arXiv:2607. 02771v1 Announce Type: new Abstract: Leadership computing facilities steward large-scale scientific datasets that routinely require substantial transformation before serving as AI training data.
By Sean R. Wilkinson, Valentine G. Anantharaj, Jong Youl Choi, Ketan Maheshwari, Marshall McDonnell, Massimiliano Lupo Pasini, Polina Shpilker, Renan Souza, Patrick Widener, Sarp Oral, Wesley Brewer
arXiv:2609.15255v1 Announce Type: new
Abstract: Ecological monitoring increasingly relies on machine learning models, whose performance depends on the quality and quantity of labelled data. However,...
By Ben McEwen, Rupa Kurinchi-Vendhan, Shiqi Zhang, Lukas Rauch, Marek Herde, Sara Beery
Flower Hub is a platform that allows researchers to publish, discover, and run federated learning (FL) benchmarks in a reproducible way. It packages benchmarks as executable, versioned applications with standardized metadata, pinned dependencies, and explicit evaluation workflows, enabling the same benchmark to run in both simulation and real deployment environments. The platform includes a multi-domain benchmark suite covering cross-silo and cross-device settings in areas such as medical imaging, finance, legal instruction tuning, phishing detection, and audio tagging, and it supports system-aware reporting of runtime and communication metrics.
By Yan Gao, Mohammad Naseri, Javier Fernandez-Marques, Dimitris Stripelis, Lorenzo Sani, Davide Eynard, Fan Zhang, Hong Jia, Ting Dang, D. B. Emerson, Fatemeh Tavakoli, Ole Werger, Lars Wulfert, Petros Demetrakopoulos, Sofia Tsekeridou, InSeo Song, KangYoon Lee, Honghao Li, Lingjuan Lyu, John P Dickerson, Daniel Janes Beutel, Nicholas D. Lane
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. 28990v1 Announce Type: new Abstract: Large language model agents have shown promising capabilities in data-driven scientific discovery tasks, where an agent interacts with an execution environment and produces a statistical claim.
By Yucheng Xu, Keyi Zhang, Yuyang Yu, Min Zhang, Shiyuan Meng, Pei Chu, Zhongying Tu
arXiv:2608. 04942v1 Announce Type: cross Abstract: CheMLFlow is an open-source platform for building and executing end-to-end, high-throughput, and agentic workflows for scientific and technological applications.
By Brendan Smith, Susana Lopez-Moreno, Eric Dolores-Cuenca, Sangil Kim, Jose L. Mendoza-Cortes, Nijamudheen Abdulrahiman