As one of the most representative DL techniques, Transformer architecture has empowered numerous advanced models, especially the large language models (LLMs) that comprise billions of parameters, becoming a cornerstone in deep learning. Despite the impressive achievements, Transformers still face inherent limitations, particularly the time-consuming inference resulting from the quadratic computation complexity of attention calculation. Recently, a novel architecture named Mamba, drawing inspiration from classical state space models (SSMs), has emerged as a promising alternative for building foundation models, delivering comparable modeling abilities to Transformers while preserving near-linear scalability concerning sequence length. This has sparked an increasing number of studies actively exploring Mamba's potential to achieve impressive performance across diverse domains. Given such rapid evolution, there is a critical need for a systematic review that consolidates existing Mamba-empowered models, offering a comprehensive understanding of this emerging model architecture. In this survey, we therefore conduct an in-depth investigation of recent Mamba-associated studies, covering three main aspects: the advancements of Mamba-based models, the techniques of adapting Mamba to diverse data, and the applications where Mamba can excel. Specifically, we first review the foundational knowledge of various representative deep learning models and the details of Mamba-1&2 as preliminaries. Then, to showcase the significance of Mamba for AI, we comprehensively review the related studies focusing on Mamba models' architecture design, data adaptability, and applications. Finally, we present a discussion of current limitations and explore various promising research directions to provide deeper insights for future investigations.
LLM agents security duality: a comprehensive survey of self-security and empowered cybersecurity
School of Cyber Science and Engineering, Wuhan University（武汉大学计算机科学与工程学院）；Department of Computer Science and Engineering, University at Buffalo（布法罗大学计算机科学与工程系）
Small Vision-Language Models: A Survey on Compact Architectures and Techniques
School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University（卡林加工业技术学院（KIIT）德emed大学计算机工程学院）；Indian Institute of Management Visakhapatnam（印度管理学院维沙卡帕特南分院）；Remodel AI
Instruction-Guided Editing Controls for Images and Multimedia: A Survey in LLM era
Griffith University（格里菲斯大学）；University of Bremen（不来梅大学）；Ho Chi Minh City University of Technology（胡志明市技术大学）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Hanoi University of Science and Technology（河内科技大学）；The University of Queensland（昆士兰大学）
State Space Model for New-Generation Network Alternative to Transformers: A Survey
School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；School of Artificial Intelligence, Anhui University（安徽大学人工智能学院）；Peng Cheng Laboratory（鹏城实验室）；Harbin Institute of Technology (HITSZ)（哈尔滨工业大学（深圳））；School of Computer Science, Peking University（北京大学计算机学院）；School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University（北京大学深圳研究生院电子与计算机工程学院）
Continual Learning for Generative AI: From LLMs to MLLMs and Beyond
School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences（中国科学院大学先进交叉学科学院）；MAIS, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所MAIS）；Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences（中国科学院香港科学与创新研究院人工智能与机器人中心）；School of Computer and Information Engineering, Xiamen University of Technology（厦门理工大学计算机与信息工程学院）
