arXivDaily arXiv每日学术速递 周一至周五更新

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University of Chinese Academy of Sciences(中国科学院大学)

2026-06-26 至 2026-06-26 共收录 11
2606.27330 2026-06-26 cs.CL cs.AI cs.CV cs.LG 新提交

Empowering GUI Agents via Autonomous Experience Exploration and Hindsight Experience Utilization for Task Planning

通过自主经验探索与事后经验利用赋能GUI智能体任务规划

Tianyi Men, Zhuoran Jin, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao

机构 * The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences(中国科学院自动化研究所复杂系统认知与决策智能重点实验室) School of Artificial Intelligence, University of Chinese Academy of Sciences(中国科学院大学人工智能学院)

AI总结 提出PEEU方法,通过自主探索环境发现经验并利用事后经验合成严格对齐的高层训练数据,提升小型多模态大语言模型在GUI任务中的规划与跨网站泛化能力。

Comments Accepted to ACL 2026 Main

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2606.27268 2026-06-26 cs.RO cs.AI 新提交

E-TTS: A New Embodied Test-Time Scaling Framework for Robotic Manipulation

E-TTS:一种新的机器人操作具身测试时缩放框架

Wen Ye, Peiyan Li, Tingyu Yuan, Yuan Xu, Xiangnan Wu, Chaoyang Zhao, Jing Liu, Nianfeng Liu, Yan Huang, Liang Wang

机构 * New Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences(中国科学院自动化研究所模式识别国家重点实验室) School of Artificial Intelligence, University of Chinese Academy of Sciences(中国科学院大学人工智能学院) Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences(中国科学院自动化研究所基础模型研究中心) FiveAges(五时代)

AI总结 提出E-TTS框架,通过历史感知迭代精炼和视觉语言验证器统一推理与动作缩放,解决具身任务中推理缩放和历史信息利用不足的问题,在仿真和真实场景中分别提升33.14%和26.62%的性能。

Comments Accepted to ECCV 2026. 44 pages, 11 figures. Project page: https://27yw.github.io/E-TTS-Web/

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2606.27140 2026-06-26 cs.LG 新提交

fTNN: a tensor neural network for fractional PDEs

fTNN:用于分数阶偏微分方程的张量神经网络

Qingkui Ma, Hehu Xie, Xiaobo Yin

机构 * Central China Normal University(华中师范大学) Academy of Mathematics and Systems Science, Chinese Academy of Sciences(中国科学院数学与系统科学研究院) University of Chinese Academy of Sciences(中国科学院大学)

AI总结 提出fTNN,一种确定性张量神经网络子空间方法,通过几何自适应积分分裂和边界奇异性感知试函数求解分数阶拉普拉斯问题,在强边界奇异性和长时间模拟中显著优于fPINN和蒙特卡洛方法。

Comments 30 pages,11 figures and 12 tables

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2606.27079 2026-06-26 cs.RO 新提交

ForesightSafety-VLA: A Unified Diagnostic Safety Benchmark for Vision-Language-Action Models

ForesightSafety-VLA:视觉-语言-动作模型的统一诊断安全基准

Mingyang Lyu, Yinqian Sun, Yiyang Jia, Sicheng Shen, Moquan Sha, Huangrui Li, Feifei Zhao, Yi Zeng

机构 * Brain-inspired Cognitive AI Lab, Institute of Automation, Chinese Academy of Sciences(中国科学院自动化研究所类脑认知智能实验室) Beijing Key Laboratory of Safe AI and Superalignment(北京市安全人工智能与超级对齐重点实验室) Beijing Institute of AI Safety and Governance(北京人工智能安全与治理研究所) Gaoling School of AI, Renmin University of China(中国人民大学高瓴人工智能学院) University of Chinese Academy of Sciences (UCAS)(中国科学院大学)

AI总结 提出ForesightSafety-VLA基准,通过13类安全分类和三维变量控制,诊断VLA模型在物理交互、指令和感知侧的安全风险,并引入累积安全成本和风险暴露时间等过程级指标。

Comments 8 pages, 5 figures, 4 tables. Submitted to IROS 2026

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2606.26801 2026-06-26 cs.RO cs.CV 新提交

Improving Vision-Language-Action Model Fine-Tuning with Structured Stage and Keyframe Supervision

通过结构化阶段和关键帧监督改进视觉-语言-动作模型微调

Yuan Xu, Yixiang Chen, Kai Wang, Jiabing Yang, Peiyan Li, Qisen Ma, Yan Huang, Liang Wang

机构 * School of Artificial Intelligence, University of Chinese Academy of Sciences(中国科学院大学人工智能学院) New Laboratory of Pattern Recognition (NLPR), State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences(中国科学院自动化研究所模式识别国家重点实验室(NLPR)多模态人工智能系统国家重点实验室(MAIS)) FiveAges

AI总结 提出StaKe框架,通过从演示中自动提取阶段分类器和关键帧预测器作为辅助监督,提升VLA模型在长时域操作任务中的微调效果,成功率相对提升14%-56%。

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2606.26722 2026-06-26 cs.AI physics.optics 新提交

Socratic agents for autonomous scientific discovery in high-dimensional physical systems

用于高维物理系统中自主科学发现的苏格拉底式智能体

Xianrui Zeng, Pengfei Liu, Yirui Zang, Yang Shen, Fei Yu, Chunlei Yu, Minghao Liu, Yang Du

机构 * Hangzhou Institute for Advanced Study(杭州高等研究 institute) University of Chinese Academy of Sciences(中国科学院大学) Shanghai Institute of Optics and Fine Mechanics(上海光学精密机械研究所) Department of Thoracic Surgery(胸外科部门) Shanghai Pulmonary Hospital(上海 pulmonary 医院) School of Medicine(医学院) Tongji University(同济大学)

AI总结 提出多智能体AI科学家AHOIS,通过苏格拉底式诘问实现闭环实验中的自主科学发现,在高维多模光纤平台上自主验证随机干涉编码假设、发现自适应稀疏测量策略并诊断故障模式。

Comments 27 pages,5 figures

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2606.26585 2026-06-26 cs.AI astro-ph.IM 新提交

A Multi-Level Validation and Traceability Framework for AI-Generated Telescope Scheduling Decisions

AI生成的望远镜调度决策的多级验证与可追溯性框架

Hengchu Xiao, Chuanjun Wang

机构 * Yunnan Observatories, Chinese Academy of Sciences(云南天文台,中国科学院) University of Chinese Academy of Sciences(中国科学院大学) Key Laboratory of the Structure and Evolution of Celestial Objects, Chinese Academy of Sciences(中国科学院天文结构与演化重点实验室) Yunnan Key Laboratory of Solar Physics and Space Science(云南太阳物理与空间科学重点实验室) Center for Astronomical Mega-Science, Chinese Academy of Sciences(中国科学院天文大科学中心)

AI总结 提出多级验证与可追溯推理框架,通过数据引用验证、逻辑一致性检查和约束验证,提高AI调度决策的可执行性和可靠性,减少瞬态机会损失。

Comments 25 pages, 8 figures, Published in Universe

Journal ref Universe, Volume 12, Issue 6, 172 (2026)

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2606.24786 2026-06-26 cs.CV 新提交

Counting Trees from Satellite Imagery with Noisy Supervision

基于噪声监督的卫星图像树木计数

Dimitri Gominski, Maurice Mugabowindekwe, Qiue Xu, Xiaowei Tong, Martin Brandt, Hieu Le, Rasmus Fensholt, Dimitris Samaras, Loic Landrieu

机构 * University of Copenhagen(哥本哈根大学) University of Rwanda(卢旺达大学) University of Chinese Academy of Sciences(中国科学院大学) University of North Carolina at Charlotte(北卡罗来纳大学夏洛特分校) Stony Brook University(石溪大学) LIGM, CNRS, Univ Gustave Eiffel, ENPC, IPP(LIGM,CNRS,古斯塔夫·埃菲尔大学,ENPC,IPP)

AI总结 针对卫星图像中树木计数任务,提出基于非平衡最优传输的空间密度匹配方法,并引入自校正机制利用传输残差逐步优化噪声监督,在跨三大洲的TinyTrees基准上优于检测、回归和传输匹配基线。

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2604.03212 2026-06-26 cs.CV 版本更新

ProtoFlow: Mitigating Forgetting in Class-Incremental Remote Sensing Segmentation via Low-Curvature Prototype Flow

ProtoFlow: 通过低曲率原型流缓解类别增量遥感分割中的遗忘

Jiekai Wu, Rong Fu, Chuangqi Li, Zijian Zhang, Guangxin Wu, Hao Zhang, Shiyin Lin, Jianyuan Ni, Yang Li, Dongxu Zhang, Amir H. Gandomi, Simon Fong, Pengbin Feng

机构 * Faculty of Health Data Science, Juntendo University(静冈大学健康数据科学学院) The Institute of Collaborative Innovation, University of Macau(澳门大学协同创新研究所) Department of Information and Computing Sciences, Faculty of Science, Utrecht University(乌得勒支大学科学学院信息与计算科学系) Department of Computer and Information Science, University of Pennsylvania(宾夕法尼亚大学计算机与信息科学系) School of Computer Science, University of Chinese Academy of Sciences(中国科学院大学计算机科学学院) Department of Computer & Information Science & Engineering, University of Florida(佛罗里达大学计算机与信息科学与工程系) Department of Computer Science, Juniata College(朱尼塔学院计算机科学系) National Engineering Research Center for Beijing Biochip Technology(北京生物芯片工程技术研究中心) CapitalBio Corporation(资本生物公司) Faculty of Engineering & Information Technology, University of Technology Sydney(悉尼科技大学工程与信息技术学院) University Research and Innovation Center (EKIK), Obuda University(布达佩斯大学研究与创新中心(EKIK)) Faculty of Science and Technology, University of Macau(澳门大学科学与技术学院) Department of Mathematics, University of Southern California(南加州大学数学系)

AI总结 本文提出ProtoFlow,一种时间感知的原型动态框架,通过将类别原型建模为轨迹并学习其演变,以缓解遥感分割中的遗忘问题,实验表明其在多个基准上取得了显著提升。

Comments In the previous version, Juntendo University was erroneously listed as the affiliation; we must clarify that this paper has absolutely no relation to Juntendo University. Therefore, we have replaced this affiliation in the new version

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2510.17459 2026-06-26 astro-ph.EP astro-ph.GA cs.LG 版本更新

Estimating Orbital Parameters of Direct Imaging Exoplanet Using Neural Network

使用神经网络估计直接成像系外行星的轨道参数

Bo Liang, Hanlin Song, Chang Liu, Tianyu Zhao, Yuxiang Xu, Zihao Xiao, Manjia Liang, Minghui Du, Wei-Liang Qian, Li-e Qiang, Peng Xu, Ziren Luo

机构 * Center for Gravitational Wave Experiment, National Microgravity Laboratory, Institute of Mechanics, Chinese Academy of Sciences(引力波实验中心、国家微重力实验室、力学研究所、中国科学院) Taiji Laboratory for Gravitational Wave Universe (Beijing/Hangzhou), University of Chinese Academy of Sciences (UCAS), Beijing(太极引力波宇宙实验室(北京/杭州)、中国科学院大学(UCAS)) National Space Science Center, Chinese Academy of Sciences(国家空间科学中心、中国科学院) School of Physics, Peking University(北京大学物理学院) Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences(上海光学精密机械研究所、中国科学院)

AI总结 提出流匹配马尔可夫链蒙特卡洛(FM-MCMC)算法,结合流匹配后验估计与MCMC,高效准确地推断系外行星轨道参数,在β Pictoris b上实现77.8倍加速并达到最高平均对数似然。

Comments accepted by PRR

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2510.09976 2026-06-26 cs.LG cs.RO 版本更新

Reinforcement Fine-Tuning of Flow-Matching Policies for Vision-Language-Action Models

视觉-语言-动作模型的流匹配策略的强化微调

Mingyang Lyu, Yinqian Sun, Erliang Lin, Huangrui Li, Ruolin Chen, Feifei Zhao, Yi Zeng

机构 * Brain-inspired Cognitive AI Lab, Institute of Automation, Chinese Academy of Sciences, Beijing, China(脑启发认知人工智能实验室,自动化研究所,中国科学院,北京,中国) Beijing Institute of AI Safety and Governance, China(北京人工智能安全与治理研究院,中国) State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology(脑认知与脑启发智能技术国家重点实验室) Beijing Key Laboratory of Safe AI and Superalignment, China(北京安全人工智能与超对齐重点实验室,中国) University of Chinese Academy of Sciences (UCAS), Beijing, China(中国科学院大学(UCAS),北京,中国) Long-term AI,Beijing,China(长期人工智能,北京,中国)

AI总结 针对流匹配模型强化微调中重要性采样计算困难的问题,提出流策略优化算法,通过条件流匹配目标、结构感知信用分配等技术实现稳定在线微调,在LIBERO和ALOHA任务上超越基线。

Comments Accepted to ICRA 2026

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