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University of Michigan(密歇根大学安娜堡分校)

共收录 147
2606.23658 2026-06-23 cs.RO 新提交

A Reduced Order Model for Emergent Mechanics in Woven Systems

编织系统中涌现力学的降阶模型

Anvay A. Pradhan, Evgueni T. Filipov, Talia Y. Moore

机构 * Department of Mechanical Engineering, University of Michigan(密歇根大学机械工程系) Department of Civil and Environmental Engineering, University of Michigan(密歇根大学土木与环境工程系) Department of Robotics, University of Michigan(密歇根大学机器人学系) Department of Ecology and Evolutionary Biology, Museum of Zoology, University of Michigan(密歇根大学动物学博物馆生态与进化生物学系)

AI总结 提出一种降阶模型,通过节点和四个物理可解释的刚度单元捕捉编织结构中的各向异性刚度、剪切锁定等涌现力学行为,校准后与实验误差在5%以内,并展示了连续模型无法实现的能力。

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2606.23092 2026-06-23 cs.CL 新提交

PIVOTSBench: Evaluating Fine-Grained Interpersonal Relationship Reasoning in Multimodal Large Language Models

PIVOTSBench:评估多模态大语言模型中的细粒度人际关系推理

Shuxiang Zhang, Yiting Yin, Wenxuan Song, Yuhang Wu, Miao Liu

机构 * Sun Yat-sen University(中山大学) University of Michigan(密歇根大学) Tsinghua University(清华大学)

AI总结 提出PIVOTS基准,基于Social-IQ 2.0和YouTube数据,评估多模态大语言模型在心理学研究基础上预测双向人际关系维度的能力,并包含辅助任务分析关键视觉线索。

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2606.23085 2026-06-23 cs.RO 新提交

Foresight: Failure Detection for Long-Horizon Robotic Manipulation with Action-Conditioned World Model Latents

Foresight: 基于动作条件世界模型潜在变量的长时程机器人操作故障检测

Haoran Zhang, Yifu Lu, Boyang Wang, Xuhui Kang, Yen-Ling Kuo, Zezhou Cheng, Mengdi Wang, Odest Chadwicke Jenkins

机构 * University of Michigan(密歇根大学) Princeton University(普林斯顿大学) University of Virginia(弗吉尼亚大学)

AI总结 提出Foresight框架,利用动作条件世界模型的潜在表征监测操作轨迹,仅用最终任务级标签训练,结合函数共形预测自适应校准阈值,在仿真和真实机器人长时程任务中实现高效故障检测。

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2606.22942 2026-06-23 cs.CL 新提交

Understanding Knowledge Distillation in Post-Training: When It Helps and When It Fails

理解后训练中的知识蒸馏:何时有效与何时失败

Xin Liu, Simin Ma, Shujian Liu, Song Wang, Sathish Reddy Indurthi, Haoyun Deng, Lu Wang, Kaiqiang Song

机构 * University of Michigan(密歇根大学) Zoom Video Communications(Zoom视频通信公司)

AI总结 本文系统研究后训练阶段的知识蒸馏,发现低数据场景下KD优于SFT,但数据充足时优势减弱;从更强教师蒸馏可恢复增益,并提出两阶段KD策略提升数据稀缺环境下的学生模型性能。

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2606.22454 2026-06-23 cs.CL cs.AI 新提交

CASPER in the Machine: Insights into Character Variety in LLM-Generated Stories

机器中的CASPER:LLM生成故事中角色多样性的洞察

Anneliese Brei, Abhisheik Sharma, Nicholas Sanaie, Lu Wang, Snigdha Chaturvedi

机构 * UNC Chapel Hill(北卡罗来纳大学教堂山分校) Georgia Institute of Technology(佐治亚理工学院) University of Michigan(密歇根大学)

AI总结 本文借用叙事学定义,从八个维度分析LLM与人类创作故事中角色的刻画,发现两者在角色类型和多样性上既有相似也有差异。

Comments Proceedings of ACL, 2026

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2606.22171 2026-06-23 cs.LG 新提交

Beyond Time Series: Spatial Reasoning for Epidemic Forecasting via Multimodal Learning

超越时间序列:基于多模态学习的空间推理用于流行病预测

Diana Guadalupe Gomez, Chenwei Wu, Zhiyi Wang, Liyue Shen, Alexander Rodríguez

机构 * University of Michigan(密歇根大学)

AI总结 提出M-SPICE框架,通过注意力机制融合区域级时序数据与空间辅助信号,在COVID-19、流感和ILI预测任务上超越现有基线。

Comments To appear in the Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2026), AI for Science Track

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2606.21869 2026-06-23 cs.CL cs.AI 新提交

The Language-Energy Divide: Measuring Energy Costs of Multilingual LLM Inference

语言-能量鸿沟:衡量多语言大模型推理的能量成本

Naihao Deng, Alissa Shen, Yiming Feng, Joan Nwatu, Jae-Won Chung, Mosharaf Chowdhury, Yulong Chen, Rada Mihalcea

机构 * University of Michigan(密歇根大学) University of Cambridge(剑桥大学) University of Aberdeen(阿伯丁大学)

AI总结 系统研究多语言LLM推理的能量消耗,发现不同语言间每token能耗差异达8.3倍,总能耗差异达179倍,低资源语言能耗高且准确率低,建议将能量作为评估指标。

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2606.21866 2026-06-23 cs.RO 新提交

SurGE: Surrogate Gradient-guided Evolution for Co-design of Legged Robots with Parallel Elasticity

SurGE: 基于代理梯度引导的并行弹性腿式机器人协同设计进化

Yulun Zhuang, Yue Qin, Justin Lu, Zelin Shen, Yichen Wang, Sicheng He, Yanran Ding

机构 * University of Michigan, Ann Arbor(密歇根大学安娜堡分校) University of Tennessee, Knoxville(田纳西大学诺克斯维尔分校)

AI总结 提出SurGE框架,通过可微运动动力学模型和设计感知控制策略计算代理梯度,并注入CMA-ES实现非可微协同设计,在跳跃机器人上降低37.65%的设计目标。

Comments 8 pages, 7 figures. Accepted for publication at IROS 2026. Website at https://arcad-lab-um.github.io/surge-codesign/

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2606.21861 2026-06-23 cs.CV 新提交

Zero-Shot Vision-Language Models for Classroom Engagement Recognition: A Benchmark Study of Prompt Sensitivity and Cross-Dataset Generalization

零样本视觉语言模型用于课堂投入度识别:提示敏感性与跨数据集泛化的基准研究

Aman Goyal, Kshama Nitin Shah, Kemmannu Vineet Venkatesh Rao

机构 * Carnegie Mellon University(卡内基梅隆大学) University of Michigan, Ann Arbor(密歇根大学安娜堡分校) Magna International(麦格纳国际)

AI总结 本研究系统评估五种视觉语言模型在零样本条件下识别课堂投入度的表现,发现三个主要失败模式:个体学生识别近乎随机、类别崩溃和极端提示敏感性,但场景级分类效果较好。

Comments 11 pages, 6 figures, including supplementary material. Presented as a non-archival paper at the CV4Edu Workshop, CVPR 2026

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2606.21830 2026-06-23 cs.LG 新提交

Mat-Pref: Verifiable-Reward Training Improves Compositional Reasoning in Inorganic Materials

Mat-Pref: 可验证奖励训练提升无机材料中的组合推理能力

Sarrah R. Mikhail Leung, Taehan Kim, Jeongbin Park

机构 * University of California, Berkeley(加州大学伯克利分校) University of Michigan(密歇根大学)

AI总结 提出Mat-Pref基准,通过可验证奖励强化学习(GRPO)提升无机材料组合推理,在结构泛化和属性迁移上超越大模型。

Comments 10 pages, 4 figures, Accepted at ICML AI4Physics 2026 Workshop

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2606.21119 2026-06-23 cs.CV cs.AI 新提交

MammoExpert: Benchmarking Chain-of-Thought Reasoning in Mammography Diagnosis

MammoExpert:乳腺X线诊断中的思维链推理基准测试

Di Dai, Bo Liu, Youcheng Li, Haojun Yu, Zhouhang Bian, Quanlin Wu, Dong Wang, Sichen Meng, Hongye Xuan, Zijie Lan, Shenda Hong, Liwei Wang

机构 * State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University(北京大学智能科学与技术学院通用人工智能国家重点实验室) School of Computer Science and Engineer, Beijing University of Aeronautics and Astronautics(北京航空航天大学计算机科学与工程学院) Center for Data Science, Peking University(北京大学数据科学中心) Yizhun co. ltd(医准有限公司) International School, Beijing University of Post and Telecommunications(北京邮电大学国际学院) School of Public Health, University of Michigan, Ann Arbor(密歇根大学安娜堡分校公共卫生学院) Future Technology College, Xi'an Jiaotong University(西安交通大学未来技术学院) Peking University(北京大学)

AI总结 提出首个包含思维链推理标注的乳腺X线数据集MammoExpert,覆盖2379张图像和67种病理亚型,通过三阶段推理提升病灶分类准确率,在多个数据集上验证了有效性。

Comments KDD 2026

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2606.20905 2026-06-23 cs.RO cs.AI 新提交

Vesta: A Generalist Embodied Reasoning Model

Vesta: 一种通用具身推理模型

Johan Bjorck, Zhiqi Li, Yunze Man, Jing Wang, An-Chieh Cheng, Sifei Liu, Shihao Wang, Zhiding Yu, Abhishek Badki, Stan Birchfield, Valts Blukis, Yevgen Chebotar, Siyi Chen, Sicong Leng, Yu-Cheng Chou, Tianli Ding, Boyi Li, Zhengyi Luo, Hang Su, Jonathan Tremblay, Tingwu Wang, Bowen Wen, Jimmy Wu, Xianghui Xie, Hanrong Ye, Hongxu Yin, K. R. Zentner, Liangyan Gui, Yu-Xiong Wang, Yuke Zhu, Linxi "Jim" Fan, Jan Kautz

机构 * University of Illinois Urbana-Champaign(伊利诺伊大学厄巴纳-香槟分校) University of California, San Diego(加州大学圣地亚哥分校) The Hong Kong Polytechnic University(香港理工大学) University of Michigan(密歇根大学) Nanyang Technological University(南洋理工大学) Johns Hopkins University(约翰霍普金斯大学) University of Tübingen(图宾根大学)

AI总结 提出统一的基础模型Vesta,整合定位、空间推理、导航和长时规划能力,通过大规模空间感知语料库和简单多模态记忆机制,在多个基准上平均优于专用模型20%以上,在真实机器人任务中成功率提升35%。

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2606.20772 2026-06-23 cs.RO eess.IV 新提交

Mind the Privileged-to-Camera Gap: Actor-Centric Sidecar Supervision for Camera-First Open-Loop Waypoint Prediction

注意特权到摄像头的差距:面向摄像头优先开环路径预测的以参与者为中心的侧车监督

Feeza Khan Khanzada, Jaerock Kwon

机构 * University of Michigan-Dearborn(密歇根大学迪尔伯恩分校)

AI总结 针对摄像头优先开环路径预测中缺乏对道路参与者显式监督的问题,提出侧车监督方法,利用模拟器生成的参与者标签(如相关性、短时运动)训练模型,在部署时无需额外输入,最终将终点位移误差降低32.6%。

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2606.20752 2026-06-23 cs.CV cs.CR 新提交

Mirage: a Clean-Label Backdoor against LiDAR 3D Object Detection

Mirage:针对LiDAR 3D目标检测的干净标签后门攻击

Ziba Parsons, Ang Li

机构 * University of Michigan - Dearborn(密歇根大学迪尔伯恩分校)

AI总结 提出Mirage,一种黑盒、干净标签的后门攻击方法,通过注入少量标签一致的毒化样本,使LiDAR 3D目标检测模型学习触发器与目标类别的恶意关联,实现73%误分类成功率且仅需0.5%毒化率。

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2606.20643 2026-06-23 cs.AI cs.CV 新提交

SPARC: A Multi-Agent System for Electrical Circuit Question Answering

SPARC:用于电路问答的多智能体系统

Mushtari Sadia, Zhenning Yang, Umme Habiba Lamia, Nishat Shawrin, Ang Chen, Amrita Roy Chowdhury

机构 * University of Michigan(密歇根大学) Bangladesh University of Engineering and Technology(孟加拉工程与技术大学)

AI总结 提出SPARC多智能体系统,通过可执行的物理仿真程序进行推理,在电路问答任务上实现83%准确率,较基线提升高达58%。

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2606.23627 2026-06-23 stat.ML cs.LG math.ST stat.TH 新提交

Diffusion Models Adapt to Low-Dimensional Structure Under Flexible Coefficient Choices

扩散模型在灵活系数选择下适应低维结构

Changxiao Cai, Yuchen Jiao, Gen Li

机构 * Department of Industrial and Operations Engineering, University of Michigan(工业与运营工程系,密歇根大学) Department of Statistics and Data Science, Chinese University of Hong Kong(统计与数据科学系,中国香港大学)

AI总结 本文证明扩散模型在广泛系数选择下,仅需O(k/ε)次迭代即可生成ε-精确样本,独立于环境维度,从而验证了其适应低维结构的鲁棒性。

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2606.21701 2026-06-23 astro-ph.EP astro-ph.IM cs.DB cs.LG 新提交

ARCO-Mars: A Unified Cloud-Optimized Archive of Mars Atmosphere Reanalysis

ARCO-Mars:统一云优化的火星大气再分析档案

Ananyo Bhattacharya

机构 * University of Michigan, Department of Climate and Space Sciences and Engineering(密歇根大学气候与空间科学与工程系)

AI总结 提出ARCO-Mars,一个统一的分析就绪云优化数据集,整合三种火星大气再分析产品(EMARS、MACDA、OpenMARS),覆盖火星年24-35,以Zarr v3格式存储于HuggingFace,支持高效云访问,并比较了系统差异。

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2606.15980 2026-06-23 cs.LG cs.AI cs.CL 新提交

Do Activation Monitors Survive Model Updates? Benchmarking, Predicting, and Repairing Activation-Monitor Staleness

安全监控器在更新后是否仍可靠?激活监控器陈旧性的基准测试与预测

Evan Duan

机构 * University of Michigan(密歇根大学)

AI总结 研究语言模型更新后激活监控器是否仍可靠,发现量化更新影响小,微调更新常导致监控器失效,且可通过预部署特征预测退化。

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2606.20206 2026-06-19 stat.ML cs.LG 新提交

Off-Policy Evaluation for Missingness-Aware Policies in MDPs with Rewards Missing Not at Random

马尔可夫决策过程中奖励非随机缺失的缺失感知策略的离线评估

Ziheng Wei, Annie Qu, Rui Miao

机构 * Department of Statistics, University of Michigan at Ann Arbor(密歇根大学安娜堡分校统计学系) Department of Statistics(统计学系) Applied Probability, University of California at Santa Barbara(加州大学圣巴巴拉分校应用概率系) Department of Mathematical Sciences, University of Texas at Dallas(德克萨斯大学达拉斯分校数学科学系)

AI总结 针对奖励非随机缺失的离线强化学习问题,提出基于未来状态作为影子变量的识别方法,并利用桥函数和min-max估计器恢复条件均值奖励,实现缺失感知策略的离线评估。

Comments Accepted at ICML 2026. 31 pages, 6 figures

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2606.19539 2026-06-19 astro-ph.SR cs.AI 新提交

Review of Machine Learning Models for Solar Energetic Particle Prediction

太阳高能粒子预测的机器学习模型综述

Spiridon Kasapis, Pouya Hosseinzadeh, Kathryn Whitman, Ricky Egeland, Manolis Georgoulis, Angelos Vourlidas, Athanasios Papaioannou, Eleni Lavasa, Anastasios Anastasiadis, Giorgos Giannopoulos, Andres Munoz-Jaramillo, Bala Poduval, Irina N. Kitiashvili, Alexander G. Kosovichev, Viacheslav Sadykov, Soukaina Filali Boubrahimi, Tate T. Hutchins, Hameedullah A. Farooki, Manuel E. Cuesta, Leng Y. Khoo, Sungmin Pak, Robert Czarnota, Jamie S. Rankin, Jamey Szalay, Mitchell M. Shen, Georgios Livadiotis, Zigong Xu, David J. McComas, Nikolaos Sarlis, Dionissios Hristopulos, Arik Posner, Alec J. Engell, Mohammed AbuBakr Ali, Ali G. A. Abdelkawy, Abdelrazek M. K. Shaltout, M. M. Beheary, Christina O. Lee, Sigiava Aminalragia-Giamini, Constantinos Papadimitriou, Ingmar Sandberg, Savvas Raptis, Shah Muhammad Hamdi, Monica Laurenza, Mirko Stumpo, Sumanth A. Rotti, India Jackson, Aatiya Ali, Atilim Gunes Baydin, Nathan Schwadron, Subhamoy Chatterjee, Maher A. Dayeh, Gelu M. Nita, Patrick M. O'Keefe, Chun Jie Chong, Paul Kosovich, Russell D. Marroquin, Berkay Aydin, Petrus C. Martens, Lulu Zhao, Yang Chen, Yian Yu, Monica G. Bobra, Ward Manchester, Tamas Gombosi, Ming Zhang, Jesse Torres, Philip K. Chan, Mohamed Nedal, Kamen Kozarev, Peijin Zhang, Kimberly Moreland, Hazel M. Bain, Samuel Hart, Michael J. Starkey, Alan G. Ling, Simone Benella

机构 * Department of Astrophysical Sciences, Princeton University, Princeton, NJ, USA Computational Physics Branch, NASA Ames Research Center, Moffett Field, CA, USA Department of Computer Science, Utah State University, Logan, UT, USA Space Radiation Analysis Group, NASA Johnson Space Center, Houston, TX, USA Johns Hopkins Applied Physics Lab, 11100 Johns Hopkins Rd, Laurel, MD 20723, United States Research Center for Astronomy Applied Mathematics of the Academy of Athens, 4 Soranou Efesiou Street, Athens 11527, Greece Institute for Astronomy, Astrophysics, Space Applications Southwest Research Institute, Boulder, CO, USA Space Science Center, University of New Hampshire, Durham, NH, USA Department of Physics, New Jersey Institute of Technology, Newark, NJ, USA Astronomy Department, Georgia State University, Atlanta, GA, USA Department of Computer Science, Princeton University, Princeton, NJ, USA Department of Mathematics, Rowan University, Glassboro, NJ, USA Astronomy, California Institute of Technology, Pasadena, CA, USA Department of Physics, National Kapodistrian University of Athens, Athens, Greece School of Electrical Computer Engineering, Technical University of Crete, Chania, Greece Department of Astronomy Meteorology, Faculty of Science, Al-Azhar University, Cairo, Egypt Space Sciences Lab, University of California, Berkeley, CA, USA Research Consultancy, Athens, Greece Institute for Space Astrophysics Department of Physics Astronomy, Georgia State University, Atlanta, GA 30303, USA Aryabhatta Research Institute of Observational Sciences (ARIES), Manora Peak, Nainital-263001, Uttarakhand, India Department of Computer Science, Oxford University, Oxford, England Southwest Research Institute, San Antonio, TX, USA Computer Science Department, New Jersey Institute of Technology, Newark, NJ, USA Department of Physics, University of California San Diego, La Jolla, CA 92093, USA Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA Department of Climate Engineering, University of Michigan, Ann Arbor, MI, USA Department of Statistics, University of Michigan, Ann Arbor, MI, USA Department of Electrical Engineering Computer Science, Florida Institute of Technology, Melbourne, FL, USA Astrophysics Section, School of Cosmic Physics, Dublin Institute for Advanced Studies, DIAS Dunsink Observatory, Dublin D15 XR2R, Ireland Institute of Astronomy of the Bulgarian Academy of Sciences, Sofia, Bulgaria Center for Solar-Terrestrial Research, New Jersey Institute of Technology, Newark, NJ 07102, USA Cooperative Programs for the Advancement of Earth System Science, University Corporation for Atmospheric Research, Boulder, CO, USA CIRES, University of Colorado Boulder, Boulder, CO, USA Space Weather Prediction Center, NOAA, Boulder, CO, USA Astronomy, College of Science, The University of Texas at San Antonio, San Antonio, TX, USA Space Weather Prediction Center, National Oceanic The University of Texas at San Antonio, San Antonio, TX, USA Environmental Research, Inc., MA, USA

AI总结 综述了用于太阳高能粒子预测的机器学习模型,包括数据集、架构、输入输出比较,并提出了未来研究建议。

Comments Review Paper, Maine text: 23 pages, References: 5 pages, Appendix: 42 pages

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2606.17054 2026-06-19 cs.RO cs.AI cs.CV cs.LG 新提交

Human Universal Grasping

人类通用抓取

Kevin Yuanbo Wu, Tianxing Zhou, Isaac Tu, Billy Yan, Irmak Guzey, David Fouhey, Dandan Shan, Lerrel Pinto

机构 * New York University(纽约大学) Tsinghua University(清华大学) University of Michigan(密歇根大学)

AI总结 提出HUG模型,利用人类抓取数据(1M-HUG数据集)和流匹配方法,从单张RGB-D图像生成多样化抓取姿态,并重定向到机器人手,实现零样本抓取,在HUG-Bench上超越基线23%-34%。

Comments 28 pages, 20 figures, 7 tables

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2606.19233 2026-06-18 cs.RO 新提交

Mobile Pedipulation for Object Sliding via Hierarchical Control on a Wheeled Bipedal Robot

基于轮式双足机器人分层控制的移动式腿部操作物体滑动

Yue Qin, Yulun Zhuang, Zelin Shen, Yanran Ding

机构 * Department of Robotics, University of Michigan(密歇根大学机器人系)

AI总结 提出一种分层控制框架,使轮式双足机器人能用腿部滑动平面物体,通过简化三刚体动力学模型和轨迹优化运动规划器,在实验中成功实现1kg物体取回和4kg物体滑动。

Comments 8 pages, 7 figures

Journal ref IEEE Robotics and Automation Letters, vol. 11, no. 8, pp. 9787-9794, Aug. 2026

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2606.18656 2026-06-18 cs.CL 新提交

The Wrong Kind of Right: Quantifying and Localizing Misfired Alignment in LLMs

错误的正确:量化和定位大语言模型中的失调对齐

Naihao Deng, Yiming Feng, Chimaobi Okite, Kaijian Zou, Lu Wang, Rada Mihalcea, Yulong Chen

机构 * University of Michigan(密歇根大学) University of Cambridge(剑桥大学) University of Aberdeen(阿伯丁大学)

AI总结 本文提出VETO基准和失调对齐率(MAR)指标,发现所有LLM在刻板印象相关问题上均存在非平凡的失调对齐,且人类为0%,机制分析表明对齐诱导的线索会放大该现象。

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2606.18471 2026-06-18 cs.CL 新提交

Possible or Definite? A Benchmark for Evaluating Diagnostic Uncertainty Preservation in Clinical Text

可能还是确定?评估临床文本中诊断不确定性保留的基准

Hongbo Du, Zixin Lu, Jiaming Qu

机构 * Trine University(特里尼大学) University of Michigan(密歇根大学) Amazon(亚马逊)

AI总结 构建包含9184个不确定性标注的基准,评估LLM在临床文本中保留诊断不确定性的能力,发现LLM保留原始不确定性线索不足一半,且难以区分相邻级别。

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2606.18438 2026-06-18 math.OC cs.LG 新提交

Sequential Hiring of Contingent Workers Through Learning-Based Optimization

基于学习优化的临时工顺序雇佣

Chris Lee, Xiuli Chao, Izak Duenyas

机构 * Department of Industrial and Operations Engineering, University of Michigan(工业与运营工程系,密歇根大学) Ross School of Business, University of Michigan(罗斯商学院,密歇根大学)

AI总结 针对临时工场景中工人产能和劳动力供给的不确定性,提出DR-UCB策略,通过学习周期顺序决策替换与雇佣,实现累积利润最大化,并证明其遗憾下界匹配。

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2606.15633 2026-06-18 cs.LG 新提交

Formalizing and Mitigating Structural Distortion in LLM Attention for Graph Reasoning

形式化并缓解大语言模型注意力中的结构失真以实现零样本图推理

Donald Loveland, Puja Trivedi, Ari Weinstein, Edward W Huang, Danai Koutra

机构 * University of Michigan(密歇根大学) Amazon(亚马逊)

AI总结 本文形式化了大语言模型处理文本属性图时因图线性化导致的结构失真机制,并提出轻量级推理时修改方法GaLA,通过校正注意力偏差提升零样本图推理性能。

Comments Accepted to KDD 2026

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2606.18119 2026-06-17 cs.AI 新提交

First Proof Second Batch

首次证明第二批

Mohammed Abouzaid, Nikhil Srivastava, Rachel Ward, Lauren Williams

机构 * Stanford University(斯坦福大学) University of California, Berkeley(加州大学伯克利分校) University of Texas at Austin(德克萨斯大学奥斯汀分校) Harvard University(哈佛大学) Polish Academy of Sciences(波兰科学院) UC Berkeley(加州大学伯克利分校) Brown University(布朗大学) ETH Zürich(苏黎世联邦理工学院) MIT(麻省理工学院) Weierstrass Institute(魏尔斯特拉斯研究所) Duke University(杜克大学) Sorbonne Université(索邦大学) Boston College(波士顿学院) Université du Québec à Montréal(魁北克大学蒙特利尔分校) UCLA(加州大学洛杉矶分校) University of Michigan(密歇根大学) University of Maryland(马里兰大学)

AI总结 测试多个AI系统在十个数学研究问题上的解题能力,评估当前AI解决研究级数学问题的水平。

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2606.17887 2026-06-17 cs.HC cs.AI 新提交

AI Adoption Across a Multinational Workforce: Sociotechnical Conditions for GenAI Acceptance in Human Resources

AI在跨国劳动力中的采纳:人力资源中GenAI接受的社会技术条件

Dalia Ali, Maria José Rodríguez Velázquez, Manoel Horta Ribeiro, Vera Liao, Orestis Papakyriakopoulos

机构 * Technical University of Munich(慕尼黑技术大学) University of Michigan(密歇根大学) Princeton University(普林斯顿大学)

AI总结 研究跨国科技公司从传统HR系统转向GenAI系统过程中,员工采纳受情境适配、搜索素养和信任校准等社会技术条件影响,并提出了包容性部署的设计建议。

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2606.17645 2026-06-17 cs.AI cs.CL cs.LG 新提交

Beyond Domains: Reusing Web Skills via Transferable Interaction Patterns

超越领域:通过可迁移交互模式重用网络技能

Shiqi He, Yue Cui, Feijie Wu, Xinyu Ma, Jiaheng Lu, Yaliang Li, Bolin Ding, Mosharaf Chowdhury

机构 * University of Michigan(密歇根大学) Alibaba Group(阿里巴巴集团) Purdue University(普渡大学) McMaster University(麦克马斯特大学) University of Pennsylvania(宾夕法尼亚大学)

AI总结 提出SkillMigrator代理,通过学习可迁移交互模式(TIP)匹配布局结构而非元素引用,实现跨站点技能重用,在WebArena和Mind2Web上成功轨迹的LLM动作数减少8-10%。

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2606.17310 2026-06-17 cs.CV 新提交

SierpinskiCam: Camera-Controlled Video Retaking with Sierpinski Triangle Pattern Cues

SierpinskiCam: 基于谢尔宾斯基三角形图案线索的相机控制视频重拍

Suttisak Wizadwongsa, Hyelin Nam, Supasorn Suwajanakorn, Jeong Joon Park

机构 * University of Michigan, Ann Arbor(密歇根大学安娜堡分校) VISTEC, Thailand(泰国威斯泰克科学技术研究院)

AI总结 提出SierpinskiCam方法,通过谢尔宾斯基圆顶纹理线索增强几何引导,并引入参考视频条件机制,解决单目视频重拍中相机大角度偏离时的稀疏区域问题,提升相机可控性、几何一致性和视频质量。

Comments 20 pages, 13 figures

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