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

2026-02-24 至 2026-02-24 共收录 13
2602.19976 2026-02-24 cs.SD

SongEcho: Towards Cover Song Generation via Instance-Adaptive Element-wise Linear Modulation

SongEcho:通过实例自适应元素线性调制实现覆盖歌曲生成

Sifei Li, Yang Li, Zizhou Wang, Yuxin Zhang, Fuzhang Wu, Oliver Deussen, Tong-Yee Lee, Weiming Dong

机构 * MAIS, Institute of Automation, Chinese Academy of Sciences(自动化研究所,中国科学院) School of Artificial Intelligence, University of Chinese Academy of Sciences(中国科学院大学人工智能学院) ISRC, Institute of Software, Chinese Academy of Sciences(软件研究所,中国科学院) University of Konstanz(康斯坦茨大学) National Cheng-Kung University(国立成功大学)

AI总结 SongEcho通过实例自适应元素线性调制实现覆盖歌曲生成,生成高质量歌曲并减少参数使用。

Comments Accepted at ICLR 2026. 21 pages (10 pages main text), 5 figures

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2408.07543 2026-02-24 cs.CV cs.CL

MathScape: Benchmarking Multimodal Large Language Models in Real-World Mathematical Contexts

MathScape:在现实数学情境中评估多模态大语言模型

Hao Liang, Linzhuang Sun, Minxuan Zhou, Zirong Chen, Meiyi Qiang, Mingan Lin, Tianpeng Li, Fan Yang, Zenan Zhou, Wentao Zhang

机构 * Peking University(北京大学) University of Chinese Academy of Sciences(中国科学院大学) Nankai University(南开大学) Beijing Institute of Technology(北京理工大学) Baichuan Inc.(百度文心)

AI总结 MathScape是一个评估多模态大语言模型在现实数学情境中推理能力的新基准,揭示了现有模型在现实任务中的局限性。

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2602.19870 2026-02-24 cs.CV

ApET: Approximation-Error Guided Token Compression for Efficient VLMs

ApET:基于近似误差的令牌压缩用于高效的视觉语言模型

Qiankun Ma, Ziyao Zhang, Haofei Wang, Jie Chen, Zhen Song, Hairong Zheng

机构 * Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences(中国科学院深圳先进技术研究所) Peng Cheng Laboratory(鹏城实验室) University of Chinese Academy of Sciences(中国科学院大学) Harbin Institute of Technology(哈尔滨工业大学) Peking University(北京大学)

AI总结 ApET通过近似误差指导的令牌压缩,在不使用注意力机制的情况下高效压缩视觉语言模型的令牌预算,提升推理效率。

Comments CVPR2026

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2602.19526 2026-02-24 cs.CL

How to Train Your Deep Research Agent? Prompt, Reward, and Policy Optimization in Search-R1

如何训练你的深度研究代理?搜索-R1中的提示、奖励和策略优化

Yinuo Xu, Shuo Lu, Jianjie Cheng, Meng Wang, Qianlong Xie, Xingxing Wang, Ran He, Jian Liang

机构 * CASIA(中国科学院自动化研究所) NLPR & MAIS(自然语言处理与人工智能研究室) School of AI UCAS(中国科学院大学人工智能学院) Meituan Inc(美团公司)

AI总结 本文提出通过优化提示模板、奖励函数和策略优化方法来提升深度研究代理的性能,通过实验发现快速思考模板和REINFORCE在性能和稳定性上表现更优,同时引入Search-R1++基线提升了Search-R1的性能。

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2602.19138 2026-02-24 q-bio.NC cs.AI

CRCC: Contrast-Based Robust Cross-Subject and Cross-Site Representation Learning for EEG

CRCC: 基于对比的跨受试者和跨站点表示学习

Xiaobin Wong, Zhonghua Zhao, Haoran Guo, Zhengyi Liu, Yu Wu, Feng Yan, Zhiren Wang, Sen Song

机构 * Tsinghua Laboratory of Brain(清华大学脑科学实验室) School of Biomedical Engineering, Tsinghua University(清华大学生物医学工程学院) Institute of Automation, Chinese Academy of Sciences(中国科学院自动化研究所) University of Chinese Academy of Sciences(中国科学院大学) Weixian College, Tsinghua University(清华大学魏先学院) School of Artificial Intelligence, Beijing University of Posts(北京邮电大学人工智能学院) School of Computer Science(计算机科学学院) Technology, Northwestern Polytechnical University, Xi'an, China(技术,西北工业大学,西安,中国) Beijing Huilongguan Hospital, Capital Medical University(北京回龙观医院,首都医科大学) Peking University Huilongguan Clinical Medical School(北京大学回龙观临床医学院)

AI总结 CRCC通过对比学习和对抗优化提升跨站点EEG表示学习的泛化能力,实现10.7个百分点的准确率提升。

Comments First edition

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2602.19068 2026-02-24 cs.LG

TimeRadar: A Domain-Rotatable Foundation Model for Time Series Anomaly Detection

TimeRadar: 一个用于时间序列异常检测的领域可旋转基础模型

Hui He, Hezhe Qiao, Yutong Chen, Kun Yi, Guansong Pang

机构 * School of Computing and Information Systems(计算与信息系) Singapore Management University(新加坡管理大学) Chongqing Institute of Green and Intelligent Technology(绿色与智能技术研究所) University of Chinese Academy of Sciences(中国科学院大学) State Information Center(信息中心)

AI总结 TimeRadar通过分数时间-频率域旋转和FTFRecon组件实现跨不同数据集的通用时间序列异常检测。

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2602.19064 2026-02-24 cs.CV

L3DR: 3D-aware LiDAR Diffusion and Rectification

L3DR:基于3D感知的LiDAR扩散与校正

Quan Liu, Xiaoqin Zhang, Ling Shao, Shijian Lu

机构 * Nanyang Technological University(南洋理工大学) Zhejiang University of Technology(浙江工业大学) University of Chinese Academy of Sciences(中国科学院大学)

AI总结 L3DR通过3D-aware的LiDAR扩散和校正框架,在3D空间中消除RV伪影并恢复局部几何结构,实现更真实的3D几何生成。

Comments In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026

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2412.17596 2026-02-24 cs.CL cs.AI

Evaluating LLMs' Divergent Thinking Capabilities for Scientific Idea Generation with Minimal Context

评估LLMs在科学想法生成中的发散思维能力:基于最小上下文

Kai Ruan, Xuan Wang, Jixiang Hong, Peng Wang, Yang Liu, Hao Sun

机构 * Gaoling School of Artificial Intelligence, Renmin University of China(中国人民大学人工智能学院) College of Computer Science and Technology, Zhejiang University(浙江大学计算机科学与技术学院) Bank of China(中国银行) School of Engineering Science, University of Chinese Academy of Sciences(中国科学院大学工程科学学院) State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences(中国科学院力学研究所非线性力学重点实验室)

AI总结 本研究提出LiveIdeaBench,通过单关键词提示评估LLMs在科学想法生成中的发散思维能力,揭示通用智能指标无法有效预测该能力,表明需专门的评估基准和不同的训练策略来提升科学想法生成能力。

Comments Updated manuscript and title

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2405.14504 2026-02-24 cs.CV cs.AI

Adaptive Runge-Kutta Dynamics for Spatiotemporal Prediction

自适应龙格-库塔动力学用于时空预测

Xuanle Zhao, Yue Sun, Ziyi Wang, Bo Xu, Tielin Zhang

机构 * School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China(人工智能学院,中国科学院大学,北京,中国)

AI总结 本文提出了一种基于自适应龙格-库塔方法和频率增强傅里叶模块的物理引导神经网络,用于更精确地建模时空动态,并在多个任务中取得了优于现有方法的性能。

Comments Accepted by ICASSP 2026

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2602.18811 2026-02-24 cs.CV

Learning Multi-Modal Prototypes for Cross-Domain Few-Shot Object Detection

跨域少样本目标检测中的多模态原型学习

Wanqi Wang, Jingcai Guo, Yuxiang Cai, Zhi Chen

机构 * University of Chinese Academy of Sciences(中国科学院大学) The Hong Kong Polytechnic University(香港理工大学) Zhejiang University(浙江大学) The University of Southern Queensland(昆士兰大学)

AI总结 本文提出LMP方法,通过结合文本和视觉信息,提升跨域少样本目标检测的精度和性能。

Comments Accepted to CVPR 2026 Findings

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2602.18493 2026-02-24 cs.LG cs.AI

Learning to Remember: End-to-End Training of Memory Agents for Long-Context Reasoning

学习记忆:为长上下文推理端到端训练记忆代理

Kehao Zhang, Shangtong Gui, Sheng Yang, Wei Chen, Yang Feng

机构 * Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences (ICT/CAS)(智能信息处理重点实验室,计算技术研究所,中国科学院) Key Laboratory of AI Safety, Chinese Academy of Sciences(人工智能安全重点实验室,中国科学院) University of Chinese Academy of Sciences, Beijing, China(中国科学院大学,北京,中国) Li Auto Inc.(利亚德公司)

AI总结 UMA通过端到端强化学习框架,整合记忆操作与问答,提升长上下文推理任务的性能。

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2504.18880 2026-02-24 cs.AI cond-mat.mtrl-sci cs.CL

Reshaping MOFs text mining with a dynamic multi-agents framework of large language model

用大语言模型的动态多智能体框架重塑MOFs文本挖掘

Zuhong Lin, Daoyuan Ren, Kai Ran, Jing Sun, Songlin Yu, Xuefeng Bai, Xiaotian Huang, Haiyang He, Pengxu Pan, Ying Fang, Zhanglin Li, Haipu Li, Jingjing Yao

机构 * Center for Environment and Water Resources, College of Chemistry and Chemical Engineering, Central South University(环境与水资源中心,化学与化工学院,中南大学) Key Laboratory of Hunan Province for Water Environment and Agriculture Product Safety(湖南省水环境与农产品安全重点实验室) School of Resources and Environment, Hunan University of Technology and Business(资源与环境学院,湖南工业大学) School of Artificial Intelligence, University of Chinese Academy of Sciences(人工智能学院,中国科学院大学) Faculty of Data Science, City University of Macau(数据科学学院,澳门城市大学) State Key Laboratory of High Performance Ceramics and Superfine Microstructure, Shanghai Institute of Ceramics, Chinese Academy of Sciences(高性能陶瓷与超细微结构重点实验室,上海陶瓷研究所,中国科学院) Beijing Key Laboratory for Green Catalysis and Separation, Department of Chemical Engineering, College of Materials Science and Engineering, Beijing University of Technology(绿色催化与分离北京市重点实验室,化学工程系,材料科学与工程学院,北京理工大学) State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University(环境模拟与污染控制国家重点联合实验室,环境学院,清华大学) School of Chemical Engineering and Materials Science, Yueyang University(化学工程与材料科学学院,岳阳大学) School of Computer Science and Engineering, Central South University(计算机科学与工程学院,中南大学) School of Software Engineering, Sun Yat-sen University(软件工程学院,中山大学)

AI总结 MOFh6利用大语言模型的动态多智能体框架,实现MOFs合成条件的高效提取与标准化,提升材料发现的效率和可扩展性。

Comments Accepted by TRAMAT 2 (2026) 100176

Journal ref Transactions of Materials Research, 2026, 2(1), 100176

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2504.12796 2026-02-24 cs.MM cs.SD eess.AS

A Survey on Cross-Modal Interaction Between Music and Multimodal Data

多模态数据与音乐交叉交互的综述

Sifei Li, Mining Tan, Feier Shen, Minyan Luo, Zijiao Yin, Fan Tang, Weiming Dong, Changsheng Xu

机构 * MAIS, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. E-mail: S. Li School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China. E-mail: F. Shen Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China. E-mail: .

AI总结 本文综述了音乐与多模态数据的交叉交互,探讨了音乐在多模态学习中的作用,并提出了未来研究的方向。

Comments 34 pages, 7 figures

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