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University of Oxford(牛津大学)

共收录 186
2606.20891 2026-06-23 cs.CV cs.LG 新提交

Go-with-the-Track: Video Compositing and Motion Control with Point Tracking

Go-with-the-Track: 基于点追踪的视频合成与运动控制

Koichi Namekata, Yash Kant, Zhizheng Liu, Ryan D Burgert, Yuancheng Xu, Kuan Heng Lin, Emmett Steven, Julien Philip, Li Ma, Andrea Vedaldi, Paul Debevec, Ning Yu

机构 * Netflix USA(Netflix美国) Eyeline Labs USA(Eyeline Labs美国) University of Oxford(牛津大学) Eyeline Labs Los Angeles USA(Eyeline Labs洛杉矶美国) University of California, Los Angeles(加州大学洛杉矶分校) Stony Brook University USA(石溪大学美国) Columbia University USA(哥伦比亚大学美国) Eyeline Labs United Kingdom(Eyeline Labs英国) Netflix Los Angeles USA(Netflix洛杉矶美国)

AI总结 提出Go-with-the-Track,通过联合条件多参考图像和参考锚定点轨迹,统一视频合成与运动控制,实现精确合成与运动控制。

Comments SIGGRAPH 2026, Project page: https://eyeline-labs.github.io/Go-with-the-Track/

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2606.20823 2026-06-23 cs.CV 新提交

NeoLoc-68: End-to-end 68-point neonatal facial landmark localisation in neonatal clinical environments

NeoLoc-68:新生儿临床环境中的端到端68点面部关键点定位

Abdullah Bin-Obaid, Maria M. Cobo, Rebeccah Slater, Lionel Tarassenko, Mauricio Villarroel

机构 * Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford(牛津大学工程科学系生物医学工程研究所) Department of Paediatrics, University of Oxford(牛津大学儿科学系) Universidad San Francisco de Quito USFQ, Colegio de Ciencias Biologicas y Ambientales(基多圣弗朗西斯科大学生物与环境科学学院)

AI总结 提出首个端到端68点新生儿面部关键点检测模型NeoLoc-68,通过结合公开数据集和临床标注数据,基于YOLO架构适应新生儿面部特征,在临床环境中实现低检测失败率和强泛化能力。

Comments 38 pages, 6 figures, journal paper

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2606.20707 2026-06-23 cs.CV cs.AI 新提交

GEOPHYS: The Geometry of Physical Plausibility

GEOPHYS: 物理合理性的几何学

Christian Internò, Alexander Pondaven, Habon Issa, Fabio Pizzati, Francesco Pinto, Markus Olhofer, Ivan Laptev, Philip Torr, Eero P. Simoncelli, Barbara Hammer, David Klindt

机构 * Bielefeld University(比勒费尔德大学) University of Oxford(牛津大学) Cold Spring Harbor Laboratory(冷泉港实验室) MBZUAI(穆罕默德·本·扎耶德人工智能大学) Independent(独立作者) Honda Research Institute EU(本田欧洲研究院) New York University(纽约大学) Flatiron Institute, Simons Foundation(西蒙斯基金会熨斗研究所)

AI总结 提出GEOPHYS方法,利用冻结图像编码器的每帧嵌入的五个几何特征检测视频中的物理不合理性,在物理违反检测上达到SOTA,并作为验证器提升视频生成模型的物理一致性。

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2606.20663 2026-06-23 cs.AI cs.CL cs.LG 新提交

DrugBench: Evaluating AI Control Protocols for Medication Harm Mitigation

DrugBench:评估用于减轻药物危害的AI控制协议

Guido Freire, Agustín Martínez-Suñé, Viviana Cotik

机构 * Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Computación(布宜诺斯艾利斯大学,精确与自然科学学院,计算机系) AI Safety Argentina (AISAR)(阿根廷人工智能安全组织 (AISAR)) Department of Computer Science, University of Oxford(牛津大学计算机科学系) CONICET-Universidad de Buenos Aires, Instituto de Ciencias de la Computación (ICC)(阿根廷国家科学与技术研究理事会-布宜诺斯艾利斯大学,计算机科学研究所 (ICC))

AI总结 提出DrugBench基准,结合医疗对话与FDA标签,评估AI控制协议在减轻药物交互、禁忌症等四类危害中的有效性,并引入基于严重性的监控方法。

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2606.20638 2026-06-23 cs.AI cs.LG 新提交

RIZZ: Routing Interactions to Near Zero-Interference Zones for Continual Adaptation of Black-Box Agents

RIZZ: 将交互路由到近零干扰区域以实现黑盒智能体的持续适应

Sonali Goel, Pranav Vaidhyanathan, Lucas Schorling, Natalia Ares, Maike Osborne

机构 * University of Oxford(牛津大学)

AI总结 提出RIZZ框架,通过验证器门控记忆、路由和提示编译,实现黑盒语言模型系统在非平稳流数据上的持续适应,有效控制干扰并提升性能。

Comments 20 pages, 2 figures

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2606.17168 2026-06-23 cs.CL 新提交

RepSelect: Robust LLM Unlearning via Representation Selectivity

RepSelect: 通过表示选择性实现鲁棒的大语言模型遗忘

Filip Sondej, Yushi Yang, Adam Mahdi

机构 * Independent(独立) University of Oxford(牛津大学)

AI总结 针对现有遗忘方法易被微调或少样本提示逆转的问题,提出RepSelect方法,通过梯度主成分坍塌隔离遗忘集表示,实现深度鲁棒遗忘。

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2606.16412 2026-06-23 cs.SD eess.AS math.HO math.NT 新提交

An Asymmetric Formula for Interval Consonance and its Relation to Harmonic Coincidence

区间协和的不对称公式及其与谐波重合的关系

David De Roure

机构 * University of Oxford(牛津大学) Royal Northern College of Music(皇家北方音乐学院)

AI总结 提出一个不对称公式 f(p/q) = p + Ω(q) 用于度量音程协和度,并证明在标准协和数据上表现良好,同时揭示了与欧拉和谐波重合模型的联系。

Comments v2: minor revision. Tightened the partial-beating argument in Sec. 9, added an acknowledgement, and updated references to the now-approved OEIS sequences A397104 and A397106. 18 pages

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2606.20449 2026-06-19 cs.CV 新提交

InfantFace: Detecting infant faces in neonatal clinical environments

InfantFace:新生儿临床环境中的婴儿面部检测

Abdullah Bin-Obaid, Maria M. Cobo, Rebeccah Slater, Lionel Tarassenko, Mauricio Villarroel

机构 * Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford(牛津大学生物医学工程研究所、工程科学系) Department of Paediatrics, University of Oxford(牛津大学儿科系) Universidad San Francisco de Quito USFQ, Colegio de Ciencias Biologicas y Ambientales(奎托大学圣弗朗西斯科德奎托大学,生物科学与环境学院)

AI总结 针对新生儿临床环境中的遮挡和光照问题,提出基于YOLOv11m的单阶段面部检测模型,在多个公开数据集预训练后,通过临床数据微调,AP50从0.87提升至0.96。

Comments 32 pages, 7 figures, 4 tables; supplementary information included

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2606.19390 2026-06-19 cs.SE cs.AI 新提交

Execution-bound advisory automation for agentic AI: a reproducible AIBOM-driven CSAF-VEX framework

面向执行约束的自主AI自动化:一种可复现的AIBOM驱动的CSAF-VEX框架

Petar Radanliev, Omar Santos, Carsten Maple, Kay Atefi

机构 * University of Oxford(牛津大学) Cisco Systems(思科系统) The Alan Turing Institute(艾伦·图灵研究所) University of Warwick – WMG(沃里克大学 – WMG) University of Hull(哈罗德大学)

AI总结 提出一种协议驱动框架,通过绑定SBOM和AIBOM工件与确定性环境捕获及结构化运行时遥测,结合静态与运行时证据生成CSAF VEX公告,经密码签名和确定性重放验证,在合成自主AI工作负载上评估。

Journal ref Execution-bound advisory automation for agentic AI: a reproducible AIBOM-driven CSAF-VEX framework. Front Artif Intell 9, (May 2026), 1826384

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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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2606.15015 2026-06-19 cs.CV cs.AI 新提交

NEXUS: Neural Energy Fields for Physically Consistent Contact-Rich 3D Object Dynamics

NEXUS: 用于物理一致的高接触3D物体动力学的神经能量场

Qizhen Ying, Guangming Wang, Yangchen Pan, Victor Adrian Prisacariu, Brian Sheil, Yixiong Jing

机构 * University of Oxford(牛津大学) University of Cambridge(剑桥大学)

AI总结 提出神经能量场框架NEXUS,通过标量能量和耗散项建模保守与非保守动力学,提升高接触3D场景下的长时程轨迹精度并指导视频生成。

Comments 18 pages, 4 figures, 6 tables. Preprint

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2606.19156 2026-06-18 cs.CV 新提交

Hand-4DGS: Feed-Forward 3D Gaussian Splatting for 4D Hand Reconstruction from Egocentric Videos

Hand-4DGS: 用于从第一人称视频进行4D手部重建的前馈3D高斯泼溅方法

Jeongmin Bae, Seoha Kim, Marc Pollefeys, Mahdi Rad, Youngjung Uh, Taein Kwon

机构 * Yonsei University(延世大学) Electronics and Telecommunications Research Institute(电子电信研究院) ETH Zurich(苏黎世联邦理工学院) Microsoft Spatial AI Lab(微软空间AI实验室) VGG, University of Oxford(VGG,牛津大学)

AI总结 提出Hand-4DGS,首个前馈框架,从第一人称视频直接重建动态4D手部,利用网格引导表示和时间卷积,实现快速推理和强泛化,无需3D真值标注。

Comments Project page: https://jeongminb.github.io/hand-4dgs/

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2606.18650 2026-06-18 cs.LG 新提交

BLADE: Scalable Bi-level Adaptive Data Selection for LLM Training

BLADE: 面向LLM训练的可扩展双层自适应数据选择

Jiaxing Wang, Deping Xiang, Jin Xu, Zirui Liu, Zicheng Zhang, Guoqiang Gong, Jun Fang, Chao Liu, Pengzhang Liu, Tongxuan Liu, Ke Zhang, Qixia Jiang

机构 * University of Oxford(牛津大学) Renmin University of China(中国人民大学) University of Chinese Academy of Sciences(中国科学院大学)

AI总结 提出BLADE框架,通过拉格朗日乘子将双层优化转化为单层惩罚目标,避免逆Hessian计算,实现动态参考模型,理论保证一阶收敛,实验优于现有方法。

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2606.18338 2026-06-18 cs.LG astro-ph.EP astro-ph.IM 新提交

ThousandWorlds: A benchmark for climate emulation of potentially habitable exoplanets

ThousandWorlds: 一个用于潜在宜居系外行星气候模拟的基准数据集

Edward T. Stevenson, Mei Ting Mak, Eric Wolf, Denis E. Sergeev, Tobi Hammond, N. J. Mayne, Miles Cranmer

机构 * University of Cambridge(剑桥大学) University of Oxford(牛津大学) University of Colorado Boulder(科罗拉多大学博尔德分校) University of Bristol(布里斯托大学) Purdue University(普渡大学) University of Exeter(埃克塞特大学)

AI总结 为加速系外行星气候模拟,提出ThousandWorlds基准数据集,包含五个全球气候模型的约1800次模拟,用于评估机器学习模拟器在低数据、多模拟器参数到场回归任务中的性能。

Comments 10 pages main text, 26 pages references/appendix, plus NeurIPS checklist. Data at https://doi.org/10.57967/hf/8695. Code at https://github.com/edstevenson/ThousandWorlds

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2606.18464 2026-06-18 astro-ph.IM astro-ph.EP cs.LG 新提交

Modeling Doppler Shifts in Radial-Velocity Data with Deep Learning toward Earth-mass Exoplanet Detection

利用深度学习建模径向速度数据中的多普勒频移以探测地球质量系外行星

Isidro Gómez-Vargas, Xavier Dumusque, Yinan Zhao, Khaled Al Moulla, Michael Cretignier

机构 * Department of Astronomy, University of Geneva 51 chemin de Pegasi, 1290 Versoix, Switzerland. Instituto de Astrofı\'isica de Andaluc\'ia (CSIC), Glorieta de la Astronom\'ia s/n, E-18008 Granada, Spain. Institute of Space Sciences (CSIC), Carrer de Can Magrans s/n, E-08193 Barcelona, Spain. Department of Astronomy, University of Texas at Austin, 2515 Speedway, Austin, TX 78712, USA. Instituto de Astrofísica e Ciências do Espaço, Universidade do Porto, CAUP, Rua das Estrelas, 4150-762 Porto, Portugal. Department of Physics, University of Oxford, OX13RH Oxford, UK.

AI总结 针对恒星活动干扰,提出结合物理启发光谱表示与深度学习的框架,通过交叉验证和遗传算法优化,可靠恢复振幅≥25 cm/s、周期10-550天的行星信号,并发布Python包doppleriann。

Comments 20 pages, 14 figures. Accepted for publication in Astronomy & Astrophysics

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2606.11918 2026-06-18 cs.AI 新提交

The Art of Interrogation: Consistency Amplifies Factuality in Spatial Reasoning

提问的艺术:一致性增强空间推理中的事实性

Theo Uscidda, Marta Tintore Gazulla, Maks Ovsjanikov, Federico Tombari, Leonidas Guibas

机构 * The University of California, Berkeley(加州大学伯克利分校) ETH Zurich(苏黎世联邦理工学院) University of Oxford(牛津大学) Stanford University(斯坦福大学)

AI总结 提出自监督强化学习框架,通过几何与语义一致性验证器(如图像翻转、文本对象顺序交换)对齐预训练模型的内在空间推理能力,无需标注数据即可达到接近监督方法的精度。

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2606.17065 2026-06-17 q-fin.CP cs.AI cs.LG 新提交

PIVOT: Bridging Black-Scholes Implied-Volatility and Price Objectives via Differentiable Jäckel Operator

PIVOT: 通过可微分的Jäckel算子桥接Black-Scholes隐含波动率与价格目标

Raeid Saqur, Yannick Limmer, Anastasis Kratsios, Blanka Horvath, Hans Buehler

机构 * Mathematical Institute, University of Oxford(牛津大学数学研究所) McMaster University(麦基尔大学) Vector Institute for AI(人工智能矢量研究所) DRW

AI总结 提出PIVOT层,通过隐式微分保留Jäckel求解器的前向精度,并利用门控机制处理低vega区域的奇异性,实现价格与隐含波动率空间的高效可微转换。

Comments 30 pages, 17 figures, 12 tables

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2606.17286 2026-06-17 cs.CY cs.AI 新提交

From Democracies to Autocracies: How AI Systems Enable Authoritarianism by Design

从民主到专制:AI系统如何通过设计实现威权主义

Jeba Sania, Marta Ziosi, Fazl Barez

机构 * Harvard Kennedy School(哈佛肯尼迪学校) University of Oxford(牛津大学)

AI总结 本文通过比较美国到中国的六种AI系统生命周期,识别出集中行政数据、监管漏洞、弱用户合规性及编码受保护群体特征等关键特征,揭示AI系统在不同政体中促成威权主义的机制。

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2606.17530 2026-06-17 physics.soc-ph cs.LG econ.GN q-fin.EC stat.AP 新提交

Public transit gains and spatially uneven travel demand changes after NYC congestion pricing

纽约市拥堵收费后公共交通增益与空间不均的出行需求变化

Donghang Li, Dingyi Zhuang, Yunlin Li, Chenan Shen, Nina Cao, Yunhan Zheng, Shenhao Wang, Jinhua Zhao

机构 * Department of Civil and Environmental Engineering, Massachusetts Institute of Technology(麻省理工学院土木与环境工程系) Department of Urban Studies and Planning, Massachusetts Institute of Technology(麻省理工学院城市研究与规划系) Mathematical Institute, University of Oxford(牛津大学数学院) Department of Mechanical Engineering, Massachusetts Institute of Technology(麻省理工学院机械工程系) College of Urban and Environmental Sciences, Peking University(北京大学城市与环境科学学院) Department of Urban and Regional Planning, University of Florida(佛罗里达大学城市与区域规划系) 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)

AI总结 利用时间序列基础模型生成概率反事实预测,评估纽约市2025年实施的拥堵收费政策,发现公交和地铁客流量显著增加,但总体出行需求略有下降,且影响存在空间异质性。

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2606.08402 2026-06-17 cs.CV cs.AI cs.MA 新提交

SceneConductor: 3D Scene Generation from a Single Image with Multi-Agent Orchestration

SceneConductor: 基于多智能体编排的单图像3D场景生成

Jeonghwan Kim, Yushi Lan, Yongwei Chen, Hieu Trung Nguyen, Chuanyu Pan, Xingang Pan

机构 * Nanyang Technological University(南洋理工大学) University of Oxford(牛津大学) Meshy AI

AI总结 提出多智能体编排框架,将单图像3D场景生成分解为场景初始化、环境构建和多智能体细化三个阶段,并引入几何感知布局预测器,在几何精度、空间一致性和感知真实性上超越现有方法。

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2606.17048 2026-06-16 cs.LG cs.CV stat.ML 新提交

Exact Posterior Score Estimation for Solving Linear Inverse Problems

精确后验分数估计用于求解线性逆问题

Abbas Mammadov, Ozgur Kara, Kaan Oktay, Iskander Azangulov, Adil Kaan Akan, Hyungjin Chung, James Matthew Rehg, Yee Whye Teh

机构 * University of Oxford(牛津大学) UIUC(伊利诺伊大学厄巴纳-香槟分校) EverEx

AI总结 提出精确后验分数(EPS)方法,通过闭式后验分数将线性逆问题转化为去噪问题,无需梯度或投影,在FFHQ和ImageNet上优于现有方法。

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2606.17000 2026-06-16 cs.CC cs.GT cs.LG math.OC 新提交

The Complexity of Min-Max Optimization for Quadratic Polynomials

二次多项式极小极大优化的复杂性

Martino Bernasconi, Matteo Castiglioni, Andrea Celli, Alexandros Hollender

机构 * Bocconi University(博科尼大学) Politecnico di Milano(米兰理工学院) University of Oxford(牛津大学)

AI总结 证明超立方体上极小极大优化的近似稳定点计算对二次多项式是PPAD难的,即使多项式是多线性的且每个变量最多出现在三个单项式中。

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2606.16587 2026-06-16 physics.flu-dyn cs.AI cs.LG physics.comp-ph 新提交

Learning Interface Breakup: A Geometry-Conditioned Latent Surrogate for Spray Formation

学习界面破碎:一种用于喷雾形成的几何条件潜在代理模型

Julius H Ramlau, Friedrich Hastedt, Tolga Birdal, Ehecatl-Antonio del Río Chanona, Nausheen S Basha, Omar K Matar

机构 * University of California, Berkeley(加州大学伯克利分校) Technical University of Munich(慕尼黑技术大学) Istanbul Technology University(伊斯坦布尔技术大学) University of Texas at Austin(德克萨斯大学奥斯汀分校) University of Cambridge(剑桥大学) University of Oxford(牛津大学)

AI总结 提出一种几何条件潜在代理模型,通过编码自适应网格细化(AMR)的单元密度场,在797个两相喷嘴模拟上训练,实现瞬态破碎动力学的高效预测,推理速度比Basilisk CFD快6×10^4倍。

Comments 11 pages, 5 figures, accepted to ICML AI4Physics 2026

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2606.16569 2026-06-16 cs.CV cs.RO 新提交

PROSE: Training-Free Egocentric Scene Registration with Vision-Language Models

PROSE: 基于视觉语言模型的无训练自我中心场景配准

Zhiang Chen, Nahyuk Lee, Boyang Sun, Taein Kwon, Marc Pollefeys, Zuria Bauer, Sunghwan Hong

机构 * ETH Zurich(苏黎世联邦理工学院) VGG, University of Oxford(牛津大学VGG实验室) ETH AI Center(苏黎世联邦理工学院人工智能中心)

AI总结 提出PROSE方法,利用预训练视觉语言模型将RGB序列提升为对象级3D场景图,通过对象高度先验和相同/不同查询匹配实例,无需训练或深度传感器即可实现自我中心场景配准,在Aria基准上超越几何和场景图基线。

Comments Project page: https://rckola.github.io/prose/

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2606.16564 2026-06-16 cs.RO cs.LG 新提交

Elastic ODYN: Differentiable Optimization for Infeasible Control and Learning in Robotics

Elastic ODYN:面向机器人中不可行控制与学习的可微优化

Aristotelis Papatheodorou, Jose Rojas, Ioannis Havoutis, Carlos Mastalli

机构 * University of Oxford(牛津大学) Heriot-Watt University(赫瑞瓦特大学)

AI总结 提出Elastic ODYN,一种通过平滑平方ℓ2弹性松弛处理不可行二次规划(QP)的原始-对偶非内点求解器,支持热启动,在无可行点时收敛到最接近可行解,并基于此开发可微QP层和不可行感知SQP方法,在基准QP、奇异接触力学、可微参数辨识及四足/人形机器人轨迹优化中优于现有方法。

Comments 8 pages, 5 figures, 2 tables

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2606.16475 2026-06-16 cs.CY cs.AI 新提交

AI systems out-persuade expert humans

AI系统在说服力上超越人类专家

Kobi Hackenburg, Caroline Wagner, Luke Hewitt, Ben M. Tappin, Ed Saunders, Hannah Rose Kirk, Helen Margetts, Christopher Summerfield

机构 * University of Oxford(牛津大学) UK AI Security Institute(英国人工智能安全研究所) Stanford University(斯坦福大学) London School of Economics and Political Science(伦敦政治经济学院)

AI总结 通过四项预注册实验(n=18,978次对话),发现AI系统在说服力上可靠地超越人类专家,包括专业拉票者和世界辩论冠军,其优势源于快速部署大量信息,并扩展到现实世界筹款行为。

Comments 16 pages, 4 figures

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2606.15989 2026-06-16 q-bio.NC cs.AI 新提交

Task-guided cross-subject latent alignment: a multi-encoder-decoder VAE

任务引导的跨被试潜在对齐:一种多编码器-解码器VAE

Angeliki Papathanasiou, Jascha Achterberg, Thomas E. Nichols, Rui Ponte Costa

机构 * Centre for Neural Circuits and Behaviour Department of Physiology Anatomy and Genetics University of Oxford(神经回路与行为中心 生理解剖与遗传学系 牛津大学) Big Data Institute Nuffield Department of Medicine University of Oxford(大数据研究所 纳菲尔德医学系 牛津大学)

AI总结 提出MED-VAE模型,通过预训练ANN锚定表征,实现无共享刺激的跨被试神经对齐,在自然场景数据集上优于传统方法,并支持跨被试图像解码。

Comments In Proceedings of the 9th Conference on Cognitive Computational Neuroscience, New York, NY, USA, 2026

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2606.15983 2026-06-16 quant-ph cond-mat.mtrl-sci cs.LG 新提交

Learning ground state observables from quantum computing experiments

从量子计算实验中学习基态可观测量

Ben Jaderberg, Freya Shah, Minjun Jeon, M. Emre Sahin, Christa Zoufal, Kunal Sharma

机构 * IBM Quantum, IBM Research Europe, Hursley, Winchester, SO21 2JN, United Kingdom(IBM量子、IBM欧洲研究院,赫尔斯利,温切斯特,SO21 2JN,英国) Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, United Kingdom(工程科学系,牛津大学,帕克斯路,牛津 OX1 3PJ,英国) IBM Quantum, T. J. Watson Research Center, Yorktown Heights, NY 10598, USA(IBM量子、T.J. Watson研究中心,扬斯敦高地,纽约 10598,美国) Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH, United Kingdom(材料系,牛津大学,帕克斯路,牛津 OX1 3PH,英国) The Hartree Centre, STFC, Sci-Tech Daresbury, Warrington WA4 4AD, UK(哈特里中心,STFC,科技达尔斯伯里,沃林顿 WA4 4AD,英国) IBM Quantum, IBM Research Europe — Zurich, Ruschlikon 8803, Switzerland(IBM量子、IBM欧洲研究院——苏黎世,卢斯利康 8803,瑞士) IBM Research, Chicago, IL 60606, USA(IBM研究院,芝加哥,伊利诺伊 60606,美国)

AI总结 本文在115量子比特的二维海森堡XXZ模型中,利用近似基态的实验数据训练神经网络,成功预测了未见哈密顿量参数下的空间分辨可观测量,展示了从量子数据学习的实际可行性。

Comments 20 pages, 14 figures

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2606.15949 2026-06-16 cs.CL 新提交

FinBalance: A Multi-Document Accounting Reconciliation Benchmark

FinBalance:多文档会计对账基准

Sasank Tumpati, Devansh Agarwal, Ayush Kedia, Arjun Neekhra, Murari Mandal, Krishna Garg, Yash Sinha, Suman Gupta, Dhruv Kumar

机构 * BITS Pilani(比拉理工学院皮拉尼校区) KIIT Bhubaneswar(KIIT布巴内斯瓦尔) University of Oxford(牛津大学)

AI总结 提出FinBalance基准,通过多行业源文档构建会计对账任务,评估LLM在生成资产负债表和检测不一致性上的表现,发现模型在文档绑定和一致性聚合上存在显著差距。

Comments 18 pages, 12 figures. Code and data: https://github.com/Devansh1105/finbalance

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2606.15897 2026-06-16 cs.LG cs.AI stat.ML 新提交

Topological Flow Matching

拓扑流匹配

Kacper Wyrwal, İsmail İlkan Ceylan, Alexander Tong

机构 * University of Oxford(牛津大学) TU Wien(维也纳技术大学) AITHYRA

AI总结 提出拓扑流匹配,通过拉普拉斯漂移增强参考过程,在保留流匹配稳定性和无模拟目标的同时,捕捉底层域拓扑结构,适用于脑fMRI、洋流等结构化数据。

Comments Accepted at ICLR 2026. 26 pages, 24 figures. Code: https://github.com/KacperWyrwal/topological-flow-matching

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