arXiv:2607. 15606v2 Announce Type: replace Abstract: Synthetic sequential tabular data are increasingly used for privacy-preserving data sharing and data-driven research, but evaluating their fidelity remains difficult because temporal structure is easily lost under conventional tabular metrics.
By Kiwan Kwon, Kangmin Kim, Hojin Lee, Yeseong Jung, Hyeongwoo Kong, Vamsi K. Potluru, Saerom Park, Yongjae Lee
One of the most important concepts in DAX is lineage. It’s about the information on where something comes from.
By Salvatore Cagliari
arXiv:2606. 02433v1 Announce Type: cross Abstract: The rapid development of LLMs has significantly advanced tabular question answering, but most systems cannot perform future-oriented numerical prediction.
By Zhensheng Wang, Xiaole Liu, Wenmian Yang, Kun Zhou, Yiquan Zhang, Weijia Jia
arXiv:2606. 30452v1 Announce Type: new Abstract: Tabular data dominate the landscape of data science, increasingly attracting innovative machine learning models and tailored benchmarks.
By Myung Jun Kim, Maximilian Schambach, Frank Essenberger, Andre Sres, Johannes H\"ohne
arXiv:2607. 15606v1 Announce Type: new Abstract: Synthetic sequential tabular data are increasingly used for privacy-preserving data sharing, yet a generator can reproduce every marginal and every foreign-key relationship while emitting timestamps that run backwards or repeat, and while sending entities along paths that no real entity followed.
By Kiwan Kwon, Kangmin Kim, Hojin Lee, Yeseong Jung, Hyeongwoo Kong, Vamsi K. Potluru, Saerom Park, Yongjae Lee
Tabular foundation models predict the missing column of any spreadsheet zero-shot, the way an LLM completes text — and on the TabArena benchmark they now sit above fully tuned gradient-boosted trees. An introduction to how they work, an independent reproduction of the strongest open one, and a map of where XGBoost still wins.
By Sean Moran