arXiv:2604. 10311v2 Announce Type: replace Abstract: Artificial Intelligence (AI) models, encompassing both traditional machine learning (ML) and more advanced approaches such as deep learning and large language models (LLMs), play a central role in modern applications.
By Fabio Porto, Eduardo Ogasawara, Gabriela Moraes Botaro, Julia Neumann Bastos, Augusto Fonseca, Esther Pacitti, Patrick Valduriez
arXiv:2607. 26652v1 Announce Type: new Abstract: The responsible development and deployment of artificial intelligence (AI) systems requires rigorous documentation of their constituent artifacts, e.
By Federica Pepe, Daniele Bifolco, Costantino Martignetti, Aureliano D'Amici, Fabiano Izzo, Damian A. Tamburri, Massimiliano Di Penta
arXiv:2607. 09224v1 Announce Type: cross Abstract: Version control systems are essential for collaborative software development, yet tools like git remain challenging for many practitioners.
By Alfredo Garrach\'on Ruiz, Tom\'as de la Rosa, Daniel Borrajo
arXiv:2607. 22677v1 Announce Type: cross Abstract: Scientific datasets intended for AI use require both computational readiness for model training and metadata readiness for discovery, sharing, and reuse.
By Sean R. Wilkinson, Polina Shpilker, Wesley Brewer
arXiv:2607. 18816v1 Announce Type: cross Abstract: LLM-powered agents increasingly tackle complex tasks by invoking tools, querying databases, executing code, and manipulating intermediate artifacts.
By Eden Wu, Sonia Castelo, Yurong Liu, Cl\'audio T. Silva, Juliana Freire
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