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Imperial College London(帝国理工学院)

共收录 1182
2606.07157 2026-08-05 cs.AI 版本更新

Think Fast: Estimating No-CoT Task-Completion Time Horizons of Frontier AI Models

快速思考:估计前沿AI模型的无思维链任务完成时间范围

Dewi Gould, Francis Rhys Ward, Anders Cairns Woodruff, Rauno Arike, Josh Hills, Alex Serrano, Ida Caspary, Jason Ross Brown, Jo J. Jiao, Patrick Leask, Twm Stone, Ram Potham, Ionut Gabriel Stan, Harry Mayne, Simeon Hellsten, Shubhorup Biswas, Ariana Azarbal, William L. Anderson, Elle Najt, Ryan Greenblatt, Julian Stastny

机构 * Redwood Research(红木研究) Astra Fellows Program(Astra 后援计划) Aether Research(Aether 研究) MATS Research(MATS 研究) Polytechnic University of Catalonia(加泰罗尼亚理工大学) Imperial College London(伦敦帝国理工学院) University of Cambridge(剑桥大学) University of Chicago(芝加哥大学) Durham University(杜伦大学) MIT(麻省理工学院) University of Oxford(牛津大学) University of Glasgow(格拉斯哥大学) Constellation(星座)

AI总结 本研究通过超过3万个问题测试前沿AI模型在无思维链推理下的表现,估计其50%任务完成时间范围,发现该时间每约两年翻一番,GPT-5.5已达3分钟以上。

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2510.17568 2026-08-05 cs.CV

PAGE-4D: Disentangled pose and geometry estimation for vggt-4d perception

PAGE-4D: 通过解耦姿态与几何估计实现VGGT-4D感知

Kaichen Zhou, Yuhan Wang, Grace Chen, Xinhai Chang, Gaspard Beaudouin, Fangneng Zhan, Paul Pu Liang, Mengyu Wang

机构 * Harvard AI and Robotics Lab, Harvard University(哈佛人工智能与机器人实验室,哈佛大学) Media Lab and Electrical Engineering and Computer Science, Massachusetts Institute of Technology(媒体实验室和电气工程与计算机科学,麻省理工学院) Department of Computing, Imperial College London(计算系,帝国理工学院) Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University(哈佛大学自然与人工智能研究学院)

AI总结 提出PAGE-4D,扩展VGGT到动态场景,通过动态感知聚合器解耦静态与动态信息,同时提升相机姿态估计、深度预测和点云重建性能。

Comments ICLR 2026, VGGT-4D, Dynamic VGGT

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2603.20381 2026-08-05 cs.CL cs.AI cs.HC 版本更新

The production of meaning in the processing of natural language

自然语言处理中意义产生的机制

Christopher J. Agostino, Quan Le Thien, Nayan D'Souza, Louis van der Elst

机构 * Department of Physics, Indiana University(印第安纳大学物理系) Department of Linguistics, Indiana University(印第安纳大学语言学系) Imperial College London(伦敦帝国学院)

AI总结 研究自然语言处理中意义产生的机制,探讨量子逻辑与经典布尔理论在语义处理中的差异,分析模型在不同参数下的表现及对安全交互的影响。

Comments Accepted to QNLP 2026, 9 pages, 3 figures, 2 tables

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2601.14871 2026-08-05 cs.RO 版本更新

On-the-fly hand-eye calibration for the da Vinci surgical robot

达芬奇手术机器人的在线手眼标定

Zejian Cui, Ferdinando Rodriguez y Baena

机构 * Department of Mechanical Engineering, Imperial College London(帝国理工学院机械工程系) Mechatronics in Medicine Laboratory(医学机电实验室) Hamlyn Centre for Robotics Surgery(机器人外科哈姆林中心)

AI总结 针对达芬奇机器人因编码器误差导致工具定位不准的问题,提出一种在线计算手眼变换矩阵的标定框架,通过特征关联和手眼标定两个模块实现无预训练的关键点匹配,在多种手术场景下显著降低定位误差且时间效率高。

Comments 18 pages, 17 figures, 5 tables

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2511.20532 2026-08-05 q-bio.NC cs.AI cs.RO 版本更新

MIMIC-MJX: Neuromechanical Emulation of Animal Behavior

MIMIC-MJX:动物行为的神经机械模拟

Charles Y. Zhang, Yuanjia Yang, Aidan Sirbu, Elliott T. T. Abe, Emil Wärnberg, Eric J. Leonardis, Diego E. Aldarondo, Adam Lee, Aaditya Prasad, Jason Foat, Kaiwen Bian, Joshua Park, Rusham Bhatt, Vyom N. Patel, Hutton Saunders, Austin O. Barbano, Akira Nagamori, Ayesha R. Thanawalla, Kee Wui Huang, Fabian Plum, Hendrik K. Beck, Steven W. Flavell, David Labonte, Blake A. Richards, Bingni W. Brunton, Eiman Azim, Bence P. Ölveczky, Talmo D. Pereira

机构 * Department of Organismic and Evolutionary Biology(有机与进化生物学系) Harvard University(哈佛大学) Computational Neurobiology Laboratory(计算神经生物学实验室) Salk Institute for Biological Studies(生物研究 institute) Neurosciences Graduate Program(神经科学研究生项目) University of California San Diego(加州大学圣地亚哥分校) Mila School of Computer Science(计算机科学学院) McGill University(麦吉尔大学) University of Washington(华盛顿大学) eScience Institute(eScience 院) Computational Neuroscience Center(计算神经科学中心) Department of Brain and Cognitive Sciences(脑与认知科学系) Massachusetts Institute of Technology(麻省理工学院) Picower Institute for Learning and Memory(记忆学习研究所) Molecular Neurobiology Laboratory(分子神经生物学实验室) Department of Bioengineering(生物工程系) Imperial College London(帝国理工学院) Howard Hughes Medical Institute(霍华德·休斯医学研究所)

AI总结 MIMIC-MJX通过学习生物合理的神经控制策略,实现了对动物行为的神经机械模拟,具有高准确性和广泛适用性。

Comments Project page available at https://mimic-mjx.talmolab.org

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2608.02005 2026-08-04 cs.AI 新提交

Evolving in the Agent Jungle via History-Informed Opponent Awareness

在智能体丛林中通过历史感知的对手意识进化

Zhaofeng Zhang, Linhan Xia, Rui Liu, Yihao Wang, Binrui Shen, Shengxin Zhu

机构 * University of Edinburgh(爱丁堡大学) University of Oklahoma(俄克拉荷马大学) Imperial College London(伦敦帝国学院) University of Michigan(密歇根大学) University of Southern California(南加州大学) Tencent(腾讯) Beijing Normal University(北京师范大学) Beijing Normal–Hong Kong Baptist University(北京师范大学-香港浸会大学联合国际学院)

AI总结 针对多智能体环境中对手策略持续进化导致静态技能修改方法失效的问题,提出OASE方法,通过历史快照锚定的配对比较选择有益技能修改,在两类场景中实现更低均衡距离与更少无效策略变更。

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2608.01426 2026-08-04 cs.LG cs.DC 新提交

Cluster-Aware Over-the-Air Federated Learning with Energy-Harvesting Devices: From Global Training to Model Personalization

面向带能量收集设备的集群感知空中联邦学习:从全局训练到模型个性化

Furkan Bagci, Busra Tegin, Mohammad Kazemi, Tolga M. Duman

机构 * Bilkent University(比尔肯特大学) University of Illinois Chicago(伊利诺伊大学芝加哥分校) CentraleSupélec(中央理工学院) Imperial College London(帝国理工学院)

AI总结 本研究针对带能量收集设备的异质性数据分布场景,提出统一集群感知空中联邦学习框架,可分别实现全局训练的公平性优化与模型个性化,同时降低通信开销。

Comments 17 pages

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2608.01319 2026-08-04 cs.AI 新提交

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models

大语言模型自适应元推理的认知需求引导

John Scoville, Shengzhuang Chen, Yejin Bang, Stefan Winzeck, Jonathan Richard Schwarz

机构 * Thomson Reuters Foundational Research(汤森路透基础研究部) Imperial College London(帝国理工学院)

AI总结 该研究提出无需训练的元推理框架CDS,通过认知量表刻画需求,在三类大模型和六个基准上较直接调用、标准CoT分别提升21.9%、9%准确率,难数学编码任务增益显著。

Comments 21 pages, 3 figures

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2608.00752 2026-08-04 cs.CV 新提交

NISF++: Geometrically-grounded implicit representations of 3D+time cardiac function from 2D short- and long-axis MR views

NISF++:基于几何基础的、从2D短轴和长轴MR视图重建心脏3D+时间功能的隐式表示

Nil Stolt-Ansó, Maik Dannecker, Steven Jia, Julian McGinnis, Daniel Rueckert

机构 * Technical University Munich(慕尼黑工业大学) TUM University Hospital(慕尼黑工业大学附属医院) Aix-Marseille Université(艾克斯-马赛大学) Imperial College London(伦敦帝国学院)

AI总结 该研究提出NISF++架构,从2D短轴和长轴MR视图构建心脏3D+时间隐式表示,实现时空一致性与运动校正,在UK-Biobank120人队列中获良好分割与运动校正效果。

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2608.00147 2026-08-04 cs.CV cs.LG 新提交

RadPRISM: Schema-stratified radiology-report supervision for concept-disentangled image representations and visual grounding

RadPRISM:用于概念解耦图像表示与视觉定位的模式分层放射学报告监督方法

Fabian Drexel, Marlene Fritzsche, Era Stambollxhiu, Miriam Kumpf, Lena Schmitzer, Lea Schumann, Jannik Kahmann, Friedrich Puttkammer, Johannes Moll, Jannik Lübberstedt, Zeineb Ben Chaaben, Anirudh Narayanan, Cosmin I. Bercea, Sebastian Ziegelmayer, Marcus R. Makowski, Daniel Rueckert, Lisa C. Adams, Keno K. Bressem

机构 * Technical University of Munich (TUM)(慕尼黑工业大学(TUM)) TUM University Hospital(慕尼黑工业大学医院) Technical University of Munich, School of Medicine and Health(慕尼黑工业大学医学与健康学院) Klinikum rechts der Isar(右伊萨尔医院) Charité – Universitätsmedizin Berlin(柏林夏里特医学院) Freie Universität Berlin(柏林自由大学) Humboldt Universität zu Berlin(柏林洪堡大学) Imperial College London(伦敦帝国理工学院) Munich Center for Machine Learning (MCML)(慕尼黑机器学习中心(MCML)) University Hospital Essen (AöR)(埃森大学医院(AöR)) Institute for Artificial Intelligence in Medicine (IKIM)(医学人工智能研究所(IKIM)) Institute of Interventional and Diagnostic Radiology and Neuroradiology(介入与诊断放射学及神经放射学研究所) National Center for Tumor Diseases West(西部肿瘤疾病国家中心)

AI总结 RadPRISM将放射学模式作为分层轴,通过专用视觉子空间对齐临床概念,提升零样本分类与视觉定位性能,实现可透明检查的概念解耦医学图像表示。

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2512.04452 2026-08-04 physics.ao-ph cs.AI cs.LG physics.comp-ph physics.flu-dyn 版本更新

NORi: An ML-Augmented Ocean Boundary Layer Parameterization

NORi:一种融合机器学习的海洋边界层参数化方法

Xin Kai Lee, Ali Ramadhan, Andre Souza, Gregory LeClaire Wagner, Simone Silvestri, John Marshall, Raffaele Ferrari

机构 * Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology(麻省理工学院地球、大气与行星科学系) Center for Computational Science and Engineering, Massachusetts Institute of Technology(麻省理工学院计算科学与工程中心) Department of Physics, Imperial College London(伦敦帝国学院物理系) atdepth Aeolus Labs(Aeolus实验室) Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino(托里诺理工学院环境、土地与基础设施工程系)

AI总结 NORi是一种基于物理并结合神经网络的机器学习海洋边界层湍流参数化方法,通过训练大规模涡旋模拟来捕捉边界层底部的混合过程,展示了在不同对流强度、背景层结、旋转和风力作用下的预测和泛化能力。

Comments 59 pages, 20 figures, submitted to Journal of Advances in Modeling Earth Systems (JAMES). This is version 3, updated based on reviews from 3 anonymous reviewers after initial submission to JAMES

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2603.02028 2026-08-04 cs.LG

Latent attention on masked patches for flow reconstruction

潜在注意力在遮蔽块上的应用用于流体重构

Ben Eze, Luca Magri, Andrea Nóvoa

机构 * Aeronautics Dept., Imperial College London(伦敦帝国理工学院航空系) DIMEAS, Politecnico di Torino(都灵理工大学机械与航空航天工程系) I-X, Imperial College London(伦敦帝国理工学院I-X)

AI总结 本文提出LAMP模型,通过分块、降维和单层Transformer回归实现遮蔽流体重构,展示了在层流和湍流中的应用效果。

Comments 8 pages, 5 figures, accepted for publication in Springer's LNCS Series and for poster presentation at ICCS (International Conference on Computational Science) 2026

Journal ref Lecture Notes in Computer Science, vol 16788, pp. 181-188. Springer, Cham (2026)

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2409.06857 2026-08-04 cs.CL

What is the Role of Small Models in the LLM Era: A Survey

在大语言模型时代,小型模型的作用是什么:一项调查

Lihu Chen, Gaël Varoquaux

机构 * Imperial College London, UK(伦敦帝国学院) Inria Saclay, France(法国萨克利研究所)

AI总结 本文通过系统分析LLMs与SMs的协作与竞争关系,探讨小型模型在大语言模型时代的作用,旨在为实践者提供深入理解与高效资源利用的参考。

Comments a survey paper of small models

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2506.02976 2026-08-04 cs.CV cs.AI 版本更新

Deep Learning for Retinal Degeneration Assessment: A Comprehensive Analysis of the MARIO Challenge

利用深度学习评估视网膜退化:对MARIO挑战的全面分析

Rachid Zeghlache, Ikram Brahim, Pierre-Henri Conze, Mathieu Lamard, Mohammed El Amine Lazouni, Zineb Aziza Elaouaber, Leila Ryma Lazouni, Christopher Nielsen, Ahmad O. Ahsan, Matthias Wilms, Nils D. Forkert, Lovre Antonio Budimir, Ivana Matovinović, Donik Vršnak, Sven Lončarić, Philippe Zhang, Weili Jiang, Yihao Li, Yiding Hao, Markus Frohmann, Patrick Binder, Marcel Huber, Taha Emre, Teresa Finisterra Araújo, Marzieh Oghbaie, Hrvoje Bogunović, Amerens A. Bekkers, Nina M. van Liebergen, Hugo J. Kuijf, Abdul Qayyum, Moona Mazher, Steven A. Niederer, Alberto J. Beltrán-Carrero, Juan J. Gómez-Valverde, Javier Torresano-Rodríquez, Álvaro Caballero-Sastre, María J. Ledesma Carbayo, Yosuke Yamagishi, Yi Ding, Robin Peretzke, Alexandra Ertl, Maximilian Fischer, Jessica Kächele, Sofiane Zehar, Karim Boukli Hacene, Thomas Monfort, Béatrice Cochener, Mostafa El Habib Daho, Anas-Alexis Benyoussef, Gwenolé Quellec

机构 * University of Western Brittany, Brest, France University of Tlemcen, Algeria Ophthalmology Department, CHRU Brest, Brest, France Imperial College London, United Kingdom Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan Evolucare Technologies, France College of Computer Science, Sichuan University, China Medical University of Vienna, Austria TNO, The Hague, The Netherlands Image Sciences Institute, UMC Utrecht, Utrecht, The Netherlands Johannes Kepler University Linz, Austria University of Zagreb, Faculty of Electrical Engineering Department of Radiology, University of Calgary, Calgary, AB, Canada Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada Department of Pediatrics, University of Calgary, Calgary, AB, Canada Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada Department of Clinical Neuroscience, University of Calgary, Calgary, AB, Canada University of Calgary, Calgary, AB, Canada German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Germany Medical Faculty Heidelberg, Heidelberg University, Germany Biomedical Image Technologies (BIT), ETSI Telecomunicación, Universidad Politécnica de Madrid, Spain Ophthalmology Service of the Provincial Ophthalmic Institute, Hospital Universitario Gregorio Marañón, Madrid, Spain University of Edinburgh, Scotland Lung Institute, Faculty of Medicine, Imperial College London, United Kingdom Hawkes Institute, Department of Computer Science, University College London, London, United Kingdom

AI总结 本文通过MARIO挑战展示了深度学习在AMD监测中的应用,验证了AI在检测AMD进展方面的有效性,但尚未实现对未来演变的预测。

Comments MARIO-MICCAI-CHALLENGE 2024

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2509.16577 2026-08-04 cs.LG eess.SP 版本更新

Learned Digital Over-the-Air Computing for Federated Edge Learning

用于联邦边缘学习的学习型数字无线聚合计算

Antonio Tarizzo, Mohammad Kazemi, Deniz Gündüz

机构 * Department of Electrical \& Electronic Engineering, Imperial College London, London, UK

AI总结 该研究针对联邦边缘学习中低信噪比下数字OTA设计性能差的问题,提出联合优化URA码本与AMP解码器的学习型框架,可扩展SNR范围约7dB且泛化性良好。

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2607.28677 2026-08-03 cs.AI 新提交

Reasoning in Real World Clinical Care: Why Large Language Models Are Not Yet Safe for Autonomous Clinical Decision Support

现实世界临床护理中的推理:为何大型语言模型(LLM)尚不适合自主临床决策支持

Shayndhan Sivanathan, Shravan Nageswaran, Mehdi Zadem, Ryaan Sultan, Nicolas von Mallinckrodt, Max Solovyev, Alexey Matyushkin, Sumon Sadhu, Gabriele C DeLuca, Sanjeeva Jeyaretna, James Hillis, Manoj Ramachandran, Prakash Jayakumar

机构 * Atman Labs(阿特曼实验室) Oxford University Hospitals(牛津大学医院) University of Oxford(牛津大学) NIHR Oxford Biomedical Research Centre(英国国立卫生研究院牛津生物医学研究中心) Imperial College London(伦敦帝国理工学院) Technical University of Munich(慕尼黑工业大学) Massachusetts General Hospital(麻省总医院) Mass General Brigham(麻省总医院布里格姆医疗系统) Harvard Medical School(哈佛医学院) Barts Health NHS Trust(巴特保健国民保健信托基金会) the University of Texas at Austin(德克萨斯大学奥斯汀分校)

AI总结 本文指出,尽管LLM在医学考试和部分病例推理上表现出色,但因存在信息收集缺陷等问题,尚不适合用于无医生参与的自主临床分诊。

Comments 3 figures

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2605.11567 2026-08-03 cs.CV 版本更新

Dynamic Execution Commitment of Vision-Language-Action Models

视觉-语言-动作模型的动态执行承诺

Feng Chen, Xianghui Wang, Yuxuan Chen, Boying Li, Yefei He, Zeyu Zhang, Yicheng Wu

机构 * University of Adelaide(阿德莱德大学) Sichuan University(四川大学) Shanghai Jiao Tong University(上海交通大学) Monash University(墨尔本大学) Zhejiang University(浙江大学) Imperial College London(伦敦帝国理工学院)

AI总结 本文提出A3机制,通过将动态执行承诺重新定义为自推测前缀验证问题,解决了视觉-语言-动作模型在动态或分布外情况下执行鲁棒性和推理吞吐量之间的平衡问题。

Comments code is available at https://inceptionwang.github.io/A3/

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2607.28466 2026-07-31 cs.AI 新提交

A report-grounded vision-language foundation model for colonoscopy from 280000 routine reports

基于28万份常规报告的肠镜报告驱动的视觉-语言基础模型

Jia Yu, Yan Zhu, Yili He, Zilong Wang, Xinyang Jiang, Peiyao Fu, Ruijie Yang, Tianyi Chen, Siyuan Li, Zhihua Wang, Fei Wu, Quanlin Li, Xian Yang, Pinghong Zhou, Shuo Wang

机构 * Digital Medical Research Center, School of Basic Medical Sciences, Fudan University(复旦大学基础医学院数字医学研究中心) Shanghai Collaborative Innovation Center of Endoscopy(上海内镜诊疗协同创新中心) Zhejiang University(浙江大学) Shanghai Institute for Advanced Study of Zhejiang University(浙江大学上海高等研究院) Alliance Manchester Business School, The University of Manchester(曼彻斯特大学联盟曼彻斯特商学院) Data Science Institute, Imperial College London(伦敦帝国理工学院数据科学研究所) Microsoft Research Asia(微软亚洲研究院)

AI总结 该研究开发了肠镜视觉-语言基础模型EndoCLIP,利用28万份常规肠镜记录恢复的图像-文本对训练,在多项任务中优于通用模型,良恶性分类性能接近专家,可实现临床目标的语言指定。

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2607.28127 2026-07-31 cs.CL cs.LG q-fin.ST q-fin.TR 新提交

FinSMART: Financial Sentiment Analysis for Algorithmic Trading through Market-Aligned Reinforcement Learning

FinSMART:基于市场对齐强化学习的算法交易金融情感分析

Giorgos Iacovides, Wuyang Zhou, Danilo Mandic

机构 * Imperial College London(帝国理工学院)

AI总结 FinSMART是首个基于市场对齐强化学习的金融情感分析框架,通过市场感知数据过滤与非对称交易奖励优化情感信号,在交易回报等指标上较基线提升220%,可自适应市场动态。

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2607.28079 2026-07-31 cs.LG cs.AI 新提交

Chem World: A Large-Scale Benchmark and Physics-Informed Framework for Trustworthy Chemical Property Prediction

Chem World:用于可信赖化学性质预测的大规模基准及物理信息框架

Tianyou Bai, Huan Wang, Mingchen Gao, Fangyue Lin, Pinze Ren, Zhenlin Zhao, Siming Dong

机构 * Cleer Science(克利尔科学公司) Institute of Automation, Chinese Academy of Sciences(中国科学院自动化研究所) Imperial College London(伦敦帝国学院) Tsinghua University(清华大学)

AI总结 本研究推出整合17类超80万分子样本的Chem World化学性质预测基准,并提出Mixture-PINN物理信息神经网络框架,经实验验证其可提升预测性能,为可信赖AI系统研发奠定基础。

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2607.27378 2026-07-31 cs.CV cs.MM 新提交

PanDent: Toward Comprehensive Tooth-Level Structure-Language Consistency in Dental Radiology

PanDent:面向牙科放射学中全面的牙级结构-语言一致性

Xiaohan Li, Xinyu Liu, Chang Liu, Sum Wing Au Yeung, Jun Liu, Yixuan Yuan, Hui Chen

机构 * Faculty of Dentistry, The University of Hong Kong(香港大学牙医学院) Imperial College London(帝国理工学院) University of Science and Technology of China(中国科学技术大学) Department of Data and Systems Engineering, The University of Hong Kong(香港大学数据与系统工程系) Department of Electronic Engineering, The Chinese University of Hong Kong(香港中文大学电子工程系)

AI总结 本研究推出PanDent牙科OPG基准,经实验发现现有MLLM生成的牙科报告流畅但临床一致性差,在PanDent上微调可提升其结构-语言一致性,该基准可用于评估MLLM的牙级临床推理能力。

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2607.27023 2026-07-30 cs.LG cs.AI stat.ML 新提交

BayesAME: Bayesian Active Model Evaluation

BayesAME:贝叶斯主动模型评估

Paula Cordero Encinar, Taylan Cemgil, Arnaud Doucet, Virginia Aglietti, Silvia Chiappa

机构 * Imperial College London(帝国理工学院) Google DeepMind(谷歌DeepMind)

AI总结 BayesAME是一种序列贝叶斯框架,可自动确定核心集大小以高效评估大型生成模型,其性能优于现有方法,且证实非随机核心集选择及连续响应对数似然的优势。

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2607.26980 2026-07-30 cs.RO eess.SP 新提交

Dense Soft Weighting for Radar Ego-Velocity Estimation

用于雷达自速度估计的密集软加权方法

Atar Babgei, Chenyu Zhao, Michael Breza, Julie A. McCann

机构 * Imperial College London(帝国理工学院)

AI总结 针对视觉退化环境中雷达自速度估计的传统CFAR方法易丢失有效线索的问题,提出密集软加权雷达前端,结合鲁棒加权最小二乘估计自速度,在多数据集上显著降低位姿误差且可实时运行。

Comments Submitted to

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2403.15152 2026-07-30 cs.CV

Caption-Matching: A Multimodal Approach for Cross-Domain Image Retrieval

图像跨域检索:一种多模态方法

Lucas Iijima, Nikos Giakoumoglou, Tania Stathaki

机构 * Department of Electrical and Electronic Engineering, Imperial College London(帝国理工学院电气与电子工程系)

AI总结 本文提出Caption-Matching方法,利用预训练视觉语言模型生成的图像描述作为中间表示,实现跨域图像检索,无需标注数据或进一步训练,在Office-Home和DomainNet上取得显著提升。

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2509.02029 2026-07-30 cs.CV cs.AI

Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives

Nikos Giakoumoglou, Andreas Floros, Kleanthis Marios Papadopoulos, Tania Stathaki

机构 * Imperial College London(伦敦帝国学院)

Comments ICCV 2025 Workshop LIMIT

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2509.02024 2026-07-30 cs.CV cs.AI

Unsupervised Training of Vision Transformers with Synthetic Negatives

Nikos Giakoumoglou, Andreas Floros, Kleanthis Marios Papadopoulos, Tania Stathaki

机构 * Imperial College London(伦敦帝国学院)

Comments CVPR 2025 Workshop VisCon

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2407.12073 2026-07-30 cs.CV cs.AI

Relational Representation Distillation

Nikos Giakoumoglou, Tania Stathaki

机构 * Imperial College London(伦敦帝国学院)

Comments Preprint. Code: https://github.com/giakoumoglou/distillers, Supplementary: https://giakoumoglou.com/src/rrd_suppl.pdf

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2607.25807 2026-07-29 cs.RO 新提交

Modular Robotic Catheters for Endovascular Aneurysm Repair

用于血管内动脉瘤修复的模块化机器人导管

Alex Ranne, Jinshi Zhao, Ali Anil Demircali, Songli Moey, Ayhan Aktas, Burak Temelkuran, Nassir Navab, Ferdinando Rodriguez y Baena

机构 * Hamlyn Centre for Robotic Surgery, Department of Mechanical Engineering, Imperial College London(伦敦帝国理工学院机械工程系机器人手术哈姆林中心) Department of Metabolism, Digestion and Reproduction, Imperial College London(伦敦帝国理工学院代谢、消化与生殖系) Department of Mechanical Engineering, Bursa Uludag University(布尔萨乌鲁达大学机械工程系) Chair for Computer Aided Medical Procedures and Augmented Reality (CAMP), Technical University of Munich(慕尼黑工业大学计算机辅助医疗程序与增强现实主席)

AI总结 针对血管内动脉瘤修复中导管操作难题,提出定制两段式可转向导管及模块化肌腱驱动平台,利用热纤维拉伸和激光微加工制造,经模拟和体外实验评估,有望缩短手术时间,助力解决复杂临床病例。

Comments 8 pages, 8 figures

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2607.25546 2026-07-29 cs.AI 新提交

From Training to Deployment: Post-Hoc Causal Feature Identification via Sensitivity Ratios

从训练到部署:通过灵敏度比率进行事后因果特征识别

Athanasios Vlontzos, Giorgos Papanastasiou, Bernhard Kainz, Sotirios Tsaftaris

机构 * Hologen AI(全息人工智能公司) Mathematics Research Centre Academy of Athens(雅典科学院数学研究中心) FAU Erlangen Nuremberg(埃尔朗根 - 纽伦堡大学) Imperial College London(伦敦帝国理工学院) University of Edinburgh(爱丁堡大学)

AI总结 研究已训练模型所依赖因果与虚假特征的问题,提出归一化灵敏度比率(NSR)方法,在结构化转移情况下可事后诊断。在特定线性结构因果模型下能精确识别,刻画了失败情况及有限样本率,实验验证了该方法在多方面的有效性。

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2607.25532 2026-07-29 cs.AI 新提交

Entangled by Design: Spurious Intra-Variable Signal Routing in Tabular In-Context Learners

设计中的纠缠:表格上下文学习器中的虚假变量内信号路由

Athanasios Vlontzos, Giorgos Papanastasiou, Bernhard Kainz, Sotirios Tsaftaris

机构 * Hologen AI(全息人工智能公司) Mathematics Research Centre Academy of Athens(雅典科学院数学研究中心) FAU Erlangen Nuremberg(埃尔朗根 - 纽伦堡大学) Imperial College London(伦敦帝国理工学院) University of Edinburgh(爱丁堡大学)

AI总结 研究表格上下文学习器中虚假变量内信号路由问题,证明岭回归上下文学习器下该路由不可避免,推导出相关特征。发现更大上下文会放大虚假路由,更具表现力模型更脆弱。引入两种轻量级缓解措施,有效减少虚假路由并提高因果敏感性。

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