are increasingly evaluated on their ability to use tools, plan multi-step tasks, coordinate with other agents, and operate over extended horizons. Reported benchmark gains often obscure recurring failure modes documented across otherwise unrelated evaluation efforts.
arXiv:2609.01045v1 Announce Type: new
Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities as powerful components in agentic systems, enabling sophisticated reasoning and...
By Enci Zhang, Haofeng Wang, Yuesheng Zhu, Xiaole Cui, Guibo Luo