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

共收录 143
2607.03387 2026-07-21 cs.RO 版本更新

Feeling the Unexpected: ResTacVLA for Contact-Rich Manipulation via Residual Tactile Representation

感受意外:通过残差触觉表示实现用于丰富接触操作的ResTacVLA

Pengwei Zhang, Bin Xie, Xinpan Meng, Xinyu Guo, Ce Hao, Fang Deng, Long Cheng, Tiancai 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) Institute of Automation, Chinese Academy of Sciences(中国科学院自动化研究所) Zhongguancun Academy(中关村科学城) Dexmal(德克斯马尔)

AI总结 为解决触觉感知融入视觉语言动作模型时的模态坍塌问题,提出ResTacVLA,将触觉数据转为残差触觉表示,过滤视觉可预测动态,经矢量量化瓶颈离散化,利用视觉先验不确定性自适应控制触觉整合。

Comments 8 pages, 6 figures, 3 tables. Accepted by IROS 2026, Project page: https://awilekong.github.io/ResTacVLA/

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2605.28634 2026-07-21 cs.RO 版本更新

PrimitiveVLA: Learning Reusable Motion Primitives for Efficient and Generalizable Robotic Manipulation

PrimitiveVLA:学习可复用的运动基元以实现高效且可泛化的机器人操作

Yutai Li, Shaohui Peng, Jiaming Guo, Di Huang, Zihao Zhang, Yuxuan Guo, Yunkai Gao, Siming Lan, Ling Li, Xing Hu, Yunji Chen

机构 * State Key Lab of Processors, Institute of Computing Technology, CAS(处理器国家重点实验室,计算技术研究所,中国科学院) Jiangsu Key Laboratory of AI for Industries, Institute of AI for Industries, CAS(江苏人工智能工业重点实验室,人工智能工业研究所,中国科学院) University of Chinese Academy of Sciences(中国科学院大学) Cambricon Technologies(寒武科技) Intelligent Software Research Center, Institute of Software, CAS(软件研究所智能软件研究中心,中国科学院) University of Science and Technology of China(中国科学技术大学)

AI总结 提出PrimitiveVLA框架,通过将视觉-语言-动作模型从直接指令到控制映射转向以基元为中心的拆解与组装范式,利用多模态规范表示和自动化流水线,提升数据效率并实现零样本泛化。

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2602.07008 2026-07-21 cs.CV cs.LG 版本更新

Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making

不应学习的地方:基于子集归因约束的先验对齐训练以实现可靠的决策制定

Ruoyu Chen, Shangquan Sun, Xiaoqing Guo, Sanyi Zhang, Kangwei Liu, Shiming Liu, Zhangcheng Wang, Qunli Zhang, Wei Wang, Hua Zhang, Xiaochun Cao

机构 * Institute of Information Engineering, Chinese Academy of Sciences(中国科学院信息工程研究所) University of Chinese Academy of Sciences(中国科学院大学) College of Computing and Data Science, Nanyang Technological University(南洋理工大学计算与数据科学学院) Department of Computer Science, Hong Kong Baptist University(香港 Baptist 大学计算机科学系) Communication University of China(中国传媒大学) Imperial College London(伦敦帝国学院) School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University(中山大学深圳校区网络科学与技术学院)

AI总结 本文提出了一种基于归因的先验对齐方法,通过子集选择归因技术约束模型依赖于人类先验区域,从而提升决策的可靠性。

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2602.08556 2026-07-21 cs.SD 版本更新

Global Rotation Equivariant Phase Modeling for Speech Enhancement with Deep Magnitude-Phase Interaction

全局旋转等变相位建模用于语音增强的深度幅度-相位交互

Chengzhong Wang, Andong Li, Dingding Yao, Junfeng Li

机构 * Institute of Acoustics, Chinese Academy of Sciences(中国科学院声学研究所) Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences(中国科学院声学研究所语音声学与内容理解重点实验室) University of Chinese Academy of Sciences(中国科学院大学)

AI总结 本文提出一种全局旋转等变的幅度-相位双流框架,通过强制相位流保持全局旋转等变性,提升语音增强中的相位建模效果,实验显示在相位检索、去噪、去回声和带宽扩展任务中均优于现有方法。

Comments Accepted to IEEE Transactions on Audio, Speech and Language Processing

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2509.02522 2026-07-21 cs.CL cs.LG 版本更新

Implicit Actor Critic Coupling via a Supervised Learning Framework for RLVR

通过监督学习框架实现隐式Actor-Critic耦合的RLVR方法

Jiaming Li, Longze Chen, Ze Gong, Yukun Chen, Lu Wang, Wanwei He, Run Luo, Min Yang

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

AI总结 PACS通过监督学习框架实现隐式Actor-Critic耦合,提升RLVR中奖励信号的稳定性与训练效率。

Comments ICML 2026

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2511.13420 2026-07-21 cs.CV 版本更新

VOPE: Revisiting Hallucination of Vision-Language Models in Voluntary Imagination Task

VOPE:重新审视视觉语言模型在自愿想象任务中的幻觉现象

Xingming Long, Jie Zhang, Shiguang Shan, Xilin Chen

机构 * Key Laboratory of AI Safety of CAS, Institute of Computing Technology, Chinese Academy of Sciences (CAS)(中国科学院人工智能安全重点实验室,计算技术研究所,中国科学院) University of Chinese Academy of Sciences(中国科学院大学) Zhongguancun Academy(中关村学院)

AI总结 研究聚焦视觉语言模型在自愿想象任务中的幻觉问题,引入VOPE评估基准,通过构建数据集应用于主流模型和缓解方法,发现多数模型幻觉严重,现有缓解方法效果有限,为该领域未来研究指明重要方向。

Comments Accepted at ACM MM 2026 Dataset Track

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2504.17356 2026-07-21 cs.AI cs.LG 版本更新

Comprehend, Divide, and Conquer: Feature Subspace Exploration via Multi-Agent Hierarchical Reinforcement Learning

理解、划分与征服:通过多智能体分层强化学习进行特征子空间探索

Weiliang Zhang, Xiaohan Huang, Yi Du, Ziyue Qiao, Qingqing Long, Zhen Meng, 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) Great Bay University(Great Bay大学) Duke-NUS Medical School, National University of Singapore(新加坡国立大学杜克-奈素医学院)

AI总结 研究针对特征选择问题,提出HRLFS方法,先利用基于大语言模型的混合状态提取器捕捉特征特性并聚类,构建分层智能体,通过多智能体分层强化学习进行特征子空间探索,提升了下游机器学习性能并加速运行

Comments 25 pages, keywords: Automated Feature Engineering, Tabular Dataset, Multi-Agent Reinforcement Learning, Feature Selection, Accepted by ACM Transactions on Knowledge Discovery from Data

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2509.00930 2026-07-21 cs.AI cs.LG cs.LO 版本更新

SATQuest: A Verifier for Logical Reasoning Evaluation and Reinforcement Fine-Tuning of LLMs

SATQuest:用于大语言模型逻辑推理评估和强化微调的验证器

Yanxiao Zhao, Yaqian Li, Zihao Bo, Rinyoichi Takezoe, Haojia Hui, Mo Guang, Lei Ren, Xiaolin Qin, Kaiwen Long

机构 * Chengdu Institute of Computer Applications, Chinese Academy of Sciences(中国科学院成都计算机应用研究所) 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)

AI总结 研究旨在为大语言模型逻辑推理评估及强化微调提供工具。核心方法是SATQuest,它能从CNF实例生成推理任务并客观检查答案,从多维度评估。主要贡献是通过该工具发现模型逻辑推理差距,证明强化微调有效果但跨格式鲁棒性待提升,还提供了相关研究基础设施。

Comments 23 pages, 8 figures. ACL 2026 Main Conference long paper (oral presentation)

Journal ref Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2109-2131. Association for Computational Linguistics, 2026

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2506.07691 2026-07-21 cs.CL cs.LG 版本更新

Breaking the Block: Preserving Data Continuity to Train Superior SAEs for Instruct Models

打破模块限制:保留数据连续性以训练用于指令模型的卓越稀疏自编码器

Jiaming Li, Haoran Ye, Yukun Chen, Xinyue Li, Lei Zhang, Hamid Alinejad-Rokny, Jimmy Chih-Hsien Peng, Min Yang

机构 * Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China(中国科学院深圳先进技术研究所) University of Chinese Academy of Sciences, China(中国科学院大学) National University of Singapore, Singapore(新加坡国立大学) The University of New South Wales, Australia(新南威尔士大学) Shenzhen University of Advanced Technology, China(深圳先进技术大学)

AI总结 研究针对现有SAEs训练方法在指令模型中因注意力泄漏引入梯度噪声问题,提出FAST顺序训练范式,通过与数据分布和激活模式对齐,提升重建保真度和特征可解释性,实验显示该方法效果显著优于基线。

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2506.04141 2026-07-21 cs.CV cs.CL 版本更新

MMR-V: What's Left Unsaid? A Benchmark for Multimodal Deep Reasoning in Videos

MMR-V:未言明的是什么?视频中多模态深度推理的基准测试

Kejian Zhu, Zhuoran Jin, Hongbang Yuan, Jiachun Li, Shangqing Tu, 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, Beijing, China(认知与决策智能复杂系统重点实验室,自动化研究所,中国科学院,北京,中国) School of Artificial Intelligence, University of Chinese Academy of Sciences(人工智能学院,中国科学院大学) Tsinghua University(清华大学)

AI总结 针对视频多模态推理挑战,提出MMR-V基准测试,具有长程多帧推理等特点。该基准测试含317个视频和1257个任务,实验发现当前模型多模态推理困难,最佳模型准确率低,现有增强策略收益有限,旨在推动多模态推理能力研究。

Comments Accepted at ICLR 2026. Camera-ready version

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2603.27556 2026-07-20 cs.CV 版本更新

Towards Domain-Generalized Open-Vocabulary Object Detection: A Progressive Domain-invariant Cross-modal Alignment Method

迈向领域通用的开放词汇物体检测:一种渐进领域不变跨模态对齐方法

Xiaoran Xu, Xiaoshan Yang, Jiangang Yang, Yifan Xu, Jian Liu, Changsheng Xu

机构 * MAIS, Institute of Automation, Chinese Academy of Sciences(中国科学院自动化研究所多模态人工智能系统实验室) Institute of Microelectronics, University of Chinese Academy of Sciences(中国科学院大学微电子学院) School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences(中国科学院大学前沿交叉科学学院)

AI总结 本文提出PICA方法,通过多级模糊性和信号强度课程,解决开放词汇检测中领域变化导致的跨模态空间崩溃问题,提升模型鲁棒性。

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2601.00898 2026-07-20 cs.LG cs.RO 版本更新

Dichotomous Diffusion Policy Optimization

二元扩散策略优化

Ruiming Liang, Yinan Zheng, Kexin Zheng, Tianyi Tan, Jianxiong Li, Liyuan Mao, Zhihao Wang, Guang Chen, Hangjun Ye, Jingjing Liu, Jinqiao Wang, Xianyuan Zhan

机构 * Fundation Model Research Center, Institute of Automation, Chinese Academy of Sciences(基础模型研究中心,自动化研究所,中国科学院) School of Artificial Intelligence, University of Chinese Academy of Sciences(人工智能学院,中国科学院大学) Institute for AI Industry Research (AIR), Tsinghua University(人工智能产业研究院(AIR),清华大学) The Chinese University of Hong Kong(香港中文大学) Shanghai Jiao Tong University(上海交通大学) Peking University(北京大学) Xiaomi EV(小米电动车)

AI总结 DIPOLE是一种新的RL算法,通过二元策略分解实现稳定可控的扩散策略优化,适用于复杂现实应用。

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2601.13020 2026-07-20 cs.LG cs.AI 版本更新

PASs-MoE: Mitigating Misaligned Co-drift among Router and Experts via Pathway Activation Subspaces for Continual Learning

PASs-MoE:通过路径激活子空间减轻路由器与专家之间的错位协同漂移以进行持续学习

Zhiyan Hou, Haiyun Guo, Haokai Ma, Yandu Sun, Yonghui Yang, Jinqiao Wang

机构 * 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) National University of Singapore(新加坡国立大学) Southeast University, Nanjing, China(南京东南大学) Wuhan AI Research, Wuhan, China(武汉人工智能研究院)

AI总结 研究持续指令调整中多模态大语言模型的问题,提出基于路径激活子空间的固定容量PASs - MoE - LoRA方法,含PAS引导的重新加权和PAS感知的秩稳定,实验表明该方法在准确性和抗遗忘性上优于基线和变体且不增参数。

Comments Published in the Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), Volume 1: Long Papers. 14 pages. Code is available at https://github.com/yueluoshuangtian/PASs-MoE

Journal ref Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 31959--31972, San Diego, California, United States, July 2026. Association for Computational Linguistics

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2607.13418 2026-07-17 cs.IR cs.AI 版本更新

Can We Steer the Black-Box? Towards Controllability-Centric Evaluation of Recommender Systems with Collaborative Agents

我们能操控黑箱吗?迈向以可控性为中心的协同代理推荐系统评估

Jiwen Zhou, Xiang Liu, Mingming Li, Pengbo Mo, Jiao Dai, Honglei Lv, Jizhong Han, Songlin Hu

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

AI总结 针对推荐系统缺乏可控性的问题,提出CtrlBench-Rec协同多智能体框架,形式化三个基本任务衡量可操纵性,通过实验有效量化可控性、揭示系统瓶颈,为可控推荐研究等提供首个标准化工具包。

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2607.10079 2026-07-17 cs.AI cs.CL 版本更新

MAG: A Web-Agent Benchmark and Harness for Multimodal Action and Guide Generation

MAG:用于多模态动作与引导生成的网络智能体基准测试与工具包

Chengguang Gan, Hanjun Wei, Yunhao Liang, Zhixi Cai, Qinghao Zhang, Shiwen Ni

机构 * University of Chinese Academy of Sciences(中国科学院大学) Monash University(莫纳什大学) Pusan National University(釜山国立大学) Shenzhen University of Advanced Technology(深圳先进技术大学)

AI总结 介绍MAG这一网络智能体基准测试,统一任务执行与引导写作,有基于截图的定位方案和完整工具包。用其评估模型并详细分析,还设计GRPO训练方法,提升智能体成功率与引导质量,指出当前模型任务完成率低,为后续研究提供方向。

Comments 8 pages main text, 21 pages total including appendices; 11 figures, 7 tables, 2 algorithms. Benchmark, harness, and model checkpoints to be released

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2512.12477 2026-07-17 cs.AI cs.LG 版本更新

DualHNIE: Dual-Channel Hypergraph Learning for Node Importance Estimation in Heterogeneous Knowledge Graphs

MetaHGNIE:异构知识图谱中的元路径诱导超图对比学习

Jiawen Chen, Yanyan He, Qi Shao, Mengli Wei, Duxin Chen, Wenwu Yu, Yanlong Zhao

机构 * Jiangsu Key Laboratory of Networked Collective Intelligence, School of Mathematics, Southeast University(江苏网络集体智能重点实验室,数学学院,东南大学) Jiangsu Key Laboratory of Networked Collective Intelligence, School of Cyber Science and Engineering, Southeast University(江苏网络集体智能重点实验室,网络科学与工程学院,东南大学) State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, University of Chinese Academy of Sciences(数学科学国家重点实验室,数学与系统科学研究院,中国科学院大学)

AI总结 研究异构知识图谱中节点重要性估计问题,提出DualHNIE框架,通过构建高阶知识图谱、引入互补编码器及对比对齐机制进行显式高阶建模和解缠双通道表示学习,实验验证其优于现有方法。

Comments Accepted by IEEE Transactions on Artificial Intelligence

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2510.21324 2026-07-17 cs.AI cs.MA 版本更新

CXRAgent: Director-Orchestrated Multi-Stage Reasoning for Chest X-Ray Interpretation

CXRAgent:用于胸部X光解读的由主任编排的多阶段推理

Jinhui Lou, Yan Yang, Zhou Yu, Zhenqi Fu, Weidong Han, Qingming Huang, Jun Yu

机构 * School of Computer Science, Hangzhou Dianzi University(杭州电子科技大学计算机科学学院) Department of Automation, Tsinghua University(清华大学自动化系) Department of Colorectal Medical Oncology, Zhejiang Cancer Hospital(浙江省肿瘤医院结直肠医学肿瘤科) School of Computer and Control Engineering, University of Chinese Academy of Sciences(中国科学院大学计算机与控制工程学院) 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 针对胸部X光解读中模型适应性差等问题,提出CXRAgent,通过中央主任协调多阶段,包括工具调用、诊断规划和协作决策,实验证明该方法性能出色,能提供视觉证据并适应不同临床任务。

Comments 10 pages, 4 figures, 7 Tables

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2607.12752 2026-07-16 cs.CV cs.AI 版本更新

Hallo4D: Multi-Modal Hallucination Mitigation for Consistent Spatio-Temporal Generation

Hallo4D:用于一致时空生成的多模态幻觉缓解

Hongbo Wang, Huaibo Huang, Jie Cao, Jin Liu, Haoyang Tong, Ran He

机构 * Institute of Automation, Chinese Academy of Science(中国科学院自动化研究所) University of Chinese Academy of Sciences(中国科学院大学) Shanghaitech University(上海科技大学)

AI总结 研究针对3D和4D内容生成中的时空幻觉问题,提出Hallo4D框架,利用多模态语言模型,通过生成-检测-校正范式及多种技术提升一致性,实验证明其在多样生成设置下优于基线,提供了可扩展通用的一致性感知内容生成方案。

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2607.00927 2026-07-16 cs.CV cs.AI 版本更新

Post-Training Pruning for Diffusion Transformers

扩散变换器的训练后剪枝

Chengzhi Hu, Xuewen Liu, Jing Zhang, Mengjuan Chen, Zhikai Li, Qingyi Gu

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总结 针对扩散变换器(DiTs)计算开销大的问题,提出DiT-Pruning方法,通过能量感知的显著性度量与聚类感知的剪枝粒度,在50%稀疏度下仅损失0.001 CLIP分数。

Comments 15 pages, 13 figures

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2604.23620 2026-07-16 cs.RO 版本更新

Move-Then-Operate: Behavioral Phasing for Human-Like Robotic Manipulation

移动-然后-操作:用于类人机器人操作的行为相位

Haoming Xu, Lei Lei, Jie Gu, Chu Tang, Jingmin Chen, Ruiqi Wang

机构 * Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China.(杭州高等研究院,中国科学院大学,中国杭州) University of Science and Technology of China, Hefei, China.(中国科学技术大学,中国合肥) School of Aritificial intelligence, Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China(人工智能学院,人工智能研究院,北京科技大学,中国北京)

AI总结 本文提出Move-Then-Operate框架,将机器人操作分为粗略移动和接触关键交互两个阶段,通过双专家策略提升操作精度与效率。

Comments 15 pages, 10 figures

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2509.22415 2026-07-16 cs.CV cs.AI 版本更新

Evidence Recomposition and Predictive Context Residualization for Visual Attribution in Multimodal Large Language Models

多模态大语言模型中用于视觉归因的证据重组与预测上下文残差化

Jiawei Liang, Jianjie Huang, Ruoyu Chen, Xianghao Jiao, Siyuan Liang, Shiming Liu, Xiaochun Cao

机构 * Shenzhen Campus of Sun Yat-sen University(中山大学深圳校区) Zhongguancun Academy(中关村学院) University of Chinese Academy of Sciences(中国科学院大学) Nanyang Technological University(南洋理工大学) Department of Mechanical Engineering, Imperial College London(伦敦帝国理工学院机械工程系)

AI总结 研究多模态大语言模型token级视觉证据难检查问题,提出基于证据重组和预测上下文残差化的ERCR框架,经实验验证该框架能改善目标token视觉证据、减轻上下文干扰,为视觉证据检查提供实用改进。

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2502.16167 2026-07-16 cs.CV cs.AI 版本更新

PersGuard: Preventing Malicious Personalization in Text-to-Image Diffusion Models via Model Backdoors

PersGuard:通过模型后门防止文本到图像扩散模型中的恶意个性化

Xinwei Liu, Xiaojun Jia, Yuan Xun, Hua Zhang, Xiaochun Cao

机构 * Institute of Information Engineering, Chinese Academy of Sciences(信息工程研究所,中国科学院) School of Cyber Security, University of Chinese Academy of Sciences(中国科学院大学网络安全学院) BraneMatrix AI School of Cyber Science and Technology, Shenzhen Campus, Sun Yat-sen University(中山大学深圳校区计算机科学与技术学院)

AI总结 研究针对文本到图像扩散模型个性化引发的隐私问题,提出PersGuard框架,通过在模型发布前嵌入保护后门,结合统一优化问题制定后门注入,经实验验证其在隐私保护方面优于现有基于扰动的方法。

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2607.11638 2026-07-15 cs.RO 版本更新

DA-Nav: Direction-Aware City-Scale Vision-Language Navigation

DA-Nav:方向感知的城市规模视觉语言导航

Ye Yuan, Kehan Chen, Xinqiang Yu, Wentao Xu, Heng Wang, Libo Huang, Chuanguang Yang, Yan Huang, Jiawei He, Zhulin An

机构 * School of Information Science and Technology, ShanghaiTech University(上海科技大学信息科学与技术学院) Institute of Computing Technology, Chinese Academy of Sciences(中国科学院计算技术研究所) State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences(中国科学院计算技术研究所人工智能安全国家重点实验室) National 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) XYZ Embodied AI(XYZ具身人工智能)

AI总结 研究城市规模户外导航难题,提出DA-Nav框架,利用商业导航工具方向指示,经思维链推理实现轨迹恢复,引入ReDA数据集。实验表明其在未见环境成功率高,优于现有方法,还能适应多种机器人实现稳定户外导航。

Comments 9 pages, 8 figures

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2602.05513 2026-07-15 cs.RO cs.AI 版本更新

DECO: Decoupled Multimodal Diffusion Transformer for Bimanual Dexterous Manipulation with a Plugin Tactile Adapter

DECO:解耦多模态扩散变压器用于配备插件触觉适配器的双臂灵巧操作

Xukun Li, Yu Sun, Lei Zhang, Bosheng Huang, Yibo Peng, Yuan Meng, Haojun Jiang, Shaoxuan Xie, Guocai Yao, Alois Knoll, Zhenshan Bing, Xinlong Wang, Zhenguo Sun

机构 * Beijing Academy of Artificial Intelligence, Beijing, China(北京人工智能研究院) School of Computation, Information and Technology, Technical University of Munich, Garching, Germany(慕尼黑技术大学计算与信息学院) Department of Shenyang Institute of Computing Technology, University of Chinese Academy of Sciences, Beijing, China(中国科学院沈阳计算技术研究所部门) State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China(新型软件技术国家重点实验室) Department of Computer Science and Technology, Tsinghua University, Beijing, China(清华大学计算机科学与技术系)

AI总结 DECO通过解耦多模态输入和触觉适配器,实现了双臂灵巧操作的高效整合与高成功率

Comments 17 pages, 8 figures. Project Page: https://baai-humanoid.github.io/DECO-webpage/

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2312.17670 2026-07-15 cs.CV cs.LG q-bio.QM q-bio.TO 版本更新

The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography

TopCoW挑战——用于CT和MR血管造影的拓扑感知Willis环分割

Kaiyuan Yang, Fabio Musio, Yihui Ma, Norman Juchler, Johannes C. Paetzold, Rami Al-Maskari, Luciano Höher, Hongwei Bran Li, Ibrahim Ethem Hamamci, Anjany Sekuboyina, Suprosanna Shit, Houjing Huang, Chinmay Prabhakar, Ezequiel de la Rosa, Bastian Wittmann, Diana Waldmannstetter, Florian Kofler, Fernando Navarro, Martin J. Menten, Ivan Ezhov, Daniel Rueckert, Iris N. Vos, Ynte M. Ruigrok, Birgitta K. Velthuis, Hugo J. Kuijf, Pengcheng Shi, Wei Liu, Ting Ma, Maximilian R. Rokuss, Yannick Kirchhoff, Fabian Isensee, Klaus Maier-Hein, Chengcheng Zhu, Huilin Zhao, Philippe Bijlenga, Julien Hämmerli, Catherine Wurster, Laura Westphal, Jeroen Bisschop, Elisa Colombo, Hakim Baazaoui, Hannah-Lea Handelsmann, Andrew Makmur, James Hallinan, Amrish Soundararajan, Benedikt Wiestler, Jan S. Kirschke, Evamaria O. Riedel, Roland Wiest, Emmanuel Montagnon, Laurent Letourneau-Guillon, Kwanseok Oh, Dahye Lee, Orhun Utku Aydin, Adam Hilbert, Jana Rieger, Dimitrios Rallios, Satoru Tanioka, Alexander Koch, Dietmar Frey, Abdul Qayyum, Moona Mazher, Steven Niederer, Nico Disch, Julius C. Holzschuh, Dominic LaBella, Francesco Galati, Daniele Falcetta, Maria A. Zuluaga, Chaolong Lin, Haoran Zhao, Zehan Zhang, Minghui Zhang, Xin You, Hanxiao Zhang, Guang-Zhong Yang, Yun Gu, Sinyoung Ra, Jongyun Hwang, Hyunjin Park, Junqiang Chen, Marek Wodzinski, Henning Müller, Nesrin Mansouri, Florent Autrusseau, Cansu Yalcin, Rachika E. Hamadache, Clara Lisazo, Joaquim Salvi, Adrià Casamitjana, Xavier Lladó, Uma Maria Lal-Trehan Estrada, Valeriia Abramova, Luca Giancardo, Arnau Oliver, Paula Casademunt, Adrian Galdran, Matteo Delucchi, Oscar Camara, Jialu Liu, Haibin Huang, Yue Cui, Zehang Lin, Yusheng Liu, Shunzhi Zhu, Tatsat R. Patel, Adnan H. Siddiqui, Vincent M. Tutino, Maysam Orouskhani, Huayu Wang, Mahmud Mossa-Basha, Yuki Sato, Sven Hirsch, Susanne Wegener, Bjoern Menze

机构 * Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland Institute of Computational Life Sciences, Zurich University of Applied Sciences (ZHAW), Waedenswil, Switzerland Department of Neuroradiology, University Hospital of Zurich, Zurich, Switzerland Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China Department of Radiology at Weill Cornell Medicine, Cornell University, New York, USA Institute for Tissue Engineering School of Computation, Information Technology, Technical University of Munich, Germany Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, USA School of Medicine Health, TUM Klinikum, Technical University of Munich, Germany Munich Center for Machine Learning, Munich, Germany Department of Computing, Imperial College London, London, UK Image Sciences Institute, UMC Utrecht, Utrecht, The Netherlands Department of Neurology Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands Electronic \& Information Engineering School, Harbin Institute of Technology (Shenzhen), China Peng Cheng Laboratory, Shenzhen, China Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany Faculty of Mathematics Computer Science, Heidelberg University, Germany Helmholtz Imaging, German Cancer Research Center, Heidelberg, Germany Data Science School for Health, Karlsruhe/Heidelberg, Germany Learning Group, Department of Radiation Oncology, Heidelberg University Hospital Department of Radiology, University of Washington, Seattle, WA, USA Department of Radiology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China Department of Clinical Neurosciences, Division of Neurosurgery, Geneva University Hospitals, Geneva, Switzerland Department of Neurology, University Hospital of Zurich, Zurich, Switzerland Department of Physiology, University of Toronto, Canada Department of Neurosurgery, University Hospital of Zurich, Zurich, Switzerland Department of Diagnostic Imaging, National University Hospital, Singapore University of Chicago, USA Department of Diagnostic Interventional Neuroradiology, University Hospital Berne University of Berne, Berne, Switzerland Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), Montréal, Québec, Canada DEEPNOID Inc., Seoul, South Korea Department of Artificial Intelligence, Korea University, Seoul, South Korea Charité Lab for AI in Medicine (CLAIM), Charité Universitätsmedizin Berlin, Berlin, Germany Lung Institute, Faculty of Medicine, Imperial College London, London, UK Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA Institute of Medical Technology, Peking University Health Science Center, Beijing, China Hangzhou Genlight MedTech Co., Ltd., China Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China Department of Automation, Shanghai Jiao Tong University, Shanghai, China Department of Artificial Intelligence, Sungkyunkwan University, Seoul, South Korea Department of Electrical Computer Engineering, Sungkyunkwan University, Seoul, South Korea Shanghai MediWorks Precision Instruments Co., Ltd., China Institute of Informatics, HES-SO Valais-Wallis, Switzerland Department of Measurement Electronics, AGH University of Krakow, Poland Laboratoire de Thermique et Energie de Nantes (LTeN), Université Nantes, Polytech’Nantes, Nantes, France Research Institute of Computer Vision Center for Precision Health, McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, USA Physense, BCN-Medtech, Department of Communication Information Technologies, Universitat Pompeu Fabra, Barcelona, Spain Department of Mathematical Modeling Machine Learning, University of Zurich, Zurich, Switzerland Laboratory of Brain Atlas Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China School of Computer Information Engineering, Xiamen University of Technology, Xiamen, China Vascular Research Center, University at Buffalo, NY, USA Department of Pathology Anatomical Sciences, University at Buffalo, NY, USA Department of Neurosurgery, University at Buffalo, NY, USA LPIXEL Inc., Tokyo, Japan

AI总结 组织TopCoW基准挑战,发布含125对MRA和CTA扫描的注释数据集,参与者提交CoW分割和变体分类算法,经评估,最佳算法在多任务中表现出色,证明CoW分割算法对下游临床应用有可解释性效用。

Comments Summary paper for the TopCoW Challenge: 4 figures, 1 table, and supplementary material in appendix. Accepted for publication in NEJM AI. Datasets and best-performing algorithm Dockers are available at https://zenodo.org/records/15692630 and https://zenodo.org/records/15665435

Journal ref NEJM AI 2026;3(8)

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2505.16157 2026-07-15 cs.CV 版本更新

Fast and Accurate Image Restoration and Generation with Rank Enhanced Linear Attention

基于秩增强线性注意力的快速准确图像恢复与生成

Yuang Ai

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

AI总结 研究旨在解决Transformer自注意力二次复杂度问题,提出秩增强线性注意力(RELA)及高效视觉Transformer(LAformer),通过集成深度卷积丰富特征表示,经实验验证LAformer在图像恢复和生成任务中性能优且计算高效。

Comments Code: https://github.com/shallowdream204/LAformer

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2607.05241 2026-07-14 cs.RO 版本更新

GelNeuro: A Sensing-Computing Integrated Neuromorphic Tactile System for Texture Recognition

GelNeuro:面向纹理识别的感算融合神经形态触觉系统

Luoyang Bian, Xinpan Meng, Zhenghua Ma, Houcheng Li, Long Cheng

机构 * State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences(中国科学院自动化研究所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)

AI总结 针对现有神经形态触觉系统依赖主机预处理的问题,提出GelNeuro感算融合系统,搭配硬件感知权重钳位策略,实现低功耗高准确率的端侧纹理识别。

Comments The authors withdraw this preprint as the work requires substantial revision and additional validation. The current version is not suitable for public dissemination, and further development of the research is needed

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2606.24844 2026-07-14 cs.CV 版本更新

Bridging the Manifold Gap: Riemannian Residual Line Search for One-Step Image Editing

弥合流形差距:黎曼残差线搜索用于一步图像编辑

Hongzhu Yi, Zhongtian Luo, Tong Li, Yiyan Fan, Jungang Xu

机构 * UCAS(中国科学院大学) WashU(华盛顿大学) SHU(上海大学)

AI总结 针对一步扩散编辑中固定更新强度无法兼顾目标提示和源图像保真度的问题,提出黎曼残差线搜索方法,通过局部时间曲率估计和残差路径选择,在PIE-Bench++上达到SOTA。

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2605.18601 2026-07-14 cs.CV 版本更新

Incantation: Natural Language as the Action Interface for Multi-Entity Video World Models

Incantation: 自然语言作为多实体视频世界模型的动作接口

Shangwen Zhu, Qianyu Peng, Zhao Pu, Zhilei Shu, Xiangrui Ke, Zhaohu Xing, Zizhao Tong, Zeqing Wang, Xinyu Cui, Zian Zheng, Huangji Wang, Jian Zhao, Yeying Jin, Fan Cheng, Ruili Feng

机构 * SJTU(上海交通大学) NVIDIA Research(英伟达研究) USTC(中国科学技术大学) UCAS(乌兹别克斯坦科学院) NUS(新加坡国立大学) UWaterloo(滑铁卢大学) HKUST(香港理工大学) HKU(香港大学) ZGCA(浙江大学)

AI总结 本研究提出了一种基于自然语言的动作接口,用于多实体视频世界模型,解决了传统接口在细粒度多实体控制和跨实体、跨世界泛化能力上的不足,通过引入自然语言条件化实现了更强大的表达能力。

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2602.23024 2026-07-14 cs.RO 版本更新

InCoM: Intent-Driven Perception and Structured Coordination for Mobile Manipulation

InCoM:基于意图的感知与结构化协调用于移动操作

Jiahao Liu, Cui Wenbo, Zhongpu Xia, Yongliang Wang, Haoran Li, Dongbin Zhao

机构 * Institute of Automation, Chinese Academy of Sciences(中国科学院自动化研究所) School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences(中国科学院大学先进交叉学科学院) AnyverseDynamics

AI总结 InCoM通过意图驱动的感知和结构化协调框架,解决移动操作中基座与机械臂动作耦合及感知注意力分配问题,提升任务成功率。

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