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Trajectory Densification and Depth from Perspective-based Blur | In the absence of a mechanical stabilizer, the camera undergoes inevitable rotational dynamics during capturing, which induces perspective-based blur especially under long-exposure scenarios. From an optical standpoint, perspective-based blur is depth-position-dependent: objects residing at distinct spatial locations i... | cs.CV | cs.CV | 2025-12-09 | 2512.08627v1 | http://arxiv.org/abs/2512.08627v1 | 0.95195 | 0.949373 | 0.935895 | false | null | Tianchen Qiu; Qirun Zhang; Jiajian He; Zhengyue Zhuge; Jiahui Xu; Yueting Chen | 6 | 22 | 22 | 22 | null | 22 | 2,980 | 2,980 | 2,980 | 32 | 32 | false | true | 0 | false | ; ; ; ; ; Shanghai University; Shanghai Jiao Tong University; Shanghai Chest Hospital; Seattle University; Zhejiang University | ; ; ; ; ; https://openalex.org/A5003982996 | Tianchen Qiu; Qirun Zhang; Jiajian He; Zhengyue Zhuge; Jiahui Xu; Yueting Chen | ; ; ; ; ; https://orcid.org/0000-0002-2759-9784 | ; ; ; ; ; 22 | ; ; ; ; ; 59 | ; ; ; ; ; 2980 | ; ; ; ; ; 230 | ; ; ; ; ; 3.1298701298701297 | ; ; ; ; ; 33 | ; ; ; ; ; 1993 | ; ; ; ; ; 2025 | ; ; ; ; ; 32 | ; ; ; ; ; 95 | ; ; ; ; ; 13.045 | ; ; ; ; ; 562 |
Towards Visual Re-Identification of Fish using Fine-Grained Classification for Electronic Monitoring in Fisheries | Accurate fisheries data are crucial for effective and sustainable marine resource management. With the recent adoption of Electronic Monitoring (EM) systems, more video data is now being collected than can be feasibly reviewed manually. This paper addresses this challenge by developing an optimized deep learning pipeli... | cs.CV | cs.CV | 2025-12-09 | 2512.08400v1 | http://arxiv.org/abs/2512.08400v1 | -2.251374 | 0.939626 | 0.925051 | true | Comment match: The paper has been accepted for publication at Northern Lights Deep Learning (NLDL) Conference 2025 | Samitha Nuwan Thilakarathna; Ercan Avsar; Martin Mathias Nielsen; Malte Pedersen | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | null | null | null | null | 0 | false | Yamanashi Prefectural Fisheries Technology Center; Department of Fisheries; ES Technology (United Kingdom) | https://openalex.org/A3040899779 | Avsar Ercan | 1 | 0 | 1 | 5 | 0.0 | ||||||||
SPROCKET: Extending ROCKET to Distance-Based Time-Series Transformations With Prototypes | Classical Time Series Classification algorithms are dominated by feature engineering strategies. One of the most prominent of these transforms is ROCKET, which achieves strong performance through random kernel features. We introduce SPROCKET (Selected Prototype Random Convolutional Kernel Transform), which implements a... | cs.LG | cs.LG | 2025-12-09 | 2512.08246v1 | http://arxiv.org/abs/2512.08246v1 | 0.259915 | 0.932883 | 0.925819 | false | null | Nicholas Harner | 1 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | Nicholas Harner | |||||||||||||||
From Cells to Survival: Hierarchical Analysis of Cell Inter-Relations in Multiplex Microscopy for Lung Cancer Prognosis | The tumor microenvironment (TME) has emerged as a promising source of prognostic biomarkers. To fully leverage its potential, analysis methods must capture complex interactions between different cell types. We propose HiGINE -- a hierarchical graph-based approach to predict patient survival (short vs. long) from TME ch... | cs.CV | cs.CV | 2025-12-09 | 2512.08572v1 | http://arxiv.org/abs/2512.08572v1 | -2.662529 | 0.937067 | 0.931133 | false | null | Olle Edgren Schüllerqvist; Jens Baumann; Joakim Lindblad; Love Nordling; Artur Mezheyeuski; Patrick Micke; Nataša Sladoje | 7 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; ; ; ; | ; ; ; ; ; ; | Olle Edgren Schüllerqvist; Jens Baumann; Joakim Lindblad; Love Nordling; Artur Mezheyeuski; Patrick Micke; Nataša Sladoje | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; |
Fully Decentralized Certified Unlearning | Machine unlearning (MU) seeks to remove the influence of specified data from a trained model in response to privacy requests or data poisoning. While certified unlearning has been analyzed in centralized and server-orchestrated federated settings (via guarantees analogous to differential privacy, DP), the decentralized... | cs.LG | cs.LG | 2025-12-09 | 2512.08443v1 | http://arxiv.org/abs/2512.08443v1 | 1.076929 | 0.955837 | 0.943076 | false | null | Hithem Lamri; Michail Maniatakos | 2 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; | ; | Hithem Lamri; Michail Maniatakos | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; |
What really matters for person re-identification? A Mixture-of-Experts Framework for Semantic Attribute Importance | State-of-the-art person re-identification methods achieve impressive accuracy but remain largely opaque, leaving open the question: which high-level semantic attributes do these models actually rely on? We propose MoSAIC-ReID, a Mixture-of-Experts framework that systematically quantifies the importance of pedestrian at... | cs.CV | cs.CV | 2025-12-09 | 2512.08697v1 | http://arxiv.org/abs/2512.08697v1 | 0.639082 | 0.952075 | 0.940744 | false | null | Athena Psalta; Vasileios Tsironis; Konstantinos Karantzalos | 2 | 31 | 17 | 3 | 3 | 31 | 4,015 | 2,050.5 | 4,101 | 22 | 12.5 | true | true | 14 | false | National Technical University of Athens; National Technical University of Athens | https://openalex.org/A5022759615; https://openalex.org/A5064461457 | Vasileios Tsironis; Κωνσταντίνος Καράντζαλος | ; https://orcid.org/0000-0001-8730-6245 | 3; 31 | 2; 64 | 86; 4015 | 14; 211 | 6.583333333333333; 2.4035087719298245 | 4; 23 | 2022; 2003 | 2025; 2025 | 3; 22 | 12; 88 | 5.8; 19.445 | 63; 971 |
Soil Compaction Parameters Prediction Based on Automated Machine Learning Approach | Soil compaction is critical in construction engineering to ensure the stability of structures like road embankments and earth dams. Traditional methods for determining optimum moisture content (OMC) and maximum dry density (MDD) involve labor-intensive laboratory experiments, and empirical regression models have limite... | cs.AI | cs.AI | 2025-12-09 | 2512.08343v1 | http://arxiv.org/abs/2512.08343v1 | -3.5017 | 0.942007 | 0.909807 | true | Journal Ref: Proc. of the 13th Int. Symp. on Intelligent Manufacturing and Service Systems, pp. 571-578, 2025, ISBN 978-625-00-3472-9 | Caner Erden; Alparslan Serhat Demir; Abdullah Hulusi Kokcam; Talas Fikret Kurnaz; Ugur Dagdeviren | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | null | null | 0 | false | Sakarya University; Sakarya Uygulamalı Bilimler Üniversitesi; Sakarya University; Sakarya University; Mersin Üniversitesi; Dumlupinar University | https://openalex.org/A2315251305; https://openalex.org/A2921899262; https://openalex.org/A2600700350; https://openalex.org/A2793238053; https://openalex.org/A2504784148 | Caner Erden; Alparslan Serhat Demir; Abdullah Hulusi Kökçam; Talas Fikret Kurnaz; Ugur Dagdeviren | https://orcid.org/0000-0002-7311-862X; https://orcid.org/0000-0003-3415-8116; https://orcid.org/0000-0002-4757-1594; ; | 0; 0; 0; 0; 0 | 0; 0; 0; 0; 0 | 0; 0; 0; 0; 0 | 5; 4; 7; 4; 2 | 0.0; 0.0; 0.0; 0.0; 0.0 | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; |
MIRAGE: Misleading Retrieval-Augmented Generation via Black-box and Query-agnostic Poisoning Attacks | Retrieval-Augmented Generation (RAG) systems enhance LLMs with external knowledge but introduce a critical attack surface: corpus poisoning. While recent studies have demonstrated the potential of such attacks, they typically rely on impractical assumptions, such as white-box access or known user queries, thereby under... | cs.CR | cs.CR | 2025-12-09 | 2512.08289v1 | http://arxiv.org/abs/2512.08289v1 | 0.63841 | 0.960792 | 0.95153 | false | null | Tailun Chen; Yu He; Yan Wang; Shuo Shao; Haolun Zheng; Zhihao Liu; Jinfeng Li; Yuefeng Chen; Zhixuan Chu; Zhan Qin | 10 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; | Tailun Chen; Yu He; Yan Wang; Shuo Shao; Haolun Zheng; Zhihao Liu; Jinfeng Li; Yuefeng Chen; Zhixuan Chu; Zhan Qin | ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; |
Scalable Back-End for an AI-Based Diabetes Prediction Application | The rising global prevalence of diabetes necessitates early detection to prevent severe complications. While AI-powered prediction applications offer a promising solution, they require a responsive and scalable back-end architecture to serve a large user base effectively. This paper details the development and evaluati... | cs.AI; cs.SE | cs.AI | 2025-12-09 | 2512.08147v1 | http://arxiv.org/abs/2512.08147v1 | -3.50093 | 0.911728 | 0.899331 | true | Comment match: This paper was accepted and presented at the 2025 IEEE International Conference on Data and Software Engineering (ICoDSE) on 28 October 2025 in Batam, Indonesia, and is currently awaiting publication | Henry Anand Septian Radityo; Bernardus Willson; Reynard Tanadi; Latifa Dwiyanti; Saiful Akbar | 5 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; ; | ; ; ; ; | Henry Anand Septian Radityo; Bernardus Willson; Reynard Tanadi; Latifa Dwiyanti; Saiful Akbar | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; |
A Practical Guide for Designing, Developing, and Deploying Production-Grade Agentic AI Workflows | Agentic AI marks a major shift in how autonomous systems reason, plan, and execute multi-step tasks. Unlike traditional single model prompting, agentic workflows integrate multiple specialized agents with different Large Language Models(LLMs), tool-augmented capabilities, orchestration logic, and external system intera... | cs.AI | cs.AI | 2025-12-09 | 2512.08769v1 | http://arxiv.org/abs/2512.08769v1 | -2.614305 | 0.95895 | 0.950017 | false | null | Eranga Bandara; Ross Gore; Peter Foytik; Sachin Shetty; Ravi Mukkamala; Abdul Rahman; Xueping Liang; Safdar H. Bouk; Amin Hass; Sachini Rajapakse; Ng Wee Keong; Kasun De Zoysa; Aruna Withanage; Nilaan Loganathan | 14 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; ; ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; ; ; ; ; | Eranga Bandara; Ross Gore; Peter Foytik; Sachin Shetty; Ravi Mukkamala; Abdul Rahman; Xueping Liang; Safdar H. Bouk; Amin Hass; Sachini Rajapakse; Ng Wee Keong; Kasun De Zoysa; Aruna Withanage; Nilaan Loganathan | ; ; ; ; ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; ; ; ; ; |
Geometry-Aware Sparse Depth Sampling for High-Fidelity RGB-D Depth Completion in Robotic Systems | Accurate three-dimensional perception is essential for modern industrial robotic systems that perform manipulation, inspection, and navigation tasks. RGB-D and stereo vision sensors are widely used for this purpose, but the depth maps they produce are often noisy, incomplete, or biased due to sensor limitations and env... | cs.CV; cs.RO | cs.CV | 2025-12-09 | 2512.08229v1 | http://arxiv.org/abs/2512.08229v1 | -3.12779 | 0.949258 | 0.93961 | false | null | Tony Salloom; Dandi Zhou; Xinhai Sun | 3 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; | ; ; | Tony Salloom; Dandi Zhou; Xinhai Sun | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; |
SATGround: A Spatially-Aware Approach for Visual Grounding in Remote Sensing | Vision-language models (VLMs) are emerging as powerful generalist tools for remote sensing, capable of integrating information across diverse tasks and enabling flexible, instruction-based interactions via a chat interface. In this work, we enhance VLM-based visual grounding in satellite imagery by proposing a novel st... | cs.CV | cs.CV | 2025-12-09 | 2512.08881v1 | http://arxiv.org/abs/2512.08881v1 | 0.253698 | 0.937827 | 0.934666 | false | null | Aysim Toker; Andreea-Maria Oncescu; Roy Miles; Ismail Elezi; Jiankang Deng | 5 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; ; | ; ; ; ; | Aysim Toker; Andreea-Maria Oncescu; Roy Miles; Ismail Elezi; Jiankang Deng | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; |
Wavelet-Accelerated Physics-Informed Quantum Neural Network for Multiscale Partial Differential Equations | This work proposes a wavelet-based physics-informed quantum neural network framework to efficiently address multiscale partial differential equations that involve sharp gradients, stiffness, rapid local variations, and highly oscillatory behavior. Traditional physics-informed neural networks (PINNs) have demonstrated s... | cs.LG; math.AP; math.QA | cs.LG | 2025-12-09 | 2512.08256v1 | http://arxiv.org/abs/2512.08256v1 | -2.267665 | 0.94296 | 0.932374 | false | null | Deepak Gupta; Himanshu Pandey; Ratikanta Behera | 3 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; | ; ; | Deepak Gupta; Himanshu Pandey; Ratikanta Behera | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; |
Democratizing ML for Enterprise Security: A Self-Sustained Attack Detection Framework | Despite advancements in machine learning for security, rule-based detection remains prevalent in Security Operations Centers due to the resource intensiveness and skill gap associated with ML solutions. While traditional rule-based methods offer efficiency, their rigidity leads to high false positives or negatives and ... | cs.CR; cs.AI | cs.CR | 2025-12-09 | 2512.08802v1 | http://arxiv.org/abs/2512.08802v1 | -3.498886 | 0.949088 | 0.939132 | true | Comment match: published in CAMLIS 2025, https://www.camlis.org/ | Sadegh Momeni; Ge Zhang; Birkett Huber; Hamza Harkous; Sam Lipton; Benoit Seguin; Yanis Pavlidis | 7 | 81 | 81 | 81 | null | null | 27,863 | 27,863 | 27,863 | 40 | 40 | false | true | 0 | true | ; Jiangxi University of Traditional Chinese Medicine; Ningbo University; Ministry of Public Security of the People's Republic of China; Zhongyuan University of Technology; Shandong University of Traditional Chinese Medicine; Hong Kong Baptist University; Xidian University; Hunan University; Northwestern Polytechnical U... | ; https://openalex.org/A5100326074; ; ; ; ; | Sadegh Momeni; Ge Zhang; Birkett Huber; Hamza Harkous; Sam Lipton; Benoit Seguin; Yanis Pavlidis | ; https://orcid.org/0000-0002-7807-7695; ; ; ; ; | ; 81; ; ; ; ; | ; 456; ; ; ; ; | ; 27863; ; ; ; ; | ; 974; ; ; ; ; | ; 5.874418604651162; ; ; ; ; | ; 41; ; ; ; ; | ; 1985; ; ; ; ; | ; 2025; ; ; ; ; | ; 40; ; ; ; ; | ; 264; ; ; ; ; | ; 5.17; ; ; ; ; | ; 69; ; ; ; ; |
PR-CapsNet: Pseudo-Riemannian Capsule Network with Adaptive Curvature Routing for Graph Learning | Capsule Networks (CapsNets) show exceptional graph representation capacity via dynamic routing and vectorized hierarchical representations, but they model the complex geometries of real\-world graphs poorly by fixed\-curvature space due to the inherent geodesical disconnectedness issues, leading to suboptimal performan... | cs.LG; cs.AI | cs.LG | 2025-12-09 | 2512.08218v1 | http://arxiv.org/abs/2512.08218v1 | 1.440715 | 0.956511 | 0.941412 | true | Comment match: To appear in WSDM 2026 (ACM International Conference on Web Search and Data Mining) | Ye Qin; Jingchao Wang; Yang Shi; Haiying Huang; Junxu Li; Weijian Liu; Tinghui Chen; Jinghui Qin | 8 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | Ye Qin; Jingchao Wang; Yang Shi; Haiying Huang; Junxu Li; Weijian Liu; Tinghui Chen; Jinghui Qin | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; |
See-Control: A Multimodal Agent Framework for Smartphone Interaction with a Robotic Arm | Recent advances in Multimodal Large Language Models (MLLMs) have enabled their use as intelligent agents for smartphone operation. However, existing methods depend on the Android Debug Bridge (ADB) for data transmission and action execution, limiting their applicability to Android devices. In this work, we introduce th... | cs.AI; cs.CV; cs.HC | cs.AI | 2025-12-09 | 2512.08629v1 | http://arxiv.org/abs/2512.08629v1 | 1.097584 | 0.945316 | 0.937418 | false | null | Haoyu Zhao; Weizhong Ding; Yuhao Yang; Zheng Tian; Linyi Yang; Kun Shao; Jun Wang | 7 | 227 | 94.333333 | 17 | null | 227 | 377,517 | 128,760 | 386,280 | 38 | 24.333333 | false | true | 94.238468 | true | ; ; ; ; Westlake University; Xi'an University of Architecture and Technology; Dalian University of Technology; China University of Geosciences; Shaoguan University; Dalian University; Ministry of Health and Welfare; Tsinghua University; Chinese Academy of Tropical Agricultural Sciences; Jiangnan University; Nanjing Uni... | ; ; ; ; https://openalex.org/A5035082722; https://openalex.org/A5032730804; https://openalex.org/A5111964102 | Haoyu Zhao; Weizhong Ding; Yuhao Yang; Zheng Tian; Linyi Yang; Kun Shao; Jun Wang | ; ; ; ; https://orcid.org/0000-0003-0667-7349; https://orcid.org/0000-0001-6876-4697; https://orcid.org/0009-0008-8475-2934 | ; ; ; ; 17; 39; 227 | ; ; ; ; 21; 49; 1564 | ; ; ; ; 2550; 6213; 377517 | ; ; ; ; 73; 94; 3176 | ; ; ; ; 44.625; 5.448275862068965; 4.299424184261037 | ; ; ; ; 10; 27; 39 | ; ; ; ; 2016; 1999; 1987 | ; ; ; ; 2025; 2025; 2025 | ; ; ; ; 9; 26; 38 | ; ; ; ; 59; 32; 764 | ; ; ; ; 36.33783783783784; 65.90526315789474; 2.645 | ; ; ; ; 1719; 615; 28 |
Reinforcement Learning From State and Temporal Differences | TD($λ$) with function approximation has proved empirically successful for some complex reinforcement learning problems. For linear approximation, TD($λ$) has been shown to minimise the squared error between the approximate value of each state and the true value. However, as far as policy is concerned, it is error in th... | cs.LG | cs.LG | 2025-12-09 | 2512.08855v1 | http://arxiv.org/abs/2512.08855v1 | -2.282495 | 0.950343 | 0.943512 | false | null | Lex Weaver; Jonathan Baxter | 2 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; | ; | Lex Weaver; Jonathan Baxter | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; |
Chat with UAV -- Human-UAV Interaction Based on Large Language Models | The future of UAV interaction systems is evolving from engineer-driven to user-driven, aiming to replace traditional predefined Human-UAV Interaction designs. This shift focuses on enabling more personalized task planning and design, thereby achieving a higher quality of interaction experience and greater flexibility, ... | cs.RO; cs.AI | cs.RO | 2025-12-09 | 2512.08145v1 | http://arxiv.org/abs/2512.08145v1 | -3.493536 | 0.944237 | 0.934391 | false | null | Haoran Wang; Zhuohang Chen; Guang Li; Bo Ma; Chuanghuang Li | 5 | 38 | 38 | 38 | 38 | null | 5,473 | 5,473 | 5,473 | 24 | 24 | false | true | 0 | false | Qingdao University; Hebei Medical University; Nanjing University of Chinese Medicine; Shanghai University of Engineering Science; Southwest University; Sun Yat-sen University; Guangdong Pharmaceutical University; Jilin University; China University of Mining and Technology; Academy of Military Medical Sciences; Shantou ... | https://openalex.org/A5100324821; ; ; ; | Haoran Wang; Zhuohang Chen; Guang Li; Bo Ma; Chuanghuang Li | https://orcid.org/0000-0002-1630-4813; ; ; ; | 38; ; ; ; | 118; ; ; ; | 5473; ; ; ; | 333; ; ; ; | 5.05625; ; ; ; | 25; ; ; ; | 2001; ; ; ; | 2025; ; ; ; | 24; ; ; ; | 193; ; ; ; | 7.165; ; ; ; | 108; ; ; ; |
Prismatic World Model: Learning Compositional Dynamics for Planning in Hybrid Systems | Model-based planning in robotic domains is fundamentally challenged by the hybrid nature of physical dynamics, where continuous motion is punctuated by discrete events such as contacts and impacts. Conventional latent world models typically employ monolithic neural networks that enforce global continuity, inevitably ov... | cs.AI; cs.RO | cs.AI | 2025-12-09 | 2512.08411v1 | http://arxiv.org/abs/2512.08411v1 | 1.433895 | 0.948429 | 0.944804 | false | null | Mingwei Li; Xiaoyuan Zhang; Chengwei Yang; Zilong Zheng; Yaodong Yang | 5 | 37 | 33.5 | 30 | null | 30 | 5,106 | 4,398.5 | 8,797 | 22 | 17.5 | false | true | 3.5 | false | ; ; ; Fudan University; Beijing University of Technology; Beijing Academy of Artificial Intelligence; Materials Science & Engineering; Collaborative Innovation Center of Chemistry for Energy Materials; Beijing Institute for General Artificial Intelligence; King University; Peking University; Beijing Academy of Artifici... | ; ; ; https://openalex.org/A5055357816; https://openalex.org/A5090073634 | Mingwei Li; Xiaoyuan Zhang; Chengwei Yang; Zilong Zheng; Yaodong Yang | ; ; ; https://orcid.org/0000-0003-4310-2755; https://orcid.org/0000-0001-8132-5613 | ; ; ; 37; 30 | ; ; ; 70; 66 | ; ; ; 5106; 3691 | ; ; ; 180; 302 | ; ; ; 8.369565217391305; 5.049689440993789 | ; ; ; 23; 14 | ; ; ; 2003; 2012 | ; ; ; 2025; 2025 | ; ; ; 22; 13 | ; ; ; 109; 226 | ; ; ; 28.02173913043478; 6.42 | ; ; ; 902; 101 |
Team-Aware Football Player Tracking with SAM: An Appearance-Based Approach to Occlusion Recovery | Football player tracking is challenged by frequent occlusions, similar appearances, and rapid motion in crowded scenes. This paper presents a lightweight SAM-based tracking method combining the Segment Anything Model (SAM) with CSRT trackers and jersey color-based appearance models. We propose a team-aware tracking sys... | cs.CV | cs.CV | 2025-12-09 | 2512.08467v1 | http://arxiv.org/abs/2512.08467v1 | -2.912146 | 0.922849 | 0.91788 | false | null | Chamath Ranasinghe; Uthayasanker Thayasivam | 2 | 11 | 11 | 11 | null | 11 | 367 | 367 | 367 | 19 | 19 | false | false | 0 | false | ; University of Moratuwa | ; https://openalex.org/A5049911731 | Chamath Ranasinghe; Uthayasanker Thayasivam | ; https://orcid.org/0000-0002-3936-8174 | ; 11 | ; 11 | ; 367 | ; 88 | ; 0.45454545454545453 | ; 20 | ; 2006 | ; 2025 | ; 19 | ; 32 | ; 3.978494623655914 | ; 41 |
Disrupting Hierarchical Reasoning: Adversarial Protection for Geographic Privacy in Multimodal Reasoning Models | Multi-modal large reasoning models (MLRMs) pose significant privacy risks by inferring precise geographic locations from personal images through hierarchical chain-of-thought reasoning. Existing privacy protection techniques, primarily designed for perception-based models, prove ineffective against MLRMs' sophisticated... | cs.CV; cs.AI | cs.CV | 2025-12-09 | 2512.08503v1 | http://arxiv.org/abs/2512.08503v1 | -0.317842 | 0.964097 | 0.92504 | false | null | Jiaming Zhang; Che Wang; Yang Cao; Longtao Huang; Wei Yang Bryan Lim | 5 | 74 | 74 | 74 | null | null | 25,016 | 25,016 | 25,016 | 39 | 39 | false | true | 0 | true | ; ; University of Science and Technology of China; Union Hospital; Hunan University of Traditional Chinese Medicine; Harbin Medical University; Jinan University; Shanghai Jiao Tong University; Dalian Medical University; Chinese Academy of Medical Sciences & Peking Union Medical College; Augusta University; Peking Union... | ; ; https://openalex.org/A5014323068; ; | Jiaming Zhang; Che Wang; Yang Cao; Longtao Huang; Wei Yang Bryan Lim | ; ; https://orcid.org/0000-0002-0058-614X; ; | ; ; 74; ; | ; ; 403; ; | ; ; 25016; ; | ; ; 1035; ; | ; ; 6.916666666666667; ; | ; ; 40; ; | ; ; 1986; ; | ; ; 2025; ; | ; ; 39; ; | ; ; 306; ; | ; ; 7.02; ; | ; ; 131; ; |
Evaluating Vulnerabilities of Connected Vehicles Under Cyber Attacks by Attack-Defense Tree | Connected vehicles represent a key enabler of intelligent transportation systems, where vehicles are equipped with advanced communication, sensing, and computing technologies to interact not only with one another but also with surrounding infrastructures and the environment. Through continuous data exchange, such vehic... | cs.CR; cs.NI | cs.CR | 2025-12-09 | 2512.08204v1 | http://arxiv.org/abs/2512.08204v1 | -3.488722 | 0.964696 | 0.944139 | false | null | Muhammad Baqer Mollah; Honggang Wang; Hua Fang | 3 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; | ; ; | Muhammad Baqer Mollah; Honggang Wang; Hua Fang | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; |
ContextDrag: Precise Drag-Based Image Editing via Context-Preserving Token Injection and Position-Consistent Attention | Drag-based image editing aims to modify visual content followed by user-specified drag operations. Despite existing methods having made notable progress, they still fail to fully exploit the contextual information in the reference image, including fine-grained texture details, leading to edits with limited coherence an... | cs.CV; cs.AI | cs.CV | 2025-12-09 | 2512.08477v1 | http://arxiv.org/abs/2512.08477v1 | -2.249568 | 0.955725 | 0.947092 | false | null | Huiguo He; Pengyu Yan; Ziqi Yi; Weizhi Zhong; Zheng Liu; Yejun Tang; Huan Yang; Kun Gai; Guanbin Li; Lianwen Jin | 10 | 132 | 70.666667 | 28 | null | null | 76,806 | 33,166.666667 | 99,500 | 40 | 27 | false | true | 44.462219 | true | ; ; ; ; Beijing Institute of Petrochemical Technology; Zhejiang Sci-Tech University; The University of Texas MD Anderson Cancer Center; Sun Yat-sen University; National University of Singapore; Nanyang Technological University; South China Normal University; Chinese Academy of Medical Sciences & Peking Union Medical Co... | ; ; ; ; https://openalex.org/A5100423704; ; https://openalex.org/A5004532503; https://openalex.org/A5062939922; ; | Huiguo He; Pengyu Yan; Ziqi Yi; Weizhi Zhong; Zheng Liu; Yejun Tang; Huan Yang; Kun Gai; Guanbin Li; Lianwen Jin | ; ; ; ; https://orcid.org/0000-0002-8825-7198; ; https://orcid.org/0000-0001-8796-5523; https://orcid.org/0000-0002-3636-3618; ; | ; ; ; ; 132; ; 52; 28; ; | ; ; ; ; 548; ; 83; 51; ; | ; ; ; ; 76806; ; 16913; 5781; ; | ; ; ; ; 1007; ; 133; 179; ; | ; ; ; ; 11.275985663082437; ; 15.76923076923077; 4.973684210526316; ; | ; ; ; ; 41; ; 25; 18; ; | ; ; ; ; 1985; ; 2001; 2008; ; | ; ; ; ; 2025; ; 2025; 2025; ; | ; ; ; ; 40; ; 24; 17; ; | ; ; ; ; 403; ; 19; 124; ; | ; ; ; ; 8.045; ; 127.14285714285714; 31.321052631578947; ; | ; ; ; ; 104; ; 2899; 1901; ; |
Towards Effective and Efficient Long Video Understanding of Multimodal Large Language Models via One-shot Clip Retrieval | Due to excessive memory overhead, most Multimodal Large Language Models (MLLMs) can only process videos of limited frames. In this paper, we propose an effective and efficient paradigm to remedy this shortcoming, termed One-shot video-Clip based Retrieval AuGmentation (OneClip-RAG). Compared with existing video RAG met... | cs.CV | cs.CV | 2025-12-09 | 2512.08410v1 | http://arxiv.org/abs/2512.08410v1 | 1.428897 | 0.972817 | 0.963008 | false | null | Tao Chen; Shaobo Ju; Qiong Wu; Chenxin Fang; Kun Zhang; Jun Peng; Hui Li; Yiyi Zhou; Rongrong Ji | 9 | 84 | 66.25 | 43 | null | 84 | 61,028 | 35,608.25 | 142,433 | 34 | 27.25 | false | true | 17.020209 | true | ; ; East China University of Science and Technology; Dalian Medical University; Jilin University; Harbin Institute of Technology; Sichuan University; Renji Hospital; Dalian University; Shanghai University of Traditional Chinese Medicine; Traditional Chinese Medicine Hospital of Kunshan; Twelfth Guangzhou City People's ... | ; ; https://openalex.org/A5100638546; ; https://openalex.org/A5100342359; ; https://openalex.org/A5057824494; ; https://openalex.org/A5016080094 | Tao Chen; Shaobo Ju; Qiong Wu; Chenxin Fang; Kun Zhang; Jun Peng; Hui Li; Yiyi Zhou; Rongrong Ji | ; ; https://orcid.org/0000-0003-3305-6652; ; https://orcid.org/0000-0002-7596-5224; ; https://orcid.org/0000-0002-8751-3065; ; https://orcid.org/0000-0001-9163-2932 | ; ; 57; ; 81; ; 43; ; 84 | ; ; 190; ; 237; ; 205; ; 385 | ; ; 11680; ; 38987; ; 61028; ; 30738 | ; ; 428; ; 521; ; 621; ; 884 | ; ; 4.155172413793103; ; 17.415254237288135; ; 4.869565217391305; ; 5.355400696864112 | ; ; 24; ; 35; ; 34; ; 20 | ; ; 2002; ; 1991; ; 1992; ; 2006 | ; ; 2025; ; 2025; ; 2025; ; 2025 | ; ; 23; ; 34; ; 33; ; 19 | ; ; 174; ; 171; ; 89; ; 446 | ; ; 7.815; ; 18.05; ; 270.0; ; 2.49 | ; ; 174; ; 564; ; 51105; ; 99 |
Information-Dense Reasoning for Efficient and Auditable Security Alert Triage | Security Operations Centers face massive, heterogeneous alert streams under minute-level service windows, creating the Alert Triage Latency Paradox: verbose reasoning chains ensure accuracy and compliance but incur prohibitive latency and token costs, while minimal chains sacrifice transparency and auditability. Existi... | cs.CR; cs.AI | cs.CR | 2025-12-09 | 2512.08169v1 | http://arxiv.org/abs/2512.08169v1 | -2.569729 | 0.935855 | 0.922343 | false | null | Guangze Zhao; Yongzheng Zhang; Changbo Tian; Dan Xie; Hongri Liu; Bailing Wang | 6 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; ; ; | ; ; ; ; ; | Guangze Zhao; Yongzheng Zhang; Changbo Tian; Dan Xie; Hongri Liu; Bailing Wang | ; ; ; ; ; | ; ; ; ; ; | ; ; ; ; ; | ; ; ; ; ; | ; ; ; ; ; | ; ; ; ; ; | ; ; ; ; ; | ; ; ; ; ; | ; ; ; ; ; | ; ; ; ; ; | ; ; ; ; ; | ; ; ; ; ; | ; ; ; ; ; |
Distilling Future Temporal Knowledge with Masked Feature Reconstruction for 3D Object Detection | Camera-based temporal 3D object detection has shown impressive results in autonomous driving, with offline models improving accuracy by using future frames. Knowledge distillation (KD) can be an appealing framework for transferring rich information from offline models to online models. However, existing KD methods over... | cs.CV; cs.AI | cs.CV | 2025-12-09 | 2512.08247v1 | http://arxiv.org/abs/2512.08247v1 | 1.427907 | 0.950978 | 0.944436 | false | null | Haowen Zheng; Hu Zhu; Lu Deng; Weihao Gu; Yang Yang; Yanyan Liang | 6 | 45 | 45 | 45 | null | null | 866,464 | 866,464 | 866,464 | 31 | 31 | false | true | 0 | false | ; ; ; ; Shanghai University; Zhongyuan University of Technology; China University of Petroleum, East China; Henan Normal University; Xi'an Jiaotong University; | ; ; ; ; https://openalex.org/A5045798956; | Haowen Zheng; Hu Zhu; Lu Deng; Weihao Gu; Yang Liu; Yanyan Liang | ; ; ; ; https://orcid.org/0000-0002-0858-8166; | ; ; ; ; 45; | ; ; ; ; 120; | ; ; ; ; 866464; | ; ; ; ; 197; | ; ; ; ; 10.59322033898305; | ; ; ; ; 32; | ; ; ; ; 1994; | ; ; ; ; 2025; | ; ; ; ; 31; | ; ; ; ; 71; | ; ; ; ; 43.85353535353536; | ; ; ; ; 749; |
Simultaneous Enhancement and Noise Suppression under Complex Illumination Conditions | Under challenging light conditions, captured images often suffer from various degradations, leading to a decline in the performance of vision-based applications. Although numerous methods have been proposed to enhance image quality, they either significantly amplify inherent noise or are only effective under specific i... | cs.CV | cs.CV | 2025-12-09 | 2512.08378v1 | http://arxiv.org/abs/2512.08378v1 | -3.138138 | 0.946538 | 0.937032 | true | Comment match: The paper has been accepted and officially published by IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT | Jing Tao; You Li; Banglei Guan; Yang Shang; Qifeng Yu | 5 | 15 | 5 | 2 | 2 | 4 | 632 | 149 | 745 | 36 | 17.2 | true | true | 5.059644 | false | National University of Defense Technology; Jiaxing University; China Astronaut Research and Training Center; National University of Defense Technology; Institute of Navigation; National University of Defense Technology; National University of Defense Technology | https://openalex.org/A5076261630; https://openalex.org/A5086774086; https://openalex.org/A5023955044; https://openalex.org/A5015599737; https://openalex.org/A5103960608 | Jing Tao; You Li; Banglei Guan; Yang Shang; Qifeng Yu | ; ; https://orcid.org/0000-0003-2123-0182; https://orcid.org/0009-0008-0511-0547; https://orcid.org/0000-0002-9286-7642 | 2; 2; 15; 2; 4 | 0; 0; 19; 0; 3 | 10; 17; 632; 14; 72 | 2; 6; 102; 3; 11 | 7.0; 3.5; 3.5357142857142856; 7.0; 7.2 | 11; 25; 12; 3; 37 | 2014; 2000; 2014; 2022; 1989 | 2024; 2024; 2025; 2024; 2025 | 11; 25; 11; 3; 36 | 1; 1; 57; 3; 10 | 5.0; 2.8333333333333335; 6.174757281553398; 5.0; 6.636363636363637 | 7; 7; 46; 8; 29 |
Ground Slow, Move Fast: A Dual-System Foundation Model for Generalizable Vision-and-Language Navigation | While recent large vision-language models (VLMs) have improved generalization in vision-language navigation (VLN), existing methods typically rely on end-to-end pipelines that map vision-language inputs directly to short-horizon discrete actions. Such designs often produce fragmented motions, incur high latency, and st... | cs.RO | cs.RO | 2025-12-09 | 2512.08186v1 | http://arxiv.org/abs/2512.08186v1 | 0.489038 | 0.965366 | 0.956421 | false | null | Meng Wei; Chenyang Wan; Jiaqi Peng; Xiqian Yu; Yuqiang Yang; Delin Feng; Wenzhe Cai; Chenming Zhu; Tai Wang; Jiangmiao Pang; Xihui Liu | 11 | 25 | 25 | 25 | null | null | 7,906 | 7,906 | 7,906 | 23 | 23 | false | true | 0 | false | ; ; ; ; ; ; ; ; ; Beijing Academy of Artificial Intelligence; ShangHai JiAi Genetics & IVF Institute; Shanghai Artificial Intelligence Laboratory; | ; ; ; ; ; ; ; ; ; https://openalex.org/A5020672335; | Meng Wei; Chenyang Wan; Jiaqi Peng; Xiqian Yu; Yuqiang Yang; Delin Feng; Wenzhe Cai; Chenming Zhu; Tai Wang; Jiangmiao Pang; Xihui Liu | ; ; ; ; ; ; ; ; ; https://orcid.org/0000-0002-6711-9319; | ; ; ; ; ; ; ; ; ; 25; | ; ; ; ; ; ; ; ; ; 36; | ; ; ; ; ; ; ; ; ; 7906; | ; ; ; ; ; ; ; ; ; 94; | ; ; ; ; ; ; ; ; ; 34.131147540983605; | ; ; ; ; ; ; ; ; ; 24; | ; ; ; ; ; ; ; ; ; 2002; | ; ; ; ; ; ; ; ; ; 2025; | ; ; ; ; ; ; ; ; ; 23; | ; ; ; ; ; ; ; ; ; 69; | ; ; ; ; ; ; ; ; ; 79.47524752475248; | ; ; ; ; ; ; ; ; ; 1634; |
Learning Robot Manipulation from Audio World Models | World models have demonstrated impressive performance on robotic learning tasks. Many such tasks inherently demand multimodal reasoning; for example, filling a bottle with water will lead to visual information alone being ambiguous or incomplete, thereby requiring reasoning over the temporal evolution of audio, account... | cs.RO | cs.RO | 2025-12-09 | 2512.08405v1 | http://arxiv.org/abs/2512.08405v1 | -0.298821 | 0.935823 | 0.929214 | false | null | Fan Zhang; Michael Gienger | 2 | 123 | 123 | 123 | 123 | null | 51,382 | 51,382 | 51,382 | 34 | 34 | false | true | 0 | true | Zhejiang Chinese Medical University; Tongji University; Central South University; Qingdao University of Science and Technology; Shandong University; Harbin Medical University; Sun Yat-sen University; Jinan University; China Pharmaceutical University; University of Sussex; Shanghai Jiao Tong University; Chinese Academy ... | https://openalex.org/A5100403400; | Fan Zhang; Michael Gienger | https://orcid.org/0000-0001-7886-6144; | 123; | 600; | 51382; | 1085; | 11.706959706959706; | 35; | 1991; | 2025; | 34; | 371; | 6.82; | 112; |
Robust Agents in Open-Ended Worlds | The growing prevalence of artificial intelligence (AI) in various applications underscores the need for agents that can successfully navigate and adapt to an ever-changing, open-ended world. A key challenge is ensuring these AI agents are robust, excelling not only in familiar settings observed during training but also... | cs.LG | cs.LG | 2025-12-09 | 2512.08139v1 | http://arxiv.org/abs/2512.08139v1 | -3.242298 | 0.947836 | 0.940597 | false | null | Mikayel Samvelyan | 1 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | Mikayel Samvelyan | |||||||||||||||
Beyond Traditional Diagnostics: Transforming Patient-Side Information into Predictive Insights with Knowledge Graphs and Prototypes | Predicting diseases solely from patient-side information, such as demographics and self-reported symptoms, has attracted significant research attention due to its potential to enhance patient awareness, facilitate early healthcare engagement, and improve healthcare system efficiency. However, existing approaches encoun... | cs.AI | cs.AI | 2025-12-09 | 2512.08261v1 | http://arxiv.org/abs/2512.08261v1 | 0.457113 | 0.950552 | 0.944425 | true | Comment match: This work has been accepted by ICDE 2026 and is available on arXiv for early access | Yibowen Zhao; Yinan Zhang; Zhixiang Su; Lizhen Cui; Chunyan Miao | 5 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; ; | ; ; ; ; | Yibowen Zhao; Yinan Zhang; Zhixiang Su; Lizhen Cui; Chunyan Miao | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; |
Beyond Real Weights: Hypercomplex Representations for Stable Quantization | Multimodal language models (MLLMs) require large parameter capacity to align high-dimensional visual features with linguistic representations, making them computationally heavy and difficult to deploy efficiently. We introduce a progressive reparameterization strategy that compresses these models by gradually replacing... | cs.CV; cs.CL | cs.CV | 2025-12-09 | 2512.08524v1 | http://arxiv.org/abs/2512.08524v1 | -0.396483 | 0.953011 | 0.946726 | true | Comment match: Accepted in Winter Conference on Applications of Computer Vision (WACV) 2026 | Jawad Ibn Ahad; Maisha Rahman; Amrijit Biswas; Muhammad Rafsan Kabir; Robin Krambroeckers; Sifat Momen; Nabeel Mohammed; Shafin Rahman | 8 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | Jawad Ibn Ahad; Maisha Rahman; Amrijit Biswas; Muhammad Rafsan Kabir; Robin Krambroeckers; Sifat Momen; Nabeel Mohammed; Shafin Rahman | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; |
Refining Visual Artifacts in Diffusion Models via Explainable AI-based Flaw Activation Maps | Diffusion models have achieved remarkable success in image synthesis. However, addressing artifacts and unrealistic regions remains a critical challenge. We propose self-refining diffusion, a novel framework that enhances image generation quality by detecting these flaws. The framework employs an explainable artificial... | cs.CV; cs.AI | cs.CV | 2025-12-09 | 2512.08774v1 | http://arxiv.org/abs/2512.08774v1 | 0.202169 | 0.949007 | 0.942704 | false | null | Seoyeon Lee; Gwangyeol Yu; Chaewon Kim; Jonghyuk Park | 4 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; | ; ; ; | Seoyeon Lee; Gwangyeol Yu; Chaewon Kim; Jonghyuk Park | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; |
Towards a Science of Scaling Agent Systems | Agents, language model (LM)-based systems that are capable of reasoning, planning, and acting are becoming the dominant paradigm for real-world AI applications. Despite this widespread adoption, the principles that determine their performance remain underexplored, leaving practitioners to rely on heuristics rather than... | cs.AI | cs.AI | 2025-12-09 | 2512.08296v1 | http://arxiv.org/abs/2512.08296v1 | -0.638302 | 0.950509 | 0.943614 | false | null | Yubin Kim; Ken Gu; Chanwoo Park; Chunjong Park; Samuel Schmidgall; A. Ali Heydari; Yao Yan; Zhihan Zhang; Yuchen Zhuang; Mark Malhotra; Paul Pu Liang; Hae Won Park; Yuzhe Yang; Xuhai Xu; Yilun Du; Shwetak Patel; Tim Althoff; Daniel McDuff; Xin Liu | 19 | 107 | 70 | 33 | null | 107 | 56,809 | 32,213 | 64,426 | 37 | 29 | false | true | 37 | true | ; ; ; ; ; ; ; ; ; ; Massachusetts Institute of Technology; Carnegie Mellon University; ; ; ; ; ; ; ; BGI Group (China); Hebei Medical University; Guangzhou University of Chinese Medicine; Nanjing Agricultural University; Central South University; Anhui Agricultural University; East China University of Science and Techn... | ; ; ; ; ; ; ; ; ; ; https://openalex.org/A5086233510; ; ; ; ; ; ; ; https://openalex.org/A5100352324 | Yubin Kim; Ken Gu; Chanwoo Park; Chunjong Park; Samuel Schmidgall; A. Ali Heydari; Yao Yan; Zhihan Zhang; Yuchen Zhuang; Mark Malhotra; Paul Pu Liang; Hae Won Park; Yuzhe Yang; Xuhai Xu; Yilun Du; Shwetak Patel; Tim Althoff; Daniel McDuff; Xin Liu | ; ; ; ; ; ; ; ; ; ; https://orcid.org/0000-0001-7768-3610; ; ; ; ; ; ; ; https://orcid.org/0000-0003-3256-2940 | ; ; ; ; ; ; ; ; ; ; 33; ; ; ; ; ; ; ; 107 | ; ; ; ; ; ; ; ; ; ; 51; ; ; ; ; ; ; ; 645 | ; ; ; ; ; ; ; ; ; ; 7617; ; ; ; ; ; ; ; 56809 | ; ; ; ; ; ; ; ; ; ; 162; ; ; ; ; ; ; ; 1615 | ; ; ; ; ; ; ; ; ; ; 11.385714285714286; ; ; ; ; ; ; ; 5.769230769230769 | ; ; ; ; ; ; ; ; ; ; 22; ; ; ; ; ; ; ; 38 | ; ; ; ; ; ; ; ; ; ; 2004; ; ; ; ; ; ; ; 1988 | ; ; ; ; ; ; ; ; ; ; 2025; ; ; ; ; ; ; ; 2025 | ; ; ; ; ; ; ; ; ; ; 21; ; ; ; ; ; ; ; 37 | ; ; ; ; ; ; ; ; ; ; 104; ; ; ; ; ; ; ; 592 | ; ; ; ; ; ; ; ; ; ; 39.67455621301775; ; ; ; ; ; ; ; 2.0 | ; ; ; ; ; ; ; ; ; ; 1024; ; ; ; ; ; ; ; 90 |
Biothreat Benchmark Generation Framework for Evaluating Frontier AI Models III: Implementing the Bacterial Biothreat Benchmark (B3) Dataset | The potential for rapidly-evolving frontier artificial intelligence (AI) models, especially large language models (LLMs), to facilitate bioterrorism or access to biological weapons has generated significant policy, academic, and public concern. Both model developers and policymakers seek to quantify and mitigate any ri... | cs.LG; cs.AI; cs.CY; cs.ET | cs.LG | 2025-12-09 | 2512.08459v1 | http://arxiv.org/abs/2512.08459v1 | -3.085575 | 0.938777 | 0.930737 | false | null | Gary Ackerman; Theodore Wilson; Zachary Kallenborn; Olivia Shoemaker; Anna Wetzel; Hayley Peterson; Abigail Danfora; Jenna LaTourette; Brandon Behlendorf; Douglas Clifford | 10 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; | Gary Ackerman; Theodore Wilson; Zachary Kallenborn; Olivia Shoemaker; Anna Wetzel; Hayley Peterson; Abigail Danfora; Jenna LaTourette; Brandon Behlendorf; Douglas Clifford | ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; |
Disturbance-Free Surgical Video Generation from Multi-Camera Shadowless Lamps for Open Surgery | Video recordings of open surgeries are greatly required for education and research purposes. However, capturing unobstructed videos is challenging since surgeons frequently block the camera field of view. To avoid occlusion, the positions and angles of the camera must be frequently adjusted, which is highly labor-inten... | cs.CV; cs.AI; cs.LG; cs.RO | cs.CV | 2025-12-09 | 2512.08577v1 | http://arxiv.org/abs/2512.08577v1 | -3.085829 | 0.920851 | 0.912223 | false | null | Yuna Kato; Shohei Mori; Hideo Saito; Yoshifumi Takatsume; Hiroki Kajita; Mariko Isogawa | 6 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; ; ; | ; ; ; ; ; | Yuna Kato; Shohei Mori; Hideo Saito; Yoshifumi Takatsume; Hiroki Kajita; Mariko Isogawa | ; ; ; ; ; | ; ; ; ; ; | ; ; ; ; ; | ; ; ; ; ; | ; ; ; ; ; | ; ; ; ; ; | ; ; ; ; ; | ; ; ; ; ; | ; ; ; ; ; | ; ; ; ; ; | ; ; ; ; ; | ; ; ; ; ; | ; ; ; ; ; |
Reasoning Models Ace the CFA Exams | Previous research has reported that large language models (LLMs) demonstrate poor performance on the Chartered Financial Analyst (CFA) exams. However, recent reasoning models have achieved strong results on graduate-level academic and professional examinations across various disciplines. In this paper, we evaluate stat... | cs.AI; cs.CL; q-fin.GN | cs.AI | 2025-12-09 | 2512.08270v1 | http://arxiv.org/abs/2512.08270v1 | -1.876998 | 0.938574 | 0.932267 | false | null | Jaisal Patel; Yunzhe Chen; Kaiwen He; Keyi Wang; David Li; Kairong Xiao; Xiao-Yang Liu | 7 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; ; ; ; | ; ; ; ; ; ; | Jaisal Patel; Yunzhe Chen; Kaiwen He; Keyi Wang; David Li; Kairong Xiao; Xiao-Yang Liu | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; |
The Unseen Bias: How Norm Discrepancy in Pre-Norm MLLMs Leads to Visual Information Loss | Multimodal Large Language Models (MLLMs), which couple pre-trained vision encoders and language models, have shown remarkable capabilities. However, their reliance on the ubiquitous Pre-Norm architecture introduces a subtle yet critical flaw: a severe norm disparity between the high-norm visual tokens and the low-norm ... | cs.CV | cs.CV | 2025-12-09 | 2512.08374v1 | http://arxiv.org/abs/2512.08374v1 | 1.341514 | 0.954807 | 0.950455 | false | null | Bozhou Li; Xinda Xue; Sihan Yang; Yang Shi; Xinlong Chen; Yushuo Guan; Yuanxing Zhang; Wentao Zhang | 8 | 65 | 65 | 65 | null | 65 | 14,086 | 14,086 | 14,086 | 31 | 31 | false | true | 0 | true | ; ; ; ; ; ; ; Hebei Agricultural University; Tongji University; University of Electronic Science and Technology of China; Guangxi University; Northeast Agricultural University; Duke University; Shanxi Medical University; Wuhan University; Guangxi University of Chinese Medicine; Peking University Shenzhen Hospital; Zhej... | ; ; ; ; ; ; ; https://openalex.org/A5100459860 | Bozhou Li; Xinda Xue; Sihan Yang; Yang Shi; Xinlong Chen; Yushuo Guan; Yuanxing Zhang; Wentao Zhang | ; ; ; ; ; ; ; https://orcid.org/0000-0002-0903-3306 | ; ; ; ; ; ; ; 65 | ; ; ; ; ; ; ; 237 | ; ; ; ; ; ; ; 14086 | ; ; ; ; ; ; ; 458 | ; ; ; ; ; ; ; 9.118421052631579 | ; ; ; ; ; ; ; 32 | ; ; ; ; ; ; ; 1994 | ; ; ; ; ; ; ; 2025 | ; ; ; ; ; ; ; 31 | ; ; ; ; ; ; ; 187 | ; ; ; ; ; ; ; 14.92 | ; ; ; ; ; ; ; 195 |
A Multivariate Bernoulli-Based Sampling Method for Multi-Label Data with Application to Meta-Research | Datasets may contain observations with multiple labels. If the labels are not mutually exclusive, and if the labels vary greatly in frequency, obtaining a sample that includes sufficient observations with scarcer labels to make inferences about those labels, and which deviates from the population frequencies in a known... | cs.LG; stat.ML | cs.LG | 2025-12-09 | 2512.08371v1 | http://arxiv.org/abs/2512.08371v1 | -2.719156 | 0.917176 | 0.913021 | false | null | Simon Chung; Colby J. Vorland; Donna L. Maney; Andrew W. Brown | 4 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; | ; ; ; | Simon Chung; Colby J. Vorland; Donna L. Maney; Andrew W. Brown | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; |
A Lightweight Transfer Learning-Based State-of-Health Monitoring with Application to Lithium-ion Batteries in Unmanned Air Vehicles | Accurate and rapid state-of-health (SOH) monitoring plays an important role in indicating energy information for lithium-ion battery-powered portable mobile devices. To confront their variable working conditions, transfer learning (TL) emerges as a promising technique for leveraging knowledge from data-rich source work... | cs.AI | cs.AI | 2025-12-09 | 2512.08512v1 | http://arxiv.org/abs/2512.08512v1 | -3.529098 | 0.938604 | 0.926405 | true | Comment match: Accepted in IEEE Transactions on Industrial Informatics | Jiang Liu; Yan Qin; Wei Dai; Chau Yuen | 4 | 69 | 64 | 59 | 69 | null | 23,849 | 18,800.5 | 37,601 | 44 | 39.5 | false | true | 5 | true | North Sichuan Medical University; Ningbo University; University of Science and Technology of China; Advanced Micro Devices (Canada); University of Nottingham Ningbo China; Johns Hopkins University; University of Shanghai for Science and Technology; Waseda University; Harbin Medical University; The University of Melbour... | https://openalex.org/A5100325209; ; https://openalex.org/A5048237280; | Jiang Liu; Yan Qin; Wei Dai; Chau Yuen | https://orcid.org/0000-0001-6281-6505; ; https://orcid.org/0000-0003-0169-8327; | 69; ; 59; | 400; ; 198; | 23849; ; 13752; | 1284; ; 438; | 3.800561797752809; ; 3.018181818181818; | 45; ; 36; | 1981; ; 1990; | 2025; ; 2025; | 44; ; 35; | 511; ; 62; | 2.145; ; 26.59; | 25; ; 1947; |
Solving Over-Smoothing in GNNs via Nonlocal Message Passing: Algebraic Smoothing and Depth Scalability | The relationship between Layer Normalization (LN) placement and the over-smoothing phenomenon remains underexplored. We identify a critical dilemma: Pre-LN architectures avoid over-smoothing but suffer from the curse of depth, while Post-LN architectures bypass the curse of depth but experience over-smoothing. To res... | cs.LG | cs.LG | 2025-12-09 | 2512.08475v1 | http://arxiv.org/abs/2512.08475v1 | 1.043901 | 0.934437 | 0.928416 | false | null | Weiqi Guan; Junlin He | 2 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; | ; | Weiqi Guan; Junlin He | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; |
Bridging Scale Discrepancies in Robotic Control via Language-Based Action Representations | Recent end-to-end robotic manipulation research increasingly adopts architectures inspired by large language models to enable robust manipulation. However, a critical challenge arises from severe distribution shifts between robotic action data, primarily due to substantial numerical variations in action commands across... | cs.RO; cs.AI | cs.RO | 2025-12-09 | 2512.08548v1 | http://arxiv.org/abs/2512.08548v1 | 1.485878 | 0.952021 | 0.94657 | false | null | Yuchi Zhang; Churui Sun; Shiqi Liang; Diyuan Liu; Chao Ji; Wei-Nan Zhang; Ting Liu | 7 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; ; ; ; | ; ; ; ; ; ; | Yuchi Zhang; Churui Sun; Shiqi Liang; Diyuan Liu; Chao Ji; Wei-Nan Zhang; Ting Liu | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; |
Persistent Topological Structures and Cohomological Flows as a Mathematical Framework for Brain-Inspired Representation Learning | This paper presents a mathematically rigorous framework for brain-inspired representation learning founded on the interplay between persistent topological structures and cohomological flows. Neural computation is reformulated as the evolution of cochain maps over dynamic simplicial complexes, enabling representations t... | cs.LG | cs.LG | 2025-12-09 | 2512.08241v1 | http://arxiv.org/abs/2512.08241v1 | -0.646571 | 0.930822 | 0.918395 | false | null | Preksha Girish; Rachana Mysore; Mahanthesha U; Shrey Kumar; Shipra Prashant | 5 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; ; | ; ; ; ; | Preksha Girish; Rachana Mysore; Mahanthesha U; Shrey Kumar; Shipra Prashant | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; |
Unsupervised Learning of Density Estimates with Topological Optimization | Kernel density estimation is a key component of a wide variety of algorithms in machine learning, Bayesian inference, stochastic dynamics and signal processing. However, the unsupervised density estimation technique requires tuning a crucial hyperparameter: the kernel bandwidth. The choice of bandwidth is critical as i... | cs.LG; stat.ML | cs.LG | 2025-12-09 | 2512.08895v1 | http://arxiv.org/abs/2512.08895v1 | -0.640462 | 0.928799 | 0.917142 | false | null | Suina Tanweer; Firas A. Khasawneh | 2 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; | ; | Suina Tanweer; Firas A. Khasawneh | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; |
Using reinforcement learning to probe the role of feedback in skill acquisition | Many high-performance human activities are executed with little or no external feedback: think of a figure skater landing a triple jump, a pitcher throwing a curveball for a strike, or a barista pouring latte art. To study the process of skill acquisition under fully controlled conditions, we bypass human subjects. Ins... | cs.AI; cs.LG; cs.RO; eess.SY | cs.AI | 2025-12-09 | 2512.08463v1 | http://arxiv.org/abs/2512.08463v1 | -2.433056 | 0.941498 | 0.937461 | false | null | Antonio Terpin; Raffaello D'Andrea | 2 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; | ; | Antonio Terpin; Raffaello D'Andrea | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; |
Dual-Branch Center-Surrounding Contrast: Rethinking Contrastive Learning for 3D Point Clouds | Most existing self-supervised learning (SSL) approaches for 3D point clouds are dominated by generative methods based on Masked Autoencoders (MAE). However, these generative methods have been proven to struggle to capture high-level discriminative features effectively, leading to poor performance on linear probing and ... | cs.CV | cs.CV | 2025-12-09 | 2512.08673v1 | http://arxiv.org/abs/2512.08673v1 | -2.694249 | 0.963489 | 0.950758 | false | null | Shaofeng Zhang; Xuanqi Chen; Xiangdong Zhang; Sitong Wu; Junchi Yan | 5 | 60 | 60 | 60 | null | 60 | 15,822 | 15,822 | 15,822 | 16 | 16 | false | false | 0 | true | ; ; ; ; Shanghai Jiao Tong University | ; ; ; ; https://openalex.org/A5087158377 | Shaofeng Zhang; Xuanqi Chen; Xiangdong Zhang; Sitong Wu; Junchi Yan | ; ; ; ; https://orcid.org/0000-0001-9639-7679 | ; ; ; ; 60 | ; ; ; ; 221 | ; ; ; ; 15822 | ; ; ; ; 584 | ; ; ; ; 9.035087719298245 | ; ; ; ; 17 | ; ; ; ; 2009 | ; ; ; ; 2025 | ; ; ; ; 16 | ; ; ; ; 359 | ; ; ; ; 3.12 | ; ; ; ; 70 |
Learning Spatiotemporal Tubes for Temporal Reach-Avoid-Stay Tasks using Physics-Informed Neural Networks | This paper presents a Spatiotemporal Tube (STT)-based control framework for general control-affine MIMO nonlinear pure-feedback systems with unknown dynamics to satisfy prescribed time reach-avoid-stay tasks under external disturbances. The STT is defined as a time-varying ball, whose center and radius are jointly appr... | cs.RO | cs.RO | 2025-12-09 | 2512.08248v1 | http://arxiv.org/abs/2512.08248v1 | -3.518855 | 0.93855 | 0.931347 | false | null | Ahan Basu; Ratnangshu Das; Pushpak Jagtap | 3 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; | ; ; | Ahan Basu; Ratnangshu Das; Pushpak Jagtap | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; |
MVP: Multiple View Prediction Improves GUI Grounding | GUI grounding, which translates natural language instructions into precise pixel coordinates, is essential for developing practical GUI agents. However, we observe that existing grounding models exhibit significant coordinate prediction instability, minor visual perturbations (e.g. cropping a few pixels) can drasticall... | cs.CV | cs.CV | 2025-12-09 | 2512.08529v1 | http://arxiv.org/abs/2512.08529v1 | 1.47676 | 0.952797 | 0.937854 | false | null | Yunzhu Zhang; Zeyu Pan; Zhengwen Zeng; Shuheng Shen; Changhua Meng; Linchao Zhu | 6 | 14 | 14 | 14 | null | null | 555 | 555 | 555 | 13 | 13 | false | false | 0 | false | ; Providence College; Inner Mongolia University of Technology; ; ; ; | ; https://openalex.org/A5043691687; ; ; ; | Yunzhu Zhang; Zeyu Pan; Zhengwen Zeng; Shuheng Shen; Changhua Meng; Linchao Zhu | ; https://orcid.org/0000-0001-8956-2349; ; ; ; | ; 14; ; ; ; | ; 18; ; ; ; | ; 555; ; ; ; | ; 63; ; ; ; | ; 2.0; ; ; ; | ; 14; ; ; ; | ; 2012; ; ; ; | ; 2025; ; ; ; | ; 13; ; ; ; | ; 6; ; ; ; | ; 8.857142857142858; ; ; ; | ; 86; ; ; ; |
C-DIRA: Computationally Efficient Dynamic ROI Routing and Domain-Invariant Adversarial Learning for Lightweight Driver Behavior Recognition | Driver distraction behavior recognition using in-vehicle cameras demands real-time inference on edge devices. However, lightweight models often fail to capture fine-grained behavioral cues, resulting in reduced performance on unseen drivers or under varying conditions. ROI-based methods also increase computational cost... | cs.CV | cs.CV | 2025-12-09 | 2512.08647v1 | http://arxiv.org/abs/2512.08647v1 | -2.677009 | 0.935289 | 0.930995 | false | null | Keito Inoshita | 1 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | Keito Inoshita | |||||||||||||||
FastBEV++: Fast by Algorithm, Deployable by Design | The advancement of camera-only Bird's-Eye-View(BEV) perception is currently impeded by a fundamental tension between state-of-the-art performance and on-vehicle deployment tractability. This bottleneck stems from a deep-rooted dependency on computationally prohibitive view transformations and bespoke, platform-specific... | cs.CV | cs.CV | 2025-12-09 | 2512.08237v1 | http://arxiv.org/abs/2512.08237v1 | -1.95701 | 0.931993 | 0.922869 | false | null | Yuanpeng Chen; Hui Song; Wei Tao; ShanHui Mo; Shuang Zhang; Xiao Hua; TianKun Zhao | 7 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; ; ; ; | ; ; ; ; ; ; | Yuanpeng Chen; Hui Song; Wei Tao; ShanHui Mo; Shuang Zhang; Xiao Hua; TianKun Zhao | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; | ; ; ; ; ; ; |
Long-only cryptocurrency portfolio management by ranking the assets: a neural network approach | This paper will propose a novel machine learning based portfolio management method in the context of the cryptocurrency market. Previous researchers mainly focus on the prediction of the movement for specific cryptocurrency such as the bitcoin(BTC) and then trade according to the prediction. In contrast to the previous... | cs.LG; cs.AI; cs.NE | cs.LG | 2025-12-09 | 2512.08124v1 | http://arxiv.org/abs/2512.08124v1 | -3.518506 | 0.937309 | 0.914004 | true | Journal Ref: 2025 International Joint Conference on Neural Networks (IJCNN), Rome, Italy, 2025, pp. 1-8 | Zijiang Yang | 1 | 1 | 1 | 1 | 1 | 1 | 5 | 5 | 5 | null | null | null | null | 0 | false | Shenzhen University | https://openalex.org/A2105069385 | Zijiang Yang | https://orcid.org/0000-0001-6067-2990 | 1 | 0 | 5 | 24 | 0.0 | |||||||
Detecting Dental Landmarks from Intraoral 3D Scans: the 3DTeethLand challenge | Teeth landmark detection is a critical task in modern clinical orthodontics. Their precise identification enables advanced diagnostics, facilitates personalized treatment strategies, and supports more effective monitoring of treatment progress in clinical dentistry. However, several significant challenges may arise due... | cs.CV | cs.CV | 2025-12-09 | 2512.08323v1 | http://arxiv.org/abs/2512.08323v1 | -3.092813 | 0.944323 | 0.909747 | true | Conference match: MICCAI 2024 | Achraf Ben-Hamadou; Nour Neifar; Ahmed Rekik; Oussama Smaoui; Firas Bouzguenda; Sergi Pujades; Niels van Nistelrooij; Shankeeth Vinayahalingam; Kaibo Shi; Hairong Jin; Youyi Zheng; Tibor Kubík; Oldřich Kodym; Petr Šilling; Kateřina Trávníčková; Tomáš Mojžiš; Jan Matula; Jeffry Hartanto; Xiaoying Zhu; Kim-Ngan Nguyen; T... | 26 | 2 | 2 | 2 | null | null | 40 | 40 | 40 | 19 | 19 | false | false | 0 | false | ; ; ; ; ; ; ; ; ; ; ; Tescan (Czechia); Brno University of Technology; ; ; ; ; ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; ; ; https://openalex.org/A5051624059; ; ; ; ; ; ; ; ; ; ; ; ; ; | Achraf Ben-Hamadou; Nour Neifar; Ahmed Rekik; Oussama Smaoui; Firas Bouzguenda; Sergi Pujades; Niels van Nistelrooij; Shankeeth Vinayahalingam; Kaibo Shi; Hairong Jin; Youyi Zheng; Tibor Kubík; Oldřich Kodym; Petr Šilling; Kateřina Trávníčková; Tomáš Mojžiš; Jan Matula; Jeffry Hartanto; Xiaoying Zhu; Kim-Ngan Nguyen; T... | ; ; ; ; ; ; ; ; ; ; ; https://orcid.org/0009-0006-8201-0035; ; ; ; ; ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; ; ; 2; ; ; ; ; ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; ; ; 1; ; ; ; ; ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; ; ; 40; ; ; ; ; ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; ; ; 7; ; ; ; ; ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; ; ; 0.0; ; ; ; ; ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; ; ; 20; ; ; ; ; ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; ; ; 2006; ; ; ; ; ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; ; ; 2025; ; ; ; ; ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; ; ; 19; ; ; ; ; ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; ; ; 4; ; ; ; ; ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; ; ; 4.444444444444445; ; ; ; ; ; ; ; ; ; ; ; ; ; | ; ; ; ; ; ; ; ; ; ; ; 37; ; ; ; ; ; ; ; ; ; ; ; ; ; |
Towards Foundation Models with Native Multi-Agent Intelligence | Foundation models (FMs) are increasingly assuming the role of the "brain" of AI agents. While recent efforts have begun to equip FMs with native single-agent abilities -- such as GUI interaction or integrated tool use -- we argue that the next frontier is endowing FMs with native multi-agent intelligence. We identify f... | cs.AI; cs.MA | cs.AI | 2025-12-09 | 2512.08743v1 | http://arxiv.org/abs/2512.08743v1 | 0.680745 | 0.963146 | 0.954997 | false | null | Shuyue Hu; Haoyang Yan; Yiqun Zhang; Yang Chen; Dongzhan Zhou; Lei Bai | 6 | 51 | 51 | 51 | null | null | 14,393 | 14,393 | 14,393 | 31 | 31 | false | true | 0 | true | ; ; ; Changchun University of Science and Technology; Qingdao University; Liaoning University; Uppsala University; Guiyang Medical University; University of Illinois Urbana-Champaign; Chongqing University; Xuzhou Medical College; Dalian Institute of Chemical Physics; Lund University; Dalian Medical University; Anhui Me... | ; ; ; https://openalex.org/A5100350371; ; | Shuyue Hu; Haoyang Yan; Yiqun Zhang; Yang Chen; Dongzhan Zhou; Lei Bai | ; ; ; https://orcid.org/0000-0001-7661-5923; ; | ; ; ; 51; ; | ; ; ; 155; ; | ; ; ; 14393; ; | ; ; ; 430; ; | ; ; ; 5.375; ; | ; ; ; 32; ; | ; ; ; 1994; ; | ; ; ; 2025; ; | ; ; ; 31; ; | ; ; ; 189; ; | ; ; ; 8.38; ; | ; ; ; 285; ; |
Chain-of-Image Generation: Toward Monitorable and Controllable Image Generation | While state-of-the-art image generation models achieve remarkable visual quality, their internal generative processes remain a "black box." This opacity limits human observation and intervention, and poses a barrier to ensuring model reliability, safety, and control. Furthermore, their non-human-like workflows make the... | cs.CV | cs.CV | 2025-12-09 | 2512.08645v1 | http://arxiv.org/abs/2512.08645v1 | 0.435389 | 0.959763 | 0.951222 | false | null | Young Kyung Kim; Oded Schlesinger; Yuzhou Zhao; J. Matias Di Martino; Guillermo Sapiro | 5 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; ; | ; ; ; ; | Young Kyung Kim; Oded Schlesinger; Yuzhou Zhao; J. Matias Di Martino; Guillermo Sapiro | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; |
DS FedProxGrad: Asymptotic Stationarity Without Noise Floor in Fair Federated Learning | Recent work \cite{arifgroup} introduced Federated Proximal Gradient \textbf{(\texttt{FedProxGrad})} for solving non-convex composite optimization problems in group fair federated learning. However, the original analysis established convergence only to a \textit{noise-dominated neighborhood of stationarity}, with explic... | cs.LG; stat.ML | cs.LG | 2025-12-09 | 2512.08671v1 | http://arxiv.org/abs/2512.08671v1 | 0.292364 | 0.939641 | 0.9314 | false | null | Huzaifa Arif | 1 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | Huzaifa Arif | |||||||||||||||
Differentially Private Synthetic Data Generation Using Context-Aware GANs | The widespread use of big data across sectors has raised major privacy concerns, especially when sensitive information is shared or analyzed. Regulations such as GDPR and HIPAA impose strict controls on data handling, making it difficult to balance the need for insights with privacy requirements. Synthetic data offers ... | cs.LG; cs.AI; cs.CR | cs.LG | 2025-12-09 | 2512.08869v1 | http://arxiv.org/abs/2512.08869v1 | -2.688904 | 0.948878 | 0.940353 | false | null | Anantaa Kotal; Anupam Joshi | 2 | 76 | 42 | 8 | 8 | 76 | 20,669 | 10,418.5 | 20,837 | 34 | 19.5 | true | true | 34 | true | The University of Texas at El Paso; University of Maryland, Baltimore; University of Maryland, Baltimore County; Indian Agricultural Research Institute; University of Maryland, College Park; University of Maryland, Baltimore County; Stanford University | https://openalex.org/A5062851464; https://openalex.org/A5020975010 | Anantaa Kotal; Anupam Joshi | https://orcid.org/0000-0003-1818-9705; https://orcid.org/0000-0002-8641-3193 | 8; 76 | 7; 291 | 168; 20669 | 26; 601 | 2.7222222222222223; 3.25 | 6; 35 | 2020; 1991 | 2025; 2025 | 5; 34 | 16; 50 | 6.296296296296297; 16.57 | 29; 168 |
Unified Diffusion Transformer for High-fidelity Text-Aware Image Restoration | Text-Aware Image Restoration (TAIR) aims to recover high-quality images from low-quality inputs containing degraded textual content. While diffusion models provide strong generative priors for general image restoration, they often produce text hallucinations in text-centric tasks due to the absence of explicit linguist... | cs.CV | cs.CV | 2025-12-09 | 2512.08922v1 | http://arxiv.org/abs/2512.08922v1 | 1.467951 | 0.973622 | 0.946789 | false | null | Jin Hyeon Kim; Paul Hyunbin Cho; Claire Kim; Jaewon Min; Jaeeun Lee; Jihye Park; Yeji Choi; Seungryong Kim | 8 | 29 | 29 | 29 | null | 29 | 2,716 | 2,716 | 2,716 | 55 | 55 | false | true | 0 | false | ; ; ; ; ; ; ; Korea Advanced Institute of Science and Technology; Korea University; Kootenay Association for Science & Technology | ; ; ; ; ; ; ; https://openalex.org/A5085363061 | Jin Hyeon Kim; Paul Hyunbin Cho; Claire Kim; Jaewon Min; Jaeeun Lee; Jihye Park; Yeji Choi; Seungryong Kim | ; ; ; ; ; ; ; https://orcid.org/0000-0003-2927-6273 | ; ; ; ; ; ; ; 29 | ; ; ; ; ; ; ; 64 | ; ; ; ; ; ; ; 2716 | ; ; ; ; ; ; ; 261 | ; ; ; ; ; ; ; 4.822916666666667 | ; ; ; ; ; ; ; 56 | ; ; ; ; ; ; ; 1970 | ; ; ; ; ; ; ; 2025 | ; ; ; ; ; ; ; 55 | ; ; ; ; ; ; ; 149 | ; ; ; ; ; ; ; 8.275 | ; ; ; ; ; ; ; 107 |
Leveraging Multispectral Sensors for Color Correction in Mobile Cameras | Recent advances in snapshot multispectral (MS) imaging have enabled compact, low-cost spectral sensors for consumer and mobile devices. By capturing richer spectral information than conventional RGB sensors, these systems can enhance key imaging tasks, including color correction. However, most existing methods treat th... | cs.CV | cs.CV | 2025-12-09 | 2512.08441v1 | http://arxiv.org/abs/2512.08441v1 | -3.104003 | 0.952832 | 0.929357 | false | null | Luca Cogo; Marco Buzzelli; Simone Bianco; Javier Vazquez-Corral; Raimondo Schettini | 5 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; ; | ; ; ; ; | Luca Cogo; Marco Buzzelli; Simone Bianco; Javier Vazquez-Corral; Raimondo Schettini | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; |
Thinking with Images via Self-Calling Agent | Thinking-with-images paradigms have showcased remarkable visual reasoning capability by integrating visual information as dynamic elements into the Chain-of-Thought (CoT). However, optimizing interleaved multimodal CoT (iMCoT) through reinforcement learning remains challenging, as it relies on scarce high-quality reaso... | cs.CV | cs.CV | 2025-12-09 | 2512.08511v1 | http://arxiv.org/abs/2512.08511v1 | 1.466028 | 0.951467 | 0.945074 | false | null | Wenxi Yang; Yuzhong Zhao; Fang Wan; Qixiang Ye | 4 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; | ; ; ; | Wenxi Yang; Yuzhong Zhao; Fang Wan; Qixiang Ye | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; |
Developing a Strong CPS Defender: An Evolutionary Approach | Cyber-physical systems (CPSs) are used extensively in critical infrastructure, underscoring the need for anomaly detection systems that are able to catch even the most motivated attackers. Traditional anomaly detection techniques typically do `one-off' training on datasets crafted by experts or generated by fuzzers, po... | cs.CR | cs.CR | 2025-12-09 | 2512.08320v1 | http://arxiv.org/abs/2512.08320v1 | -3.5058 | 0.948665 | 0.941784 | false | null | Qingyuan Hu; Christopher M. Poskitt; Jun Sun; Yuqi Chen | 4 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; | ; ; ; | Qingyuan Hu; Christopher M. Poskitt; Jun Sun; Yuqi Chen | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; |
An Agentic AI System for Multi-Framework Communication Coding | Clinical communication is central to patient outcomes, yet large-scale human annotation of patient-provider conversation remains labor-intensive, inconsistent, and difficult to scale. Existing approaches based on large language models typically rely on single-task models that lack adaptability, interpretability, and re... | cs.CL; cs.LG | cs.CL | 2025-12-09 | 2512.08659v1 | http://arxiv.org/abs/2512.08659v1 | -2.679275 | 0.948209 | 0.939959 | false | null | Bohao Yang; Rui Yang; Joshua M. Biro; Haoyuan Wang; Jessica L. Handley; Brianna Richardson; Sophia Bessias; Nicoleta Economou-Zavlanos; Armando D. Bedoya; Monica Agrawal; Michael M. Zavlanos; Anand Chowdhury; Raj M. Ratwani; Kai Sun; Kathryn I. Pollak; Michael J. Pencina; Chuan Hong | 17 | 12 | 12 | 12 | null | null | 18,545 | 18,545 | 18,545 | 23 | 23 | false | true | 0 | false | ; Harbin Institute of Technology; Beijing Forestry University; China Jiliang University; Shanxi Normal University; ; ; ; ; ; ; ; ; ; ; ; ; ; ; | ; https://openalex.org/A5111759101; ; ; ; ; ; ; ; ; ; ; ; ; ; ; | Bohao Yang; Rui Yang; Joshua M. Biro; Haoyuan Wang; Jessica L. Handley; Brianna Richardson; Sophia Bessias; Nicoleta Economou-Zavlanos; Armando D. Bedoya; Monica Agrawal; Michael M. Zavlanos; Anand Chowdhury; Raj M. Ratwani; Kai Sun; Kathryn I. Pollak; Michael J. Pencina; Chuan Hong | ; https://orcid.org/0009-0008-3970-4351; ; ; ; ; ; ; ; ; ; ; ; ; ; ; | ; 12; ; ; ; ; ; ; ; ; ; ; ; ; ; ; | ; 14; ; ; ; ; ; ; ; ; ; ; ; ; ; ; | ; 18545; ; ; ; ; ; ; ; ; ; ; ; ; ; ; | ; 34; ; ; ; ; ; ; ; ; ; ; ; ; ; ; | ; 4.764705882352941; ; ; ; ; ; ; ; ; ; ; ; ; ; ; | ; 24; ; ; ; ; ; ; ; ; ; ; ; ; ; ; | ; 2002; ; ; ; ; ; ; ; ; ; ; ; ; ; ; | ; 2025; ; ; ; ; ; ; ; ; ; ; ; ; ; ; | ; 23; ; ; ; ; ; ; ; ; ; ; ; ; ; ; | ; 17; ; ; ; ; ; ; ; ; ; ; ; ; ; ; | ; 515.0; ; ; ; ; ; ; ; ; ; ; ; ; ; ; | ; 17721; ; ; ; ; ; ; ; ; ; ; ; ; ; ; |
GeoLoom: High-quality Geometric Diagram Generation from Textual Input | High-quality geometric diagram generation presents both a challenge and an opportunity: it demands strict spatial accuracy while offering well-defined constraints to guide generation. Inspired by recent advances in geometry problem solving that employ formal languages and symbolic solvers for enhanced correctness and i... | cs.CV | cs.CV | 2025-12-09 | 2512.08180v1 | http://arxiv.org/abs/2512.08180v1 | 1.460333 | 0.937873 | 0.927817 | false | null | Xiaojing Wei; Ting Zhang; Wei He; Jingdong Wang; Hua Huang | 5 | 90 | 88 | 86 | null | null | 45,650 | 37,173 | 74,346 | 38 | 30 | false | true | 2 | true | ; ; Central South University of Forestry and Technology; Central South University; Xinzhou Teachers University; Second Affiliated Hospital of Zhejiang University; Beijing Information Science & Technology University; University of Science and Technology Beijing; Hefei University of Technology; Dezhou University; Baidu (... | ; ; https://openalex.org/A5022113595; https://openalex.org/A5075880303; | Xiaojing Wei; Ting Zhang; Wei He; Jingdong Wang; Hua Huang | ; ; https://orcid.org/0000-0002-8944-9861; https://orcid.org/0000-0002-4888-4445; | ; ; 90; 86; | ; ; 287; 295; | ; ; 28696; 45650; | ; ; 739; 639; | ; ; 4.453900709219858; 8.822727272727272; | ; ; 39; 23; | ; ; 1987; 2003; | ; ; 2025; 2025; | ; ; 38; 22; | ; ; 178; 346; | ; ; 9.08; 12.815; | ; ; 126; 794; |
Explainable Anomaly Detection for Industrial IoT Data Streams | Industrial maintenance is being transformed by the Internet of Things and edge computing, generating continuous data streams that demand real-time, adaptive decision-making under limited computational resources. While data stream mining (DSM) addresses this challenge, most methods assume fully supervised settings, yet ... | cs.LG | cs.LG | 2025-12-09 | 2512.08885v1 | http://arxiv.org/abs/2512.08885v1 | -3.474901 | 0.934049 | 0.923998 | true | Comment match: Accepted at 41st ACM/SIGAPP Symposium On Applied Computing (SAC 2026) | Ana Rita Paupério; Diogo Risca; Afonso Lourenço; Goreti Marreiros; Ricardo Martins | 5 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; ; | ; ; ; ; | Ana Rita Paupério; Diogo Risca; Afonso Lourenço; Goreti Marreiros; Ricardo Martins | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; |
Automated Pollen Recognition in Optical and Holographic Microscopy Images | This study explores the application of deep learning to improve and automate pollen grain detection and classification in both optical and holographic microscopy images, with a particular focus on veterinary cytology use cases. We used YOLOv8s for object detection and MobileNetV3L for the classification task, evaluatin... | cs.CV | cs.CV | 2025-12-09 | 2512.08589v1 | http://arxiv.org/abs/2512.08589v1 | -3.2408 | 0.927783 | 0.920489 | true | Journal Ref: 2025 3rd Cognitive Models and Artificial Intelligence Conference (AICCONF), vol. 1, no. 1, pp. 1-8, Prague, Czech Republic, IEEE, 2025 | Swarn Singh Warshaneyan; Maksims Ivanovs; Blaž Cugmas; Inese Bērziņa; Laura Goldberga; Mindaugas Tamosiunas; Roberts Kadiķis | 7 | 11 | 6.714286 | 1 | 1 | 9 | 359 | 195.857143 | 1,371 | 44 | 15.142857 | true | true | 3.76883 | false | Institute of Electronics and Computer Science; Institute of Electronics and Computer Science; University of Latvia; State Education Development Agency; University of Latvia; Vytautas Magnus University; University of Latvia; Institute of Electronics and Computer Science | https://openalex.org/A5115626264; https://openalex.org/A5013367714; https://openalex.org/A5091334196; https://openalex.org/A5086241300; https://openalex.org/A5114553926; https://openalex.org/A5064099207; https://openalex.org/A5073394716 | Swarn Singh Warshaneyan; Maksims Ivanovs; Blaž Cugmas; Inese Bērziņa; Laura Goldberga; Mindaugas Tamošiūnas; Roberts Kadiķis | ; https://orcid.org/0000-0003-2477-7327; https://orcid.org/0000-0002-3615-7443; https://orcid.org/0000-0002-9096-3542; ; https://orcid.org/0000-0001-5866-9557; https://orcid.org/0000-0001-6845-4381 | 1; 7; 9; 9; 1; 11; 9 | 0; 3; 9; 9; 0; 11; 6 | 1; 263; 227; 249; 2; 270; 359 | 2; 33; 48; 19; 3; 79; 41 | 0.5; 0.8181818181818182; 0.6923076923076923; 0.75; 0.6666666666666666; 0.6923076923076923; 1.6153846153846154 | 2; 15; 16; 18; 2; 45; 15 | 2024; 2011; 2010; 2008; 2024; 1981; 2011 | 2025; 2025; 2025; 2025; 2025; 2025; 2025 | 1; 14; 15; 17; 1; 44; 14 | 2; 15; 18; 5; 3; 18; 22 | 0.5; 8.030303030303031; 4.63265306122449; 13.105263157894736; 0.6666666666666666; 3.4177215189873418; 8.78048780487805 | 1; 169; 28; 54; 1; 48; 169 |
Siamese-Driven Optimization for Low-Resolution Image Latent Embedding in Image Captioning | Image captioning is essential in many fields including assisting visually impaired individuals, improving content management systems, and enhancing human-computer interaction. However, a recent challenge in this domain is dealing with low-resolution image (LRI). While performance can be improved by using larger models ... | cs.CV; cs.AI; cs.HC | cs.CV | 2025-12-09 | 2512.08873v1 | http://arxiv.org/abs/2512.08873v1 | -1.865983 | 0.945931 | 0.937861 | true | Journal Ref: 2024 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) | Jing Jie Tan; Anissa Mokraoui; Ban-Hoe Kwan; Danny Wee-Kiat Ng; Yan-Chai Hum | 5 | 19 | 8 | 1 | 1 | 19 | 1,163 | 354.2 | 1,771 | 31 | 19.8 | false | true | 6.228965 | false | Hengyang Normal University; Universiti Tunku Abdul Rahman; Université Sorbonne Paris Nord; University of Calgary; Universiti Tunku Abdul Rahman; Universiti Tunku Abdul Rahman | https://openalex.org/A5114163349; https://openalex.org/A5035263631; https://openalex.org/A5109030955; https://openalex.org/A5003031675; https://openalex.org/A5040863689 | Jing Tan; Anissa Mokraoui; Ban-Hoe Kwan; Danny Wee-Kiat Ng; Yan Chai Hum | https://orcid.org/0009-0003-7249-0483; https://orcid.org/0000-0001-6447-8722; ; https://orcid.org/0000-0001-9972-2676; https://orcid.org/0000-0002-9657-8311 | 1; 10; 4; 6; 19 | 0; 12; 3; 5; 32 | 4; 338; 69; 197; 1163 | 10; 109; 18; 33; 142 | 0.5; 1.2916666666666667; 1.375; 1.1333333333333333; 4.519230769230769 | 7; 32; 32; 14; 19 | 2019; 1994; 1994; 2012; 2007 | 2025; 2025; 2025; 2025; 2025 | 6; 31; 31; 13; 18 | 6; 32; 6; 18; 65 | 0.4; 3.0727272727272728; 3.8333333333333335; 6.03030303030303; 8.153846153846153 | 2; 31; 33; 57; 112 |
Data-Driven Dynamic Parameter Learning of manipulator robots | Bridging the sim-to-real gap remains a fundamental challenge in robotics, as accurate dynamic parameter estimation is essential for reliable model-based control, realistic simulation, and safe deployment of manipulators. Traditional analytical approaches often fall short when faced with complex robot structures and int... | cs.RO; cs.AI | cs.RO | 2025-12-09 | 2512.08767v1 | http://arxiv.org/abs/2512.08767v1 | -3.197043 | 0.945374 | 0.939294 | true | Comment match: Accepted for publication at SII 2026. 6 pages, 7 figures. Code is available at: https://github.com/MohamedAlsiagy/dynamic_parameter_est | Mohammed Elseiagy; Tsige Tadesse Alemayoh; Ranulfo Bezerra; Shotaro Kojima; Kazunori Ohno | 5 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; ; | ; ; ; ; | Mohammed Elseiagy; Tsige Tadesse Alemayoh; Ranulfo Bezerra; Shotaro Kojima; Kazunori Ohno | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; |
Exposing Hidden Biases in Text-to-Image Models via Automated Prompt Search | Text-to-image (TTI) diffusion models have achieved remarkable visual quality, yet they have been repeatedly shown to exhibit social biases across sensitive attributes such as gender, race and age. To mitigate these biases, existing approaches frequently depend on curated prompt datasets - either manually constructed or... | cs.LG | cs.LG | 2025-12-09 | 2512.08724v1 | http://arxiv.org/abs/2512.08724v1 | 1.332358 | 0.959022 | 0.949967 | false | null | Manos Plitsis; Giorgos Bouritsas; Vassilis Katsouros; Yannis Panagakis | 4 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; | ; ; ; | Manos Plitsis; Giorgos Bouritsas; Vassilis Katsouros; Yannis Panagakis | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; |
Efficiently Reconstructing Dynamic Scenes One D4RT at a Time | Understanding and reconstructing the complex geometry and motion of dynamic scenes from video remains a formidable challenge in computer vision. This paper introduces D4RT, a simple yet powerful feedforward model designed to efficiently solve this task. D4RT utilizes a unified transformer architecture to jointly infer ... | cs.CV | cs.CV | 2025-12-09 | 2512.08924v1 | http://arxiv.org/abs/2512.08924v1 | 1.145102 | 0.961431 | 0.956421 | false | null | Chuhan Zhang; Guillaume Le Moing; Skanda Koppula; Ignacio Rocco; Liliane Momeni; Junyu Xie; Shuyang Sun; Rahul Sukthankar; Joëlle K Barral; Raia Hadsell; Zoubin Ghahramani; Andrew Zisserman; Junlin Zhang; Mehdi SM Sajjadi | 14 | 14 | 14 | 14 | null | null | 4,242 | 4,242 | 4,242 | 12 | 12 | false | false | 0 | false | ; ; ; ; ; ; University of Oxford; ; ; ; ; ; ; | ; ; ; ; ; ; https://openalex.org/A5025536307; ; ; ; ; ; ; | Chuhan Zhang; Guillaume Le Moing; Skanda Koppula; Ignacio Rocco; Liliane Momeni; Junyu Xie; Shuyang Sun; Rahul Sukthankar; Joëlle K Barral; Raia Hadsell; Zoubin Ghahramani; Andrew Zisserman; Junlin Zhang; Mehdi SM Sajjadi | ; ; ; ; ; ; https://orcid.org/0000-0002-1486-2029; ; ; ; ; ; ; | ; ; ; ; ; ; 14; ; ; ; ; ; ; | ; ; ; ; ; ; 16; ; ; ; ; ; ; | ; ; ; ; ; ; 4242; ; ; ; ; ; ; | ; ; ; ; ; ; 33; ; ; ; ; ; ; | ; ; ; ; ; ; 84.8; ; ; ; ; ; ; | ; ; ; ; ; ; 13; ; ; ; ; ; ; | ; ; ; ; ; ; 2013; ; ; ; ; ; ; | ; ; ; ; ; ; 2025; ; ; ; ; ; ; | ; ; ; ; ; ; 12; ; ; ; ; ; ; | ; ; ; ; ; ; 23; ; ; ; ; ; ; | ; ; ; ; ; ; 129.0909090909091; ; ; ; ; ; ; | ; ; ; ; ; ; 1410; ; ; ; ; ; ; |
LayerPipe2: Multistage Pipelining and Weight Recompute via Improved Exponential Moving Average for Training Neural Networks | In our prior work, LayerPipe, we had introduced an approach to accelerate training of convolutional, fully connected, and spiking neural networks by overlapping forward and backward computation. However, despite empirical success, a principled understanding of how much gradient delay needs to be introduced at each laye... | cs.LG; cs.AI; cs.AR | cs.LG | 2025-12-09 | 2512.08160v1 | http://arxiv.org/abs/2512.08160v1 | 0.167043 | 0.938731 | 0.924918 | false | null | Nanda K. Unnikrishnan; Keshab K. Parhi | 2 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; | ; | Nanda K. Unnikrishnan; Keshab K. Parhi | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; |
Interpolation in Knowledge Representation | Craig interpolation and uniform interpolation have many applications in knowledge representation, including explainability, forgetting, modularization and reuse, and even learning. At the same time, many relevant knowledge representation formalisms do in general not have Craig or uniform interpolation, and computing in... | cs.AI; cs.LO | cs.AI | 2025-12-09 | 2512.08833v1 | http://arxiv.org/abs/2512.08833v1 | -0.113859 | 0.934479 | 0.9245 | true | Comment match: The article will appear in Balder ten Cate, Jean Christoph Jung, Patrick Koopmann, Christoph Wernhard and Frank Wolter, editors. Theory and Applications of Craig Interpolation. Ubiquity Press, 2026 | Jean Christoph Jung; Patrick Koopmann; Matthias Knorr | 3 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; | ; ; | Jean Christoph Jung; Patrick Koopmann; Matthias Knorr | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; |
Beyond the Noise: Aligning Prompts with Latent Representations in Diffusion Models | Conditional diffusion models rely on language-to-image alignment methods to steer the generation towards semantically accurate outputs. Despite the success of this architecture, misalignment and hallucinations remain common issues and require automatic misalignment detection tools to improve quality, for example by app... | cs.CV | cs.CV | 2025-12-09 | 2512.08505v1 | http://arxiv.org/abs/2512.08505v1 | 1.333516 | 0.960343 | 0.95431 | false | null | Vasco Ramos; Regev Cohen; Idan Szpektor; Joao Magalhaes | 4 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; | ; ; ; | Vasco Ramos; Regev Cohen; Idan Szpektor; Joao Magalhaes | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; |
Training-Free Dual Hyperbolic Adapters for Better Cross-Modal Reasoning | Recent research in Vision-Language Models (VLMs) has significantly advanced our capabilities in cross-modal reasoning. However, existing methods suffer from performance degradation with domain changes or require substantial computational resources for fine-tuning in new domains. To address this issue, we develop a new ... | cs.CV; cs.AI | cs.CV | 2025-12-09 | 2512.08820v1 | http://arxiv.org/abs/2512.08820v1 | 1.376685 | 0.950249 | 0.947769 | true | Comment match: Accepted in IEEE Transactions on Multimedia (TMM) | Yi Zhang; Chun-Wun Cheng; Junyi He; Ke Yu; Yushun Tang; Carola-Bibiane Schönlieb; Zhihai He; Angelica I. Aviles-Rivero | 8 | 98 | 47.666667 | 2 | 98 | null | 94,004 | 34,230.333333 | 102,691 | 35 | 20.666667 | true | true | 39.330508 | true | Wannan Medical College; Ningbo University; Fujian Medical University; Nanjing University of Chinese Medicine; Shanghai Medical College of Fudan University; Shandong University of Traditional Chinese Medicine; Central South University; Army Medical University; Pingdingshan University; Sun Yat-sen University; Nanjing For... | https://openalex.org/A5100388089; https://openalex.org/A5065750724; ; ; ; https://openalex.org/A5033880300; ; | Yi Zhang; Chun-Wun Cheng; Junyi He; Ke Yu; Yushun Tang; Carola‐Bibiane Schönlieb; Zhihai He; Angelica I. Aviles-Rivero | https://orcid.org/0000-0001-9861-4681; ; ; ; ; https://orcid.org/0000-0003-0099-6306; ; | 98; 2; ; ; ; 43; ; | 888; 0; ; ; ; 148; ; | 94004; 8; ; ; ; 8679; ; | 2205; 11; ; ; ; 636; ; | 5.783557046979865; 0.7272727272727273; ; ; ; 3.75; ; | 36; 3; ; ; ; 26; ; | 1990; 2023; ; ; ; 2000; ; | 2025; 2025; ; ; ; 2025; ; | 35; 2; ; ; ; 25; ; | 782; 9; ; ; ; 353; ; | 2.83; 0.6666666666666666; ; ; ; 3.17; ; | 178; 3; ; ; ; 125; ; |
OCCDiff: Occupancy Diffusion Model for High-Fidelity 3D Building Reconstruction from Noisy Point Clouds | A major challenge in reconstructing buildings from LiDAR point clouds lies in accurately capturing building surfaces under varying point densities and noise interference. To flexibly gather high-quality 3D profiles of the building in diverse resolution, we propose OCCDiff applying latent diffusion in the occupancy func... | cs.CV | cs.CV | 2025-12-09 | 2512.08506v1 | http://arxiv.org/abs/2512.08506v1 | 0.138074 | 0.94179 | 0.938194 | false | null | Jialu Sui; Rui Liu; Hongsheng Zhang | 3 | 63 | 63 | 63 | null | null | 15,417 | 15,417 | 15,417 | 27 | 27 | false | true | 0 | true | ; Beijing Institute of Technology; University of Science and Technology of China; North China Electric Power University; Tianjin University; University of New Mexico; Chinese Academy of Sciences; Beijing Jiaotong University; School of Advanced Study; Harbin University of Commerce; Earth Science Institute of the Slovak ... | ; https://openalex.org/A5100448332; | Jialu Sui; Rui Liu; Hongsheng Zhang | ; https://orcid.org/0000-0001-6353-6193; | ; 63; | ; 273; | ; 15417; | ; 554; | ; 7.379518072289157; | ; 28; | ; 1998; | ; 2025; | ; 27; | ; 213; | ; 8.165; | ; 157; |
Forecasting Fails: Unveiling Evasion Attacks in Weather Prediction Models | With the increasing reliance on AI models for weather forecasting, it is imperative to evaluate their vulnerability to adversarial perturbations. This work introduces Weather Adaptive Adversarial Perturbation Optimization (WAAPO), a novel framework for generating targeted adversarial perturbations that are both effecti... | cs.LG | cs.LG | 2025-12-09 | 2512.08832v1 | http://arxiv.org/abs/2512.08832v1 | -0.099863 | 0.966633 | 0.930241 | true | Journal Ref: Association for the Advancement of Artificial Intelligence 2025 | Huzaifa Arif; Pin-Yu Chen; Alex Gittens; James Diffenderfer; Bhavya Kailkhura | 5 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; ; | ; ; ; ; | Huzaifa Arif; Pin-Yu Chen; Alex Gittens; James Diffenderfer; Bhavya Kailkhura | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; |
Prospect Theory in Physical Human-Robot Interaction: A Pilot Study of Probability Perception | Understanding how humans respond to uncertainty is critical for designing safe and effective physical human-robot interaction (pHRI), as physically working with robots introduces multiple sources of uncertainty, including trust, comfort, and perceived safety. Conventional pHRI control frameworks typically build on opti... | cs.RO | cs.RO | 2025-12-09 | 2512.08481v1 | http://arxiv.org/abs/2512.08481v1 | -3.425958 | 0.924083 | 0.918918 | false | null | Yixiang Lin; Tiancheng Yang; Jonathan Eden; Ying Tan | 4 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; | ; ; ; | Yixiang Lin; Tiancheng Yang; Jonathan Eden; Ying Tan | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; |
SDT-6D: Fully Sparse Depth-Transformer for Staged End-to-End 6D Pose Estimation in Industrial Multi-View Bin Picking | Accurately recovering 6D poses in densely packed industrial bin-picking environments remain a serious challenge, owing to occlusions, reflections, and textureless parts. We introduce a holistic depth-only 6D pose estimation approach that fuses multi-view depth maps into either a fine-grained 3D point cloud in its vanil... | cs.CV; cs.RO | cs.CV | 2025-12-09 | 2512.08430v1 | http://arxiv.org/abs/2512.08430v1 | -3.230314 | 0.953517 | 0.942189 | true | Comment match: Accepted to WACV 2026. Preprint version | Nico Leuze; Maximilian Hoh; Samed Doğan; Nicolas R. -Peña; Alfred Schoettl | 5 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; ; | ; ; ; ; | Nico Leuze; Maximilian Hoh; Samed Doğan; Nicolas R. -Peña; Alfred Schoettl | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; |
On-the-fly Large-scale 3D Reconstruction from Multi-Camera Rigs | Recent advances in 3D Gaussian Splatting (3DGS) have enabled efficient free-viewpoint rendering and photorealistic scene reconstruction. While on-the-fly extensions of 3DGS have shown promise for real-time reconstruction from monocular RGB streams, they often fail to achieve complete 3D coverage due to the limited fiel... | cs.CV | cs.CV | 2025-12-09 | 2512.08498v1 | http://arxiv.org/abs/2512.08498v1 | 0.493913 | 0.957149 | 0.948358 | false | null | Yijia Guo; Tong Hu; Zhiwei Li; Liwen Hu; Keming Qian; Xitong Lin; Shengbo Chen; Tiejun Huang; Lei Ma | 9 | 58 | 36.75 | 12 | 12 | 45 | 13,548 | 6,473.75 | 25,895 | 27 | 21.75 | false | true | 16.990806 | true | Guangxi University; ; ; Fujian Medical University; Sun Yat-sen University; First Affiliated Hospital of Fujian Medical University; ; ; ; Peking University; Beijing Academy of Artificial Intelligence; Shanghai Jiao Tong University | https://openalex.org/A5100627233; ; ; https://openalex.org/A5101400037; ; ; ; https://openalex.org/A5058066577; https://openalex.org/A5006774702 | Yijia Guo; Tong Hu; Zhiwei Li; Li‐Wen Hu; Keming Qian; Xitong Lin; Shengbo Chen; Tiejun Huang; Lei Ma | https://orcid.org/0000-0003-0522-9296; ; ; https://orcid.org/0000-0002-7786-1445; ; ; ; https://orcid.org/0000-0002-4234-6099; https://orcid.org/0000-0003-2018-9915 | 12; ; ; 32; ; ; ; 58; 45 | 13; ; ; 79; ; ; ; 219; 101 | 562; ; ; 3555; ; ; ; 13548; 8230 | 41; ; ; 133; ; ; ; 595; 130 | 8.625; ; ; 5.592592592592593; ; ; ; 9.272222222222222; 9.636363636363637 | 13; ; ; 24; ; ; ; 28; 26 | 2013; ; ; 2002; ; ; ; 1998; 2000 | 2025; ; ; 2025; ; ; ; 2025; 2025 | 12; ; ; 23; ; ; ; 27; 25 | 16; ; ; 46; ; ; ; 262; 37 | 13.317073170731707; ; ; 26.984962406015036; ; ; ; 9.285; 63.89230769230769 | 111; ; ; 284; ; ; ; 373; 908 |
Mathematical Foundations of Neural Tangents and Infinite-Width Networks | We investigate the mathematical foundations of neural networks in the infinite-width regime through the Neural Tangent Kernel (NTK). We propose the NTK-Eigenvalue-Controlled Residual Network (NTK-ECRN), an architecture integrating Fourier feature embeddings, residual connections with layerwise scaling, and stochastic d... | cs.LG | cs.LG | 2025-12-09 | 2512.08264v1 | http://arxiv.org/abs/2512.08264v1 | -0.081103 | 0.959807 | 0.946236 | false | null | Rachana Mysore; Preksha Girish; Kavitha Jayaram; Shrey Kumar; Preksha Girish; Shravan Sanjeev Bagal; Kavitha Jayaram; Shreya Aravind Shastry | 8 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | Rachana Mysore; Preksha Girish; Kavitha Jayaram; Shrey Kumar; Preksha Girish; Shravan Sanjeev Bagal; Kavitha Jayaram; Shreya Aravind Shastry | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; |
A Practical Framework for Evaluating Medical AI Security: Reproducible Assessment of Jailbreaking and Privacy Vulnerabilities Across Clinical Specialties | Medical Large Language Models (LLMs) are increasingly deployed for clinical decision support across diverse specialties, yet systematic evaluation of their robustness to adversarial misuse and privacy leakage remains inaccessible to most researchers. Existing security benchmarks require GPU clusters, commercial API acc... | cs.CR; cs.AI | cs.CR | 2025-12-09 | 2512.08185v1 | http://arxiv.org/abs/2512.08185v1 | -2.466397 | 0.963257 | 0.943063 | false | null | Jinghao Wang; Ping Zhang; Carter Yagemann | 3 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; | ; ; | Jinghao Wang; Ping Zhang; Carter Yagemann | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; |
Correction of Decoupled Weight Decay | Decoupled weight decay, solely responsible for the performance advantage of AdamW over Adam, has long been set to proportional to learning rate $γ$ without questioning. Some researchers have recently challenged such assumption and argued that decoupled weight decay should be set $\propto γ^2$ instead based on orthogona... | cs.LG | cs.LG | 2025-12-09 | 2512.08217v1 | http://arxiv.org/abs/2512.08217v1 | -0.022902 | 0.933946 | 0.928664 | false | null | Jason Chuan-Chih Chou | 1 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | Jason Chuan-Chih Chou | |||||||||||||||
SOP^2: Transfer Learning with Scene-Oriented Prompt Pool on 3D Object Detection | With the rise of Large Language Models (LLMs) such as GPT-3, these models exhibit strong generalization capabilities. Through transfer learning techniques such as fine-tuning and prompt tuning, they can be adapted to various downstream tasks with minimal parameter adjustments. This approach is particularly common in th... | cs.CV | cs.CV | 2025-12-09 | 2512.08223v1 | http://arxiv.org/abs/2512.08223v1 | -0.158406 | 0.945877 | 0.93842 | false | null | Ching-Hung Cheng; Hsiu-Fu Wu; Bing-Chen Wu; Khanh-Phong Bui; Van-Tin Luu; Ching-Chun Huang | 6 | 18 | 6.333333 | 0 | 0 | 18 | 1,361 | 336.833333 | 2,021 | 30 | 16.666667 | true | true | 6.236096 | false | National Yang Ming Chiao Tung University; Guangdong University of Technology; Chunghwa Telecom (Taiwan); National Yang Ming Chiao Tung University; Fu Jen Catholic University; National Yang Ming Chiao Tung University; National Yang Ming Chiao Tung University; National Yang Ming Chiao Tung University; Toshiba (Japan) | https://openalex.org/A5020945638; https://openalex.org/A5102586272; https://openalex.org/A5075006051; https://openalex.org/A5068020135; https://openalex.org/A5083673450; https://openalex.org/A5074744839 | Ching-Hung Cheng; Huiyan Wu; Beichen Wu; Khanh-Phong Bui; Van-Tin Luu; Ching-Chun Huang | ; ; https://orcid.org/0009-0005-4249-332X; ; ; https://orcid.org/0000-0002-4382-5083 | 0; 9; 8; 1; 2; 18 | 0; 8; 7; 1; 0; 31 | 0; 474; 154; 19; 13; 1361 | 3; 25; 16; 3; 7; 141 | 0.0; 6.125; 5.25; 0.0; 0.75; 1.3157894736842106 | 20; 31; 14; 2; 6; 28 | 2004; 1995; 2012; 2021; 2020; 1998 | 2023; 2025; 2025; 2022; 2025; 2025 | 21; 30; 13; 4; 5; 27 | 1; 7; 9; 1; 3; 46 | 0.0; 19.0; 9.75; 6.333333333333333; 1.8571428571428572; 9.48611111111111 | 0; 175; 26; 18; 7; 136 |
Protein Secondary Structure Prediction Using Transformers | Predicting protein secondary structures such as alpha helices, beta sheets, and coils from amino acid sequences is essential for understanding protein function. This work presents a transformer-based model that applies attention mechanisms to protein sequence data to predict structural motifs. A sliding-window data aug... | cs.AI | cs.AI | 2025-12-09 | 2512.08613v1 | http://arxiv.org/abs/2512.08613v1 | -2.173359 | 0.950247 | 0.931449 | false | null | Manzi Kevin Maxime | 1 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | Manzi Kevin Maxime | |||||||||||||||
Multi-Agent Intelligence for Multidisciplinary Decision-Making in Gastrointestinal Oncology | Multimodal clinical reasoning in the field of gastrointestinal (GI) oncology necessitates the integrated interpretation of endoscopic imagery, radiological data, and biochemical markers. Despite the evident potential exhibited by Multimodal Large Language Models (MLLMs), they frequently encounter challenges such as con... | cs.AI; cs.MA | cs.AI | 2025-12-09 | 2512.08674v1 | http://arxiv.org/abs/2512.08674v1 | -3.198084 | 0.95872 | 0.944963 | false | null | Rongzhao Zhang; Junqiao Wang; Shuyun Yang; Mouxiao Bian; Chao Ding; Yuwei Bai; Chihao Zhang; Yuguang Shen; Lei Wang; Lei Zheng; Qiujuan Yan; Yun Zhong; Meiling Liu; Jiwei Yu; Zheng Wang; Jie Xu; Meng Luo | 17 | 133 | 99.5 | 66 | null | null | 100,684 | 60,134 | 120,268 | 125 | 81 | false | true | 33.5 | true | ; ; ; ; ; ; ; ; Xihua University; Qingdao University; University of Hawaiʻi at Mānoa; Tianjin University of Traditional Chinese Medicine; University of Science and Technology of China; Ministry of Education of the People's Republic of China; Nanjing Tech University; Nanchang University; Qingdao University of Science an... | ; ; ; ; ; ; ; ; https://openalex.org/A5058772567; ; ; ; ; ; ; https://openalex.org/A5008162410; | Rongzhao Zhang; Junqiao Wang; Shuyun Yang; Mouxiao Bian; Chao Ding; Yuwei Bai; Chihao Zhang; Yuguang Shen; Lei Wang; Lei Zheng; Qiujuan Yan; Yun Zhong; Meiling Liu; Jiwei Yu; Zheng Wang; Jie Xu; Meng Luo | ; ; ; ; ; ; ; ; https://orcid.org/0000-0001-7275-4846; ; ; ; ; ; ; https://orcid.org/0000-0002-4854-8839; | ; ; ; ; ; ; ; ; 133; ; ; ; ; ; ; 66; | ; ; ; ; ; ; ; ; 1776; ; ; ; ; ; ; 168; | ; ; ; ; ; ; ; ; 100684; ; ; ; ; ; ; 19584; | ; ; ; ; ; ; ; ; 5858; ; ; ; ; ; ; 452; | ; ; ; ; ; ; ; ; 11.739279588336192; ; ; ; ; ; ; 11.526717557251908; | ; ; ; ; ; ; ; ; 126; ; ; ; ; ; ; 38; | ; ; ; ; ; ; ; ; 1900; ; ; ; ; ; ; 1988; | ; ; ; ; ; ; ; ; 2025; ; ; ; ; ; ; 2025; | ; ; ; ; ; ; ; ; 125; ; ; ; ; ; ; 37; | ; ; ; ; ; ; ; ; 1562; ; ; ; ; ; ; 209; | ; ; ; ; ; ; ; ; 1.395; ; ; ; ; ; ; 34.965; | ; ; ; ; ; ; ; ; 17; ; ; ; ; ; ; 2249; |
Mitigating Individual Skin Tone Bias in Skin Lesion Classification through Distribution-Aware Reweighting | Skin color has historically been a focal point of discrimination, yet fairness research in machine learning for medical imaging often relies on coarse subgroup categories, overlooking individual-level variations. Such group-based approaches risk obscuring biases faced by outliers within subgroups. This study introduces... | cs.CV; cs.AI; cs.LG | cs.CV | 2025-12-09 | 2512.08733v1 | http://arxiv.org/abs/2512.08733v1 | 0.0174 | 0.957174 | 0.930103 | false | null | Kuniko Paxton; Zeinab Dehghani; Koorosh Aslansefat; Dhavalkumar Thakker; Yiannis Papadopoulos | 5 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; ; | ; ; ; ; | Kuniko Paxton; Zeinab Dehghani; Koorosh Aslansefat; Dhavalkumar Thakker; Yiannis Papadopoulos | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; |
Accuracy Does Not Guarantee Human-Likeness in Monocular Depth Estimators | Monocular depth estimation is a fundamental capability for real-world applications such as autonomous driving and robotics. Although deep neural networks (DNNs) have achieved superhuman accuracy on physical-based benchmarks, a key challenge remains: aligning model representations with human perception, a promising stra... | cs.CV | cs.CV | 2025-12-09 | 2512.08163v1 | http://arxiv.org/abs/2512.08163v1 | 0.018296 | 0.943109 | 0.936701 | false | null | Yuki Kubota; Taiki Fukiage | 2 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; | ; | Yuki Kubota; Taiki Fukiage | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; |
Repulsor: Accelerating Generative Modeling with a Contrastive Memory Bank | The dominance of denoising generative models (e.g., diffusion, flow-matching) in visual synthesis is tempered by their substantial training costs and inefficiencies in representation learning. While injecting discriminative representations via auxiliary alignment has proven effective, this approach still faces key limi... | cs.CV | cs.CV | 2025-12-09 | 2512.08648v1 | http://arxiv.org/abs/2512.08648v1 | 1.358512 | 0.960819 | 0.951895 | false | null | Shaofeng Zhang; Xuanqi Chen; Ning Liao; Haoxiang Zhao; Xiaoxing Wang; Haoru Tan; Sitong Wu; Xiaosong Jia; Qi Fan; Junchi Yan | 10 | 60 | 35 | 10 | null | 60 | 15,822 | 8,397.5 | 16,795 | 16 | 10.5 | true | false | 25 | true | ; ; ; ; ; ; ; Shanghai Jiao Tong University; ; Shanghai Jiao Tong University | ; ; ; ; ; ; ; https://openalex.org/A5015322877; ; https://openalex.org/A5087158377 | Shaofeng Zhang; Xuanqi Chen; Ning Liao; Haoxiang Zhao; Xiaoxing Wang; Haoru Tan; Sitong Wu; Xiaosong Jia; Qi Fan; Junchi Yan | ; ; ; ; ; ; ; https://orcid.org/0000-0002-5222-1476; ; https://orcid.org/0000-0001-9639-7679 | ; ; ; ; ; ; ; 10; ; 60 | ; ; ; ; ; ; ; 10; ; 221 | ; ; ; ; ; ; ; 973; ; 15822 | ; ; ; ; ; ; ; 31; ; 584 | ; ; ; ; ; ; ; 39.714285714285715; ; 9.035087719298245 | ; ; ; ; ; ; ; 6; ; 17 | ; ; ; ; ; ; ; 2020; ; 2009 | ; ; ; ; ; ; ; 2025; ; 2025 | ; ; ; ; ; ; ; 5; ; 16 | ; ; ; ; ; ; ; 23; ; 359 | ; ; ; ; ; ; ; 31.545454545454547; ; 3.12 | ; ; ; ; ; ; ; 540; ; 70 |
InfiniteVL: Synergizing Linear and Sparse Attention for Highly-Efficient, Unlimited-Input Vision-Language Models | Window attention and linear attention represent two principal strategies for mitigating the quadratic complexity and ever-growing KV cache in Vision-Language Models (VLMs). However, we observe that window-based VLMs suffer performance degradation when sequence length exceeds the window size, while linear attention unde... | cs.CV; cs.AI | cs.CV | 2025-12-09 | 2512.08829v1 | http://arxiv.org/abs/2512.08829v1 | 0.519798 | 0.967888 | 0.963049 | false | null | Hongyuan Tao; Bencheng Liao; Shaoyu Chen; Haoran Yin; Qian Zhang; Wenyu Liu; Xinggang Wang | 7 | 81 | 76.5 | 72 | null | 72 | 37,461 | 34,109.5 | 68,219 | 32 | 26.5 | false | true | 4.5 | true | ; ; ; ; ; Qingdao University; Jiangnan University; Zhejiang Wanli University; Zhejiang Sci-Tech University; Qingdao Agricultural University; Harbin Engineering University; Nanjing Normal University; Qilu University of Technology; Shandong University; Xuzhou Medical College; Yantai University; Liaocheng University; Chin... | ; ; ; ; ; https://openalex.org/A5100665053; https://openalex.org/A5037191476 | Hongyuan Tao; Bencheng Liao; Shaoyu Chen; Haoran Yin; Qian Zhang; Wenyu Liu; Xinggang Wang | ; ; ; ; ; https://orcid.org/0000-0002-4582-7488; https://orcid.org/0000-0001-6732-7823 | ; ; ; ; ; 81; 72 | ; ; ; ; ; 362; 205 | ; ; ; ; ; 37461; 30758 | ; ; ; ; ; 1023; 443 | ; ; ; ; ; 7.385802469135802; 15.241176470588234 | ; ; ; ; ; 33; 22 | ; ; ; ; ; 1993; 2004 | ; ; ; ; ; 2025; 2025 | ; ; ; ; ; 32; 21 | ; ; ; ; ; 411; 253 | ; ; ; ; ; 5.845; 18.65 | ; ; ; ; ; 351; 378 |
PolyLingua: Margin-based Inter-class Transformer for Robust Cross-domain Language Detection | Language identification is a crucial first step in multilingual systems such as chatbots and virtual assistants, enabling linguistically and culturally accurate user experiences. Errors at this stage can cascade into downstream failures, setting a high bar for accuracy. Yet, existing language identification tools strug... | cs.LG | cs.LG | 2025-12-09 | 2512.08143v1 | http://arxiv.org/abs/2512.08143v1 | 0.030749 | 0.941038 | 0.930912 | false | null | Ali Lotfi Rezaabad; Bikram Khanal; Shashwat Chaurasia; Lu Zeng; Dezhi Hong; Hossein Beshashati; Thomas Butler; Megan Ganji | 8 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | Ali Lotfi Rezaabad; Bikram Khanal; Shashwat Chaurasia; Lu Zeng; Dezhi Hong; Hossein Beshashati; Thomas Butler; Megan Ganji | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; | ; ; ; ; ; ; ; |
BrainExplore: Large-Scale Discovery of Interpretable Visual Representations in the Human Brain | Understanding how the human brain represents visual concepts, and in which brain regions these representations are encoded, remains a long-standing challenge. Decades of work have advanced our understanding of visual representations, yet brain signals remain large and complex, and the space of possible visual concepts ... | cs.CV | cs.CV | 2025-12-09 | 2512.08560v1 | http://arxiv.org/abs/2512.08560v1 | 1.338407 | 0.950288 | 0.932543 | false | null | Navve Wasserman; Matias Cosarinsky; Yuval Golbari; Aude Oliva; Antonio Torralba; Tamar Rott Shaham; Michal Irani | 7 | 124 | 124 | 124 | null | null | 95,645 | 95,645 | 95,645 | 50 | 50 | false | true | 0 | true | ; ; ; ; IIT@MIT; Massachusetts Institute of Technology; ; | ; ; ; ; https://openalex.org/A5085020955; ; | Navve Wasserman; Matias Cosarinsky; Yuval Golbari; Aude Oliva; Antonio Torralba; Tamar Rott Shaham; Michal Irani | ; ; ; ; https://orcid.org/0000-0003-4915-0256; ; | ; ; ; ; 124; ; | ; ; ; ; 307; ; | ; ; ; ; 95645; ; | ; ; ; ; 528; ; | ; ; ; ; 11.414634146341463; ; | ; ; ; ; 51; ; | ; ; ; ; 1975; ; | ; ; ; ; 2025; ; | ; ; ; ; 50; ; | ; ; ; ; 204; ; | ; ; ; ; 26.215; ; | ; ; ; ; 442; ; |
GeoDiffMM: Geometry-Guided Conditional Diffusion for Motion Magnification | Video Motion Magnification (VMM) amplifies subtle macroscopic motions to a perceptible level. Recently, existing mainstream Eulerian approaches address amplification-induced noise via decoupling representation learning such as texture, shape and frequancey schemes, but they still struggle to separate photon noise from ... | cs.CV | cs.CV | 2025-12-09 | 2512.08325v1 | http://arxiv.org/abs/2512.08325v1 | 1.35487 | 0.94753 | 0.942581 | false | null | Xuedeng Liu; Jiabao Guo; Zheng Zhang; Fei Wang; Zhi Liu; Dan Guo | 6 | 88 | 88 | 88 | null | null | 70,300 | 70,300 | 70,300 | 36 | 36 | false | true | 0 | true | ; ; Chongqing University of Posts and Telecommunications; Tongji University; Pennsylvania State University; Amazon (United States); University of Nottingham Ningbo China; Central South University; Kunming University; University of Shanghai for Science and Technology; Qilu University of Technology; University of Califor... | ; ; https://openalex.org/A5100459168; ; ; | Xuedeng Liu; Jiabao Guo; Zheng Zhang; Fei Wang; Zhi Liu; Dan Guo | ; ; https://orcid.org/0000-0003-1470-6998; ; ; | ; ; 88; ; ; | ; ; 430; ; ; | ; ; 70300; ; ; | ; ; 1204; ; ; | ; ; 5.625866050808314; ; ; | ; ; 37; ; ; | ; ; 1989; ; ; | ; ; 2025; ; ; | ; ; 36; ; ; | ; ; 618; ; ; | ; ; 1.7; ; ; | ; ; 65; ; ; |
Universal Adversarial Suffixes Using Calibrated Gumbel-Softmax Relaxation | Language models (LMs) are often used as zero-shot or few-shot classifiers by scoring label words, but they remain fragile to adversarial prompts. Prior work typically optimizes task- or model-specific triggers, making results difficult to compare and limiting transferability. We study universal adversarial suffixes: sh... | cs.CL | cs.CL | 2025-12-09 | 2512.08123v1 | http://arxiv.org/abs/2512.08123v1 | 1.340677 | 0.952052 | 0.949275 | false | null | Sampriti Soor; Suklav Ghosh; Arijit Sur | 3 | 18 | 11 | 4 | 4 | 18 | 1,107 | 580 | 1,160 | 20 | 13.5 | false | true | 7 | false | KIIT University; Indian Statistical Institute; ; Indian Institute of Technology Guwahati | https://openalex.org/A5051383030; ; https://openalex.org/A5090992514 | Sampriti Soor; Suklav Ghosh; Arijit Sur | https://orcid.org/0000-0003-0861-9329; ; https://orcid.org/0000-0002-9038-8138 | 4; ; 18 | 2; ; 42 | 53; ; 1107 | 16; ; 133 | 2.4545454545454546; ; 2.5526315789473686 | 8; ; 21 | 2018; ; 2005 | 2025; ; 2025 | 7; ; 20 | 12; ; 47 | 4.0; ; 8.235294117647058 | 17; ; 148 |
The SMART+ Framework for AI Systems | Artificial Intelligence (AI) systems are now an integral part of multiple industries. In clinical research, AI supports automated adverse event detection in clinical trials, patient eligibility screening for protocol enrollment, and data quality validation. Beyond healthcare, AI is transforming finance through real-tim... | cs.AI; cs.CY; cs.HC; eess.SY | cs.AI | 2025-12-09 | 2512.08592v1 | http://arxiv.org/abs/2512.08592v1 | -3.227242 | 0.943199 | 0.936632 | false | null | Laxmiraju Kandikatla; Branislav Radeljic | 2 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; | ; | Laxmiraju Kandikatla; Branislav Radeljic | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; |
PointDico: Contrastive 3D Representation Learning Guided by Diffusion Models | Self-supervised representation learning has shown significant improvement in Natural Language Processing and 2D Computer Vision. However, existing methods face difficulties in representing 3D data because of its unordered and uneven density. Through an in-depth analysis of mainstream contrastive and generative approach... | cs.CV | cs.CV | 2025-12-09 | 2512.08330v1 | http://arxiv.org/abs/2512.08330v1 | 0.065935 | 0.957615 | 0.951929 | true | Comment match: Accepted by IJCNN 2025 | Pengbo Li; Yiding Sun; Haozhe Cheng | 3 | 3 | 1 | 0 | 3 | 0 | 26 | 8.666667 | 26 | null | null | null | null | 1.414214 | false | Beijing University of Posts and Telecommunications; Liaoning Shihua University; Hong Kong University of Science and Technology; Xi'an Jiaotong University; Xi'an Jiaotong University | https://openalex.org/A2129406832; https://openalex.org/A2144735192; https://openalex.org/A2965617674 | Pengbo Li; Yiding Sun; Haozhe Cheng | https://orcid.org/0000-0001-5033-1252; https://orcid.org/0000-0002-6318-2206; https://orcid.org/0000-0002-1772-3948 | 3; 0; 0 | 2; 0; 0 | 26; 0; 0 | 19; 2; 4 | 0.4444444444444444; 0.0; 0.0 | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; |
Residual-SwinCA-Net: A Channel-Aware Integrated Residual CNN-Swin Transformer for Malignant Lesion Segmentation in BUSI | A novel deep hybrid Residual-SwinCA-Net segmentation framework is proposed in the study for addressing such challenges by extracting locally correlated and robust features, incorporating residual CNN modules. Furthermore, for learning global dependencies, Swin Transformer blocks are customized using internal residual p... | cs.CV; cs.AI; cs.LG | cs.CV | 2025-12-09 | 2512.08243v1 | http://arxiv.org/abs/2512.08243v1 | -3.217433 | 0.953005 | 0.94269 | false | null | Saeeda Naz; Saddam Hussain Khan | 2 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; | ; | Saeeda Naz; Saddam Hussain Khan | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; |
PaintFlow: A Unified Framework for Interactive Oil Paintings Editing and Generation | Oil painting, as a high-level medium that blends human abstract thinking with artistic expression, poses substantial challenges for digital generation and editing due to its intricate brushstroke dynamics and stylized characteristics. Existing generation and editing techniques are often constrained by the distribution ... | cs.CV | cs.CV | 2025-12-09 | 2512.08534v1 | http://arxiv.org/abs/2512.08534v1 | 1.143184 | 0.952081 | 0.947008 | false | null | Zhangli Hu; Ye Chen; Jiajun Yao; Bingbing Ni | 4 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; | ; ; ; | Zhangli Hu; Ye Chen; Jiajun Yao; Bingbing Ni | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; | ; ; ; |
A Scalable Pipeline Combining Procedural 3D Graphics and Guided Diffusion for Photorealistic Synthetic Training Data Generation in White Button Mushroom Segmentation | Industrial mushroom cultivation increasingly relies on computer vision for monitoring and automated harvesting. However, developing accurate detection and segmentation models requires large, precisely annotated datasets that are costly to produce. Synthetic data provides a scalable alternative, yet often lacks sufficie... | cs.CV | cs.CV | 2025-12-09 | 2512.08747v1 | http://arxiv.org/abs/2512.08747v1 | -2.475894 | 0.935042 | 0.923201 | false | null | Artúr I. Károly; Péter Galambos | 2 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; | ; | Artúr I. Károly; Péter Galambos | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; |
Decentralized Trust for Space AI: Blockchain-Based Federated Learning Across Multi-Vendor LEO Satellite Networks | The rise of space AI is reshaping government and industry through applications such as disaster detection, border surveillance, and climate monitoring, powered by massive data from commercial and governmental low Earth orbit (LEO) satellites. Federated satellite learning (FSL) enables joint model training without shari... | cs.CR; cs.LG | cs.CR | 2025-12-09 | 2512.08882v1 | http://arxiv.org/abs/2512.08882v1 | -3.365068 | 0.934072 | 0.923007 | false | null | Mohamed Elmahallawy; Asma Jodeiri Akbarfam | 2 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; | ; | Mohamed Elmahallawy; Asma Jodeiri Akbarfam | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; | ; |
SAM-Body4D: Training-Free 4D Human Body Mesh Recovery from Videos | Human Mesh Recovery (HMR) aims to reconstruct 3D human pose and shape from 2D observations and is fundamental to human-centric understanding in real-world scenarios. While recent image-based HMR methods such as SAM 3D Body achieve strong robustness on in-the-wild images, they rely on per-frame inference when applied to... | cs.CV | cs.CV | 2025-12-09 | 2512.08406v1 | http://arxiv.org/abs/2512.08406v1 | 1.128154 | 0.950047 | 0.945853 | false | null | Mingqi Gao; Yunqi Miao; Jungong Han | 3 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; | ; ; | Mingqi Gao; Yunqi Miao; Jungong Han | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; | ; ; |
Wan-Move: Motion-controllable Video Generation via Latent Trajectory Guidance | We present Wan-Move, a simple and scalable framework that brings motion control to video generative models. Existing motion-controllable methods typically suffer from coarse control granularity and limited scalability, leaving their outputs insufficient for practical use. We narrow this gap by achieving precise and hig... | cs.CV | cs.CV | 2025-12-09 | 2512.08765v1 | http://arxiv.org/abs/2512.08765v1 | 1.328412 | 0.961188 | 0.958229 | false | null | Ruihang Chu; Yefei He; Zhekai Chen; Shiwei Zhang; Xiaogang Xu; Bin Xia; Dingdong Wang; Hongwei Yi; Xihui Liu; Hengshuang Zhao; Yu Liu; Yingya Zhang; Yujiu Yang | 13 | 114 | 46.6 | 12 | 12 | 30 | 65,753 | 21,385.2 | 106,926 | 60 | 26.6 | false | true | 35.131752 | true | Chinese University of Hong Kong; ; ; ; Fudan University; Huashan Hospital; ; ; ; ; Chinese University of Hong Kong; HKU-Pasteur Research Pole; University of Hong Kong; Chinese Academy of Tropical Agricultural Sciences; Shanghai University; Liaoning University; Shanxi Agricultural University; Nanjing University of Chine... | https://openalex.org/A5034923822; ; ; ; https://openalex.org/A5035291206; ; ; ; ; https://openalex.org/A5078109015; https://openalex.org/A5100345666; ; https://openalex.org/A5020953714 | Ruihang Chu; Yefei He; Zhekai Chen; Shiwei Zhang; Xiaogang Xu; Bin Xia; Dingdong Wang; Hongwei Yi; Xihui Liu; Hengshuang Zhao; Yu Liu; Yingya Zhang; Yujiu Yang | https://orcid.org/0000-0001-9057-745X; ; ; ; https://orcid.org/0000-0003-1537-0250; ; ; ; ; https://orcid.org/0000-0001-8277-2706; https://orcid.org/0000-0001-8723-1896; ; https://orcid.org/0000-0002-6427-1024 | 12; ; ; ; 35; ; ; ; ; 42; 114; ; 30 | 14; ; ; ; 82; ; ; ; ; 84; 1292; ; 75 | 655; ; ; ; 3878; ; ; ; ; 32970; 65753; ; 3670 | 35; ; ; ; 161; ; ; ; ; 201; 2707; ; 349 | 4.7727272727272725; ; ; ; 3.3703703703703702; ; ; ; ; 14.685483870967742; 6.398095238095238; ; 5.137724550898204 | 17; ; ; ; 25; ; ; ; ; 12; 61; ; 23 | 2009; ; ; ; 2001; ; ; ; ; 2014; 1965; ; 2003 | 2025; ; ; ; 2025; ; ; ; ; 2025; 2025; ; 2025 | 16; ; ; ; 24; ; ; ; ; 11; 60; ; 22 | 23; ; ; ; 40; ; ; ; ; 159; 732; ; 227 | 17.076923076923077; ; ; ; 24.15527950310559; ; ; ; ; 164.34; 3.315; ; 5.36 | 194; ; ; ; 322; ; ; ; ; 14592; 84; ; 104 |
A Multi-Agent LLM Framework for Design Space Exploration in Autonomous Driving Systems | Designing autonomous driving systems requires efficient exploration of large hardware/software configuration spaces under diverse environmental conditions, e.g., with varying traffic, weather, and road layouts. Traditional design space exploration (DSE) approaches struggle with multi-modal execution outputs and complex... | cs.RO | cs.RO | 2025-12-09 | 2512.08476v1 | http://arxiv.org/abs/2512.08476v1 | -3.468253 | 0.92953 | 0.925303 | false | null | Po-An Shih; Shao-Hua Wang; Yung-Che Li; Chia-Heng Tu; Chih-Han Chang | 5 | null | null | null | null | null | null | null | null | null | null | null | null | 0 | false | ; ; ; ; | ; ; ; ; | Po-An Shih; Shao-Hua Wang; Yung-Che Li; Chia-Heng Tu; Chih-Han Chang | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; | ; ; ; ; |
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