Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts
少而精:训练数据剪枝提升事实记忆
机构 * National University of Singapore(新加坡国立大学) ; Apple(苹果公司)
AI总结 本文从信息论角度研究训练数据分布对事实准确性的影响,提出基于训练损失的数据选择方案,提升事实记忆能力,实验表明剪枝后模型可更高效记忆事实。
高校专区
少而精:训练数据剪枝提升事实记忆
机构 * National University of Singapore(新加坡国立大学) ; Apple(苹果公司)
AI总结 本文从信息论角度研究训练数据分布对事实准确性的影响,提出基于训练损失的数据选择方案,提升事实记忆能力,实验表明剪枝后模型可更高效记忆事实。
少而精:通过混合后训练实现性能与置信度忠实的和谐统一
机构 * National University of Singapore, Singapore(新加坡国立大学) ; Singapore Management University, Singapore(新加坡管理大学)
AI总结 本文提出HyTuning框架,通过混合后训练方法在有限监督下提升模型准确性并实现置信度忠实,支持'少而精'的效果。
从一叠起:多尺度自我注入用于上下文窗口扩展
机构 * Singapore University of Technology and Design (SUTD)(新加坡科技设计大学) ; Singapore Management University (SMU)(新加坡管理大学) ; Nanyang Technological University (NTU)(南洋理工大学) ; National University of Singapore (NUS)(新加坡国立大学)
AI总结 本文提出多尺度自我注入框架,通过压缩和查询感知信息获取扩展上下文窗口,实现高效且准确的长上下文处理。
Comments 20 pages, 6 figures
面向增强大语言模型推理能力的分层多步奖励模型
机构 * the University of Hong Kong(香港大学) ; Tsinghua University(清华大学) ; Georgia Institute of Technology(佐治亚理工学院) ; National University of Singapore(新加坡国立大学) ; Zhejiang University(浙江大学) ; Renmin University of China(中国人民大学) ; the Chinese University of Hong Kong(香港中文大学)
AI总结 本文提出分层奖励模型和轻量数据增强策略,解决大语言模型推理中奖励黑客问题,提升多步推理评估的稳定性和泛化能力。
PASK:迈向具有长期记忆的意图感知主动代理
机构 * Pask-Core(Pask核心) ; NTU(南洋理工大学) ; NUS(新加坡国立大学)
AI总结 本文提出PASK框架,通过意图流模型和混合记忆系统实现主动代理,在现实场景中识别更深层用户意图。
Comments Technical report; Work in progress
超越表面伪影:跨模态捕捉共享的潜在伪造知识
机构 * The University of Sydney(悉尼大学) ; Nanjing University of Posts and Telecommunications(南京邮电大学) ; National University of Singapore(新加坡国立大学)
AI总结 本文提出了一种跨模态伪造检测框架MAF,通过解耦模态特定风格,提取跨模态的潜在伪造知识,并定义了两个维度评估模型泛化能力,推动多模态防伪技术发展。
GameWorld: 向标准化和可验证的多模态游戏代理评估迈进
机构 * National University of Singapore(新加坡国立大学) ; University of Oxford(牛津大学)
AI总结 GameWorld提出一个标准化且可验证的多模态游戏代理评估基准,包含34种游戏和170个任务,通过可验证的指标评估代理能力,揭示了当前代理在视频游戏中与人类能力的差距。
Comments 23 pages, 8 figures
最近邻投影去除对抗训练
机构 * IIIT Delhi(德里印度理工学院) ; NUS Singapore(新加坡国立大学)
AI总结 本文提出一种新的对抗训练框架,通过在特征空间中去除对抗样本和干净样本间的类间依赖,增强特征分离性,提升模型鲁棒性和泛化能力。
通过依赖意识生成建模捕捉未见的空间热极值
机构 * National University of Singapore(新加坡国立大学) ; Southern University of Science and Technology(南方科技大学) ; Eastern Institute of Technology(东方理工高等研究院) ; Stanford University(斯坦福大学)
AI总结 本文提出DeepX-GAN模型,通过显式捕捉稀有极值的空间结构,模拟超出观测记录的统计合理极值,揭示未见热极值对高脆弱性国家的威胁,需空间适应性风险规划。
CAAP:针对指纹识别模型的感知-aware对抗性补丁攻击
机构 * Institute of Data Science, National University of Singapore(新加坡国立大学数据科学研究所) ; Centre for Frontier AI Research (CFAR), A*STAR(A*STAR前沿人工智能研究中心) ; School of Cyber Science and Engineering, Wuhan University(武汉大学网络空间安全学院) ; College of Computing and Data Science, Nanyang Technological University(南洋理工大学计算与数据科学学院) ; School of Engineering, Yunnan University(云南大学工程学院)
AI总结 本文提出CAAP框架,针对指纹识别模型的感知特性,设计了交叉形补丁拓扑和三个模块,有效提升对抗攻击性能,揭示了深度指纹识别系统在物理可实现的对抗性补丁攻击下的脆弱性。
行走与言说:通过多模态代理策略优化弥合图像推理与行动之间的差距
机构 * National Key Laboratory for Novel Software Technology, Nanjing University, China(南京大学计算机软件新技术国家重点实验室) ; School of Artificial Intelligence, Nanjing University, China(南京大学人工智能学院) ; AI Business, Alibaba Group(阿里巴巴集团智能计算研究院) ; School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, China(上海交通大学自动化与智能感知学院) ; School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China(中山大学智能工程学院(深圳)) ; School of Information Science and Technology, University of Science and Technology of China(中国科学技术大学信息科学技术学院) ; School of Software Technology, Zhejiang University, China(浙江大学软件学院) ; School of Computing, National University of Singapore, Singapore, Singapore(新加坡国立大学计算机学院)
AI总结 本文提出MAPO方法,通过强制生成视觉内容的显式文本描述,结合语义对齐与任务奖励,提升多模态推理能力,实验表明其在多个视觉推理基准上表现优异。
欧几里得快速数据发布(Q1)。AgileLens:一种可扩展的基于CNN的强引力透镜识别管道
机构 * Department of Physics \& Astronomy, University of California Irvine, Irvine CA 92697, USA aff1 University of Southern California, 3551 Trousdale Parkway, Los Angeles, CA 90089, USA aff2 Dipartimento di Fisica "Aldo Pontremoli", Universit\`a degli Studi di Milano, Via Celoria 16, 20133 Milano, Italy aff3 INAF-IASF Milano, Via Alfonso Corti 12, 20133 Milano, Italy aff4 Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA, 91109, USA aff5 Department of Physics ; Astronomy, University of British Columbia, Vancouver, BC V6T 1Z1, Canada aff6 INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna, Via Piero Gobetti 93/3, 40129 Bologna, Italy aff7 INFN-Sezione di Bologna, Viale Berti Pichat 6/2, 40127 Bologna, Italy aff8 Dipartimento di Fisica e Astronomia "Augusto Righi" - Alma Mater Studiorum Universit\`a di Bologna, via Piero Gobetti 93/2, 40129 Bologna, Italy aff9 INAF-Osservatorio Astronomico di Capodimonte, Via Moiariello 16, 80131 Napoli, Italy aff10 Institute of Cosmology ; Gravitation, University of Portsmouth, Portsmouth PO1 3FX, UK aff11 School of Physics, HH Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol, BS8 1TL, UK aff12 Max-Planck-Institut f\"ur Astrophysik, Karl-Schwarzschild-Str. 1, 85748 Garching, Germany aff13 Technical University of Munich, TUM School of Natural Sciences, Physics Department, James-Franck-Str. 1, 85748 Garching, Germany aff14 Departamento F\'isica Aplicada, Universidad Polit\'ecnica de Cartagena, Campus Muralla del Mar, 30202 Cartagena, Murcia, Spain aff15 Aix-Marseille Universit\'e, CNRS, CNES, LAM, Marseille, France aff16 Institut d'Astrophysique de Paris, UMR 7095, CNRS ; Sorbonne Universit\'e, 98 bis boulevard Arago, 75014 Paris, France aff17 Center for Astrophysics | Harvard \& Smithsonian, 60 Garden St., Cambridge, MA 02138, USA aff18 ESAC/ESA, Camino Bajo del Castillo, s/n., Urb. Villafranca del Castillo, 28692 Villanueva de la Ca\ nada, Madrid, Spain aff19 Universit\'e de Gen\`eve, D\'epartement de Physique Th\'eorique ; Centre for Astroparticle Physics, 24 quai Ernest-Ansermet, CH-1211 Gen\`eve 4, Switzerland aff20 Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA 94720, USA aff21 Max Planck Institute for Extraterrestrial Physics, Giessenbachstr. 1, 85748 Garching, Germany aff22 Universit\"ats-Sternwarte M\"unchen, Fakult\"at f\"ur Physik, Ludwig-Maximilians-Universit\"at M\"unchen, Scheinerstr. 1, 81679 M\"unchen, Germany aff23 STAR Institute, University of Li \`e ge, Quartier Agora, All\'ee du six Ao\ ut 19c, 4000 Li\`ege, Belgium aff24 Institut de Ci\` e ncies del Cosmos (ICCUB), Universitat de Barcelona (IEEC-UB), Mart\' i i Franqu\` e s 1, 08028 Barcelona, Spain aff25 David A. Dunlap Department of Astronomy \& Astrophysics, University of Toronto, 50 St George Street, Toronto, Ontario M5S 3H4, Canada aff26 Jodrell Bank Centre for Astrophysics, Department of Physics ; Astronomy, University of Manchester, Oxford Road, Manchester M13 9PL, UK aff27 Instituci\'o Catalana de Recerca i Estudis Avan c ats (ICREA), Passeig de Llu\' s Companys 23, 08010 Barcelona, Spain aff28 Institut de Ciencies de l'Espai (IEEC-CSIC), Campus UAB, Carrer de Can Magrans, s/n Cerdanyola del Vall\'es, 08193 Barcelona, Spain aff29 School of Mathematics ; Physics, University of Surrey, Guildford, Surrey, GU2 7XH, UK aff30 INAF-Osservatorio Astronomico di Brera, Via Brera 28, 20122 Milano, Italy aff31 IFPU, Institute for Fundamental Physics of the Universe, via Beirut 2, 34151 Trieste, Italy aff32 INAF-Osservatorio Astronomico di Trieste, Via G. B. Tiepolo 11, 34143 Trieste, Italy aff33 INFN, Sezione di Trieste, Via Valerio 2, 34127 Trieste TS, Italy aff34 SISSA, International School for Advanced Studies, Via Bonomea 265, 34136 Trieste TS, Italy aff35 Dipartimento di Fisica e Astronomia, Universit\`a di Bologna, Via Gobetti 93/2, 40129 Bologna, Italy aff36 INAF-Osservatorio Astronomico di Padova, Via dell'Osservatorio 5, 35122 Padova, Italy aff37 Dipartimento di Fisica, Universit\`a di Genova, Via Dodecaneso 33, 16146, Genova, Italy aff38 INFN-Sezione di Genova, Via Dodecaneso 33, 16146, Genova, Italy aff39 Department of Physics "E. Pancini", University Federico II, Via Cinthia 6, 80126, Napoli, Italy aff40 Dipartimento di Fisica, Universit\`a degli Studi di Torino, Via P. Giuria 1, 10125 Torino, Italy aff41 INFN-Sezione di Torino, Via P. Giuria 1, 10125 Torino, Italy aff42 INAF-Osservatorio Astrofisico di Torino, Via Osservatorio 20, 10025 Pino Torinese (TO), Italy aff43 INAF-Osservatorio Astronomico di Roma, Via Frascati 33, 00078 Monteporzio Catone, Italy aff44 INFN-Sezione di Roma, Piazzale Aldo Moro, 2 - c/o Dipartimento di Fisica, Edificio G. Marconi, 00185 Roma, Italy aff45 Centro de Investigaciones Energ\'eticas, Medioambientales y Tecnol\'ogicas (CIEMAT), Avenida Complutense 40, 28040 Madrid, Spain aff46 Port d'Informaci\' o Cient\' i fica, Campus UAB, C. Albareda s/n, 08193 Bellaterra (Barcelona), Spain aff47 Institute for Theoretical Particle Physics ; Cosmology (TTK), RWTH Aachen University, 52056 Aachen, Germany aff48 Deutsches Zentrum f\"ur Luft- und Raumfahrt e. V. (DLR), Linder H\"ohe, 51147 K\"oln, Germany aff49 INFN section of Naples, Via Cinthia 6, 80126, Napoli, Italy aff50 Dipartimento di Fisica e Astronomia "Augusto Righi" - Alma Mater Studiorum Universit\`a di Bologna, Viale Berti Pichat 6/2, 40127 Bologna, Italy aff51 Instituto de Astrof\' sica de Canarias, E-38205 La Laguna, Tenerife, Spain aff52 Institute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ, UK aff53 European Space Agency/ESRIN, Largo Galileo Galilei 1, 00044 Frascati, Roma, Italy aff54 Universit\'e Claude Bernard Lyon 1, CNRS/IN2P3, IP2I Lyon, UMR 5822, Villeurbanne, F-69100, France aff55 UCB Lyon 1, CNRS/IN2P3, IUF, IP2I Lyon, 4 rue Enrico Fermi, 69622 Villeurbanne, France aff56 Mullard Space Science Laboratory, University College London, Holmbury St Mary, Dorking, Surrey RH5 6NT, UK aff57 Departamento de F\'isica, Faculdade de Ci\ encias, Universidade de Lisboa, Edif\'icio C8, Campo Grande, PT1749-016 Lisboa, Portugal aff58 Instituto de Astrof\'isica e Ci\ encias do Espa c o, Faculdade de Ci\ encias, Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal aff59 Department of Astronomy, University of Geneva, ch. d'Ecogia 16, 1290 Versoix, Switzerland aff60 Universit\'e Paris-Saclay, CNRS, Institut d'astrophysique spatiale, 91405, Orsay, France aff61 INFN-Padova, Via Marzolo 8, 35131 Padova, Italy aff62 Aix-Marseille Universit\'e, CNRS/IN2P3, CPPM, Marseille, France aff63 INAF-Istituto di Astrofisica e Planetologia Spaziali, via del Fosso del Cavaliere, 100, 00100 Roma, Italy aff64 Universit\'e Paris-Saclay, Universit\'e Paris Cit\'e, CEA, CNRS, AIM, 91191, Gif-sur-Yvette, France aff65 INFN-Bologna, Via Irnerio 46, 40126 Bologna, Italy aff66 Institut d'Estudis Espacials de Catalunya (IEEC), Edifici RDIT, Campus UPC, 08860 Castelldefels, Barcelona, Spain aff67 Institute of Space Sciences (ICE, CSIC), Campus UAB, Carrer de Can Magrans, s/n, 08193 Barcelona, Spain aff68 University Observatory, LMU Faculty of Physics, Scheinerstr. 1, 81679 Munich, Germany aff69 FRACTAL S.L.N.E., calle Tulip\'an 2, Portal 13 1A, 28231, Las Rozas de Madrid, Spain aff70 Institute of Theoretical Astrophysics, University of Oslo, P.O. 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Box 64, University of Helsinki, 00014 Helsinki, Finland aff78 Helsinki Institute of Physics, Gustaf H \"a llstr \"o min katu 2, University of Helsinki, 00014 Helsinki, Finland aff79 Laboratoire d'etude de l'Univers et des phenomenes eXtremes, Observatoire de Paris, Universit\'e PSL, Sorbonne Universit\'e, CNRS, 92190 Meudon, France aff80 SKAO, Jodrell Bank, Lower Withington, Macclesfield SK11 9FT, UK aff81 Centre de Calcul de l'IN2P3/CNRS, 21 avenue Pierre de Coubertin 69627 Villeurbanne Cedex, France aff82 University of Applied Sciences ; Arts of Northwestern Switzerland, School of Computer Science, 5210 Windisch, Switzerland aff83 Universit\"at Bonn, Argelander-Institut f\"ur Astronomie, Auf dem H\"ugel 71, 53121 Bonn, Germany aff84 Department of Physics, Institute for Computational Cosmology, Durham University, South Road, Durham, DH1 3LE, UK aff85 Universit\'e Paris Cit\'e, CNRS, Astroparticule et Cosmologie, 75013 Paris, France aff86 CNRS-UCB International Research Laboratory, Centre Pierre Bin\'etruy, IRL2007, CPB-IN2P3, Berkeley, USA aff87 University of Applied Sciences ; Arts of Northwestern Switzerland, School of Engineering, 5210 Windisch, Switzerland aff88 Institute of Physics, Laboratory of Astrophysics, Ecole Polytechnique F\'ed\'erale de Lausanne (EPFL), Observatoire de Sauverny, 1290 Versoix, Switzerland aff89 Telespazio UK S.L. for European Space Agency (ESA), Camino bajo del Castillo, s/n, Urbanizacion Villafranca del Castillo, Villanueva de la Ca\ nada, 28692 Madrid, Spain aff90 Institut de F\' i sica d'Altes Energies (IFAE), The Barcelona Institute of Science ; Technology, Campus UAB, 08193 Bellaterra (Barcelona), Spain aff91 European Space Agency/ESTEC, Keplerlaan 1, 2201 AZ Noordwijk, The Netherlands aff92 School of Mathematics, Statistics ; Physics, Newcastle University, Herschel Building, Newcastle-upon-Tyne, NE1 7RU, UK aff93 DARK, Niels Bohr Institute, University of Copenhagen, Jagtvej 155, 2200 Copenhagen, Denmark aff94 Waterloo Centre for Astrophysics, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada aff95 Department of Physics ; Astronomy, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada aff96 Perimeter Institute for Theoretical Physics, Waterloo, Ontario N2L 2Y5, Canada aff97 Space Science Data Center, Italian Space Agency, via del Politecnico snc, 00133 Roma, Italy aff98 Centre National d'Etudes Spatiales -- Centre spatial de Toulouse, 18 avenue Edouard Belin, 31401 Toulouse Cedex 9, France aff99 Institute of Space Science, Str. 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Grenoble Alpes, CNRS, Grenoble INP, LPSC-IN2P3, 53, Avenue des Martyrs, 38000, Grenoble, France aff121 Dipartimento di Fisica, Sapienza Universit\`a di Roma, Piazzale Aldo Moro 2, 00185 Roma, Italy aff122 Aurora Technology for European Space Agency (ESA), Camino bajo del Castillo, s/n, Urbanizacion Villafranca del Castillo, Villanueva de la Ca\ nada, 28692 Madrid, Spain aff123 Dipartimento di Fisica - Sezione di Astronomia, Universit\`a di Trieste, Via Tiepolo 11, 34131 Trieste, Italy aff124 Department of Mathematics ; Physics E. 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Astrof\' sica, E-38206 La Laguna, Tenerife, Spain aff139 Ruhr University Bochum, Faculty of Physics ; Astronomy, Astronomical Institute (AIRUB), German Centre for Cosmological Lensing (GCCL), 44780 Bochum, Germany aff140 Department of Physics ; Astronomy, Vesilinnantie 5, University of Turku, 20014 Turku, Finland aff141 Finnish Centre for Astronomy with ESO (FINCA), Quantum, Vesilinnantie 5, University of Turku, 20014 Turku, Finland aff142 Serco for European Space Agency (ESA), Camino bajo del Castillo, s/n, Urbanizacion Villafranca del Castillo, Villanueva de la Ca\ nada, 28692 Madrid, Spain aff143 ARC Centre of Excellence for Dark Matter Particle Physics, Melbourne, Australia aff144 Centre for Astrophysics \& Supercomputing, Swinburne University of Technology, Hawthorn, Victoria 3122, Australia aff145 Department of Physics ; Astronomy, University of the Western Cape, Bellville, Cape Town, 7535, South Africa aff146 Departement of Theoretical Physics, University of Geneva, Switzerland aff147 Department of Physics, Centre for Extragalactic Astronomy, Durham University, South Road, Durham, DH1 3LE, UK aff148 IRFU, CEA, Universit\'e Paris-Saclay 91191 Gif-sur-Yvette Cedex, France aff149 Institute for Astronomy, University of Hawaii, 2680 Woodlawn Drive, Honolulu, HI 96822, USA aff150 INAF-Osservatorio Astrofisico di Arcetri, Largo E. Fermi 5, 50125, Firenze, Italy aff151 Centro de Astrof\' sica da Universidade do Porto, Rua das Estrelas, 4150-762 Porto, Portugal aff152 Instituto de Astrof\'isica e Ci\ encias do Espa c o, Universidade do Porto, CAUP, Rua das Estrelas, PT4150-762 Porto, Portugal aff153 HE Space for European Space Agency (ESA), Camino bajo del Castillo, s/n, Urbanizacion Villafranca del Castillo, Villanueva de la Ca\ nada, 28692 Madrid, Spain aff154 Department of Astrophysics, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland aff155 INAF - Osservatorio Astronomico d'Abruzzo, Via Maggini, 64100, Teramo, Italy aff156 Theoretical astrophysics, Department of Physics ; Astronomy, Uppsala University, Box 516, 751 37 Uppsala, Sweden aff157 Minnesota Institute for Astrophysics, University of Minnesota, 116 Church St SE, Minneapolis, MN 55455, USA aff158 Mathematical Institute, University of Leiden, Einsteinweg 55, 2333 CA Leiden, The Netherlands aff159 Leiden Observatory, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands aff160 Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK aff161 Center for Astrophysics ; Cosmology, University of Nova Gorica, Nova Gorica, Slovenia aff162 Institute for Particle Physics ; Astrophysics, Dept. of Physics, ETH Zurich, Wolfgang-Pauli-Strasse 27, 8093 Zurich, Switzerland aff163 Department of Astrophysical Sciences, Peyton Hall, Princeton University, Princeton, NJ 08544, USA aff164 Space physics ; astronomy research unit, University of Oulu, Pentti Kaiteran katu 1, FI-90014 Oulu, Finland aff165 Department of Physics ; Astronomy, Lehman College of the CUNY, Bronx, NY 10468, USA aff166 American Museum of Natural History, Department of Astrophysics, New York, NY 10024, USA aff167 International Centre for Theoretical Physics (ICTP), Strada Costiera 11, 34151 Trieste, Italy aff168 Center for Computational Astrophysics, Flatiron Institute, 162 5th Avenue, 10010, New York, NY, USA aff169
AI总结 本文提出了一种基于CNN的高效强引力透镜识别方法,通过迭代优化和数据增强,从欧几里得Q1数据中识别出441个候选系统,其中311个与现有目录重合,130个为新增,方法可扩展至未来数据发布。
Comments 30 pages, 16 figures
利用大型语言模型自动合成数据库原生函数代码
机构 * Shanghai Jiao Tong University(上海交通大学) ; Tsinghua University(清华大学) ; National University of Singapore(新加坡国立大学)
AI总结 本文提出DBCooker系统,通过多模块协同解决数据库原生函数合成难题,实现更高精度的自动代码生成,实验显示在SQLite、PostgreSQL和DuckDB上准确率提升34.55%。
Comments Please visit our homepage at: https://code4db.github.io/hi-opencook/. The code is available at: https://github.com/weAIDB/OpenCook
干预时间序列先验用于因果基础模型
机构 * National University of Singapore(新加坡国立大学)
AI总结 本文提出CausalTimePrior框架,生成合成时间结构因果模型,支持可配置因果图、非线性自回归机制和多干预类型,推动时间序列因果推断的基础模型发展。
Comments ICLR 2026 1st Workshop on Time Series in the Age of Large Models (TSALM)
dMLLM-TTS:用于扩散多模态大语言模型的自验证和高效测试时间扩展
机构 * Nanjing University(南京大学) ; Shanghai Innovation Institute(上海创新研究院) ; Shanghai AI Lab(上海人工智能实验室) ; Shanghai Jiao Tong University(上海交通大学) ; Peking University(北京大学) ; National University of Singapore(新加坡国立大学)
AI总结 本文提出dMLLM-TTS框架,通过轨迹探索扩展和迭代细化扩展两个互补的扩展轴,提升生成多样性和稳定性,同时通过自验证机制提高效率,实验表明在GenEval基准上生成质量显著提升且效率提高6倍。
Comments Project page: https://github.com/Alpha-VLLM/Lumina-DiMOO
被擦除,但未被遗忘:擦除的修正流变换器在概念攻击下仍不安全
机构 * Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, School of Artificial Intelligence, Beihang University(北京航空航天大学人工智能学院未来区块链与隐私计算北京高精尖创新中心) ; University of Science and Technology of China(中国科学技术大学) ; National University of Defense Technology(国防科技大学) ; GigaAI ; National University of Singapore(新加坡国立大学)
AI总结 本文提出ReFlux,首个针对最新修正流T2I框架评估概念擦除鲁棒性的攻击方法,通过反注意力优化和动态引导策略,验证了概念擦除在修正流变换器中的有效性。
Draw-In-Mind: 在统一多模态模型中重新平衡设计师-画家角色有助于图像编辑
机构 * Show Lab, National University of Singapore(新加坡国立大学Show Lab) ; TikTok
AI总结 本文提出Draw-In-Mind模型,通过重新分配设计职责提升图像编辑性能,展示了在统一多模态模型中平衡理解与生成模块的重要性。
Comments ICLR 2026 Camera Ready Version; Add more discussions and fix typos
LongSpec: 长上下文无损推测解码与高效草稿生成与验证
机构 * Sea AI Lab(Sea AI实验室) ; Nanyang Technological University(南洋理工大学) ; National University of Singapore(新加坡国立大学)
AI总结 LongSpec通过三种创新解决长上下文场景下的推测解码问题,实现3.26倍速度提升和2.25倍时间减少,适用于长上下文理解任务。
Comments Accepted by ACL'25 (Main)
超越损失值:通过损失轨迹对齐实现鲁棒动态剪枝
机构 * National University of Singapore(新加坡国立大学) ; Wuhan University(武汉大学) ; Sichuan University(四川大学)
AI总结 本文提出AlignPrune模块,通过动态对齐得分改进动态剪枝在标签噪声下的鲁棒性,实验表明其在多个基准上提升精度达6.3%。
Comments Published in CVPR 2026 Findings
VersaVogue: 视觉专家协作与偏好对齐的统一时尚合成
机构 * Nanjing University of Science and Technology(南京理工大学) ; National University of Singapore(新加坡国立大学) ; Nanjing University(南京大学) ; Nanjing Forestry University(南京林业大学)
AI总结 本文提出VersaVogue框架,通过 trait-routing attention 模块和自动化多视角偏好优化管道,实现多条件可控的时尚合成,提升视觉真实性和可控性。
压力下的Splats:在受约束的GPU预算下探索实时3D高斯溅射的性能-能耗权衡
机构 * National University of Singapore(新加坡国立大学) ; Telecom Paris(巴黎电信学院)
AI总结 研究在受约束的GPU预算下实时3D高斯溅射的性能-能耗权衡,通过模拟不同GPU性能层级,分析帧率、运行时间和能耗等指标。
理解并行采样与顺序采样在大推理模型中的性能差距
机构 * Google DeepMind(谷歌DeepMind) ; National University of Singapore(新加坡国立大学)
AI总结 本文研究大推理模型中并行与顺序采样性能差异,发现并行采样表现更优,提出探索不足是主要原因。
Comments Under review
考虑权重的自解释聚类用于混合类型表格数据
机构 * School of Computing, National University of Singapore(新加坡国立大学计算机学院) ; School of Intelligence Science and Engineering, Harbin Institute of Technology (Shenzhen)(哈尔滨工业大学(深圳)智能科学与工程学院)
AI总结 本文提出WISE框架,通过二进制编码与填充对齐异构特征,结合LOFO策略和两阶段加权聚类,提升混合类型表格数据的聚类质量和可解释性。
BiCoord: 一种面向长时域空间-时间协调的双臂操作基准
机构 * Beihang University(北京航空航天大学) ; Zhongguancun Academy(中关村学院) ; National University of Singapore(新加坡国立大学)
AI总结 BiCoord基准旨在解决双臂操作中长时域和紧密协调的问题,通过多样化的任务和量化指标评估协调性,揭示现有方法在长时域任务中的不足。
Comments 8 pages
RHVI-FDD:一种用于低光照图像增强的分层解耦框架
机构 * Jilin University(吉林大学) ; Dalian University of Technology(大连理工大学) ; National University of Singapore(新加坡国立大学)
AI总结 本文提出RHVI-FDD框架,通过宏微观分层解耦方法解决低光照图像中色彩失真、噪声和细节丢失问题,提升图像增强效果。
Comments 8 pages, 8 figures
黑客还是幻觉?对基于LLM的自动化渗透测试的全面分析
机构 * School of Cyber Science and Engineering, Sichuan University(四川大学网络空间安全学院) ; Institute for Network Sciences and Cyberspace, Tsinghua University(清华大学网络科学与网络空间研究院) ; College of Computing and Data Science, Nanyang Technological University(南洋理工大学计算与数据科学学院) ; National University of Singapore(新加坡国立大学) ; College of Electronic Engineering, National University of Defense Technology(国防科技大学电子工程学院) ; Gaoling School of Artificial Intelligence, Renmin University of China(中国人民大学高瓴人工智能学院) ; School of Artificial Intelligence, Wuhan University(武汉大学人工智能学院)
AI总结 本文对基于LLM的自动化渗透测试框架进行系统分析和大规模评估,揭示其架构设计和性能差异,为未来研究提供结构化分类和基准。
MARS-Dragonfly: 模块化空中机器人系统的敏捷与鲁棒飞行控制
机构 * National University of Singapore(新加坡国立大学) ; Engineering Design and Innovation Centre, National University of Singapore(新加坡国立大学工程设计与创新中心)
AI总结 本文提出MARS-Dragonfly系统,通过被动对接、无检测锁定和磁力辅助分离机制,结合力矩等效虚拟四旋翼模型,实现模块化空中机器人系统的敏捷与鲁棒飞行控制。
多模态数据的层次对比学习
机构 * University of Chinese Academy of Sciences(中国科学院大学) ; National University of Singapore(新加坡国立大学)
AI总结 本文提出层次对比学习框架,通过统一模型学习全局共享、部分共享和模态特定的表示,解决多模态数据中部分共享因素的建模问题,提升预测性能。
Comments 34 pages,11 figures
GaussFly: 用于3D高斯场中视觉-运动策略的对比学习
机构 * School of Mechanical and Aerospace Engineering, Nanyang Technological University(南洋理工大学机械与航空航天工程学院) ; School of Electrical and Electronic Engineering, Nanyang Technological University(南洋理工大学电气与电子工程学院) ; College of Design and Engineering, National University of Singapore(新加坡国立大学设计与工程学院) ; College of Automation Engineering, Nanjing University of Aeronautics and Astronautics(南京航空航天大学自动化学院)
AI总结 本文提出GaussFly框架,通过实-仿-实范式解耦表征学习与策略优化,利用3D高斯点划法重建场景并采用对比学习提取鲁棒特征,提升视觉-运动策略的样本效率和实境迁移能力。
PRIME:面向癌症预后分析的原型驱动多模态预训练方法,适用于缺失模态
机构 * University of Minnesota(明尼苏达大学) ; University of North Carolina at Chapel Hill(北卡罗来纳大学教堂山分校) ; North Carolina State University(北卡罗来纳州立大学) ; National University of Singapore(新加坡国立大学) ; Institute of High Performance Computing, Agency for Science, Technology and Research(高性能计算研究所,新加坡科技研究局)
AI总结 PRIME通过原型记忆银行实现缺失模态下的多模态自监督预训练,提升在碎片化临床数据中的预测性能,实现结构对齐和鲁棒性提升。