Latest State Space Models (Mamba) Research Papers
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- Selective Operation Quantization for Vision State Space ModelsYinglong Li, Xiaoyu Liu, Yinuo Liao, Jiacheng Li et al. · LoViF Oral · Sep 8, 2026
State Space Models (SSMs), as key components of Mamba, have gained increasing attention for vision models recently. Given the computational cost of deploying SSMs on resource-limited edge devices, Post-Training Quantization (PTQ) is a tech…
- LoRA-TSD: Tangent-Space Spectral Descent for LoRA via Muon-Style UpdatesDmitrii Andriianov, Andrey Veprikov, Aleksandr Beznosikov · arXiv · Sep 2, 2026
Low-rank adaptation (LoRA) is the standard way to fine-tune large models, yet when its two factors are trained independently, the update ignores the geometry of the low-rank weight change it induces. We introduce LoRA-TSD, an optimizer that…
- Tydra: An Efficient Hybrid Model for Tabular DataMieszko Komisarczyk, Saurabh Mathur, Maurice Kraus, Sriraam Natarajan et al. · arXiv · Aug 21, 2026
Transformer-based tabular foundation models such as TabPFN achieve strong predictive performance but incur quadratic computational cost with context length. On the other hand, subquadratic SSM-based alternatives such as Hydra trade away acc…
- On the Fragility of Self-Improving Agents: Variance, Task Order, and UnderspecificationQinyuan Ye, Yu Li, Yada Pruksachatkun, Jiaxin Zhang et al. · arXiv · Aug 18, 2026
Memory-based self-improving agents--those that learn from an online stream of tasks and improve over time by maintaining a textual memory bank--have shown great promise in recent literature. However, the reliability aspects of these methods…
- Muon Meets Mamba: Spectral Optimization for State Space ModelsArslan Battalov, Karim Kramin, Alexander Markotenko, Sofia Sinitsina · arXiv · Aug 4, 2026
Muon is a recent optimizer that orthogonalizes the update to each weight matrix with a Newton-Schulz iteration, which performs steepest descent under the spectral norm. Almost all the evidence for it comes from Transformer models, and its b…
- User-Centric Modeling of Transactional Sequences with Explainable State Space ModelsIvan Palagin · arXiv · Jul 22, 2026
We propose a hybrid approach for user-centric modeling of transactional event sequences that combines contrastive representation learning (CoLES) with State Space Models (SSMs). While contrastive methods yield high-quality compressed user r…
- DAMamba: Vision State Space Model with Dynamic Adaptive ScanTanzhe Li, Caoshuo Li, Jiayi Lyu, Hongjuan Pei et al. · NeurIPS 2025 poster · Sep 18, 2025
State space models (SSMs) have recently garnered significant attention in computer vision. However, due to the unique characteristics of image data, adapting SSMs from natural language processing to computer vision has not outperformed the …
- HG-Mamba: Heuristic-Guided State Space Model for Laparoscopic Image DesmokingShiweiWu, XiaoboZhu, Song Zhang, Yu An et al. · ICLR 2026 Conference Withdrawn Submission · Sep 16, 2025
Developing smoke removal algorithms for laparoscopic surgery is crucial for enhancing surgical visibility and supporting accurate intraoperative decision-making. Recently, Mamba, a representative state space model (SSM), has shown strong po…
- SF-Mamba: Rethinking State Space Model for VisionMasakazu Yoshimura, Teruaki Hayashi, Yuki Hoshino, Wei-Yao Wang et al. · Submitted to ICLR 2026 · Sep 14, 2025
The realm of Mamba for vision has been advanced in recent years to strike for the alternatives of Vision Transformers (ViTs) that suffer from the quadratic complexity. While the recurrent scanning mechanism of Mamba offers computational eff…
- Incomplete Multi-modal Brain Tumor Segmentation via Learnable Sorting State Space ModelZheyu Zhang, Yayuan Lu, Feipeng Ma, Yueyi Zhang et al. · Computer Vision and Pattern Recognition · Jun 10, 2025
Brain tumor segmentation plays a crucial role in clinical diagnosis, yet the frequent unavailability of certain MRI modalities poses a significant challenge. In this paper, we introduce the Learnable Sorting State Space Model (LS3M), a nove…
- Dynamic Process Monitoring Using Total Multirate Linear Gaussian State Space ModelDonglei Zheng, Le Zhou, Yi Liu, Qiang Liu · IEEE Transactions on Industrial Informatics · Jun 1, 2025
Conventional data-driven dynamic process monitoring methods usually rely on data collected at a single sampling rate. The effectiveness of these approaches typically diminishes when analyzing data from multiple sampling rates. To address th…
- Sub-Sequential Physics-Informed Learning with State Space ModelChenhui Xu, Dancheng Liu, Yuting Hu, Jiajie Li et al. · ICML 2025 poster · May 1, 2025
Physics-Informed Neural Networks (PINNs) are a kind of deep-learning-based numerical solvers for partial differential equations (PDEs). Existing PINNs often suffer from failure modes of being unable to propagate patterns of initial conditio…
- StegMamba: Distortion-Free Immune-Cover for Multi-Image Steganography With State Space ModelTing Luo, Yuhang Zhou, Zhouyan He, Gangyi Jiang et al. · IEEE transactions on circuits and systems for video technology (Print) · May 1, 2025
Multi-image steganography ensures privacy protection while avoiding suspicion from third parties by embedding multiple secret images within a cover image. However, existing multi-image steganographic methods fail to model global spatial cor…
- PoseMamba: Monocular 3D Human Pose Estimation with Bidirectional Global-Local Spatio-Temporal State Space ModelYunlong Huang, Junshuo Liu, Ke Xian, Robert C. Qiu · AAAI Conference on Artificial Intelligence · Apr 11, 2025
Transformers have significantly advanced the field of 3D human pose estimation (HPE). However, existing transformer-based methods primarily use self-attention mechanisms for spatio-temporal modeling, leading to a quadratic complexity, unidi…
- DefMamba: Deformable Visual State Space ModelLeiye Liu, Miao Zhang, Jihao Yin, Tingwei Liu et al. · Computer Vision and Pattern Recognition · Apr 8, 2025
Recently, state space models (SSM), particularly Mamba, have attracted significant attention from scholars due to their ability to effectively balance computational efficiency and performance. However, most existing visual Mamba methods fla…
- SaMam: Style-aware State Space Model for Arbitrary Image Style TransferHongda Liu, Longguang Wang, Ye Zhang, Ziru Yu et al. · Computer Vision and Pattern Recognition · Mar 20, 2025
Global effective receptive field plays a crucial role for image style transfer (ST) to obtain high-quality stylized results. However, existing ST backbones (e.g., CNNs and Transformers) suffer huge computational complexity to achieve global…
- DAMamba: Vision State Space Model with Dynamic Adaptive ScanTanzhe Li, Caoshuo Li, Jiayi Lyu, Hongjuan Pei et al. · Neural Information Processing Systems · Feb 18, 2025
State space models (SSMs) have recently garnered significant attention in computer vision. However, due to the unique characteristics of image data, adapting SSMs from natural language processing to computer vision has not outperformed the …
- Koopman-Constrained Hierarchical Deep State Space Model for Industrial Quality Prediction via Cloud-Edge Collaborative FrameworkQingkai Sui, Yalin Wang, Chenliang Liu, Minghao Han et al. · IEEE Transactions on Systems, Man & Cybernetics. Systems · Feb 1, 2025
In cloud manufacturing of industrial processes, the accurate online prediction of product quality is the basis for realizing decision-making and control of the manufacturing process. However, frequent fluctuations in working conditions and …
- Directing Mamba to Complex Textures: An Efficient Texture-Aware State Space Model for Image RestorationLong Peng, Xin Di, Zhanfeng Feng, Wenbo Li et al. · International Joint Conference on Artificial Intelligence · Jan 27, 2025
Image restoration aims to recover details and enhance contrast in degraded images. With the growing demand for high-quality imaging (e.g., 4K and 8K), achieving a balance between restoration quality and computational efficiency has become i…
- From Layers to States: A State Space Model Perspective to Deep Neural Network Layer DynamicsQinshuo Liu, Weiqin Zhao, Wei Huang, Yanwen Fang et al. · ICLR 2025 Poster · Jan 22, 2025
The depth of neural networks is a critical factor for their capability, with deeper models often demonstrating superior performance. Motivated by this, significant efforts have been made to enhance layer aggregation - reusing information fr…
- Mamba-CR: A State-Space Model for Remote Sensing Image Cloud RemovalChengyao Zhang, Fengyan Wang, Xuqing Zhang, Mingchang Wang et al. · IEEE Transactions on Geoscience and Remote Sensing · Jan 1, 2025
Cloud removal is a critical step to improve the usability of cloud-contaminated remote sensing images. Deep learning methods based on convolutional neural networks (CNNs) and transformer architectures are widely used for cloud removal tasks…
- FMambaIR: A Hybrid State-Space Model and Frequency Domain for Image RestorationXin Luan, Huijie Fan, Qiang Wang, Nan Yang et al. · IEEE Transactions on Geoscience and Remote Sensing · Jan 1, 2025
With the development of deep learning, impressive progress has been made in the field of image restoration. The existing methods mainly rely on CNNs and Transformers to obtain multiscale feature information. However, these methods rarely in…
- A Lightweight Semantic Segmentation Network Based on Self-Attention Mechanism and State Space Model for Efficient Urban Scene SegmentationLangping Li, Jizheng Yi, Hui Fan, Hui Lin · IEEE Transactions on Geoscience and Remote Sensing · Jan 1, 2025
In the semantic segmentation of remote sensing images, methods based on convolutional neural networks (CNNs) and Transformers have been extensively studied. Nevertheless, CNN struggles to capture the global context due to its local feature …
- LCCDMamba: Visual State Space Model for Land Cover Change Detection of VHR Remote Sensing ImagesJun Huang, Xiaochen Yuan, C. Lam, Yapeng Wang et al. · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · Jan 1, 2025
Land cover change detection (LCCD) is a crucial research topic for rational planning of land use and facilitation of sustainable land resource growth. However, due to the complexity of LCCD tasks, integrating global and local features and f…
- HeteGraph-Mamba: Heterogeneous Graph Learning via Selective State Space ModelZhenyu Pan, Yoonsung Jeong, Xiaoda Liu, Han Liu · ICLR 2025 Conference Withdrawn Submission · Sep 26, 2024
We propose a heterogeneous graph mamba network (HGMN) as the first exploration in leveraging the selective state space models (SSSMs) for heterogeneous graph learning. Compared with the literature, our HGMN overcomes two major challenges: (…
- MambaTree: Tree Topology is All You Need in State Space ModelYicheng Xiao, Lin Song, Shaoli Huang, Jiangshan Wang et al. · NeurIPS 2024 spotlight · Sep 25, 2024
The state space models, employing recursively propagated features, demonstrate strong representation capabilities comparable to Transformer models and superior efficiency. However, constrained by the inherent geometric constraints of sequen…
- Multi-Scale VMamba: Hierarchy in Hierarchy Visual State Space ModelYuheng Shi, Minjing Dong, Chang Xu · NeurIPS 2024 poster · Sep 25, 2024
Despite the significant achievements of Vision Transformers (ViTs) in various vision tasks, they are constrained by the quadratic complexity. Recently, State Space Models (SSMs) have garnered widespread attention due to their global recepti…
- VMamba: Visual State Space ModelYue Liu, Yunjie Tian, Yuzhong Zhao, Hongtian Yu et al. · NeurIPS 2024 spotlight · Sep 25, 2024
Designing computationally efficient network architectures remains an ongoing necessity in computer vision. In this paper, we adapt Mamba, a state-space language model, into VMamba, a vision backbone with linear time complexity. At the core …
- MS$^3$M: Multi-Stage State Space Model for Motion ForecastingChunyu Liu, Shijie Li, Xulei Yang, Jianjun Yu · Submitted to ICLR 2025 · Sep 24, 2024
Motion forecasting is a fundamental component of autonomous driving systems, as it predicts an agent's future trajectories based on its surrounding environment. Transformer architectures have dominated this domain due to their strong abilit…
- State Space Model-based Classification of Major Depressive Disorder Across Multiple Imaging SitesShusheng Li, Bo Yang, Ting Ma, Chenfei Ye · KDD-AIDSH 2024 Oral · Jun 29, 2024
Major Depressive Disorder (MDD) is a prevalent psychiatric condition characterized by persistent sadness and cognitive impairments, with high recurrence rates. This paper presents a novel state space model to classify MDD using BOLD time-se…