arXivDaily arXiv每日学术速递 周一至周五更新

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Imperial College London(帝国理工学院)

共收录 97
2603.04243 2026-08-14 cs.CV 版本更新

A Unified Framework for Joint Detection of Lacunes and Enlarged Perivascular Spaces

一种用于腔隙和扩大血管周围空间联合检测的统一框架

Lucas He, Krinos Li, Hanyuan Zhang, Runlong He, Silvia Ingala, Luigi Lorenzini, Marleen de Bruijne, Frederik Barkhof, Rhodri Davies, Carole Sudre

机构 * Hawkes Institute, University College London, UK Unit for Lifelong Health Aging, University College London, UK Bioengineering Department Imperial-X, Imperial College London, UK Department of Diagnostic Radiology, Copenhagen University Hospital, Denmark Department of Radiology \& Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, The Netherlands Department of Radiology Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands Queen Square Institute of Neurology, University College London, UK Institute of Cardiovascular Sciences, University College London, UK Barts Heart Centre, St Bartholomew's Hospital, London, UK

AI总结 本文提出了一种统一框架,通过形态解耦和混合监督策略,提高EPVS和腔隙的联合检测性能,并在大规模数据集上验证了其鲁棒性。

Comments Accepted to the 29th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2026)

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2606.17441 2026-08-12 cs.HC cs.AI cs.CY 版本更新

Patients With Personality: Realistic Patient Simulation through Controlled Diversity and Selective Disclosure

具有个性的患者:通过受控多样性与选择性披露实现逼真的患者模拟

Moritz Schlager, Friederike Jungmann, Samuel Schmidgall, Philipp Raffler, Franziska Hartl, Eva Wende, Paula Roßmüller, Conrad Ketzer, Avinatan Hassidim, Dale R. Webster, Yossi Matias, Yun Liu, Daniel Rueckert, Mike Schaekermann, Paul Hager

机构 * Technical University of Munich(慕尼黑技术大学) Munich Center for Machine Learning(慕尼黑机器学习中心) TUM University Hospital(慕尼黑技术大学医院) Google DeepMind(谷歌DeepMind) Google Research(谷歌研究) Imperial College London(伦敦帝国学院)

AI总结 提出PatientsWithPersonality框架,通过HEXACO人格参数化控制患者对话风格、合作性和信息披露,生成逼真且多样化的虚拟患者,在临床评估中接近真实演员表现。

Comments 22 pages, 11 figures

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2602.16481 2026-08-12 cs.AI 版本更新

Leveraging Large Language Models for Causal Discovery: a Constraint-based, Argumentation-driven Approach

利用大语言模型进行因果发现:一种基于约束和论证的方法

Zihao Li, Fabrizio Russo

机构 * Department of Computing(计算系) Imperial College London(帝国理工学院伦敦分校)

AI总结 本文提出利用大语言模型作为不完美的专家,结合语义结构先验和条件独立性证据,实现因果发现的先进方法。

Comments Accepted at UAI 2026. 37 pages, including appendix

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2406.12659 2026-08-12 stat.ML cs.LG math.ST stat.TH 版本更新

A variational Bayes approach to inference for low-dimensional parameters in high-dimensional linear regression

高维线性回归中低维参数推断的变分贝叶斯方法

Ismaël Castillo, Alice L'Huillier, Kolyan Ray, Luke Travis

机构 * Sorbonne Université(索邦大学) Imperial College London(帝国理工学院伦敦分校)

AI总结 针对高维线性回归的低维参数推断问题,提出一种结合平均场近似的可扩展变分贝叶斯方法,兼具计算优势与准确推断能力,且理论与数值表现均具竞争力。

Comments We have strengthened the Bernstein-von Mises results to hold in total variation and for growing parameter subsets, and generally improved the presentation

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2606.12691 2026-08-11 cs.LG cs.AI cs.SY eess.SY math.OC stat.ML 版本更新

Two-Layer Linear Auto-Regressive Models Estimate Latent States

两层线性自回归模型估计潜在状态

Yahya Sattar, Sunmook Choi, Leo Maynard-Zhang, Yassir Jedra, Maryam Fazel, Sarah Dean

机构 * Cornell(康奈尔大学) Washington(华盛顿大学) Imperial College London(伦敦帝国理工学院) Amazon(亚马逊)

AI总结 本文证明两层线性自回归模型通过经验风险最小化训练时,能近似卡尔曼滤波,恢复潜在状态估计,并提供有限样本保证。

Comments ICML 2026

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2605.00155 2026-08-11 cs.LG cs.CL math.OC stat.ML 版本更新

Wasserstein Distributionally Robust Regret Optimization for Reinforcement Learning from Human Feedback

Wasserstein分布鲁棒遗憾优化用于人类反馈的强化学习

Yikai Wang, Shang Liu, Jose Blanchet

机构 * Department of Statistics and Operations Research, University of North Carolina(统计与运筹学系,北卡罗来纳大学) Imperial Business School, Imperial College London(帝国理工学院伦敦商学院) Department of Management Science and Engineering, Stanford University(管理科学与工程系,斯坦福大学)

AI总结 本文提出Wasserstein分布鲁棒遗憾优化(DRRO)用于强化学习从人类反馈,通过简单分配模型研究提示问题,展示在ℓ1-地面成本Wasserstein模糊集下,内最坏遗憾有精确解,最优策略具有水填充结构,从而实现高效政策梯度算法。

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2601.03115 2026-08-11 cs.CL eess.AS 版本更新

Discovering and Causally Validating Emotion-Sensitive Neurons in Large Audio-Language Models

在大型音频-语言模型中发现并因果验证情绪敏感神经元

Xiutian Zhao, Björn Schuller, Berrak Sisman

机构 * Center for Language and Speech Processing (CLSP)(语言与语音处理中心) Johns Hopkins University(约翰霍普金斯大学) Group on Language, Audio & Music (GLAM)(语言、音频与音乐小组) Imperial College London(伦敦帝国学院)

AI总结 本研究通过神经元层面的干预验证了大型音频-语言模型中情绪敏感神经元的存在,并揭示了情绪识别的因果机制。

Comments Accepted to ACL 2026

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2608.03020 2026-08-10 cs.AI 版本更新

LoCA: Forward-Only LLM Tuning after One-Shot Calibration with Local Credit Assignment

LoCA:基于局部信用分配的一次性校准后仅前向的大语言模型调优

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

机构 * 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) Tencent(腾讯) University of Edinburgh(爱丁堡大学) University of Southern California(南加州大学) 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 本文提出LoCA方法,通过一次性校准替换大语言模型调优的重复反向传播,在多个基准上优于LoRA,降低了GPU峰值内存、CPU稳态内存与前向传递时间。

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2606.07419 2026-08-10 cs.CV 版本更新

DisPOSE: Projected Polystochastic Diffusion for Self-Supervised Multi-View 3D Human Pose Estimation

DisPOSE: 投影多随机扩散用于自监督多视图3D人体姿态估计

Tony Danjun Wang, Tolga Birdal, Nassir Navab, Lennart Bastian

机构 * Imperial College London(伦敦帝国学院) Technical University of Munich(慕尼黑技术大学)

AI总结 提出DisPOSE框架,将多视图人员分配问题建模为多随机张量空间上的生成扩散过程,通过可微Sinkhorn投影和超图卷积解码器实现自监督3D人体姿态估计,在标准数据集和手术室遮挡场景中表现优异。

Comments Accepted at ICML 2026

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2510.22899 2026-08-07 cs.LG stat.ML 版本更新

On the Anisotropy of Score-Based Generative Models

基于得分的生成模型的各向异性研究

Andreas Floros, Seyed-Mohsen Moosavi-Dezfooli, Pier Luigi Dragotti

机构 * Imperial College London(伦敦帝国学院) Apple(苹果公司)

AI总结 该研究针对基于得分的生成模型,引入依赖架构的得分各向异性方向(SADs),通过合成数据和图像基准验证其可捕获模型行为并关联下游性能,为预测生成模型泛化能力提供新方法。

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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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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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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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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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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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2607.05382 2026-07-28 cs.CV cs.AI 版本更新

Search Beyond What Can Be Taught: Evolving the Knowledge Boundary in Agentic Visual Generation

超越可教内容的搜索:拓展智能体视觉生成的知识边界

Haozhe Wang, Weijia Feng, Jinpeng Yu, Che Liu, Ping Nie, Fangzhen Lin, Jiaming Liu, Ruihua Huang, Jimmy Lin, Wenhu Chen, Cong Wei

机构 * Hong Kong University of Science and Technology(香港科技大学) University of Waterloo(滑铁卢大学) Qwen Applications(通义千问应用) Imperial College London(帝国理工学院)

AI总结 针对视觉生成器的世界知识瓶颈,构建专用数据集与基准,提出教-搜协同训练框架定位动态知识边界,实现知识驱动的可迭代视觉生成性能提升。

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2605.10760 2026-07-28 cs.RO 版本更新

MAGS-SLAM: Monocular Multi-Agent Gaussian Splatting SLAM for Geometrically and Photometrically Consistent Reconstruction

MAGS-SLAM:单目多智能体高斯点云SLAM用于几何和光度一致的重建

Zhihao Cao, Qi Shao, Shuhao Zhai, Jing Zhang, Anh Nguyen, Hesheng Wang, Baoru Huang

机构 * ETH Zurich(苏黎世联邦理工学院) Harbin Engineering University(哈尔滨工程大学) University of Liverpool(利物浦大学) University of Macau(澳门大学) University of Ottawa(多伦多大学) Wuhan University(武汉大学) Imperial College London(伦敦帝国学院)

AI总结 MAGS-SLAM通过单目视觉实现多智能体高斯点云SLAM,无需RGB-D传感器即可完成几何和光度一致的场景重建,实验表明其跟踪精度和渲染质量可与现有RGB-D方法媲美。

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2604.06550 2026-07-28 cs.CR cs.AI 版本更新

SkillSieve: A Hierarchical Triage Framework for Detecting Malicious AI Agent Skills

SkillSieve:一种用于检测恶意AI代理技能的分层分流框架

Yinghan Hou, Zongyou Yang

机构 * Department of Earth Science and Engineering(地球科学与工程系) Imperial College London(帝国理工学院伦敦分校) Department of Computer Science(计算机科学系) University College London(伦敦大学学院) Lingban Technology Co., Ltd.(灵伴科技有限公司) State Key Laboratory of General Artificial Intelligence, Peking University(北京大学通用人工智能国家重点实验室)

AI总结 提出SkillSieve三层检测框架,通过启发式评分、LLM子任务分析和多LLM陪审团辩论,高效检测恶意AI代理技能,在390个技能基准上达到F1=0.920。

Comments 10 pages, 2 figures, 6 tables

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2603.05607 2026-07-28 cs.CV cs.AI 版本更新

DreamCAD: Scaling Multi-modal CAD Generation using Differentiable Parametric Surfaces

DreamCAD: 通过可微参数曲面实现多模态CAD生成的扩展

Mohammad Sadil Khan, Muhammad Usama, Rolandos Alexandros Potamias, Didier Stricker, Muhammad Zeshan Afzal, Jiankang Deng, Ismail Elezi

机构 * DFKI RPTU Imperial College London(帝国理工学院伦敦分校) Huawei London Research Center(华为伦敦研究中心)

AI总结 DreamCAD通过可微参数曲面直接生成可编辑的BRep,无需CAD特定注释,实现多模态CAD生成的扩展,并在多个基准测试中取得最佳性能。

Comments For Caption Dataset: https://huggingface.co/datasets/SadilKhan/CADCap-1M

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2508.13826 2026-07-27 eess.IV cs.CV 版本更新

Latent Interpolation Learning Using Diffusion Models for Cardiac Volume Reconstruction

使用扩散模型进行心脏容积重建的潜在插值学习

Niklas Bubeck, Suprosanna Shit, Chen Chen, Can Zhao, Pengfei Guo, Dong Yang, Georg Zitzlsberger, Daguang Xu, Bernhard Kainz, Daniel Rueckert, Jiazhen Pan

机构 * School of Computation, Information and Technology, Technical University Munich(技术大学慕尼黑计算信息学院) Munich Center for Machine Learning(慕尼黑机器学习中心) Department of Quantitative Biomedicine, University of Zurich(苏黎世大学定量生物医学系) Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford(牛津大学生物医学工程研究所) Imperial College London(伦敦帝国学院) University of Sheffield(谢菲尔德大学) NVIDIA(英伟达) Department of Computing, Imperial College London(伦敦帝国学院计算机系) Department AIBE of Friedrich-Alexander-Universität Erlangen-Nürnberg(埃尔兰根-纽伦堡大学AIBE系) School of Medicine, Klinikum Rechts der Isar, Technical University of Munich(慕尼黑技术大学医学院,莱纳特医院)

AI总结 针对心脏磁共振成像二维切片采集稀疏致容积信息不完整、现有重建方法有局限的问题,提出CaLID框架,创新采用基于扩散模型的插值方案、潜在空间高效计算法,仅用稀疏二维图像输入达SOTA,扩展到二维加时间数据,提升了重建质量和效率。

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2602.13904 2026-07-24 cs.AI 版本更新

Diagnosing Pathological Chain-of-Thought in Reasoning Models

诊断推理模型中的病理链式思维

Manqing Liu, David Williams-King, Ida Caspary, Linh Le, Hannes Whittingham, Puria Radmard, Cameron Tice, Edward James Young

机构 * Department of Epidemiology, CAUSALab, Harvard University, Boston, USA(流行病学系、CAUSALab、哈佛大学) Imperial College London, London, UK(伦敦帝国学院) McGill University, Montreal, Canada(麦吉尔大学) Geodesic Research, Cambridge, UK(Geodesic研究)

AI总结 本文提出了一种评估链式思维推理模型中病态的实用工具,通过定义具体度量标准和训练特定模型生物来识别和区分三种不同的病态现象。

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2605.17086 2026-07-22 econ.GN cs.AI cs.CY q-fin.EC stat.AP 版本更新

Global Automation Atlas

全球自动化图谱

Prashant Garg, Tommaso Crosta, Jasmin Baier

机构 * Imperial College London(伦敦帝国学院) Bocconi University(博科尼大学) University of Oxford(牛津大学)

AI总结 本文提出了一种基于任务和国家特定的方法,用于全球范围内分类自动化暴露,以区分劳动力替代和增强自动化,相关技术渠道以及人工智能的物质作用。研究涵盖了124个国家,生成了覆盖全球99%人口和GDP的233万个任务-国家标签。

Comments 78 pages, 6 figures, 5 Extended Data figures. Substantially revised and expanded. Data and code: https://automationatlas.org/

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2603.11130 2026-07-22 cs.RO 版本更新

Robust Co-design Optimisation for Agile Fixed-Wing UAVs

面向敏捷固定翼无人机的鲁棒协同优化

Adrian Andrei Buda, Xavier Chen, Nicolò Botteghi, Urban Fasel

机构 * Department of Aeronautics, Imperial College London(帝国理工学院伦敦校区航空系) Department of Mathematics, Politecnico di Milano(米兰理工学院数学系)

AI总结 本文提出了一种面向敏捷固定翼无人机的鲁棒协同框架,通过整合参数不确定性与风扰动,优化物理设计与控制策略,提升在无结构环境中的鲁棒性和性能表现。

Journal ref 2026 International Conference on Unmanned Aircraft Systems (ICUAS), Corfu, Greece, 2026, pp. 9-17

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2510.15701 2026-07-22 cs.IT cs.AI eess.SP math.IT 版本更新

Beyond-Diagonal RIS Under Non-Idealities: Learning-Based Architecture Discovery and Optimization

非理想条件下超越对角线的可重构智能表面:基于学习的架构发现与优化

Binggui Zhou, Bruno Clerckx

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

AI总结 研究非理想BD-RIS性能与电路复杂度权衡问题,提出基于学习的两层架构发现框架LTTADF,由架构生成器和性能优化器联合发现最优架构,有效探索大架构空间,避免陷入局部最优,为其部署提供有价值见解。

Comments 17 pages, 16 figures, 1 table. Accepted by IEEE Transactions on Wireless Communications

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2602.07008 2026-07-21 cs.CV cs.LG 版本更新

Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making

不应学习的地方:基于子集归因约束的先验对齐训练以实现可靠的决策制定

Ruoyu Chen, Shangquan Sun, Xiaoqing Guo, Sanyi Zhang, Kangwei Liu, Shiming Liu, Zhangcheng Wang, Qunli Zhang, Wei Wang, Hua Zhang, Xiaochun Cao

机构 * Institute of Information Engineering, Chinese Academy of Sciences(中国科学院信息工程研究所) University of Chinese Academy of Sciences(中国科学院大学) College of Computing and Data Science, Nanyang Technological University(南洋理工大学计算与数据科学学院) Department of Computer Science, Hong Kong Baptist University(香港 Baptist 大学计算机科学系) Communication University of China(中国传媒大学) Imperial College London(伦敦帝国学院) School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University(中山大学深圳校区网络科学与技术学院)

AI总结 本文提出了一种基于归因的先验对齐方法,通过子集选择归因技术约束模型依赖于人类先验区域,从而提升决策的可靠性。

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2601.22259 2026-07-21 cs.LG 版本更新

Tabular Foundation Models Can Do Survival Analysis

表格基础模型也能进行生存分析

Da In Kim, Wei Siang Lai, Kelly W. Zhang

机构 * Department of Computing, Imperial College London(帝国理工学院计算机系) Department of Mathematics, Imperial College London(帝国理工学院数学系)

AI总结 本文提出了一种基于分类的框架,通过将生存分析转化为二分类问题,使表格基础模型能够无需显式训练即可进行生存分析,并在多个数据集上验证了其有效性。

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2512.13247 2026-07-21 cs.CV 版本更新

STARCaster: Spatio-Temporal AutoRegressive Video Diffusion for Identity- and View-Aware Talking Portraits

STARCaster:用于身份和视图感知的会说话肖像的时空自回归视频扩散

Foivos Paraperas Papantoniou, Stathis Galanakis, Rolandos Alexandros Potamias, Bernhard Kainz, Stefanos Zafeiriou

机构 * Imperial College London, UK(伦敦帝国理工学院) FAU Erlangen–Nürnberg, Germany(埃朗根-纽伦堡大学)

AI总结 研究提出STARCaster模型,通过重新思考参考和几何范式,采用组合方法及解耦学习,解决语音驱动肖像动画和动态视点控制问题,能有效泛化,超越先前方法。

Comments ICML 2026

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