State Space Models

Also known as SSM

State space models describe how hidden states evolve over time and generate observations, supporting both probabilistic system identification and neural sequence learning. Approaches range from Kalman filtering, smoothing, and particle or variational inference to structured S4/HiPPO models, selective Mamba scans, and hybrids that combine recurrence with attention. They are applied to long-context language and video, time-series forecasting, and signals such as EEG, with evaluation spanning retrieval and sequence benchmarks as well as real-world prediction tasks. Their recurrent structure can offer linear-time processing and bounded memory, but efficient computation does not guarantee reliable long-range retrieval or state tracking, and practical speed, robustness, and accuracy remain important trade-offs.

Latest papers

299 papers30 papers in the last four weeks, down 12% on the four weeks before. 0.2% of all new papers.

RSGMamba: Reliability-Aware Self-Gated State Space Model for Multimodal Semantic Segmentation
Apr 14, 2026Guoan Xu, Yang Xiao, Guangwei Gao +3Multimodal Semantic Segmentation Semantic Segmentation
Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia · PCA Lab, Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China · Bionic Vision Systems Laboratory, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China +2
Physics-Informed State Space Models for Reliable Solar Irradiance Forecasting in Off-Grid Systems
Apr 13, 2026Mohammed Ezzaldin Babiker AbdullahSolar Irradiance Forecasting Time Series Forecasting
Department of Electrical and Electronic Engineering, Faculty of Engineering Sciences, Omdurman Islamic University, Omdurman, Sudan
COREY: Entropy-Guided Runtime Chunk Scheduling for Selective Scan Kernels
Apr 12, 2026Bo Ma, Jinsong Wu, Weiqi YanLLM Inference Scheduling State Space Models
Auckland University of Technology; Resideo Technologies, Inc. · Guilin University of Electronic Technology · Auckland University of Technology
Stability Enhanced Gaussian Process Variational Autoencoders
Apr 10, 2026Carl R. Richardson, Jichen Zhang, Ethan King +1Latent Dynamics Modeling Variational Autoencoders
Johns Hopkins University, Baltimore, Maryland, 21218, USA
State Space Models are Effective Sign Language Learners: Exploiting Phonological Compositionality for Vocabulary-Scale Recognition
Apr 9, 2026Bryan Cheng, Austin Jin, Jasper ZhangSign Language Recognition Compositional Representation Learning
William A. Shine Great Neck South High School
ABMAMBA: Multimodal Large Language Model with Aligned Hierarchical Bidirectional Scan for Efficient Video Captioning
Apr 9, 2026Daichi Yashima, Shuhei Kurita, Yusuke Oda +3Video Captioning Multimodal Large Language Models
Keio University, 3-14-1, Kohoku Ward, Yokohama, Kanagawa 223-8522, Japan · National Institute of Informatics Research and Development Center for Large Language Models, 1-1-1, Hitotsubashi, Chiyoda, Tokyo 100-0003, Japan · National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda, Tokyo 101-8430, Japan
Beyond the Autoregressive Horizon: A Comprehensive Survey of Diffusion Models, World Modelling, and State Space Models for Code
Apr 9, 2026Kishan Maharaj, Ashita Saxena, Srikanth TamilselvamCode World Models LLM Code Generation
IBM
Controller Design for Structured State-space Models via Contraction Theory
Apr 8, 2026Muhammad Zakwan, Vaibhav Gupta, Alireza Karimi +2Contraction Theory Feedback Control
Control & Automation Group, Inspire AG, 8005 Zürich, Switzerland · Automatic Control Laboratory (IfA), ETH Zürich, 8092 Zürich, Switzerland. · Laboratoire d’Automatique, EPFL, 1015 Lausanne, Switzerland.
The UNDO Flip-Flop: A Controlled Probe for Reversible Semantic State Management in State Space Model
Apr 7, 2026Hongxu ZhouState Space Models State Tracking
Erasmus Mundus Language and Communication Technologies Saarland University Saarbrücken, Germany
StateSMix: Online Lossless Compression via Mamba State Space Models and Sparse N-gram Context Mixing
Apr 5, 2026Roberto TacconelliLossless Data Compression State Space Models
Independent Researcher
Safety, Security, and Cognitive Risks in State-Space Models: A Systematic Threat Analysis with Spectral, Stateful, and Capacity Attacks
Apr 4, 2026Manoj ParmarState Space Models Adversarial Attacks
SovereignAI Security Labs, Bengaluru, India
CAGMamba: Context-Aware Gated Cross-Modal Mamba Network for Multimodal Sentiment Analysis
Apr 4, 2026Minghai Jiao, Jing Xiao, Peng Xiao +6Multimodal Sentiment Analysis Multimodal Fusion
School of Computer Science and Engineering, Northeastern University, Shenyang, China · Xingning Power Supply Bureau, Guangdong Power Grid Co., Ltd., Meizhou, China
Learning Nonlinear Regime Transitions via Semi-Parametric State-Space Models
Apr 3, 2026Prakul Sunil HiremathRegime-Switching Dynamical Systems State Space Models
Visvesvaraya Technological University, Belagavi, India · Aliens on Earth (AoE) Autonomous Research Group, Belagavi, India
Adversarial Robustness of Deep State Space Models for Forecasting
Apr 3, 2026Sribalaji C. Anand, George J. PappasAdversarial Robustness State Space Models
Department of Electrical and Systems Engineering, University of Pennsylvania, United States · KTH Royal Institute of Technology, Sweden
AEGIS: Adversarial Entropy-Guided Immune System -- Thermodynamic State Space Models for Zero-Day Network Evasion Detection
Apr 2, 2026Vickson FerrelNetwork Intrusion Detection Encrypted Network Traffic Analysis
Faculty of Computer Science & Information Technology Universiti Malaysia Sarawak (UNIMAS), Kota Samarahan, Malaysia · Founder & Lead Architect, Vixero Technology Enterprise, Kuching, Sarawak, Malaysia
Attention to Mamba: A Recipe for Cross-Architecture Distillation
Apr 1, 2026Abhinav Moudgil, Ningyuan Huang, Eeshan Gunesh Dhekane +3Cross-Architecture Knowledge Distillation Language Model Distillation
Apple · MILA Research Institute · Flat Iron Institute
Reservoir observer enhanced with residual calibration and attention mechanism
Apr 1, 2026Yichen Liu, Wei Xiao, Tianguang ChuReservoir Computing State Space Models
School of Advanced Manufacturing and Robotics, Peking University, Beijing 100871, China
MATHENA: Mamba-based Architectural Tooth Hierarchical Estimator and Holistic Evaluation Network for Anatomy
Apr 1, 2026Kyeonghun Kim, Jaehyung Park, Youngung Han +17Dental Imaging Medical Imaging
OUTTA, Seoul, South Korea · Seoul National University, Seoul, South Korea · Sangmyung University, Seoul, South Korea +4
Learning Surrogate LPV State-Space Models with Uncertainty Quantification
Mar 31, 2026E. Javier Olucha, Amritam Das, Roland TóthSurrogate Modeling State Space Models
Control Systems Group, Eindhoven University of Technology, The Netherlands · Systems and Control Laboratory, HUN-REN Institute for Computer Science and Control, Hungary
Mamba-driven MRI-to-CT Synthesis for MRI-only Radiotherapy Planning
Mar 24, 2026Konstantinos Barmpounakis, Theodoros P. Vagenas, Maria Vakalopoulou +1Medical Image Synthesis State Space Models
School of Electrical and Computer Engineering, National Technical University of Athens · Archimedes, Athena Research Center · CentraleSupélec, University of Paris-Saclay
SegMaFormer: A Hybrid State-Space and Transformer Model for Efficient Segmentation
Mar 23, 2026Duy D. Nguyen, Phat T. Tran-TruongHybrid Transformer-State Space Model Architectures 3D Medical Image Segmentation
Department of Software Engineering, Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, Dien Hong Ward, Ho Chi Minh City, Vietnam · Vietnam National University Ho Chi Minh City, Linh Trung Ward, Ho Chi Minh City, Vietnam
Auto-differentiable data assimilation: Co-learning of states, dynamics, and filtering algorithms
Mar 21, 2026Melissa Adrian, Daniel Sanz-Alonso, Rebecca WillettData Assimilation State Space Models
School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907 · Department of Statistics, The University of Chicago, Chicago, IL 60637 · Department of Computer Science, The University of Chicago, Chicago, IL 60637 +1
Do VLMs Need Vision Transformers? Evaluating State Space Models as Vision Encoders
Mar 19, 2026Shang-Jui Ray Kuo, Paola Cascante-BonillaVision-Language Models State Space Models
Stony Brook University
Extending SSMs with the Exponentially Weighted Signature
Mar 19, 2026Alexandre Bloch, Benjamin Walker, Joël Mouterde +3Dynamical Systems Time Series Classification
Mathematical Institute, University of Oxford · SKF · Department of Mathematics, Imperial College London
DA-Mamba: Learning Domain-Aware State Space Model for Global-Local Alignment in Domain Adaptive Object Detection
Mar 19, 2026Haochen Li, Rui Zhang, Hantao Yao +5Object Detection Unsupervised Domain Adaptation
Intelligent Software Research Center, Institute of Software, CAS · University of Chinese Academy of Sciences · State Key Lab of Processors, Institute of Computing Technology, CAS +1
AlignMamba-2: Enhancing Multimodal Fusion and Sentiment Analysis with Modality-Aware Mamba
Mar 19, 2026Yan Li, Yifei Xing, Xiangyuan Lan +3Multimodal Sentiment Analysis Multimodal Fusion
Pengcheng Laboratory, Shenzhen, China · Shaanxi University of Science & Technology, Xi’an, China · School of Computer Science, Northwestern Polytechnical University, Xi’an, China
Atomic Trajectory Modeling with State Space Models for Biomolecular Dynamics
Mar 18, 2026Liang Shi, Jiarui Lu, Junqi Liu +3Molecular Dynamics Simulation State Space Models
School of Computer Science, Peking University · BioGeometry · Mila - Québec AI Institute +3
S3T-Former: A Purely Spike-Driven State-Space Topology Transformer for Skeleton Action Recognition
Mar 18, 2026Naichuan Zheng, Hailun Xia, Zepeng Sun +2Skeleton-Based Action Recognition Spiking Transformers
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China · International School, Beijing University of Posts and Telecommunications, Beijing, China · School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China
FEAT: A Linear-Complexity Foundation Model for Extremely Large Structured Data
Mar 17, 2026Zhenghang Song, Tang Qian, Lu Chen +7Tabular Foundation Models Linear Attention
Zhejiang University · Ant Group · Aalborg University
HGP-Mamba: Integrating Histology and Generated Protein Features for Mamba-based Multimodal Survival Risk Prediction
Mar 17, 2026Jing Dai, Chen Wu, Ming Wu +4Cancer Survival Prediction Multimodal Survival Analysis
Cancer Hospital of Dalian University of Technology, Shenyang, China · School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, China · Key Laboratory of Integrated Circuit and Biomedical Electronic System, Dalian University of Technology, Dalian, China

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RSGMamba: Reliability-Aware Self-Gated State Space Model for Multimodal Semantic Segmentation

Guoan Xu, Yang Xiao, Guangwei Gao, Dongchen Zhu+2

Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia · PCA Lab, Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China · Bionic Vision Systems Laboratory, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China+2

Multimodal Semantic Segmentation Semantic Segmentation Reliability-Aware Multimodal Fusion Multimodal Fusion Multimodal Robustness State Space Models

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Multimodal semantic segmentation has emerged as a powerful paradigm for enhancing scene understanding by leveraging complementary information from multiple sensing modalities (e.g., RGB, depth, and thermal). However, existing cross-modal fusion methods often implicitly assume that all modalities are equally reliable, which can lead to feature degradation when auxiliary modalities are noisy, misaligned, or incomplete. In this paper, we revisit cross-modal fusion from the perspective of modality reliability and propose a novel framework termed the Reliability-aware Self-Gated State Space Model (RSGMamba). At the core of our method is the Reliability-aware Self-Gated Mamba Block (RSGMB), which explicitly models modality reliability and dynamically regulates cross-modal interactions through a self-gating mechanism. Unlike conventional fusion strategies that indiscriminately exchange information across modalities, RSGMB enables reliability-aware feature selection and enhancing informative feature aggregation. In addition, a lightweight Local Cross-Gated Modulation (LCGM) is incorporated to refine fine-grained spatial details, complementing the global modeling capability of RSGMB. Extensive experiments demonstrate that RSGMamba achieves state-of-the-art performance on both RGB-D and RGB-T semantic segmentation benchmarks, resulting 58.8% / 54.0% mIoU on NYUDepth V2 and SUN-RGBD (+0.4% / +0.7% over prior best), and 61.1% / 88.9% mIoU on MFNet and PST900 (up to +1.6%), with only 48.6M parameters, thereby validating the effectiveness and superiority of the proposed approach.