Latest State Space Models (Mamba) Research Papers
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- OncoTraj: a public benchmark for longitudinal resistance prediction in EGFR-mutant non-small-cell lung cancer on osimertinibAbhijoy Sarkar, Aarchi Singh Thakur · arXiv · Jun 9, 2026
Resistance to first-line osimertinib in EGFR-mutant non-small-cell lung cancer (NSCLC) is the canonical example of predictable clonal evolution under therapeutic pressure, yet no public benchmark exists for training or evaluating computatio…
- CaMBRAIN: Real-time, Continuous EEG Inference with Causal State Space ModelsAbhilash Durgam, Nyle Siddiqui, Jeffrey A. Chan-Santiago, Qiushi Fu et al. · arXiv · May 27, 2026
Electroencephalography (EEG) is a critical, non-invasive method to monitor electrical brain activity. EEGs can span anywhere from a couple seconds to multiple hours, posing a major hurdle for existing deep learning methods due to two major …
- Multi-Mixer Models: Flexible Sequence Modeling with Shared RepresentationsKevin Y. Li, Asher Trockman, Ananda Theertha Suresh, Ziteng Sun · arXiv · May 27, 2026
Softmax attention is the cornerstone of modern large language models, but its memory scales linearly and compute quadratically with sequence length. Linear recurrent models, such as linear attention and state space models, have become widel…
- MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking DataAmir Mousavi, Mohammad Sadegh Sirjani, Erfan Nourbakhsh, Mimi Xie et al. · arXiv · May 21, 2026
Real-time cognitive load assessment from eye-tracking signals could potentially enable adaptive human-centered-AI such as safety-critical applications such as driver vigilance monitoring or automated flight deck assistance, yet two challeng…
- How Long Does Infinite Width Last? Signal Propagation in Long-Range Linear RecurrencesMariia Seleznova · arXiv · May 6, 2026
We study signal propagation in linear recurrent models at finite width. While existing signal propagation theory relies predominantly on the infinite-width limit, it remains unclear for how long that approximation remains accurate when recu…
- Turning the TIDE: Cross-Architecture Distillation for Diffusion Large Language ModelsGongbo Zhang, Wen Wang, Ye Tian, Li Yuan · arXiv · Apr 29, 2026
Diffusion large language models (dLLMs) offer parallel decoding and bidirectional context, but state-of-the-art dLLMs require billions of parameters for competitive performance. While existing distillation methods for dLLMs reduce inference…
- State space modeling of variation propagation in multistage machining systems with generalized fixture layouts: A case study from an engine manufacturing enterpriseFangrui Li, Shichang Du, Chen Zhao · Proceedings of the Institut... · Mar 25, 2026
In multistage machining systems (MMSs), dimensional variation propagates across stages due to fixture and datum deviations, compounded by process-induced deformation. Accurate modeling of this propagation remains challenging, as each stage …
- Latent Matters: Learning Deep State-Space ModelsAlexej Klushyn, Richard Kurle, Maximilian Soelch, Botond Cseke et al. · arXiv (Cornell University) · Feb 26, 2026
Deep state-space models (DSSMs) enable temporal predictions by learning the underlying dynamics of observed sequence data. They are often trained by maximising the evidence lower bound. However, as we show, this does not ensure the model ac…
- 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…
- ProtMamba: a homology-aware but alignment-free protein state space modelDamiano Sgarbossa, Cyril Malbranke, Anne-Florence Bitbol · bioRxiv · Jun 1, 2025
Motivation Protein language models are enabling advances in elucidating the sequence-to-function mapping, and have important applications in protein design. Models based on multiple sequence alignments efficiently capture the evolutionary i…
- 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…
- InsectMamba: State Space Model with Adaptive Composite Features for Insect RecognitionQianning Wang, Yucheng Zhou, Zhixin Lai, Yucheng Zhou · IEEE International Conference on Acoustics, Speech, and Signal Processing · Apr 6, 2025
The recognition of insect pests is a critical task in agricultural technology, vital for ensuring food security and environmental sustainability. However, due to factors like high camouflage and species diversity, the complexity of pest ide…
- 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…
- 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, and 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…
- CD-Lamba: Boosting Remote Sensing Change Detection via a Cross-Temporal Locally Adaptive State Space ModelZhenkai Wu, Xiaowen Ma, Rongrong Lian, Kai Zheng et al. · arXiv.org · Jan 26, 2025
Mamba, with its advantages of global perception and linear complexity, has been widely applied to identify changes of the target regions within the remote sensing (RS) images captured under complex scenarios and varied conditions. However, …
- 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…
- GLVMamba: A Global–Local Visual State-Space Model for Remote Sensing Image SegmentationHuihui Li, Huajian Pan, Xiaoyong Liu, Jinchang Ren et al. · IEEE Transactions on Geoscience and Remote Sensing · Jan 1, 2025
Semantic segmentation of remote sensing images (RSIs) has significant advances with the adoption of deep neural networks, taking the advantages of convolutional neural networks (CNNs) in local feature extraction with transformers in global …
- 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…
- 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…
- 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…