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2512.20084 2025-12-24 cs.LG cs.AI

QE-Catalytic: A Graph-Language Multimodal Base Model for Relaxed-Energy Prediction in Catalytic Adsorption

QE-Catalytic: 一种图-语言多模态基础模型,用于催化吸附中放松能量的预测

Yanjie Li, Jian Xu, Xueqing Chen, Lina Yu, Shiming Xiang, Weijun Li, Cheng-lin Liu

机构 * AnnLab(安实验室) Institute of Semiconductors, Chinese Academy of Sciences(半导体研究所,中国科学院) Zhongguancun Academy(中关村学院) State Key Laboratory of Multimodal Artificial Intelligence Systems(多模态人工智能系统国家重点实验室) Institute of Automation, Chinese Academy of Sciences(自动化研究所,中国科学院) University of Chinese Academy of Sciences(中国科学院大学) Computer Network Information Center(计算机网络信息中心)

AI总结 QE-Catalytic结合语言模型与图Transformer,实现高精度催化吸附能量预测及逆向设计

Comments 25 pages

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2512.15649 2025-12-24 cs.CV cs.AI cs.CL

VTCBench: Can Vision-Language Models Understand Long Context with Vision-Text Compression?

VTCBench: 视觉-语言模型能否通过视觉-文本压缩理解长上下文?

Hongbo Zhao, Meng Wang, Fei Zhu, Wenzhuo Liu, Bolin Ni, Fanhu Zeng, Gaofeng Meng, Zhaoxiang Zhang

机构 * 1 Institute of Automation, Chinese Academy of Sciences 2 School of Artificial Intelligence, University of Chinese Academy of Sciences 3 Centre for Artificial Intelligence Robotics, Hong Kong Institute of Science \& Innovation, CAS 4 Independent Researcher

AI总结 VTCBench评估视觉-文本压缩对视觉语言模型长上下文理解能力的影响,发现多数模型在处理压缩信息时表现不佳。

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2508.16580 2025-12-24 cs.HC cs.AI

Adaptive Command: Real-Time Policy Adjustment via Language Models in StarCraft II

自适应命令:通过语言模型在星际争霸II中实现实时策略调整

Weiyu Ma, Dongyu Xu, Shu Lin, Haifeng Zhang, Jun Wang

机构 * Institute of Automation, Chinese Academy of Sciences(中国科学院自动化研究所) School of Artificial Intelligence, University of Chinese Academy of Sciences(中国科学院大学人工智能学院) AI Centre, Department of Computer Science, UCL(UCL计算机科学系人工智能中心) Nanjing Artificial Intelligence Research of IA, China(南京人工智能研究院)

AI总结 自适应命令通过整合大型语言模型和行为树,在星际争霸II中实现实时策略调整,提升人类与AI协作的决策能力。

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2502.15802 2025-12-24 cs.LG cs.AI cs.IT math.IT

A General Error-Theoretical Analysis Framework for Constructing Compression Strategies

一种用于构建压缩策略的通用误差理论分析框架

Boyang Zhang, Daning Cheng, Yunquan Zhang, Meiqi Tu, Fangming Liu, Jiake Tian

机构 * Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China(中国科学院计算技术研究所) University of Chinese Academy of Sciences, Beijing, China(中国科学院大学) Peng Cheng Laboratory, Shenzhen, China(鹏城实验室) the School of Microelectronics, South China University of Technology, Guangzhou, China(华南理工大学微电子学院) The University of Hong Kong(香港大学)

AI总结 本文提出压缩误差理论框架,通过几何方法优化各层压缩水平,实现高效参数压缩且性能损失小。

Comments Under Review

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2412.06868 2025-12-24 cs.CV cs.AI

Compression for Better: A General and Stable Lossless Compression Framework

压缩以更好:一种通用且稳定的无损压缩框架

Boyang Zhang, Daning Cheng, Yunquan Zhang, Fangming Liu, Wenguang Chen

机构 * Institute of Computing Technology, Chinese Academy of Sciences(中国科学院计算技术研究所) University of Chinese Academy of Sciences(中国科学院大学) Peng Cheng Laboratory(鹏城实验室) Tsinghua University(清华大学)

AI总结 本文提出LLC框架,通过总微分界定压缩邻域和边界,实现无损模型压缩,提升效率并保持性能。

Comments Under Review

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2412.06867 2025-12-24 cs.LG cs.AI cs.CC

Lossless Model Compression via Joint Low-Rank Factorization Optimization

通过联合低秩分解优化实现无损模型压缩

Boyang Zhang, Daning Cheng, Yunquan Zhang, Fangming Liu, Jiake Tian

机构 * Institute of Computing Technology, Chinese Academy of Sciences(中国科学院计算技术研究所) University of Chinese Academy of Sciences(中国科学院大学) Peng Cheng Laboratory(鹏城实验室) the School of Microelectronics, South China University of Technology(华南理工大学微电子学院)

AI总结 本文提出了一种联合优化策略,通过无损压缩实现模型性能的提升,适用于多种深度学习任务。

Comments Under Review

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2412.06865 2025-12-24 cs.LG cs.AI

FP=xINT:Representing Neural Networks via Low-Bit Series Basis Functions

FP=xINT:通过低比特级数基函数表示神经网络

Boyang Zhang, Daning Cheng, Yunquan Zhang, Jiake Tian, Jing Li, Fangming Liu

机构 * Institute of Computing Technology, Chinese Academy of Sciences(中国科学院计算技术研究所) Pengcheng 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) South China University of Technology(华南理工大学)

AI总结 本文提出通过低比特级数基函数表示神经网络的方法,实现无需校准集的高精度量化,实验表明在4比特设置下ResNet-50的精度达到77.03%。

Comments AAAI2026

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2412.11154 2025-12-23 cs.CV

From Easy to Hard: Progressive Active Learning Framework for Infrared Small Target Detection with Single Point Supervision

从易到难:基于单点监督的红外小目标检测渐进主动学习框架

Chuang Yu, Jinmiao Zhao, Yunpeng Liu, Sicheng Zhao, Yimian Dai, Xiangyu Yue

机构 * Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences(光电信息处理重点实验室,中国科学院) Shenyang Institute of Automation, Chinese Academy of Sciences(沈阳自动化研究所,中国科学院) University of Chinese Academy of Sciences(中国科学院大学) Tsinghua University(清华大学) Nankai University(南开大学) MMLab, The Chinese University of Hong Kong(香港中文大学MMLab) CPII under InnoHK(创新香港下的CPII)

AI总结 本文提出渐进主动学习框架,通过模型预启动和双更新策略提升单点监督下红外小目标检测的性能和稳定性。

Comments Accepted by ICCV 2025

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2512.19271 2025-12-23 cs.CV

3SGen: Unified Subject, Style, and Structure-Driven Image Generation with Adaptive Task-specific Memory

3SGen: 一种统一的主体、风格和结构驱动的图像生成方法,具有自适应任务特定记忆

Xinyang Song, Libin Wang, Weining Wang, Zhiwei Li, Jianxin Sun, Dandan Zheng, Jingdong Chen, Qi Li, Zhenan Sun

机构 * School of Artificial Intelligence, UCAS(人工智能学院,UCAS) CASIA AntGroup(蚂蚁集团)

AI总结 3SGen通过统一的框架实现主体、风格和结构驱动的图像生成,采用自适应任务特定记忆模块提升生成质量和跨任务迁移性。

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2512.19081 2025-12-23 cs.AI

Population-Evolve: a Parallel Sampling and Evolutionary Method for LLM Math Reasoning

Population-Evolve: 一种用于LLM数学推理的并行采样和进化方法

Yanzhi Zhang, Yitong Duan, Zhaoxi Zhang, Jiyan He, Shuxin Zheng

机构 * 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) Zhongguancun Academy(中关村学院) Zhongguancun Institute of Artificial Intelligence(中关村人工智能研究院)

AI总结 Population-Evolve通过并行推理和进化提示提升LLM数学推理能力,实现高准确性和低计算成本。

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2512.18651 2025-12-23 cs.CV

Adversarial Robustness in Zero-Shot Learning:An Empirical Study on Class and Concept-Level Vulnerabilities

零样本学习中的对抗鲁棒性:对类别和概念层面脆弱性的实证研究

Zhiyuan Peng, Zihan Ye, Shreyank N Gowda, Yuping Yan, Haotian Xu, Ling Shao

机构 * iFLYTEK Co., Ltd.(iFLYTEK公司) UCAS-Terminus AI Lab, University of Chinese Academy of Sciences(中国科学院大学Terminus AI实验室) School of Computer Science, the University of Nottingham(诺丁汉大学计算机学院) TGAI lab, the Westlake University(西湖大学TGAI实验室) RippleInfo Co., Ltd(RippleInfo公司)

AI总结 本研究通过实证分析揭示了零样本学习模型在类别和概念层面的对抗脆弱性,并提出了改进对抗鲁棒性的方法。

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2512.17370 2025-12-23 cs.RO cs.AI

TakeAD: Preference-based Post-optimization for End-to-end Autonomous Driving with Expert Takeover Data

TakeAD: 基于偏好的端到端自动驾驶后优化方法与专家接管数据

Deqing Liu, Yinfeng Gao, Deheng Qian, Qichao Zhang, Xiaoqing Ye, Junyu Han, Yupeng Zheng, Xueyi Liu, Zhongpu Xia, Dawei Ding, Yifeng Pan, Dongbin Zhao

机构 * The State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences(多模态人工智能系统国家重点实验室,中国科学院自动化研究所) School of Artificial Intelligence, University of Chinese Academy of Sciences(中国科学院大学人工智能学院) School of Automation and Electrical Engineering, University of Science and Technology Beijing(北京科技大学自动化与电气工程学院) Chongqing Chang’an Technology Co., Ltd.(重庆长安科技有限公司)

AI总结 TakeAD通过基于偏好的后优化框架利用专家接管数据,提升端到端自动驾驶闭环性能。

Comments This work has been accepted by IEEE RA-L. Manuscript submitted: July, 8, 2025; Accepted: November, 24, 2025

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2504.12826 2025-12-23 cs.RO cs.CV

UncAD: Towards Safe End-to-end Autonomous Driving via Online Map Uncertainty

UncAD: 向通过在线地图不确定性实现安全端到端自动驾驶迈进

Pengxuan Yang, Yupeng Zheng, Qichao Zhang, Kefei Zhu, Zebin Xing, Qiao Lin, Yun-Fu Liu, Zhiguo Su, Dongbin Zhao

机构 * Key Laboratory of Safety Intelligent Mining in Non-coal Open-pit Mines, National Mine safety Administration, Guangdong Guangzhou, 510000, China(安全智能采矿非煤矿山重点实验室,国家矿山安全监察局,广东广州,510000,中国) The State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences(多模态人工智能系统国家重点实验室,自动化研究所,中国科学院) 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) EACON, Fujian, China(福建中国EACON)

AI总结 UncAD通过引入在线地图不确定性,提升自动驾驶安全性,减少碰撞和冲突率。

Journal ref 2025 IEEE International Conference on Robotics and Automation (ICRA)

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2503.11185 2025-12-23 cs.CR cs.AI

Bleeding Pathways: Vanishing Discriminability in LLM Hidden States Fuels Jailbreak Attacks

出血路径:LLM隐藏状态中的判别能力消失加剧了 jailbreak 攻击

Yingjie Zhang, Tong Liu, Zhe Zhao, Guozhu Meng, Kai Chen

机构 * Institute of Information Engineering, Chinese Academy of Sciences(中国科学院信息工程研究所) School of Cyber Security, University of Chinese Academy of Sciences(中国科学院大学网络安全学院) Ant Group(蚂蚁集团)

AI总结 本研究提出DEEPALIGN框架,通过增强LLM隐藏状态的分离度,有效缓解jailbreak攻击,提升安全性和实用性。

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2501.17642 2025-12-23 cs.CV

Efficient Redundancy Reduction for Open-Vocabulary Semantic Segmentation

高效开放词汇语义分割中的冗余减少

Lin Chen, Qi Yang, Kun Ding, Zhihao Li, Gang Shen, Fei Li, Qiyuan Cao, Shiming Xiang

机构 * State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences(多模态人工智能系统国家重点实验室(MAIS)、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) School of Software, Shandong University(山东大学软件学院) China Tower Corporation Limited(中国铁塔股份有限公司)

AI总结 本文提出ERR-Seg,通过减少冗余信息和优化序列建模,提升开放词汇语义分割的效率与性能。

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2512.18571 2025-12-23 cs.AI cs.CV

ESearch-R1: Learning Cost-Aware MLLM Agents for Interactive Embodied Search via Reinforcement Learning

ESearch-R1: 通过强化学习学习成本感知的多模态大语言模型代理以进行交互式具身搜索

Weijie Zhou, Xuangtang Xiong, Ye Tian, Lijun Yue, Xinyu Wu, Wei Li, Chaoyang Zhao, Honghui Dong, Ming Tang, Jinqiao Wang, Zhengyou Zhang

机构 * School of Traffic and Transportation, Beijing Jiaotong University(交通与运输学院,北京交通大学) Tencent Robotics X & Futian Laboratory(腾讯机器人X与福田实验室) Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences(基础模型研究中心,中国科学院自动化研究所) University of Chinese Academy of Sciences(中国科学院大学)

AI总结 ESearch-R1通过强化学习方法,结合交互对话、记忆检索和导航,实现成本感知的多模态大语言模型代理,有效降低任务执行成本并提高成功率。

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2512.18411 2025-12-23 cs.CV cs.AI

AmPLe: Supporting Vision-Language Models via Adaptive-Debiased Ensemble Multi-Prompt Learning

AmPLe: 通过自适应去偏集成多提示学习支持视觉-语言模型

Fei Song, Yi Li, Jiangmeng Li, Rui Wang, Changwen Zheng, Fanjiang Xu, Hui Xiong

机构 * National Key Laboratory of Space Integrated Information System, Institute of Software, Chinese Academy of Sciences(中国科学院空间信息集成系统国家重点实验室,软件研究所) University of Chinese Academy of Sciences(中国科学院大学) The Hong Kong University of Science and Technology (Guangzhou)(香港科技大学(广州)) The Hong Kong University of Science and Technology(香港科技大学)

AI总结 AmPLe通过自适应去偏集成多提示学习方法,解决模型-提示匹配偏差和样本-提示匹配偏差,提升视觉-语言模型在下游任务中的性能。

Comments Accepted by IJCV2025

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2512.18251 2025-12-23 cond-mat.mtrl-sci cs.LG physics.comp-ph

CrystalFormer-CSP: Thinking Fast and Slow for Crystal Structure Prediction

CrystalFormer-CSP: 快速与缓慢思考的晶体结构预测

Zhendong Cao, Shigang Ou, Lei Wang

机构 * Institute of Physics, Chinese Academy of Sciences, Beijing, China(中国科学院物理研究所) School of Physics, University of Chinese Academy of Sciences, Beijing, China(中国科学院大学物理学院)

AI总结 CrystalFormer-CSP结合数据驱动和物理驱动方法,通过预训练生成模型和力场优化,高效预测稳定晶体结构,并通过强化微调提升准确性。

Comments 11 pages, 4 figures

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2506.07576 2025-12-23 cs.CV

Super Encoding Network: Recursive Association of Multi-Modal Encoders for Video Understanding

超级编码网络:多模态编码器的递归关联用于视频理解

Boyu Chen, Siran Chen, Kunchang Li, Qinglin Xu, Yu Qiao, Yali Wang

机构 * Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences(中国科学院深圳先进技术研究院) the School of Artificial Intelligence, University of Chinese Academy of Sciences(中国科学院大学人工智能学院) Shanghai AI Laboratory(上海人工智能实验室)

AI总结 本文提出超级编码网络,通过递归关联多模态编码器提升视频理解性能,显著提升跟踪、识别、聊天和编辑等任务效果。

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2307.14596 2025-12-23 cs.LG

HUTFormer: Hierarchical U-Net Transformer for Long-Term Traffic Forecasting

HUTFormer:分层U-Net变换器用于长期交通预测

Zezhi Shao, Fei Wang, Tao Sun, Chengqing Yu, Yuchen Fang, Guangyin Jin, Zhulin An, Yang Liu, Xiaobo Qu, Yongjun Xu

机构 * Institute of Computing Technology, Chinese Academy of Sciences(中国科学院计算技术研究所) School of Computer Science and Technology, University of Chinese Academy of Sciences(中国科学院大学计算机科学与技术学院) School of Computer Science and Engineering, University of Electronic Science and Technology of China(电子科技大学计算机科学与工程学院) Department of Planning, Design, and Technology of Architecture, Sapienza University of Rome(罗马大学建筑规划、设计与技术系) School of Vehicle and Mobility, Tsinghua University(清华大学车辆与移动性学院)

AI总结 HUTFormer通过分层U-Net变换器解决长期交通预测问题,采用多尺度表示和高效嵌入策略提升预测性能。

Comments Accepted for publication in the Communications in Transportation Research on December 2025. 38 paqes with 9 fiqures and 6 tables

Journal ref Communications in Transportation Research. 5, 100218 (2025)

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2512.17667 2025-12-22 cs.CR cs.AI cs.NI

STAR: Semantic-Traffic Alignment and Retrieval for Zero-Shot HTTPS Website Fingerprinting

STAR:语义-交通对齐与检索用于零样本HTTPS网站指纹识别

Yifei Cheng, Yujia Zhu, Baiyang Li, Xinhao Deng, Yitong Cai, Yaochen Ren, Qingyun Liu

机构 * Institute of Information Engineering, Chinese Academy of Sciences(中国科学院信息工程研究所) School of Cyber Security, University of Chinese Academy of Sciences(中国科学院大学网络与信息安全学院) Institute for Network Sciences and Cyberspace, Tsinghua University(清华大学网络科学与空间研究院)

AI总结 STAR通过零样本跨模态检索方法,实现对未见过网站的高准确率指纹识别,提升HTTPS加密流量的隐私保护能力。

Comments Accepted by IEEE INFOCOM 2026. Camera-ready version

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2512.15528 2025-12-22 cs.CV

EmoCaliber: Advancing Reliable Visual Emotion Comprehension via Confidence Verbalization and Calibration

EmoCaliber: 通过置信度 verbalization 和校准推进可靠的视觉情绪理解

Daiqing Wu, Dongbao Yang, Can Ma, Yu Zhou

机构 * IIE, Chinese Academy of Sciences(中国科学院信息研究所) Nankai University(南开大学) University of Chinese Academy of Sciences(中国科学院大学)

AI总结 EmoCaliber通过置信度 verbalization 和校准提升视觉情绪理解的可靠性。

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2507.01939 2025-12-22 astro-ph.IM astro-ph.SR cs.AI cs.LG

SpecCLIP: Aligning and Translating Spectroscopic Measurements for Stars

SpecCLIP:对齐和翻译恒星光谱测量

Xiaosheng Zhao, Yang Huang, Guirong Xue, Xiao Kong, Jifeng Liu, Xiaoyu Tang, Timothy C. Beers, Yuan-Sen Ting, A-Li Luo

机构 * School of Astronomy Space Science, University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, People's Republic of China Department of Physics \& Astronomy, The Johns Hopkins University, Baltimore, MD 21218, USA Zhejiang Laboratory, Hangzhou 311121, People's Republic of China Research Center for Astronomical Computing, Zhejiang Laboratory, Hangzhou 311121, People's Republic of China Department of Physics Astronomy, University of Notre Dame, Notre Dame, IN 46556, USA Joint Institute for Nuclear Astrophysics -- Center for the Evolution of the Elements (JINA-CEE), USA Department of Astronomy, The Ohio State University, 140 West 18th Avenue, Columbus, OH 43210, USA Center for Cosmology AstroParticle Physics (CCAPP), The Ohio State University, Columbus, OH 43210, USA

AI总结 SpecCLIP通过对比学习和光谱意识解码器提升恒星光谱分析的精度和应用灵活性。

Comments 29 pages, 8 figures, 6 tables. Accepted for publication in ApJ. Comments welcome

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2512.14121 2025-12-22 cs.CV cs.AI

SportsGPT: An LLM-driven Framework for Interpretable Sports Motion Assessment and Training Guidance

SportsGPT: 一种基于大语言模型的可解释性体育动作评估与训练指导框架

Wenbo Tian, Ruting Lin, Hongxian Zheng, Yaodong Yang, Geng Wu, Zihao Zhang, Zhang Zhang

机构 * School of Artificial Intelligence, UCAS, Beijing, China(人工智能学院) MAIS, NLPR, Institute of Automation, CAS, Beijing, China(自动化研究所) College of Education, Beijing Sport University, Beijing, China(北京体育大学) KuTi Sports Technology, Shaanxi, China(KuTi体育科技) Haoxiong Technology, Shanghai, China(浩鸿科技)

AI总结 SportsGPT通过结合大语言模型和运动分析技术,实现可解释的体育动作评估与专业训练指导。

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2512.16724 2025-12-19 cs.RO cs.CV

VERM: Leveraging Foundation Models to Create a Virtual Eye for Efficient 3D Robotic Manipulation

VERM:利用基础模型创建虚拟眼睛以实现高效的3D机器人操作

Yixiang Chen, Yan Huang, Keji He, Peiyan Li, Liang Wang

机构 * 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新技术实验室,多模态人工智能系统国家重点实验室) School of Artificial Intelligence, University of Chinese Academy of Sciences(中国科学院大学人工智能学院) FiveAges Shandong University(山东大学)

AI总结 VERM通过利用基础模型创建虚拟视图,提升3D机器人操作的效率和准确性,实现训练和推理速度的显著提升。

Comments Accepted at RA-L 2025

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2512.16439 2025-12-19 cs.CR cs.CL

From Essence to Defense: Adaptive Semantic-aware Watermarking for Embedding-as-a-Service Copyright Protection

从本质到防御:面向嵌入即服务版权保护的自适应语义感知水印技术

Hao Li, Yubing Ren, Yanan Cao, Yingjie Li, Fang Fang, Xuebin Wang

机构 * Institute of Information Engineering, Chinese Academy of Sciences(中国科学院信息工程研究所) School of Cyber Security, University of Chinese Academy of Sciences(中国科学院大学网络安全学院)

AI总结 SemMark是一种基于语义的嵌入即服务版权保护水印技术,通过语义感知水印和自适应权重机制提升防护效果。

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2512.16247 2025-12-19 cs.LG

Sharpness-aware Federated Graph Learning

精确性感知的联邦图学习

Ruiyu Li, Peige Zhao, Guangxia Li, Pengcheng Wu, Xingyu Gao, Zhiqiang Xu

机构 * Xidian University(西安电子科技大学) Nanyang Technological University(南洋理工大学) University of Chinese Academy of Sciences(中国科学院大学)

AI总结 SEAL算法通过优化模型尖锐性提升联邦图学习中本地模型的分类准确性和泛化能力。

Comments Accepted by WSDM'26

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2504.19565 2025-12-19 cs.CL cs.AI q-bio.QM

Knowledge-Driven Agentic Scientific Corpus Distillation Framework for Biomedical Large Language Models Training

面向生物医学大语言模型训练的知识驱动代理科学语料库精简框架

Meng Xiao, Xunxin Cai, Qingqing Long, Chengrui Wang, Yuanchun Zhou, Hengshu Zhu

机构 * Computer Network Information Center, Chinese Academy of Sciences, Beijing, China(中国科学院计算机网络信息中心) University of Chinese Academy of Sciences, Beijing, China(中国科学院大学) Duke-NUS Medical School, National University of Singapore, Singapore(新加坡国立大学杜克-新加坡医学部)

AI总结 本文提出一种知识驱动的代理框架,用于生物医学大语言模型训练中的语料库精简,通过多代理协作提升问答任务性能。

Comments Biomedical Large Language Models, Agentic Corpus Distillation, Synthetic Question-Answer Generation, Agentic AI, Knowledge Hierarchy Guidance

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2501.15108 2025-12-19 cs.CL

Knowledge Hierarchy Guided Biological-Medical Dataset Distillation for Domain LLM Training

基于知识层级的生物医学数据集蒸馏用于领域大语言模型训练

Xunxin Cai, Chengrui Wang, Qingqing Long, Yuanchun Zhou, Meng Xiao

机构 * Computer Network Information Center, Chinese Academy of Sciences(中国科学院计算机网络信息中心) University of Chinese Academy of Sciences(中国科学院大学) Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences(中国科学院大学杭州高等研究院)

AI总结 本文提出基于生物医学知识层级的自动数据蒸馏框架,提升领域大语言模型的训练效果,使Llama3-70B在参数量上超越GPT-4。

Comments 10 pages

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2402.18951 2025-12-19 cs.CV

Percept, Chat, and then Adapt: Multimodal Knowledge Transfer of Foundation Models for Open-World Video Recognition

感知、对话,然后适应:面向开放世界视频识别的多模态基础模型知识迁移

Boyu Chen, Siran Chen, Kunchang Li, Qinglin Xu, Yu Qiao, Yali Wang

机构 * Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences(中国科学院深圳先进技术研究院) the School of Artificial Intelligence, University of Chinese Academy of Sciences(中国科学院大学人工智能学院) Shanghai AI Laboratory(上海人工智能实验室)

AI总结 本文提出PCA框架,通过感知、对话和适应三个阶段,利用多模态知识提升开放世界视频识别的性能。

Comments 35 pages, 6 figures, 8 tables

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