State Space Models

Also known as SSM

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28 papers in the last four weeks, up 250% on the four weeks before. 0.3% of all new papers.

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Lyapunov-Inspired LyRIC Activation and GLARE Attention in Chaos-Guided State Space Modeling for EMG-To-Speech (ETS) Synthesis
Oct 6, 2026Sajid Fardin Dipto, Tarikul Islam Tamiti, Luke Baja-Ricketts +2State Space Models Chaotic Dynamical Systems
Department of Cyber Security Engineering George Mason University Fairfax, VA, USA
MaRK: Markov-adapted Recurrent Kernels for Dynamic Operator Conditioning in State Space Models
Oct 6, 2026Syed Ibrahim Omer, Ginny Y. Wong, Xiangyu ZhaoState Space Models Diffusion Model Fine-Tuning
Department of Data Science City University of Hong Kong
WaveGSSM: Graph Wave State Space Models for Propagating Spatio-Temporal Patterns
Oct 5, 2026Junyou Zhu, Fenying Cai, Ping Xiong +5Spatiotemporal Forecasting State Space Models
Potsdam Institute for Climate Impact Research, Germany · Machine Learning Group, Technical University of Berlin, Germany · Department of Computer Science, University of Illinois at Chicago, USA +2
A Structured State Space Sequence Model for Multi-Class Classification of Malware
Oct 1, 2026Emmanuela Andam, Rana Shaaban, Emanuel Grant +1Cybersecurity Malware Classification
Artificial Intelligence Research (AIR) Center · School of Electrical Engineering and Computer Science, College of Engineering & Mines University of North Dakota, Grand Forks, ND 58201, USA
Action-On-Item Preference Flow: A Shared Event Schema for Predictive and Generative Personalization
Oct 1, 2026Parthiv Chatterjee, Kashish Kanjaria, Vashisth Purani +2User Modeling State Tracking
KDM Lab, Dhirubhai Ambani University · Dhirubhai Ambani University · LCS2 Lab, Indian Institute of Technology Delhi
CAMOS: Coupled Oscillatory State-Space Model for Multimodal Clinical Time-Series
Sep 30, 2026Maxx Richard Rahman, Mostafa Hammouda, Wolfgang MaassIrregular Time-Series Modeling Missing-Modality Learning
Saarland University and German Research Center for Artificial Intelligence (DFKI) Saarbrücken, Germany · Saarland University Saarbrücken, Germany
SCORE-LM: State-Space Radar Representations with Language Models for Fault Diagnosis
Sep 30, 2026Mainak Mallick, Seung-Kyum ChoiFault Detection Fault Diagnosis
Georgia Institute of Technology, Atlanta, GA, USA
Riccati State Space Models: Non-iterative Parallelization for Nonlinear Sequence Modeling
Sep 28, 2026Mónika Farsang, Ramin Hasani, Daniela Rus +1Sequence Modeling State Space Models
TU Wien · MIT CSAIL · Liquid AI
GT-PSSM: Unified Probabilistic Framework for Stochastic Dynamics Modeling and Dependency Learning in Multivariate Time Series Anomaly Detection
Sep 28, 2026Wonmo Koo, Jaeyeong Lee, Taeseong Yoon +1Time-Series Anomaly Detection State Space Models
KAIST Daejeon, Republic of Korea
ADPTNet: Adaptive with Prescriptive Timescales Non-Linear SSM for Sequence Modelling
Sep 27, 2026Matei-Ioan Stan, Oliver RhodesRecurrent Neural Networks Neuromorphic Computing
International Centre for Neuromorphic Systems The University of Manchester Manchester, United Kingdom
Correct then Forecast: Observer State-Space Models for Time Series Forecasting
Sep 27, 2026Alexis-Raja Brachet, Guillaume Clavier--Frémond, Abdelhakim Ziani +2Latent Dynamics Modeling Time Series Forecasting
MICS, CentraleSup´elec, Universit´e Paris-Saclay, France · CentraleSup´elec, IETR UMR CNRS 6164, France · Universit`a di Torino, Torino, Italy.
Identifying the Predictable Drift of a Semimartingale from Marginal Laws
Sep 27, 2026Jakub Marecek, Enrico Biffis, Abigail Langbridge +1Latent Dynamics Modeling Constrained Optimization
Department of Computer Science, Czech Technical University in Prague, Czechia · Department of Finance, Imperial College London, UK · Dyson School of Design Engineering, Imperial College London, UK
Baseline Shape Decides the Verdict: A Controlled Re-Examination of Ternary Language Models at 60K Parameters
Sep 24, 2026Gautam VeldandaTernary Quantization Quantization-Aware Training
Independent Researcher
Physics and Data Driven Transformer-Mamba Framework for Flow Field
Sep 24, 2026Zhuo Zhang, Shun Zou, Canqun Yang +1PDE Surrogate Modeling PDE Operator Learning
College of Computer Science and Technology, National University of Defense Technology, Changsha, China · College of Artificial Intelligence, Nanjing Agricultural University, NanJing, China · National SuperComputer Center in Tianjin, Binhai New Area, Tianjin, China +1
Non-Commutative State Tracking with Input-Dependent Low-Rank Updates in Mamba-3
Sep 23, 2026Hiroki Fujii, Masaki YamakitaMamba State Tracking
Department of Systems and Control Engineering, Institute of Science Tokyo, Tokyo, Japan
When Labels Are Scarce: An Oscillatory State Space Model for Vibration Diagnosis
Sep 23, 2026Mainak Mallick, Seung-Kyum ChoiPredictive Maintenance Tiny ML
Georgia Institute of Technology
Full-Covariance Smoothing of Bayesian Neural Networks for Online Adaptation
Sep 23, 2026Oren Wright, Haoming Jing, Qiaoan Shen +3Bayesian Neural Networks Bayesian Filtering
Department of Electrical and Computer Engineering, Carnegie Mellon University · Software Engineering Institute, Carnegie Mellon University · Fujitsu Research of America
Temporally Ordered Region-Token Mamba with Logit-Space Diffusion for Remote Sensing Change Detection
Sep 22, 2026Anuvab Sen, Maneet Chatterjee, Aparup Ghosh +3Remote Sensing Change Detection Diffusion Models
Georgia Institute of Technology · Indian Institute of Engineering Science and Technology (IIEST), Shibpur · Nanyang Technological University +2
PR-Smoother: Simulator-Preserving Non-Gaussian Smoothing for Data Assimilation
Sep 22, 2026Yuta TarumiAmortized Inference Data Assimilation
iTHEMS RIKEN Kobe, Japan
Comparing Latent Concept Formation in State Space Models and Transformers via Sparse Autoencoders
Sep 21, 2026Rithin Nagaraj, Rupa Laalasa Oruganti, Prerna Subhashchandra Kunder +1Transformer Interpretability Language Modeling
Department of Computer Science and Engineering PES University Bangalore, India
Pharmacokinetic State Space Models for Unbiased Prediction of Haemodynamic Collapse
Sep 21, 2026Rithin Nagaraj, Sudiksha Chindula, Bhaskarjyoti DasClinical Prediction Clinical Risk Prediction
Department of Computer Science and Engineering, PES University, Bengaluru, 560085, India · Department of Computer Science and Engineering (AIML), PES University, Bengaluru, 560085, India
REACT: A Fully Spiking State-Space Model for Real-Time Event-Driven Temporal Perception
Sep 16, 2026Geoffroy Keime, Nicolas Cuperlier, Benoit R. CottereauCollision Avoidance Event-Based Vision
CerCo, CNRS UMR5549 Université de Toulouse Toulouse, France · IPAL, CNRS IRL, Singapore · ETIS, CY Cergy Paris Université CNRS, ENSEA Cergy, France
Long-Context Demonstration Selection Using State Space Models
Sep 15, 2026Ziniu Zhang, Zhenshuo Zhang, Ruoxuan Xiong +2In-Context Example Selection In-Context Learning
Northeastern University · Emory University
On the Importance of Gating: Memorization vs. In-Context Learning in State Space Models
Sep 15, 2026William L. Tong, Aryo Lotfi, Emmanuel Abbe +6Memorization in Language Models Long-Context Modeling
Harvard University · Apple
Anatomy of Associative Recall in Fixed-State Recurrences: A Matched-State Decomposition, an Interference Wall, and a Curriculum That Breaks It
Sep 14, 2026Julian Boesch, Andrew WeeRecurrent Neural Networks Curriculum Learning
1Purdue University · 2Obit Research
Fixed State, Long Reach: What a Constant-Size Cache Buys Block Diffusion at Scale
Sep 14, 2026Vaibhav Singh, Pierre-André Noël, Torsten Scholak +2Blockwise Diffusion Language Models Diffusion Language Model Inference
Mila · Concordia University · ServiceNow Research
OphBiWSSD: Scaling Temporal Action Localization in Ophthalmic Surgeries with Bidirectional Weight-tied State Space Duality
Sep 14, 2026Yang Liu, Qionghong Ma, Joongwon Chae +10Surgical Video Understanding Temporal Action Detection
Institute of Biopharmaceutics and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen 518055, China · Department of Ophthalmology, The Fifth Affiliated Hospital of Wenzhou Medical University, Wenzhou Medical University, Lishui 323000, China · School of Optometry, The Hong Kong Polytechnic University +4
PAC-Bayesian Bounds for Learning Partially Observed Stochastic Linear Time-Invariant State-Space Systems with Inputs and Sub-Gaussian Noise
Sep 8, 2026Mihaly Petreczky, Mohamad Al Ahdab, John LethPAC-Bayesian Generalization Bounds Stochastic Linear Dynamical Systems
CRIStAL, Centrale Lille, Universit´e de Lille, France · Department of Electronic Systems, Aalborg University, Denmark
Global Divergence, Local Convergence: Representation Geometry in SSMs and Transformers
Sep 8, 2026Amit Ben-Artzy, Roy SchwartzTransformer Representation Geometry in Language Models
Computer Science, The Hebrew University of Jerusalem
On the Recall Scaling Laws in Mamba: A Theoretical and Mechanistic Study via Hashing
Sep 7, 2026Yuval Koren, Assaf Ben-Kish, Raja Giryes +2Mechanistic Interpretability Scaling Laws
Blavatnik School of Computer Science and AI, Tel Aviv University

Oct 6, 2026, cs.SDJ/K move · Enter open · S save

Lyapunov-Inspired LyRIC Activation and GLARE Attention in Chaos-Guided State Space Modeling for EMG-To-Speech (ETS) Synthesis

Sajid Fardin Dipto, Tarikul Islam Tamiti, Luke Baja-Ricketts, David Vergano+1

Department of Cyber Security Engineering George Mason University Fairfax, VA, USA

State Space Models Chaotic Dynamical Systems

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Electromyography-to-Speech (ETS) synthesis is typically a non-linear, chaotic dynamical system. However, no prior work has studied the chaotic behavior of ETS synthesis to date. Yet, prior works strictly rely on standard reconstruction metrics with parameter-heavy transformers that systematically over-smooth natural acoustic dynamics. To close this gap, for the first time, we propose a chaos-inspired Lyapunov-derived activation function (LyRIC) with two novel chaotic loss functions, Lyapunov Exponent Regularization and Multi-Scale Detrended Fluctuation Analysis, to explicitly capture the deterministic chaos of human phonation. In addition, we introduce a compressed novel encoder, GLAME, which synergizes global Mamba state-space modeling with localized GLARE attention. We comprehensively perform frame-level acoustic evaluation in a multilingual and multi-speaker setup using English and Mandarin datasets. The proposed system outperforms the established baseline with a 4.69x increase in objective intelligibility (STOI: 0.61 vs. 0.13) and a 2.08x improvement in spectral reconstruction (LSD: 1.08 vs. 2.25). Importantly, this improvement is achieved with 73.49% fewer parameters (14.34M vs. 54.10M), establishing a new baseline for ETS synthesis. To the best of our knowledge, this is the first work demonstrating that integrating non-linear chaotic physics into neural networks yields superior yet compact inductive biases for real-time ETS synthesis.

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