Standard Transformer architectures rely heavily on dense linear transformations, treating feature projection as a monolithic, full-rank operation. We argue that this formulation is inefficient and lacks the structural inductive bias necessary for distinguishing between local feature preservation and global context integration. To address this, we introduce the Hybrid Dual-Path Linear (HDPL) operator, which decomposes the affine transformation into two topologically distinct pathways: a sparse block-diagonal component for high-rank local processing, and a low-rank Variational Autoencoder (VAE) bottleneck for global context regularization. By "surgically" replacing specific projections (Query, Key, Value, Gate, Up) with HDPL operators while retaining standard dense layers for aggregation (Output, Down), we achieve a superior balance of efficiency and representational power. Experiments on the FineWeb-Edu dataset demonstrate that the HDPL architecture outperforms a standard Llama-style baseline, reducing validation loss while simultaneously reducing parameter count by 6.8%. Beyond immediate performance gains, we discuss how the explicit materialization of a probabilistic latent space within the Transformer backbone serves as a vital architectural affordance, offering new pathways for inference-time or hypernetwork induced control, continual adaptation, interpretability, and cross-model or cross-modal synchronization. The code is available at https://github.com/VladimerKhasia/HDPL
Hybrid Convolution and Vision Transformer NAS Search Space for TinyML Image Classification
Chair of Electronic Design Automation, Technical University of Munich（电子设计自动化研究所，慕尼黑技术大学）；Department of Computer Science, University of Tübingen（计算机科学系，图宾根大学）；Institute of Computer Engineering, TU Wien（计算机工程研究所，维也纳技术大学）
Hybrid Attention-Based Transformer Block Model for Distant Supervision Relation Extraction
College of Information Science and Technology, Donghua University（东华大学信息科学与技术学院）；University of Surrey（萨里大学）；Southern University of Science and Technology（南方科技大学）
随着各种数字文本信息呈指数级爆炸式增长，如何从海量的非结构化文本信息中有效地获取特定知识成为一个挑战。关系抽取是自然语言处理的基本任务之一，其目的是从给定的文本中抽取实体对之间的语义关系。为了避免人工标注数据集，远距离监督关系抽取（DSRE）被广泛应用，旨在利用知识库自动标注数据集。不幸的是，由于潜在的强假设，这种方法严重遭受错误的标签。为了解决这个问题，我们提出了一个新的框架，使用混合注意力为基础的Transformer块与多实例学习来执行DSRE任务。具体地说，首先使用Transformer块作为句子编码器来捕获句子的句法信息，其主要利用多头自注意从词级提取特征。然后，采用一种更为简洁的句子级注意力机制来构造袋子表示，旨在整合句子的有效信息来有效地表示袋子。在公开数据集New York Times（NYT）上的实验结果表明，该方法在评估数据集上的性能优于现有算法，验证了该模型在DSRE任务中的有效性.
Tree Meets Transformer: A Hybrid Architecture for Scalable Power Allocation in Cell-Free Networks
Smart Networks and Services Joint Undertaking (SNS JU)（智能网络与服务联合体）；Italian Ministry of Education and Research (MUR)（意大利教育与研究部）；HORIZON-JU-SNS-2022 EU project TIMES（HORIZON-JU-SNS-2022 EU项目TIMES）；European Union under the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU（欧盟在意大利国家复苏与韧性计划（NRRP）下的NextGenerationEU）
HybriDNA: A Hybrid Transformer-Mamba2 Long-Range DNA Language Model
Microsoft Research（微软研究院）；UM-SJTU Joint Institute（上海交通大学密歇根联合学院）；Princeton University（普林斯顿大学）；Carnegie Mellon University（卡内基梅隆大学）；Renmin University of China（中国人民大学）
Hybrid Quantum Vision Transformers for Event Classification in High Energy Physics
University of Florida（佛罗里达大学）；Polytechnic University of Catalonia（加泰罗尼亚理工大学）；Indian Institute of Technology Bhilai（贝拉理工学院）；University of Kansas（堪萨斯大学）；University of Alabama（阿拉巴马大学）；Software Engineering Institute, Carnegie Mellon University（卡内基梅隆大学软件工程研究所）；Technische University of München（慕尼黑工业大学）
Spatio-Temporal Hybrid Fusion of CAE and SWIn Transformers for Lung Cancer Malignancy Prediction
Concordia University（康考迪亚大学）；Concordia Institute for Information Systems Engineering（康考迪亚信息系统工程研究所）；Sunnybrook Health Sciences Centre（桑尼布鲁克健康科学中心）；University of Toronto（多伦多大学）
HTMformer: Hybrid Time and Multivariate Transformer for Time Series Forecasting
Northwestern Polytechnical University（北华理工大学）；MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University（人工智能教育部重点实验室，上海交通大学AI研究院）；Ningbo Institute of Digital Twin, Eastern Institute of Technology（宁波数字孪生研究所，东部技术研究所）；Ningbo Key Laboratory of Spatial Intelligence and Digital Derivative（宁波空间智能与数字衍生关键实验室）
Parameter Efficient Hybrid Transformer (PEHT) for Network Traffic Prediction via Dynamic Urban Congestion Integration
National Science Foundation（国家科学基金会）；U.S. Department of Transportation（美国交通部）；University Transportation Center (UTC)（大学交通中心）；Transportation Cybersecurity Center for Advanced Research and Education (CYBER-CARE)（交通网络安全高级研究与教育中心）；NVIDIA Academic Grant Program（NVIDIA学术资助计划）
Hybrid Mamba-Transformer Decoder for Error-Correcting Codes
School of Electrical and Computer Engineering（电气与计算机工程学院）；Ben-Gurion University of the Negev（贝内尔-加利利大学内盖夫分校）；Blavatnik School of Computer Science（Blavatnik计算机科学学院）；Tel Aviv University（特拉维夫大学）
Technical University of Denmark（丹麦技术大学）；University of Oxford（牛津大学）；Danish Research Centre for Magnetic Resonance（丹麦磁共振研究中心）；Copenhagen University Hospital Hvidovre（哥本哈根大学哈维德夫医院）
TOAST: Transformer Optimization using Adaptive and Simple Transformations
Department of Computer Science, ETH Zurich（苏黎世联邦理工学院计算机科学系）；CISPA Helmholtz Center for Information Security（信息安全赫尔姆霍兹中心）；Sapienza University of Rome（罗马大学萨皮恩扎大学）；University of Fribourg（弗里堡大学）
MSHT: Multi-stage Hybrid Transformer for the ROSE Image Analysis of Pancreatic Cancer
Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University（北京航空航天大学生物科学与医学工程学院北京生物医学工程高精尖创新中心）；Department of Gastroenterology, Peking Union Medical College Hospital（北京协和医院消化内科）；Department of Pathology, Peking Union Medical College Hospital（北京协和医院病理科）；School of Electronics & Computer Science, University of Southampton（南安普顿大学电子与计算机科学学院）；School of Biological Science and Medical Engineering, Beihang University（北京航空航天大学生物科学与医学工程学院）
DSAT-HD: Dual-Stream Adaptive Transformer with Hybrid Decomposition for Multivariate Time Series Forecasting
Computer Science and Technology（计算机科学与技术）；Harbin Engineering University（哈尔滨工程大学）；Traditional Chinese Medicine Data Center（传统中医数据中心）；China Academy of Chinese Medical Sciences（中国中医科学院）
Placenta Accreta Spectrum Detection Using an MRI-based Hybrid CNN-Transformer Model
Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University（计算机科学系，计算与信息科技学院，国王阿卜杜勒阿齐兹大学）；Faculty of Medicine, King Abdulaziz University（医学学院，国王阿卜杜勒阿齐兹大学）；Department of Radiology, King Abdulaziz University Hospital（放射科，国王阿卜杜勒阿齐兹大学医院）
