Pigeonholing: how bad prompts hurt models, causing collapse and mistakes
鸽笼效应:不良提示导致模型崩溃和犯错
机构 * Stanford University(斯坦福大学) ; University of Washington(华盛顿大学)
AI总结 研究不良上下文导致大语言模型性能下降和模式崩溃的“鸽笼效应”,发现重复错误答案、收敛于狭窄答案集等问题,并提出RLVR合成错误缓解方法。
Comments 10 pages
高校专区
鸽笼效应:不良提示导致模型崩溃和犯错
机构 * Stanford University(斯坦福大学) ; University of Washington(华盛顿大学)
AI总结 研究不良上下文导致大语言模型性能下降和模式崩溃的“鸽笼效应”,发现重复错误答案、收敛于狭窄答案集等问题,并提出RLVR合成错误缓解方法。
Comments 10 pages
TopCoW挑战——用于CT和MR血管造影的拓扑感知Willis环分割
机构 * Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland ; Institute of Computational Life Sciences, Zurich University of Applied Sciences (ZHAW), Waedenswil, Switzerland ; Department of Neuroradiology, University Hospital of Zurich, Zurich, Switzerland ; Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China ; Department of Radiology at Weill Cornell Medicine, Cornell University, New York, USA ; Institute for Tissue Engineering ; School of Computation, Information ; Technology, Technical University of Munich, Germany ; Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, USA ; School of Medicine ; Health, TUM Klinikum, Technical University of Munich, Germany ; Munich Center for Machine Learning, Munich, Germany ; Department of Computing, Imperial College London, London, UK ; Image Sciences Institute, UMC Utrecht, Utrecht, The Netherlands ; Department of Neurology ; Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands ; Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands ; Electronic \& Information Engineering School, Harbin Institute of Technology (Shenzhen), China ; Peng Cheng Laboratory, Shenzhen, China ; Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany ; Faculty of Mathematics ; Computer Science, Heidelberg University, Germany ; Helmholtz Imaging, German Cancer Research Center, Heidelberg, Germany ; Data Science School for Health, Karlsruhe/Heidelberg, Germany ; Learning Group, Department of Radiation Oncology, Heidelberg University Hospital ; Department of Radiology, University of Washington, Seattle, WA, USA ; Department of Radiology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China ; Department of Clinical Neurosciences, Division of Neurosurgery, Geneva University Hospitals, Geneva, Switzerland ; Department of Neurology, University Hospital of Zurich, Zurich, Switzerland ; Department of Physiology, University of Toronto, Canada ; Department of Neurosurgery, University Hospital of Zurich, Zurich, Switzerland ; Department of Diagnostic Imaging, National University Hospital, Singapore ; University of Chicago, USA ; Department of Diagnostic ; Interventional Neuroradiology, University Hospital Berne ; University of Berne, Berne, Switzerland ; Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), Montréal, Québec, Canada ; DEEPNOID Inc., Seoul, South Korea ; Department of Artificial Intelligence, Korea University, Seoul, South Korea ; Charité Lab for AI in Medicine (CLAIM), Charité Universitätsmedizin Berlin, Berlin, Germany ; Lung Institute, Faculty of Medicine, Imperial College London, London, UK ; Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK ; Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA ; Institute of Medical Technology, Peking University Health Science Center, Beijing, China ; Hangzhou Genlight MedTech Co., Ltd., China ; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China ; Department of Automation, Shanghai Jiao Tong University, Shanghai, China ; Department of Artificial Intelligence, Sungkyunkwan University, Seoul, South Korea ; Department of Electrical ; Computer Engineering, Sungkyunkwan University, Seoul, South Korea ; Shanghai MediWorks Precision Instruments Co., Ltd., China ; Institute of Informatics, HES-SO Valais-Wallis, Switzerland ; Department of Measurement ; Electronics, AGH University of Krakow, Poland ; Laboratoire de Thermique et Energie de Nantes (LTeN), Université Nantes, Polytech’Nantes, Nantes, France ; Research Institute of Computer Vision ; Center for Precision Health, McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, USA ; Physense, BCN-Medtech, Department of Communication ; Information Technologies, Universitat Pompeu Fabra, Barcelona, Spain ; Department of Mathematical Modeling ; Machine Learning, University of Zurich, Zurich, Switzerland ; Laboratory of Brain Atlas ; Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China ; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China ; School of Computer ; Information Engineering, Xiamen University of Technology, Xiamen, China ; Vascular Research Center, University at Buffalo, NY, USA ; Department of Pathology ; Anatomical Sciences, University at Buffalo, NY, USA ; Department of Neurosurgery, University at Buffalo, NY, USA ; LPIXEL Inc., Tokyo, Japan
AI总结 组织TopCoW基准挑战,发布含125对MRA和CTA扫描的注释数据集,参与者提交CoW分割和变体分类算法,经评估,最佳算法在多任务中表现出色,证明CoW分割算法对下游临床应用有可解释性效用。
Comments Summary paper for the TopCoW Challenge: 4 figures, 1 table, and supplementary material in appendix. Accepted for publication in NEJM AI. Datasets and best-performing algorithm Dockers are available at https://zenodo.org/records/15692630 and https://zenodo.org/records/15665435
Journal ref NEJM AI 2026;3(8)