arXiv:2610.34860v1 Announce Type: new
Abstract: Much of the information needed for data analysis is buried in text columns: an analyst may infer customer frustration from complaint text and use it to...
By Jinxiang Xie, Zihao Li, Rui Ding, Bartuer Zhou, Shi Han, Dongmei Zhang
EGI‑BENCH is a unified benchmark suite for evaluating large language model agents in enterprise settings, consolidating eight existing benchmarks (CRMARENA, HERB, PERSONABENCH, USERBENCH, MCP‑UNIVERSE, MCPEVAL, BENCHFORCE, LOCOBENCH) under a single command‑line interface with two runtime‑budgeted modes. It introduces the Enterprise General Intelligence (EGI) Score, a six‑dimensional framework (M1‑M6) that aggregates fine‑grained sub‑metrics from each benchmark rather than simple averages, providing a more nuanced view of model capabilities. The paper reports results for fourteen LLMs across GPT, Claude, and Gemini families, showing varied strengths across task outcome, planning, retrieval, tool actioning, safety, and engineering quality.
By Zhiwei Liu, Zhujun Lan, Jielin Qiu, Zixiang Chen, Liangwei Yang, Ming Zhu, Akshara Prabhakar, Wenting Zhao, John Emmons, Jason Wu, Shafiq Rayhan Joty, Junnan Li, Shelby Heinecke, Silvio Savarese, Huan Wang
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
arXiv:2606. 28370v1 Announce Type: cross Abstract: Enterprise business intelligence queries span structured warehouses and unstructured document repositories -- modalities with fundamentally different access methods, cost profiles, and correctness semantics.
By Darshita Rathore, Vineet Kumar, Vaibhav Singal, Ankur Vivek Singh, Anindya Moitra
arXiv:2605. 28787v2 Announce Type: replace-cross Abstract: In the era of autonomous agents, machine-actionable data is critical for data-driven workflows.
By Shiyu Chen, Tarfah Alrashed, Alon Halevy, Natasha Noy
arXiv:2610.20959v2 Announce Type: replace-cross
Abstract: Answering questions over heterogeneous data means combining evidence from files, databases, documents and the web. Data agents automate this,...
By Zhivar Sourati, Marianne Menglin Liu, Zheng Wang, Rongguang Wang, Sai Ashish Somayajula, Tao Sheng, Kyu J. Han, Sujith Ravi, Dan Roth