Tootfinder

Community Report from the 2025 SNOLAB Future Projects Workshop
M. D. Diamond, P. Abbamonte, A. Arvanitaki, D. M. Asner, D. Balut, D. Baxter, C. Blanco, D. Boreham, M. Boulay, B. Broerman, T. Brunner, E. Caden, A. Chavarria, M. Chen, J. P. Davis, A. Drlica-Wagner, J. Estrada, N. Fatemighomi, J. Foster, D. Freedman, C. Gao, J. Hall, S. Hall, W. Halperin, M. Hirschel, N. Hoch, Z. Hong, A. Ianni, C. Jillings, D. Johnson, Y. Kahn, C. B. Krauss, T. Laframboise, M. Lai, M. R. Lapointe, B. Lil…

Community Report from the 2025 SNOLAB Future Projects Workshop
SNOLAB hosts a biannual Future Projects Workshop (FPW) with the goal of encouraging future project stakeholders to present ideas, concepts, and needs for experiments or programs that could one day be hosted at SNOLAB. The 2025 FPW was held in the larger context of a 15-year planning exercise requested by the Canada Foundation for Innovation. This report collects input from the community, including both contributions to the workshop and contributions that could not be scheduled in the workshop b…

CARES: Collaborative Agentic Reasoning for Error Detection in Surgery
Chang Han Low, Zhu Zhuo, Ziyue Wang, Jialang Xu, Haofeng Liu, Nazir Sirajudeen, Matthew Boal, Philip J. Edwards, Danail Stoyanov, Nader Francis, Jiehui Zhong, Di Gu, Evangelos B. Mazomenos, Yueming Jin
https://arxiv.org/abs/2508.08764

CARES: Collaborative Agentic Reasoning for Error Detection in Surgery
Robotic-assisted surgery (RAS) introduces complex challenges that current surgical error detection methods struggle to address effectively due to limited training data and methodological constraints. Therefore, we construct MERP (Multi-class Error in Robotic Prostatectomy), a comprehensive dataset for error detection in robotic prostatectomy with frame-level annotations featuring six clinically aligned error categories. In addition, we propose CARES (Collaborative Agentic Reasoning for Error De…