arXivDaily arXiv每日学术速递 周一至周五更新

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University of Washington(华盛顿大学)

2026-01-27 至 2026-01-27 共收录 7
2601.18735 2026-01-27 cs.AI cs.LG

Why Keep Your Doubts to Yourself? Trading Visual Uncertainties in Multi-Agent Bandit Systems

为何保留你的怀疑?多智能体老虎机系统中的视觉不确定性交易

Jusheng Zhang, Yijia Fan, Kaitong Cai, Jing Yang, Jiawei Yao, Jian Wang, Guanlong Qu, Ziliang Chen, Keze Wang

机构 * Sun Yat-sen University(中山大学) University of Washington(华盛顿大学) Snap Inc.(Snap公司) Syracuse University(雪城大学)

AI总结 Agora通过去中心化市场交易机制提升多智能体系统在视觉任务中的协调效率与经济性。

Comments Accepted to ICLR 2026

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2601.18218 2026-01-27 cs.HC cs.AI cs.CL

PaperTok: Exploring the Use of Generative AI for Creating Short-form Videos for Research Communication

PaperTok: 探索生成式AI在科研传播中制作短视频的应用

Meziah Ruby Cristobal, Hyeonjeong Byeon, Tze-Yu Chen, Ruoxi Shang, Donghoon Shin, Ruican Zhong, Tony Zhou, Gary Hsieh

机构 * University of Washington(华盛顿大学)

AI总结 PaperTok利用生成式AI帮助研究人员将学术论文转化为短视频,通过自动化脚本和视听内容生成,提升科研传播效率。

Journal ref In Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI '26), Apr 13-17, 2026, Barcelona, Spain. ACM, New York, NY, USA

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2601.17449 2026-01-27 cs.LG

DREAM: Dual-Standard Semantic Homogeneity with Dynamic Optimization for Graph Learning with Label Noise

DREAM: 图学习中带标签噪声的双标准语义同质性与动态优化

Yusheng Zhao, Jiaye Xie, Qixin Zhang, Weizhi Zhang, Xiao Luo, Zhiping Xiao, Philip S. Yu, Ming Zhang

机构 * National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University-Anker Embodied AI Lab, Peking University(国家多媒体信息处理重点实验室,计算机科学学院,北京大学-安ker具身AI实验室,北京大学) College of Computing and Data Science, Nanyang Technological University(计算与数据科学学院,南洋理工大学) Department of Computer Science, University of Illinois Chicago(计算机科学系,伊利诺伊大学香槟分校) Paul G. Allen School of Computer Science and Engineering, University of Washington(保罗·G·阿伦计算机科学与工程学院,华盛顿大学) Department of Statistics, University of Wisconsin–Madison(统计系,威斯康星大学麦迪逊分校)

AI总结 DREAM通过双标准策略和动态优化解决图学习中标签噪声问题,提升图结构中节点可靠性与关系信息的利用。

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2601.17126 2026-01-27 hep-ex cs.LG hep-ph

EveNet: A Foundation Model for Particle Collision Data Analysis

EveNet:粒子碰撞数据分析的基础模型

Ting-Hsiang Hsu, Bai-Hong Zhou, Qibin Liu, Yue Xu, Shu Li, George Wei-Shu Hou, Benjamin Nachman, Shih-Chieh Hsu, Vinicius Mikuni, Yuan-Tang Chou, Yulei Zhang

机构 * Department of Physics, National Taiwan University, Taipei, Taiwan(国立台湾大学物理系) Tsung-Dao Lee Institute, Shanghai Jiao Tong University, Shanghai, China(李政道研究所) Fundamental Physics Directorate, SLAC National Accelerator Laboratory, Menlo Park, USA(SLAC国家加速器实验室基础物理主任) Department of Physics, University of Washington, Seattle, Washington, USA(华盛顿大学物理系) Department of Particle Physics and Astrophysics, Stanford University, Stanford, USA(斯坦福大学粒子物理与天体物理学系) Kobayashi-Maskawa Institute, Nagoya University, Nagoya, Japan(小林信三研究所)

AI总结 EveNet通过预训练在大量模拟碰撞事件上,实现了对粒子碰撞数据分析的高效处理,展示了在多种任务上的优越性能和在低统计数据下的高效性。

Comments 26 pages, 8 figures

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2509.23525 2026-01-27 cs.HC cs.AI

Privy: Envisioning and Mitigating Privacy Risks for Consumer-facing AI Product Concepts

Privy:面向消费者的人工智能产品概念中的隐私风险展望与缓解

Hao-Ping Lee, Yu-Ju Yang, Matthew Bilik, Isadora Krsek, Thomas Serban von Davier, Kyzyl Monteiro, Jason Lin, Shivani Agarwal, Jodi Forlizzi, Sauvik Das

机构 * Carnegie Mellon University(卡内基梅隆大学) University of Illinois Urbana-Champaign(伊利诺伊大学厄巴纳-香槟分校) University of Washington(华盛顿大学)

AI总结 Privy通过结构化隐私影响评估帮助无隐私专业知识的从业者识别和缓解人工智能产品概念中的隐私风险,其基于大语言模型的版本效果更佳。

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2508.18741 2026-01-27 cs.LG

Stability and Generalization for Bellman Residuals

贝尔曼残差的稳定性与泛化能力

Enoch H. Kang, Kyoungseok Jang

机构 * University of Washington(华盛顿大学) Chung-Ang University(Chung-Ang 大学)

AI总结 本文研究了贝尔曼残差最小化在离线强化学习中的稳定性与泛化能力,提出了一种新的方法通过李雅普诺夫势能实现O(1/n)的稳定性界,无需额外正则化或独立性假设。

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2503.21679 2026-01-27 cs.CL cs.CY

JiraiBench: A Bilingual Benchmark for Evaluating Large Language Models' Detection of Human Self-Destructive Behavior Content in Jirai Community

JiraiBench:一个双语基准,用于评估大型语言模型在Jirai社区中检测人类自毁行为内容的效果

Yunze Xiao, Tingyu He, Lionel Z. Wang, Yiming Ma, Xingyu Song, Xiaohang Xu, Mona Diab, Irene Li, Ka Chung Ng

机构 * Carnegie Mellon University(卡内基梅隆大学) University of Washington(华盛顿大学) The Hong Kong Polytechnic University(香港理工大学) The University of Tokyo(东京大学)

AI总结 JiraiBench通过双语评估揭示文化接近性在检测自毁内容中的重要性,展示跨语言知识迁移的潜力。

Comments 20 pages, 1 figures

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