Harvard University（哈佛大学）

Can Induced Emotion Bias LLM Behaviors in Sequential Decision Making?

诱导情绪会影响大语言模型在序列决策中的行为吗？

Minh Khoi Ho, Zihao Zhu, Runchuan Zhu, Levina Li, Zhiwen Fan, Zhangyang Wang, Junyuan Hong

机构 * MBZUAI（穆罕默德·本·扎耶德人工智能大学） ； Texas A&M University（德州农工大学） ； National University of Singapore（新加坡国立大学） ； UCLA（加州大学洛杉矶分校） ； University of Texas at Austin（德克萨斯大学奥斯汀分校） ； Mass General Hospital（麻省总医院） ； Harvard Medical School（哈佛医学院） 纠错

AI总结 研究探讨诱导情绪对大语言模型在序列决策中行为的影响，采用爱荷华赌博任务结合情绪诱导程序，发现诱导情绪平均不显著影响其决策动态，但愤怒有条件地影响决策，揭示了与人类行为的差异，为相关研究提供工具。

Atlas H&E-TME: Scalable AI-Based Tissue Profiling at Expert Pathologist-Level Accuracy

Atlas H&E-TME：基于AI的可扩展组织分析，达到专家病理学家级别的准确性

Kai Standvoss, Miriam Hägele, Rosemarie Krupar, Julika Ribbat-Idel, Jennifer Altschüler, Gerrit Erdmann, Hans Pinckaers, Evelyn Ramberger, Madleen Drinkwitz, Ádám Nárai, Alexander Möllers, Katja Lingelbach, Sebastian Kons, Lukas Hönig, Recepcan Adigüzel, Joana Baião, Alberto Megina Gonzalo, Marius Teodorescu, Marie-Lisa Eich, Paolo Chetta, Shakil Merchant, Verena Aumiller, Simon Schallenberg, Andrew Norgan, Klaus-Robert Müller, Lukas Ruff, Maximilian Alber, Frederick Klauschen

机构 * Aignostics, Germany（Aignostics，德国） ； Institute of Pathology, Charité – Universitätsmedizin Berlin, Germany（柏林夏里特医学院病理学研究所） ； Berlin Institute of Health, Charité – Universitätsmedizin Berlin, Germany（柏林夏里特医学院柏林健康研究所） ； Massachusetts General Hospital, Department of Pathology, Harvard Medical School, Boston, MA, US（哈佛医学院麻省总医院病理学系） ； Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, US（梅奥诊所检验医学与病理学系） ； Machine Learning Group, Technische Universität Berlin, Germany（柏林工业大学机器学习组） ； BIFOLD – Berlin Institute for the Foundations of Learning and Data, Germany（柏林学习与数据基础研究所） ； Department of Artificial Intelligence, Korea University, Republic of Korea（高丽大学人工智能系） ； Max-Planck Institute for Informatics, Germany（马克斯·普朗克信息学研究所） ； German Cancer Research Center (DKFZ) & German Cancer Consortium (DKTK), Berlin & Munich Partner Sites, Germany（德国癌症研究中心及德国癌症联盟柏林和慕尼黑合作站点） ； Institute of Pathology, Ludwig-Maximilians-Universität München, Germany（慕尼黑大学病理学研究所） ； Bavarian Cancer Research Center (BZKF), Germany（巴伐利亚癌症研究中心） 纠错

AI总结 提出Atlas H&E-TME系统，利用病理基础模型预测组织质量、区域和细胞类型，通过IHC共识验证和20万+注释基准，在多种癌症中达到或超越病理学家水平。

Inference-Time Machine Unlearning via Gated Activation Redirection

推理时的机器去学习 via 门控激活重定向

Vinícius Conte Turani, Otávio Parraga, João Vitor Boer Abitante, Kristen K. Arguello, Joana Pasquali, Ramiro N. Barros, Flavio du Pin Calmon, Christian Mattjie, Rodrigo C. Barros, Lucas S. Kupssinskü

机构 * MALTA, Machine Learning Theory and Applications Lab, PUCRS, Porto Alegre, Brazil（MALTA机器学习理论与应用实验室，PUCRS，波士顿-阿尔格雷，巴西） ； Harvard University（哈佛大学） ； Kunumi Institute, Brazil（库努米研究所，巴西） 纠错

AI总结 本文提出了一种无需训练和梯度的机器去学习方法GUARD-IT，通过在推理时依赖输入的激活引导来消除特定数据集的影响，同时保持模型性能，且在量化部署下仍有效。

Same Compression Principle, Different Geometry: Rate-Distortion Signatures Dissociate Biological and Artificial Visual Systems

泛化与信息权衡的率-失真签名

Leyla Roksan Caglar, Pedro A. M. Mediano, Baihan Lin

机构 * Windreich Department of AI ； Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA ； Department of Computing, Imperial College London, London, UK ； Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA ； Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA ； Berkman Klein Center for Internet \& Society, Harvard University, Cambridge, MA, USA 纠错

AI总结 本文提出率-失真理论框架，通过斜率和曲率签名分析系统泛化与鲁棒性权衡，揭示生物与人工系统在RD空间中的不同表现。

First, do NOHARM: a medical safety benchmark and randomized study of physician and AI teaming on clinical consultations

首先，不伤害：迈向临床安全的大语言模型

David Wu, Fateme Nateghi Haredasht, Saloni Kumar Maharaj, Priyank Jain, Jessica Tran, Matthew Gwiazdon, Arjun Rustagi, Jenelle Jindal, Jacob M. Koshy, Vinay Kadiyala, Anup Agarwal, Bassman Tappuni, Brianna French, Sirus Jesudasen, Christopher V. Cosgriff, Rebanta Chakraborty, Jillian Caldwell, Susan Ziolkowski, David J. Iberri, Robert Diep, Rahul S. Dalal, Kira L. Newman, Kristin Galetta, J. Carl Pallais, Nancy Wei, Kathleen M. Buchheit, David I. Hong, Vartan Pahalyants, Ernest Y. Lee, Allen Shih, Tamara B. Kaplan, Vishnu Ravi, Sarita Khemani, Thomas A. Buckley, April S. Liang, Daniel Shirvani, Advait Patil, Nicholas Marshall, Kanav Chopra, Joel Koh, Adi Badhwar, Anastasia Perez, Austin J. Schoeffler, Mahbuba Tusty, Chase M. Walton, Liam G. McCoy, David J. H. Wu, Yingjie Weng, Sumant Ranji, Kevin Schulman, Nigam H. Shah, Jason Hom, Arnold Milstein, Arjun K. Manrai, Adam Rodman, Jonathan H. Chen, Ethan Goh

机构 * Harvard Combined Dermatology Program（哈佛联合皮肤科项目） ； Department of Dermatology, Mass General Brigham（麻省总医院皮肤科） ； Harvard Medical School（哈佛医学院） ； Stanford Center for Biomedical Informatics Research（斯坦福生物医学信息学研究中心） ； Stanford University（斯坦福大学） ； Division of Hospital Medicine, Department of Medicine, Stanford University School of Medicine（斯坦福大学医学院医院医学科） ； Department of Medicine, Cambridge Health Alliance（剑桥健康联盟医学科） ； Beth Israel Deaconess Hospital–Plymouth（贝塞斯达德acons医院-普利茅斯） ； Department of Medicine, University of California, San Francisco（加州大学旧金山分校医学科） ； Department of Neurology, Stanford University School of Medicine（斯坦福大学医学院神经科） ； Department of Medicine, Beth Israel Deaconess Medical Center（贝塞斯达德acons医学中心医学科） ； Division of Cardiology, Department of Medicine, Cambridge Health Alliance（剑桥健康联盟心脏病科） ； Department of Cardiovascular Medicine, Summa Health System（Summa健康系统心血管医学科） ； Division of Allergy, Pulmonary, and Critical Care Medicine, Department of Medicine, University of Wisconsin-Madison（威斯康星大学麦迪逊分校医学科过敏、呼吸科和危重医学科） ； Division of Pulmonary and Critical Care Medicine, Department of Medicine, Massachusetts General Hospital（麻省总医院呼吸科和危重医学科） ； Center for Immunology and Inflammatory Diseases, Department of Medicine, Massachusetts General Hospital（麻省总医院免疫和炎症疾病中心） ； Broad Institute of MIT and Harvard（MIT和哈佛Broad研究所） ； Division of Pulmonary, Critical Care, and Sleep Medicine, Cambridge Health Alliance（剑桥健康联盟呼吸科、危重医学科和睡眠医学科） 纠错

AI总结 提出NOHARM基准，包含1100个初级到专科咨询案例，评估28个LLM的医疗建议安全性，发现高达22.6%的案例存在严重危害风险，其中遗漏错误占80%以上。

RippleBench: Capturing Ripple Effects Using Existing Knowledge Repositories

RippleBench: 利用现有知识库捕捉涟漪效应

Roy Rinberg, Usha Bhalla, Igor Shilov, Flavio P. Calmon, Rohit Gandikota

机构 * Harvard University（哈佛大学） ； Imperial College London（伦敦帝国学院） ； Northeastern University（东北大学） 纠错

AI总结 提出RippleBench-Maker自动管道，从知识库检索语义邻居生成选择题，评估八种遗忘方法在Llama3-8B-Instruct上的涟漪效应，发现准确率下降随语义距离衰减且跨模型一致。

Optimal and Provable Calibration in High-Dimensional Binary Classification: Angular Calibration and Platt Scaling

高维二分类中的最优且可证明的校准：角度校准与Platt缩放

Yufan Li, Pragya Sur

机构 * Harvard University（哈佛大学） 纠错

AI总结 针对高维高斯特征下的线性二分类器，提出基于估计权重与真实权重夹角的角度校准方法，证明其可校准且唯一Bregman最优，并揭示Platt缩放在高维下收敛于该最优解。

The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography

TopCoW挑战——用于CT和MR血管造影的拓扑感知Willis环分割

Kaiyuan Yang, Fabio Musio, Yihui Ma, Norman Juchler, Johannes C. Paetzold, Rami Al-Maskari, Luciano Höher, Hongwei Bran Li, Ibrahim Ethem Hamamci, Anjany Sekuboyina, Suprosanna Shit, Houjing Huang, Chinmay Prabhakar, Ezequiel de la Rosa, Bastian Wittmann, Diana Waldmannstetter, Florian Kofler, Fernando Navarro, Martin J. Menten, Ivan Ezhov, Daniel Rueckert, Iris N. Vos, Ynte M. Ruigrok, Birgitta K. Velthuis, Hugo J. Kuijf, Pengcheng Shi, Wei Liu, Ting Ma, Maximilian R. Rokuss, Yannick Kirchhoff, Fabian Isensee, Klaus Maier-Hein, Chengcheng Zhu, Huilin Zhao, Philippe Bijlenga, Julien Hämmerli, Catherine Wurster, Laura Westphal, Jeroen Bisschop, Elisa Colombo, Hakim Baazaoui, Hannah-Lea Handelsmann, Andrew Makmur, James Hallinan, Amrish Soundararajan, Benedikt Wiestler, Jan S. Kirschke, Evamaria O. Riedel, Roland Wiest, Emmanuel Montagnon, Laurent Letourneau-Guillon, Kwanseok Oh, Dahye Lee, Orhun Utku Aydin, Adam Hilbert, Jana Rieger, Dimitrios Rallios, Satoru Tanioka, Alexander Koch, Dietmar Frey, Abdul Qayyum, Moona Mazher, Steven Niederer, Nico Disch, Julius C. Holzschuh, Dominic LaBella, Francesco Galati, Daniele Falcetta, Maria A. Zuluaga, Chaolong Lin, Haoran Zhao, Zehan Zhang, Minghui Zhang, Xin You, Hanxiao Zhang, Guang-Zhong Yang, Yun Gu, Sinyoung Ra, Jongyun Hwang, Hyunjin Park, Junqiang Chen, Marek Wodzinski, Henning Müller, Nesrin Mansouri, Florent Autrusseau, Cansu Yalcin, Rachika E. Hamadache, Clara Lisazo, Joaquim Salvi, Adrià Casamitjana, Xavier Lladó, Uma Maria Lal-Trehan Estrada, Valeriia Abramova, Luca Giancardo, Arnau Oliver, Paula Casademunt, Adrian Galdran, Matteo Delucchi, Oscar Camara, Jialu Liu, Haibin Huang, Yue Cui, Zehang Lin, Yusheng Liu, Shunzhi Zhu, Tatsat R. Patel, Adnan H. Siddiqui, Vincent M. Tutino, Maysam Orouskhani, Huayu Wang, Mahmud Mossa-Basha, Yuki Sato, Sven Hirsch, Susanne Wegener, Bjoern Menze

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 纠错

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)