Datasets:

fineset-io
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state-space-models-papers

id string	sources list	title string	abstract string	authors list	categories list	fields_of_study list	published_date timestamp[s]	url string	pdf_url string	arxiv_id string	doi string	citation_count int64	influential_citation_count int64	has_code bool	code_url string	venue string	quality_score float64
aeaf83abc516eb6a5a39fd49f5063a479468a00f591416b968e28cf4a998d5af	[ "arxiv" ]	Ternary Mamba: Grouped Quantization-Aware Training of W1.58A16 State Space Models	State Space Models (SSMs) such as Mamba-2 offer linear-time inference but their memory footprint limits edge deployment. Prior ternary SSM work (Slender-Mamba) trains from scratch on 150B tokens; we show a pretrained checkpoint suffices, reducing the marginal token budget by 1,000x. Using grouped quantization-aware tra...	[ "Ramprasath Ganesaraja", "Sahil Dilip Panse", "Swathika N" ]	[ "cs.LG", "cs.AI" ]	[]	2026-06-16T00:00:00	https://arxiv.org/abs/2606.18114	https://arxiv.org/pdf/2606.18114v1	2606.18114	null	0	0	false	null	null	0.35
a9da9090e15f2acbf70dd9f926ea0f53ce40b2b9c9df5f291a652cdd0ca3d51d	[ "arxiv" ]	Reload-Mamba: Hierarchical Anti-Dilution State-Space Modeling for Multi-Class Semantic Segmentation	Mamba-based state space models offer linear-time long-range modeling for high-resolution dense prediction, but sequential state-space propagation can attenuate boundary-sensitive and detail-sensitive responses that are critical in multi-class semantic segmentation. We propose Reload-Mamba, a semantic segmentation frame...	[ "Sheng-Wei Chan", "Hsin-Jui Pan", "Jen-Shiun Chiang" ]	[ "cs.CV" ]	[]	2026-06-16T00:00:00	https://arxiv.org/abs/2606.17966	https://arxiv.org/pdf/2606.17966v1	2606.17966	null	0	0	false	null	null	0.35
28890da352d2d2348f1e45e05f8bb8bd71ab5fd0755926ee200db6c818f73759	[ "arxiv" ]	Task-Restricted Symmetries in Recurrent Weight Space	Recurrent networks can contain substantial functional redundancy in weight space: changing a recurrent matrix may leave the input-output rollout nearly unchanged on a task distribution, while similar-scale changes can destroy the same behavior. We study this redundancy in one-layer tanh RNNs using ordered real Schur co...	[ "Simon Dräger" ]	[ "cs.LG" ]	[]	2026-06-16T00:00:00	https://arxiv.org/abs/2606.18457	https://arxiv.org/pdf/2606.18457v1	2606.18457	null	0	0	false	null	null	0.35
83f36ca26767a9fe5efc32e4a75cce5c1f5b5f49b77b7877a7b7210049ee1671	[ "arxiv", "semantic_scholar" ]	DeepMine-Mamba: Mitigating Information Dilution in Mamba-Based State Space Models for Document Image Binarization	Document image binarization aims to separate foreground text from degraded backgrounds while preserving thin, broken, and low-contrast strokes. Although deep learning methods have improved binarization performance, most existing approaches rely on convolutional, transformer-based, or generative architectures, while Mam...	[ "Sheng-Wei Chan", "Yung-Che Wang", "Hsin-Jui Pan", "Chia-Min Lin", "Jen-Shiun Chiang" ]	[ "cs.CV" ]	[ "Computer Science" ]	2026-06-07T00:00:00	https://arxiv.org/abs/2606.08781	https://arxiv.org/pdf/2606.08781v2	2606.08781	null	0	0	true	https://github.com/henrychan0719/Deep-Mine-Mamba	null	0.65
e5958c80e2f110d394e2e03639bf45e36990796fcc3336897801fc8566cb4997	[ "arxiv", "semantic_scholar" ]	Advancing Heliophysics and Space Weather Modeling through Open Science	We present a community-wide effort to develop a strategy and action plan to advance heliophysics and space weather modeling through open science. While open science has the potential to enhance the quality and pace of scientific discovery, its application to scientific modeling requires more careful consideration regar...	[ "C. Corti", "M. M. Kuznetsova", "M. A. Reiss", "J. Yue", "J. Karpen", "C. N. Arge", "F. Bacchini", "C. Bard", "S. Bruinsma", "R. M. Caplan", "L. K. S. Daldorff", "P. J. Deka", "C. R. DeVore", "S. Elvidge", "N. Ganushkina", "J. D. Huba", "B. V. Jackson", "V. Jordanova", "J. A. Lin...	[ "physics.space-ph", "astro-ph.IM", "astro-ph.SR", "physics.comp-ph" ]	[ "Physics" ]	2026-05-28T00:00:00	https://arxiv.org/abs/2605.30626	https://arxiv.org/pdf/2605.30626v1	2605.30626	null	1	0	false	null	null	0.35
fa77155c03cbe72999517c78d920b1d7c0a1f565fbcf60f87a08d2efe99f601b	[ "arxiv", "semantic_scholar" ]	SO-Mamba: State-Ownership Mamba for Unrolled MRI Reconstruction	Accelerated MRI reconstruction requires recovering missing details while preserving anatomically coherent structures across large spatial regions. State-space models such as Mamba provide efficient long-range modeling, making them attractive learned regularizers for unrolled reconstruction. However, in a data-consisten...	[ "Pengcheng Fang", "Hongli Chen", "Fangfang Tang", "Feng Liu", "Xiaohao Cai", "Shanshan Shan" ]	[ "cs.CV" ]	[ "Computer Science" ]	2026-05-21T00:00:00	https://arxiv.org/abs/2605.22031	https://arxiv.org/pdf/2605.22031v1	2605.22031	null	0	0	false	null	null	0.35
f57e7632a973cf7ec297b40929e3789b5538de80b6e0bc2e4223860173d75cde	[ "arxiv", "semantic_scholar" ]	Linear-DPO: Linear Direct Preference Optimization for Diffusion and Flow-Matching Generative Models	Direct Preference Optimization (DPO) is successful for alignment in LLMs but still faces challenges in text-to-image generation. Existing studies are confined to denoising diffusion models while overlooking flow-matching, and suffer from an objective mismatch when applying discrete NLP-based DPO to regression-based gen...	[ "Kesong Li", "Yixuan Xu", "Kuo-kun Tseng", "Weiyi Lu", "Kan Liu", "Tao Lan" ]	[ "cs.CV", "cs.LG" ]	[ "Computer Science" ]	2026-05-20T00:00:00	https://arxiv.org/abs/2605.21123	https://arxiv.org/pdf/2605.21123v1	2605.21123	null	0	0	true	https://github.com/Whynot0101/Linear-DPO	null	0.65
25482b6fc67d8efc0decb862ef004cd0d8e55ac33740811c594dd0720a784318	[ "arxiv", "semantic_scholar" ]	Flash PD-SSM: Memory-Optimized Structured Sparse State-Space Models	State-space models (SSMs) face a fundamental trade-off between efficiency and expressivity that is mainly dictated by the structure of the model's transition matrix. Unstructured transition matrices enable maximal expressivity, as measured by their ability to model finite-state automaton (FSA) transitions, but come at ...	[ "Aleksandar Terzić", "Francesco Carzaniga", "Nicolas Menet", "Yannick Biehl", "Michael Hersche", "Thomas Hofmann", "Abbas Rahimi" ]	[ "cs.LG", "cs.AI" ]	[ "Computer Science" ]	2026-05-18T00:00:00	https://arxiv.org/abs/2605.19150	https://arxiv.org/pdf/2605.19150v1	2605.19150	null	0	0	false	null	null	0.35
9aad1a1f88d08918aef24be68aaa7542d54d171526810d85b405ddae86827bf7	[ "arxiv", "semantic_scholar" ]	Patch-MoE Mamba: A Patch-Ordered Mixture-of-Experts State Space Architecture for Medical Image Segmentation	CNN- and Transformer-based architectures have achieved strong performance in medical image segmentation, but CNNs are limited in modeling long-range dependencies, while Transformers often suffer from quadratic computational and memory complexity. State space models, especially Mamba-based networks, offer an efficient a...	[ "Diego Adame", "Fabian Vazquez", "Jose A. Nunez", "Huimin Li", "Jinghao Yang", "Erik Enriquez", "DongChul Kim", "Haoteng Tang", "Bin Fu", "Pengfei Gu" ]	[ "cs.CV" ]	[ "Computer Science" ]	2026-05-18T00:00:00	https://arxiv.org/abs/2605.17719	https://arxiv.org/pdf/2605.17719v1	2605.17719	null	0	0	false	null	null	0.35
f58fff1ef4bb3bf84c6e1eabee858c75a884f30f49fe73e66f23ba21196c6ad0	[ "arxiv", "semantic_scholar" ]	Looped SSMs: Depth-Recurrence and Input Reshaping for Time Series Classification	State Space Models (SSMs) are inherently recurrent along the sequence dimension, yet depth-recurrence - reusing the same block repeatedly across layers, as recently applied in looped transformers - has not been explored in this model family. We show that a looped SSM with $k$ parameters iterated $L$ times consistently ...	[ "Mónika Farsang", "Ramin Hasani", "Daniela Rus", "Radu Grosu" ]	[ "cs.LG", "cs.AI" ]	[ "Computer Science" ]	2026-05-15T00:00:00	https://arxiv.org/abs/2605.16048	https://arxiv.org/pdf/2605.16048v1	2605.16048	null	0	0	false	null	null	0.35
9097a3ea41fde59a2e4438f05c7dcd24580ad283c3b1d8f8e661981d8924452e	[ "arxiv", "semantic_scholar" ]	MHMamba: Multi-Head Mamba for 3D Brain Tumor Segmentation	Brain tumors exhibit high heterogeneity in morphology and multimodal contrast, making manual slice-by-slice de lineation time-consuming and experience-dependent, thus necessitating efficient and stable automated segmentation methods. To address the limitations of CNNs in modeling long-range dependencies, and the heavy ...	[ "Hanjun Tao", "Hua Wang", "Fan Zhang" ]	[ "cs.CV", "cs.AI" ]	[ "Computer Science" ]	2026-05-15T00:00:00	https://arxiv.org/abs/2605.16464	https://arxiv.org/pdf/2605.16464v1	2605.16464	null	0	0	false	null	null	0.35
f2d90579d72846888f37d3a46dd82e962fcd75ea58155852d07980ff5838da3c	[ "arxiv", "semantic_scholar" ]	3DTMDet: A Dual-Path Synergy Network of Transformer and SSM for 3D Object Detection in Point Clouds	A fundamental challenge in point cloud object detection lies in the conflict between the extreme sparsity of distant points and the need for remote context understanding. The existing methods typically use 1D serialization to expand the receptive field, which inevitably discards already scarce local geometric details a...	[ "Bingwen Qiu", "Yuan Liu", "Junqi Bai", "Tong Jiang", "Ben Liang", "Fangzhou Chen", "Xiubao Sui", "Qian Chen" ]	[ "cs.CV" ]	[ "Computer Science" ]	2026-05-15T00:00:00	https://arxiv.org/abs/2605.15546	https://arxiv.org/pdf/2605.15546v1	2605.15546	null	0	0	true	https://github.com/QiuBingwen/3DTMDet	null	0.65
2d068a9aab4d33193928a82a875a930ca44ed7577e290825e309d78dc793137b	[ "arxiv", "semantic_scholar" ]	Social-Mamba: Socially-Aware Trajectory Forecasting with State-Space Models	Human trajectory forecasting is crucial for safe navigation in crowded environments, requiring models that balance accuracy with computational efficiency. Efficiently modeling social interactions is key to performance in dense crowds. Yet, most recent methods rely on attention mechanisms, which are effective at capturi...	[ "Po-Chien Luan", "Wuyang Li", "Yang Gao", "Alexandre Alahi" ]	[ "cs.CV" ]	[ "Computer Science" ]	2026-05-14T00:00:00	https://arxiv.org/abs/2605.15424	https://arxiv.org/pdf/2605.15424v1	2605.15424	null	0	0	true	https://github.com/vita-epfl/Social-Mamba	null	0.65
d39f2970790f69f67237ef438a9efc95ae312f638b5ce680614a3123f3ef27b0	[ "arxiv", "semantic_scholar" ]	TCP-SSM: Efficient Vision State Space Models with Token-Conditioned Poles	State Space Models (SSMs) have emerged as a compelling alternative to attention models for long-range vision tasks, offering input-dependent recurrence with linear complexity. However, most efficient SSM variants reduce computation cost by modifying scan routes, resolutions, or traversal patterns, while largely leaving...	[ "Sara Shoouri", "Morteza Tavakoli Taba", "Hun-Seok Kim" ]	[ "cs.CV", "cs.AI" ]	[ "Computer Science" ]	2026-05-12T00:00:00	https://arxiv.org/abs/2605.11563	https://arxiv.org/pdf/2605.11563v1	2605.11563	null	0	0	false	null	null	0.35
f27662f53c3ef332a49ee0c4c74e1c96f43760bcb5b844397e1b6d46e0dcdc12	[ "arxiv", "semantic_scholar" ]	Can Graphs Help Vision SSMs See Better?	Vision state space models inherit the efficiency and long-range modeling ability of Mamba-style selective scans. However, their performance depends critically on the representation of two-dimensional visual features as one-dimensional token sequences. Existing scan operators range from predefined geometric traversals t...	[ "Dhruv Parikh", "Anvitha Ramachandran", "Haoyang Fan", "Mustafa Munir", "Rajgopal Kannan", "Viktor Prasanna" ]	[ "cs.CV" ]	[ "Computer Science" ]	2026-05-11T00:00:00	https://arxiv.org/abs/2605.11300	https://arxiv.org/pdf/2605.11300v1	2605.11300	null	0	0	false	null	null	0.35
11310f8553106552033d2a7d1cb5801464f2793fd51a26c275c5954449306bb8	[ "arxiv", "semantic_scholar" ]	Polygon-mamba: Retinal vessel segmentation using polygon scanning mamba and space-frequency collaborative attention	Retinal vessel segmentation is crucial for diagnosis and assessment of ocular diseases. Notably, segmentation of small retinal vessels has been consistently recognized as a challenging and complex task. To tackle this challenge, we design a hybrid CNN-Mamba fusion network that integrates polygon scanning mamba and spac...	[ "Yuanyuan Peng", "Wen Li" ]	[ "cs.CV" ]	[ "Computer Science" ]	2026-05-11T00:00:00	https://arxiv.org/abs/2605.10581	https://arxiv.org/pdf/2605.10581v2	2605.10581	null	0	0	false	null	null	0.35
4e84681a8ae26f97bdcfa335aec54b0607a97f6cc824987189a21e738c7045e7	[ "arxiv", "semantic_scholar" ]	TIDES: Implicit Time-Awareness in Selective State Space Models	Selective state space models (SSMs), such as Mamba, achieve strong per-token expressivity by making the time discretization step $\TildeΔ$ a learned function of the input. However, in doing so, $\TildeΔ$ ceases to represent a physical sampling interval, limiting its irregular time series modeling capability. Continuous...	[ "Taylan Soydan", "Miguel A. Bessa", "Dirk Mohr", "Rui Barreira" ]	[ "cs.LG", "cs.AI" ]	[ "Computer Science" ]	2026-05-10T00:00:00	https://arxiv.org/abs/2605.09742	https://arxiv.org/pdf/2605.09742v1	2605.09742	null	0	0	true	https://github.com/TaylanSoydan/TIDES	null	0.65
04eecf3d30d2064968ee7896d77da14d89fef198b6548ae7de91f37c1d10999d	[ "arxiv", "semantic_scholar" ]	mHC-SSM: Manifold-Constrained Hyper-Connections for State Space Language Models with Stream-Specialized Adapters	Manifold-Constrained Hyper-Connections (mHC) introduce a stability-motivated variant of multi stream residual mixing by constraining residual stream mixing matrices to the manifold of doubly stochastic matrices via Sinkhorn-Knopp projection. In his work, we study whether mHC-style constrained multi-stream residual topo...	[ "Abdulvahap Mutlu", "Şengül Doğan", "Türker Tuncer" ]	[ "cs.LG", "cs.AI", "cs.CL" ]	[ "Computer Science" ]	2026-05-08T00:00:00	https://arxiv.org/abs/2605.08300	https://arxiv.org/pdf/2605.08300v1	2605.08300	null	0	0	true	https://github.com/abdulvahapmutlu/mhc-slm	null	0.65
207a3159e503655d2408ea4e412754892c1f4abd72b52a11d9714a376e576a4b	[ "arxiv", "semantic_scholar" ]	A Simple State Space Model Excels at Multivariate Time Series Classification	Structured state space models (SSMs) have recently emerged as a promising foundation for sequence modeling, with Mamba-based architectures demonstrating strong performance through input-dependent state transitions, albeit at considerable complexity. However, their application to time-series classification (TSC) has bee...	[ "Hassan Saadatmand", "Geoffrey I. Webb", "Hamid Rezatofighi", "Mahsa Salehi" ]	[ "cs.LG" ]	[ "Computer Science" ]	2026-05-07T00:00:00	https://arxiv.org/abs/2605.27406	https://arxiv.org/pdf/2605.27406v1	2605.27406	null	0	0	false	null	null	0.35
22af2ffff627a47e743e15060be4a6f0218e539bc37b49831dc693d9c225e578	[ "arxiv", "semantic_scholar" ]	ViM-Q: Scalable Algorithm-Hardware Co-Design for Vision Mamba Model Inference on FPGA	Vision Mamba (ViM) models offer a compelling efficiency advantage over Transformers by leveraging the linear complexity of State Space Models (SSMs), yet efficiently deploying them on FPGAs remains challenging. Linear layers struggle with dynamic activation outliers that render static quantization ineffective, while un...	[ "Shengzhe Lyu", "Yuhan She", "Patrick S. Y. Hung", "Ray C. C. Cheung", "Weitao Xu" ]	[ "cs.AR", "cs.CV", "cs.LG" ]	[ "Computer Science" ]	2026-05-03T00:00:00	https://arxiv.org/abs/2605.01935	https://arxiv.org/pdf/2605.01935v1	2605.01935	10.1109/FCCM68464.2026.00025	0	0	true	https://github.com/shengzhelyu65/ViM-Q-FCCM-2026	IEEE Symposium on Field-Programmable Custom Computing Machines	0.85
2c9231848a4656d3dde34a1ac58cf43330438ded4278f8a068492e1fcfcba018	[ "arxiv", "semantic_scholar" ]	Lost in State Space: Probing Frozen Mamba Representations	Mamba's recurrent state h_t is, by construction, a compressed summary of every token seen so far. This raises a tempting hypothesis: if we extract token-level outputs y_t at fixed patch boundaries, we obtain semantic sentence summaries for free, with no pooling head, no fine-tuning, and no [CLS] token. We test this hyp...	[ "Bhagyashree Wagh", "Akash Singh" ]	[ "cs.CL", "cs.LG" ]	[ "Computer Science" ]	2026-04-30T00:00:00	https://arxiv.org/abs/2605.00253	https://arxiv.org/pdf/2605.00253v1	2605.00253	null	0	0	false	null	null	0.35
282886064f18972324c569eb7d2725e7b7a59ef0d54134c2ae824869b46bb667	[ "arxiv", "semantic_scholar" ]	Density Field State Space Models: 1-Bit Distillation, Efficient Inference, and Knowledge Organization in Mamba-2	We present Density Field State Space Models (DF-SSM), a framework for compressing SSMs to a 1-bit scaffold with int8 low-rank correction. Applied to Mamba-2 1.3B, we achieve a 278 MB model (9.7x smaller than the 2.7 GB FP16 teacher) that runs at 21.4x faster inference on GPU (batch=1, relative to the mamba-ssm referenc...	[ "Chirag Shinde" ]	[ "cs.CL", "cs.LG" ]	[ "Computer Science" ]	2026-04-28T00:00:00	https://arxiv.org/abs/2606.10932	https://arxiv.org/pdf/2606.10932v1	2606.10932	null	0	0	true	https://github.com/cs-cmyk/df-ssm	null	0.65
c4c95a83b35cdd9adcba64ffdf1d61c50045034abe98956a446c27982841543a	[ "arxiv", "semantic_scholar" ]	BVI-Mamba: Video Enhancement Using a Visual State-Space Model for Low-Light and Underwater Environments	Videos captured in low-light and underwater conditions often suffer from distortions such as noise, low contrast, color imbalance, and blur. These issues not only limit visibility but also degrade automatic tasks like detection. Post-processing is typically required but can be time-consuming. AI-based tools for video e...	[ "Guoxi Huang", "Ruirui Lin", "Yini Li", "David R. Bull", "Nantheera Anantrasirichai" ]	[ "cs.CV" ]	[ "Computer Science", "Engineering" ]	2026-04-26T00:00:00	https://arxiv.org/abs/2604.23655	https://arxiv.org/pdf/2604.23655v1	2604.23655	10.1117/12.3053998	5	0	true	https://github.com/russellllaputa/BVI-Mamba	null	0.65
9ed4cf0afdc497b7d3c44ca3fbd57c124509216f831589cc45a8a551fe113a59	[ "arxiv", "semantic_scholar" ]	MambaCSP: Hybrid-Attention State Space Models for Hardware-Efficient Channel State Prediction	Recent works have demonstrated that attention-based transformer and large language model (LLM) architectures can achieve strong channel state prediction (CSP) performance by capturing long-range temporal dependencies across channel state information (CSI) sequences. However, these models suffer from quadratic scaling i...	[ "Aladin Djuhera", "Haris Gacanin", "Holger Boche" ]	[ "cs.IT", "cs.AI", "cs.LG", "eess.SP" ]	[ "Computer Science", "Engineering", "Mathematics" ]	2026-04-23T00:00:00	https://arxiv.org/abs/2604.21957	https://arxiv.org/pdf/2604.21957v1	2604.21957	10.48550/arXiv.2604.21957	2	0	false	null	arXiv.org	0.55
4723aaa044d53350fbf2665faa3f9beeb268caccb1292eaff7b832b3b960e8f6	[ "arxiv", "semantic_scholar" ]	Beyond ZOH: Advanced Discretization Strategies for Vision Mamba	Vision Mamba, as a state space model (SSM), employs a zero-order hold (ZOH) discretization, which assumes that input signals remain constant between sampling instants. This assumption degrades temporal fidelity in dynamic visual environments and constrains the attainable accuracy of modern SSM-based vision models. In t...	[ "Fady Ibrahim", "Guangjun Liu", "Guanghui Wang" ]	[ "cs.CV", "cs.AI" ]	[ "Computer Science" ]	2026-04-22T00:00:00	https://arxiv.org/abs/2604.20606	https://arxiv.org/pdf/2604.20606v1	2604.20606	10.48550/arXiv.2604.20606	0	0	false	null	arXiv.org	0.55
8a1d65a771c45ce3be136b67315c8dc66fa5ab29c7eeacbc8add94e061e7236c	[ "arxiv", "semantic_scholar" ]	Preconditioned DeltaNet: Curvature-aware Sequence Modeling for Linear Recurrences	To address the increasing long-context compute limitations of softmax attention, several subquadratic recurrent operators have been developed. This work includes models such as Mamba-2, DeltaNet, Gated DeltaNet (GDN), and Kimi Delta Attention (KDA). As the space of recurrences grows, a parallel line of work has arisen ...	[ "Neehal Tumma", "Noel Loo", "Daniela Rus" ]	[ "cs.LG" ]	[ "Computer Science" ]	2026-04-22T00:00:00	https://arxiv.org/abs/2604.21100	https://arxiv.org/pdf/2604.21100v1	2604.21100	10.48550/arXiv.2604.21100	2	0	false	null	arXiv.org	0.55
e6e96a498a30d7070139ffd4662426a13c9cb528fab964ea50cac47fc7c64320	[ "arxiv", "semantic_scholar" ]	DGSSM: Diffusion guided state-space models for multimodal salient object detection	Salient object detection (SOD) requires modeling both long-range contextual dependencies and fine-grained structural details, which remains challenging for convolutional, transformer-based, and Mamba-based state space models. While recent Mamba-based state space approaches enable efficient global reasoning, they often ...	[ "Suklav Ghosh", "Arijit Sur", "Pinaki Mitra" ]	[ "cs.CV", "cs.AI", "cs.LG" ]	[ "Computer Science" ]	2026-04-19T00:00:00	https://arxiv.org/abs/2604.17585	https://arxiv.org/pdf/2604.17585v1	2604.17585	10.48550/arXiv.2604.17585	0	0	false	null	arXiv.org	0.5489
44f2e0b33b57dc2f163d6649493ab822338148e1a75cc36b21e94bb2764b91f8	[ "arxiv", "semantic_scholar" ]	MambaSL: Exploring Single-Layer Mamba for Time Series Classification	Despite recent advances in state space models (SSMs) such as Mamba across various sequence domains, research on their standalone capacity for time series classification (TSC) has remained limited. We propose MambaSL, a framework that minimally redesigns the selective SSM and projection layers of a single-layer Mamba, g...	[ "Yoo-Min Jung", "Leekyung Kim" ]	[ "cs.LG", "cs.AI" ]	[ "Computer Science" ]	2026-04-16T00:00:00	https://arxiv.org/abs/2604.15174	https://arxiv.org/pdf/2604.15174v2	2604.15174	10.48550/arXiv.2604.15174	3	0	false	null	arXiv.org	0.5454
d6dc4765a7404c59fa40edd03415dbb417f1b3e19a5f803167be1fdf634b3d1e	[ "arxiv", "semantic_scholar" ]	Mamba-SSM with LLM Reasoning for Feature Selection: Faithfulness-Aware Biomarker Discovery	Gradient saliency from deep sequence models surfaces candidate biomarkers efficiently, but the resulting gene lists can be contaminated by tissue-composition confounders that degrade downstream classifiers. We study whether LLM chain-of-thought (CoT) reasoning can filter these confounders, and whether reasoning quality...	[ "Pushpa Kumar Balan", "Aijing Feng" ]	[ "q-bio.QM", "cs.AI" ]	[ "Computer Science", "Biology" ]	2026-04-15T00:00:00	https://arxiv.org/abs/2604.14334	https://arxiv.org/pdf/2604.14334v2	2604.14334	10.48550/arXiv.2604.14334	0	0	false	null	arXiv.org	0.5443
a9798221836864414913566137ab949ec37448bc7ccb7757577c3ba7b689faa5	[ "arxiv", "semantic_scholar" ]	Mamba Sequence Modeling meets Model Predictive Control	In this paper, we consider the design of Model Predictive Control (MPC) algorithms based on Mamba neural networks. Mamba is a neural network architecture capable of sub-quadratic computational scaling in sequence length with state-of-the-art modeling capabilities. We provide a consistent and complete mathematical descr...	[ "Michiel Cevaal", "Thomas de Jong", "Mircea Lazar" ]	[ "math.OC" ]	[ "Mathematics" ]	2026-04-15T00:00:00	https://arxiv.org/abs/2604.13857	https://arxiv.org/pdf/2604.13857v1	2604.13857	null	0	0	false	null	null	0.3464
6b7f69467454bf6cfcc06e21a824aacbaa38d296f8205b9b88431900f244f9a8	[ "arxiv", "semantic_scholar" ]	Structured State-Space Regularization for Generation-Friendly Image Tokenization	Image tokenizers play a central role in modern generative models, where the structure of the latent space critically determines the downstream generation performance. A key but underexplored property of effective latent representations is spectral organization, the ability to encode information across frequency compone...	[ "Jinsung Lee", "Jaemin Oh", "Namhun Kim", "Dongwon Kim", "Byung-Jun Yoon", "Suha Kwak" ]	[ "cs.CV" ]	[ "Computer Science" ]	2026-04-13T00:00:00	https://arxiv.org/abs/2604.11089	https://arxiv.org/pdf/2604.11089v2	2604.11089	null	0	0	false	null	null	0.3449
0f070942f03c8b34f9c3b2ea86ab61c84dfa3f28e845145a287035061e865565	[ "arxiv", "semantic_scholar" ]	Beyond Mamba: Enhancing State-space Models with Deformable Dilated Convolutions for Multi-scale Traffic Object Detection	In a real-world traffic scenario, varying-scale objects are usually distributed in a cluttered background, which poses great challenges to accurate detection. Although current Mamba-based methods can efficiently model long-range dependencies, they still struggle to capture small objects with abundant local details, whi...	[ "Jun Li", "Yingying Shi", "Zhixuan Ruan", "Nan Guo", "Jianhua Xu" ]	[ "cs.CV" ]	[ "Computer Science" ]	2026-04-09T00:00:00	https://arxiv.org/abs/2604.08038	https://arxiv.org/pdf/2604.08038v1	2604.08038	10.48550/arXiv.2604.08038	0	0	true	https://github.com/Bettermea/MDDCNet	arXiv.org	0.8305
43d7dbaad9981a59551aa04d38c09951248022bf4b3e448dba35cf8c24e6bc3d	[ "arxiv", "semantic_scholar" ]	Optimal Decay Spectra for Linear Recurrences	Linear recurrent models offer linear-time sequence processing but often suffer from suboptimal long-range memory. We trace this to the decay spectrum: for $N$ channels, random initialization collapses the minimum spectral gap to $O(N^{-2})$, yielding sub-exponential error $\exp(-Ω(N/\log N))$; linear spacing avoids col...	[ "Yang Cao" ]	[ "cs.LG", "cs.AI", "cs.CL" ]	[ "Computer Science" ]	2026-04-08T00:00:00	https://arxiv.org/abs/2604.07658	https://arxiv.org/pdf/2604.07658v1	2604.07658	10.48550/arXiv.2604.07658	0	0	true	https://github.com/SiLifen/PoST	arXiv.org	0.8287
93315a74a417dbacf3614cd84ad347a59e2029746cfef4e5e9e1aa743f05f90c	[ "arxiv", "semantic_scholar" ]	StateSMix: Online Lossless Compression via Mamba State Space Models and Sparse N-gram Context Mixing	We present StateSMix, a fully self-contained lossless compressor that couples an online-trained Mamba-style State Space Model (SSM) with sparse n-gram context mixing and arithmetic coding. The model is initialised from scratch and trained token-by-token on the file being compressed, requiring no pre-trained weights, no...	[ "Roberto Tacconelli" ]	[ "cs.LG", "cs.IT" ]	[ "Computer Science", "Mathematics" ]	2026-04-05T00:00:00	https://arxiv.org/abs/2605.02904	https://arxiv.org/pdf/2605.02904v1	2605.02904	null	0	0	false	null	null	0.3391
f60562b1504d3ed5cb95bc02897ba005ed8fdaf8f74f86f08730c4fcd0d70658	[ "arxiv", "semantic_scholar" ]	Attention to Mamba: A Recipe for Cross-Architecture Distillation	State Space Models (SSMs) such as Mamba have become a popular alternative to Transformer models, due to their reduced memory consumption and higher throughput at generation compared to their Attention-based counterparts. On the other hand, the community has built up a considerable body of knowledge on how to train Tran...	[ "Abhinav Moudgil", "Ningyuan Huang", "Eeshan Gunesh Dhekane", "Pau Rodríguez", "Luca Zappella", "Federico Danieli" ]	[ "cs.CL", "cs.LG" ]	[ "Computer Science" ]	2026-04-01T00:00:00	https://arxiv.org/abs/2604.14191	https://arxiv.org/pdf/2604.14191v1	2604.14191	10.48550/arXiv.2604.14191	1	0	false	null	arXiv.org	0.5282
223f5e38c10f6c8b9478e49bd2a0ae47eadd3deb037c98188b9052ea32954a98	[ "arxiv", "semantic_scholar" ]	RS-SSM: Refining Forgotten Specifics in State Space Model for Video Semantic Segmentation	Recently, state space models have demonstrated efficient video segmentation through linear-complexity state space compression. However, Video Semantic Segmentation (VSS) requires pixel-level spatiotemporal modeling capabilities to maintain temporal consistency in segmentation of semantic objects. While state space mode...	[ "Kai Zhu", "Zhenyu Cui", "Zehua Zang", "Jiahuan Zhou" ]	[ "cs.CV" ]	[ "Computer Science" ]	2026-03-25T00:00:00	https://arxiv.org/abs/2603.24295	https://arxiv.org/pdf/2603.24295v2	2603.24295	10.48550/arXiv.2603.24295	0	0	true	https://github.com/zhoujiahuan1991/CVPR2026-RS-SSM	arXiv.org	0.804
66cf824fe6a9d17f7e92d12bc06bfaa74aa64fbfbb4d2d7f9a4fde32574fedb4	[ "arxiv", "semantic_scholar" ]	MFil-Mamba: Multi-Filter Scanning for Spatial Redundancy-Aware Visual State Space Models	State Space Models (SSMs), especially recent Mamba architecture, have achieved remarkable success in sequence modeling tasks. However, extending SSMs to computer vision remains challenging due to the non-sequential structure of visual data and its complex 2D spatial dependencies. Although several early studies have exp...	[ "Puskal Khadka", "KC Santosh" ]	[ "cs.CV" ]	[ "Computer Science" ]	2026-03-20T00:00:00	https://arxiv.org/abs/2603.20074	https://arxiv.org/pdf/2603.20074v1	2603.20074	10.48550/arXiv.2603.20074	0	0	true	https://github.com/puskal-khadka/MFil-Mamba	arXiv.org	0.7951
d97aa5b4a62eb3291348d369d37bbb3de67224fe78b19e4eca09c757aef61107	[ "arxiv", "semantic_scholar" ]	CS-MUNet: A Channel-Spatial Dual-Stream Mamba Network for Multi-Organ Segmentation	Recently Mamba-based methods have shown promise in abdominal organ segmentation. However, existing approaches neglect cross-channel anatomical semantic collaboration and lack explicit boundary-aware feature fusion mechanisms. To address these limitations, we propose CS-MUNet with two purpose-built modules. The Boundary...	[ "Yuyang Zheng", "Mingda Zhang", "Jianglong Qin", "Qi Mo", "Jingdan Pan", "Haozhe Hu", "Hongyi Huang" ]	[ "cs.CV" ]	[ "Computer Science" ]	2026-03-20T00:00:00	https://arxiv.org/abs/2603.19659	https://arxiv.org/pdf/2603.19659v1	2603.19659	10.48550/arXiv.2603.19659	0	0	false	null	arXiv.org	0.5145
45d4e32f06a07f0296bd606cc1e3aa891fd64284dd5631f8efc311fa3a883c32	[ "arxiv", "semantic_scholar" ]	Do VLMs Need Vision Transformers? Evaluating State Space Models as Vision Encoders	Large vision--language models (VLMs) often use a frozen vision backbone, whose image features are mapped into a large language model through a lightweight connector. While transformer-based encoders are the standard visual backbone, we ask whether state space model (SSM) vision backbones can be a strong alternative. We...	[ "Shang-Jui Ray Kuo", "Paola Cascante-Bonilla" ]	[ "cs.CV", "cs.LG" ]	[ "Computer Science" ]	2026-03-19T00:00:00	https://arxiv.org/abs/2603.19209	https://arxiv.org/pdf/2603.19209v1	2603.19209	10.48550/arXiv.2603.19209	0	0	true	https://github.com/raykuo18/vlm-ssm-vision-encoders	arXiv.org	0.7933
0b065f743e8ab44334c534a6073f8b29affd014be63936d1ff611743fa2ccb9b	[ "arxiv", "semantic_scholar" ]	DA-Mamba: Learning Domain-Aware State Space Model for Global-Local Alignment in Domain Adaptive Object Detection	Domain Adaptive Object Detection (DAOD) aims to transfer detectors from a labeled source domain to an unlabeled target domain. Existing DAOD methods employ multi-granularity feature alignment to learn domain-invariant representations. However, the local connectivity of their CNN-based backbone and detection head restri...	[ "Haochen Li", "Rui Zhang", "Hantao Yao", "Xin Zhang", "Yifan Hao", "Shaohui Peng", "Yongwei Zhao", "Ling Li" ]	[ "cs.CV" ]	[ "Computer Science" ]	2026-03-19T00:00:00	https://arxiv.org/abs/2603.18757	https://arxiv.org/pdf/2603.18757v1	2603.18757	10.48550/arXiv.2603.18757	0	0	false	null	arXiv.org	0.5133
52d6681e0bdf75e522d49be49d0951948e677bbe40e050770e6124042cf7d955	[ "arxiv", "semantic_scholar" ]	SF-Mamba: Rethinking State Space Model for Vision	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 efficiency, it inherently limits non-causal interactions between image patches. Pri...	[ "Masakazu Yoshimura", "Teruaki Hayashi", "Yuki Hoshino", "Wei-Yao Wang", "Takeshi Ohashi" ]	[ "cs.CV", "cs.AI" ]	[ "Computer Science" ]	2026-03-17T00:00:00	https://arxiv.org/abs/2603.16423	https://arxiv.org/pdf/2603.16423v1	2603.16423	10.48550/arXiv.2603.16423	0	0	false	null	arXiv.org	0.511
c3408a88afa47a847054f9661d1b393ee33c504cf1df849623adee92c4887c08	[ "arxiv", "semantic_scholar" ]	Mamba-3: Improved Sequence Modeling using State Space Principles	Scaling inference-time compute has emerged as an important driver of LLM performance, making inference efficiency a central focus of model design alongside model quality. While the current Transformer-based models deliver strong model quality, their quadratic compute and linear memory make inference expensive. This has...	[ "Aakash Lahoti", "Kevin Y. Li", "Berlin Chen", "Caitlin Wang", "Aviv Bick", "J. Zico Kolter", "Tri Dao", "Albert Gu" ]	[ "cs.LG" ]	[ "Computer Science" ]	2026-03-16T00:00:00	https://arxiv.org/abs/2603.15569	https://arxiv.org/pdf/2603.15569v1	2603.15569	10.48550/arXiv.2603.15569	52	5	false	null	arXiv.org	0.5099
960c97f1c73793b7c469e5605601eab4b70dafe5b7d6a3b72cde424775abdb0c	[ "arxiv", "semantic_scholar" ]	PDE-SSM: A Spectral State Space Approach to Spatial Mixing in Diffusion Transformers	The success of vision transformers-especially for generative modeling-is limited by the quadratic cost and weak spatial inductive bias of self-attention. We propose PDE-SSM, a spatial state-space block that replaces attention with a learnable convection-diffusion-reaction partial differential equation. This operator en...	[ "Eshed Gal", "Moshe Eliasof", "Siddharth Rout", "Eldad Haber" ]	[ "cs.LG" ]	[ "Computer Science" ]	2026-03-14T00:00:00	https://arxiv.org/abs/2603.13663	https://arxiv.org/pdf/2603.13663v1	2603.13663	10.48550/arXiv.2603.13663	0	0	false	null	arXiv.org	0.5076
cba157c3c29f4b4d1dfa726cff45615f8f8300dac4c8fe44a71592c18fc3bc2e	[ "arxiv", "semantic_scholar" ]	SpectralGuard: Detecting Memory Collapse Attacks in State Space Models	State Space Models (SSMs) such as Mamba achieve linear-time sequence processing through input-dependent recurrence, but this mechanism introduces a critical safety vulnerability. We show that the spectral radius rho(A-bar) of the discretized transition operator governs effective memory horizon: when an adversary drives...	[ "Davi Bonetto" ]	[ "cs.LG", "cs.CR" ]	[ "Computer Science" ]	2026-03-12T00:00:00	https://arxiv.org/abs/2603.12414	https://arxiv.org/pdf/2603.12414v1	2603.12414	10.48550/arXiv.2603.12414	0	0	true	https://github.com/DaviBonetto/spectralguard	arXiv.org	0.7809
d7ca0a25811e381e486053528643c4341c3584899415a7d15de10d419669b286	[ "arxiv", "semantic_scholar" ]	Progressive Split Mamba: Effective State Space Modelling for Image Restoration	Image restoration requires simultaneously preserving fine-grained local structures and maintaining long-range spatial coherence. While convolutional networks struggle with limited receptive fields, and Transformers incur quadratic complexity for global attention, recent State Space Models (SSMs), such as Mamba, provide...	[ "Mohammed Hassanin", "Nour Moustafa", "Weijian Deng", "Ibrahim Radwan" ]	[ "cs.CV" ]	[ "Computer Science" ]	2026-03-10T00:00:00	https://arxiv.org/abs/2603.09171	https://arxiv.org/pdf/2603.09171v1	2603.09171	10.48550/arXiv.2603.09171	0	0	false	null	arXiv.org	0.503
e7d7b4ca04a0ea236814215c2f73616e152d5b3f8d112aab3ec9aad5a940a801	[ "arxiv", "semantic_scholar" ]	InfoMamba: An Attention-Free Hybrid Mamba-Transformer Model	Balancing fine-grained local modeling with long-range dependency capture under computational constraints remains a central challenge in sequence modeling. While Transformers provide strong token mixing, they suffer from quadratic complexity, whereas Mamba-style selective state-space models (SSMs) scale linearly but oft...	[ "Youjin Wang", "Jiaqiao Zhao", "Rong Fu", "Run Zhou", "Ruizhe Zhang", "Jiani Liang", "Suisuai Cao", "Feng Zhou" ]	[ "cs.LG", "cs.AI" ]	[ "Computer Science" ]	2026-03-08T00:00:00	https://arxiv.org/abs/2603.18031	https://arxiv.org/pdf/2603.18031v1	2603.18031	10.48550/arXiv.2603.18031	0	0	false	null	arXiv.org	0.5007
9d026685368f958cba1f26590dabc7d8987376f288d9157c41a505cb5db4a860	[ "arxiv", "semantic_scholar" ]	Swimba: Switch Mamba Model Scales State Space Models	Mixture-of-experts (MoE) is a common approach for increasing parameter capacity, but applying MoE to state space model (SSM) token mixers can multiply the cost of the recurrent state update. We study how to introduce expert specialization into selective SSMs while preserving computational efficiency. We show that MoE--...	[ "Zhixu Du", "Krishna Teja Chitty-Venkata", "Murali Emani", "Venkatram Vishwanath", "Hai Helen Li", "Yiran Chen" ]	[ "cs.LG" ]	[ "Computer Science" ]	2026-03-06T00:00:00	https://arxiv.org/abs/2603.06938	https://arxiv.org/pdf/2603.06938v1	2603.06938	10.48550/arXiv.2603.06938	0	0	false	null	arXiv.org	0.4984
261fb99af4d97653451ec44d6c9ad14a1c5d339690c575d7932032deb0388b7f	[ "arxiv", "semantic_scholar" ]	Mask-aware inference with State-Space Models	Many real-world computer vision tasks, such as depth completion, must handle inputs with arbitrarily shaped regions of missing or invalid data. For Convolutional Neural Networks (CNNs), Partial Convolutions solved this by a mask-aware re-normalization conditioned only on valid pixels. Recently, State Space Models (SSMs...	[ "Ignasi Mas", "Ramon Morros", "Javier-Ruiz Hidalgo", "Ivan Huerta" ]	[ "cs.CV" ]	[ "Computer Science" ]	2026-03-04T00:00:00	https://arxiv.org/abs/2603.04568	https://arxiv.org/pdf/2603.04568v1	2603.04568	10.48550/arXiv.2603.04568	0	0	false	null	arXiv.org	0.4961
6a42333ad1023e1f46932c87fc1b1c33afb4096e056235f6cbc70d6d90a3d40e	[ "arxiv", "semantic_scholar" ]	The Expressive Limits of Diagonal SSMs for State-Tracking	State-Space Models (SSMs) have recently been shown to achieve strong empirical performance on a variety of long-range sequence modeling tasks while remaining efficient and highly-parallelizable. However, the theoretical understanding of their expressive power remains limited. In this work, we study the expressivity of ...	[ "Mehran Shakerinava", "Behnoush Khavari", "Siamak Ravanbakhsh", "Sarath Chandar" ]	[ "cs.LG" ]	[ "Computer Science" ]	2026-03-02T00:00:00	https://arxiv.org/abs/2603.01959	https://arxiv.org/pdf/2603.01959v1	2603.01959	10.48550/arXiv.2603.01959	4	1	false	null	arXiv.org	0.4939
ae41dbaa6a4037110a3ff587d8a68bf300208769ac7a4d8c8b204e74af10119e	[ "arxiv", "semantic_scholar" ]	Mamba-CAD: State Space Model For 3D Computer-Aided Design Generative Modeling	Computer-Aided Design (CAD) generative modeling has a strong and long-term application in the industry. Recently, the parametric CAD sequence as the design logic of an object has been widely mined by sequence models. However, the industrial CAD models, especially in component objects, are fine-grained and complex, requ...	[ "Xueyang Li", "Yunzhong Lou", "Yu Song", "Xiangdong Zhou" ]	[ "cs.CV", "cs.AI" ]	[ "Computer Science" ]	2026-02-28T00:00:00	https://arxiv.org/abs/2603.00439	https://arxiv.org/pdf/2603.00439v1	2603.00439	10.1609/aaai.v39i5.32531	10	1	true	https://github.com/Sunny-Hack/Code-for-Mamba-CAD-AAAI-2025-	AAAI Conference on Artificial Intelligence	0.7597
9c6cc95019dce56a7d872503ab65f51bfafe72f1c7894aea5f18726fd475140d	[ "arxiv", "semantic_scholar" ]	Scaling State-Space Models on Multiple GPUs with Tensor Parallelism	Selective state space models (SSMs) have rapidly become a compelling backbone for large language models, especially for long-context workloads. Yet in deployment, their inference performance is often bounded by the memory capacity, bandwidth, and latency limits of a single GPU, making multi-GPU execution increasingly n...	[ "Anurag Dutt", "Nimit Shah", "Hazem Masarani", "Anshul Gandhi" ]	[ "cs.DC", "cs.LG" ]	[ "Computer Science" ]	2026-02-24T00:00:00	https://arxiv.org/abs/2602.21144	https://arxiv.org/pdf/2602.21144v1	2602.21144	10.48550/arXiv.2602.21144	0	0	false	null	arXiv.org	0.487
b6cbae13fbba620d54259a89bc536696708ea6d6e6d1cec05b82d6215f2bce98	[ "arxiv", "semantic_scholar" ]	CrossLLM-Mamba: Multimodal State Space Fusion of LLMs for RNA Interaction Prediction	Accurate prediction of RNA-associated interactions is essential for understanding cellular regulation and advancing drug discovery. While Biological Large Language Models (BioLLMs) such as ESM-2 and RiNALMo provide powerful sequence representations, existing methods rely on static fusion strategies that fail to capture...	[ "Rabeya Tus Sadia", "Qiang Ye", "Qiang Cheng" ]	[ "q-bio.GN", "cs.CV", "cs.LG" ]	[ "Medicine", "Biology", "Computer Science" ]	2026-02-23T00:00:00	https://arxiv.org/abs/2602.22236	https://arxiv.org/pdf/2602.22236v1	2602.22236	10.48550/arXiv.2602.22236	0	0	false	null	arXiv.org	0.4858
477be606565f92863206ae9f8944832befd9b98258f63e736f90b7a0fad45288	[ "arxiv", "semantic_scholar" ]	A Theoretical Analysis of Mamba's Training Dynamics: Filtering Relevant Features for Generalization in State Space Models	The recent empirical success of Mamba and other selective state space models (SSMs) has renewed interest in non-attention architectures for sequence modeling, yet their theoretical foundations remain underexplored. We present a first-step analysis of generalization and learning dynamics for a simplified but representat...	[ "Mugunthan Shandirasegaran", "Hongkang Li", "Songyang Zhang", "Meng Wang", "Shuai Zhang" ]	[ "cs.LG" ]	[ "Computer Science" ]	2026-02-13T00:00:00	https://arxiv.org/abs/2602.12499	https://arxiv.org/pdf/2602.12499v1	2602.12499	10.48550/arXiv.2602.12499	3	0	false	null	arXiv.org	0.4744
e41af85f7a45985621644ff6f13638929ef3d784f549a9614496628d68a5805c	[ "arxiv", "semantic_scholar" ]	Introduction to High-Temperature Superconductivity for Solid State Chemists	Superconductivity is one of the most amazing properties that metallic conductors exhibit. Electrical resistance is completely eliminated below the critical temperature (Tc), which is the most important parameter in superconductivity. Since the discovery of copper oxide superconductors 39 years ago, many solid state che...	[ "Zenji Hiroi" ]	[ "cond-mat.supr-con" ]	[ "Physics" ]	2026-02-13T00:00:00	https://arxiv.org/abs/2602.12608	https://arxiv.org/pdf/2602.12608v2	2602.12608	10.1016/j.progsolidstchem.2026.100574	0	0	false	null	Progress in Solid State Chemistry	0.4744
0b2dfcc9b6b35bfb9ebf9ac89f1a8db00e87dbd1bcfdd7d45148d6b6592dae78	[ "arxiv", "semantic_scholar" ]	Improved state mixing in higher-order and block diagonal linear recurrent networks	Linear recurrent networks (LRNNs) and linear state space models (SSMs) promise computational and memory efficiency on long-sequence modeling tasks, yet their diagonal state transitions limit expressivity. Dense and nonlinear architectures (e.g., LSTMs) on the other hand are provably more expressive, but computationally...	[ "Igor Dubinin", "Antonio Orvieto", "Felix Effenberger" ]	[ "cs.LG" ]	[ "Computer Science" ]	2026-02-12T00:00:00	https://arxiv.org/abs/2602.12021	https://arxiv.org/pdf/2602.12021v2	2602.12021	10.48550/arXiv.2602.12021	1	0	false	null	arXiv.org	0.4732
f638c2432b95a5be9d4522433254dc226f270639a2cb96dfcc013403f5b7d1fc	[ "arxiv", "semantic_scholar" ]	Retrieval-Aware Distillation for Transformer-SSM Hybrids	State-space models (SSMs) offer efficient sequence modeling but lag behind Transformers on benchmarks that require in-context retrieval. Prior work links this gap to a small set of attention heads, termed Gather-and-Aggregate (G&A), which SSMs struggle to reproduce. We propose retrieval-aware distillation, which conv...	[ "Aviv Bick", "Eric P. Xing", "Albert Gu" ]	[ "cs.LG", "cs.AI" ]	[ "Computer Science" ]	2026-02-11T00:00:00	https://arxiv.org/abs/2602.11374	https://arxiv.org/pdf/2602.11374v1	2602.11374	10.48550/arXiv.2602.11374	2	0	false	null	arXiv.org	0.4721
53839903a7aa68cce1ce14ff5708ede456232b7d895249cf375e9f5a15316bb4	[ "arxiv", "semantic_scholar" ]	Kalman Linear Attention: Parallel Bayesian Filtering For Efficient Language Modelling and State Tracking	State-space language models such as Mamba and gated linear attention (GLA) offer linear-complexity, parallelisable alternatives to transformers, but their linear state updates limit expressivity and robust state tracking. We close this gap from a probabilistic angle, casting sequence mixing as exact Bayesian filtering ...	[ "Vaisakh Shaj", "Cameron Barker", "Aidan Scannell", "Andras Szecsenyi", "Elliot J. Crowley", "Amos Storkey" ]	[ "cs.LG" ]	[ "Computer Science" ]	2026-02-11T00:00:00	https://arxiv.org/abs/2602.10743	https://arxiv.org/pdf/2602.10743v2	2602.10743	10.48550/arXiv.2602.10743	2	0	false	null	arXiv.org	0.4721
12c342082013f4e0a81ed523bb9f0a3da77c24c9159ba56a92380729f5c36bd3	[ "arxiv", "semantic_scholar" ]	DMamba: Decomposition-enhanced Mamba for Time Series Forecasting	State Space Models (SSMs), particularly Mamba, have shown potential in long-term time series forecasting. However, existing Mamba-based architectures often struggle with datasets characterized by non-stationary patterns. A key observation from time series theory is that the statistical nature of inter-variable relation...	[ "Ruxuan Chen", "Fang Sun" ]	[ "cs.LG", "cs.AI" ]	[ "Computer Science" ]	2026-02-09T00:00:00	https://arxiv.org/abs/2602.09081	https://arxiv.org/pdf/2602.09081v1	2602.09081	10.48550/arXiv.2602.09081	0	0	false	null	arXiv.org	0.4698
3516f452b9282a7c6b18fd347ee735884c9e01c6c0b357920b053ff0426aeb16	[ "arxiv", "semantic_scholar" ]	AS-Mamba: Asymmetric Self-Guided Mamba Decoupled Iterative Network for Metal Artifact Reduction	Metal artifact significantly degrades Computed Tomography (CT) image quality, impeding accurate clinical diagnosis. However, existing deep learning approaches, such as CNN and Transformer, often fail to explicitly capture the directional geometric features of artifacts, leading to compromised structural restoration. To...	[ "Bowen Ning", "Zekun Zhou", "Xinyi Zhong", "Zhongzhen Wang", "HongXin Wu", "HaiTao Wang", "Liu Shi", "Qiegen Liu" ]	[ "eess.IV", "cs.CV" ]	[ "Engineering", "Computer Science" ]	2026-02-06T00:00:00	https://arxiv.org/abs/2602.06350	https://arxiv.org/pdf/2602.06350v1	2602.06350	10.48550/arXiv.2602.06350	0	0	false	null	arXiv.org	0.4664
14d27dff8f63e3bb99fd3f11838e084b1ccd18bb3a1d8b8437127dfd2a84eae4	[ "arxiv", "semantic_scholar" ]	SMTrack: State-Aware Mamba for Efficient Temporal Modeling in Visual Tracking	Visual tracking aims to automatically estimate the state of a target object in a video sequence, which is challenging especially in dynamic scenarios. Thus, numerous methods are proposed to introduce temporal cues to enhance tracking robustness. However, conventional CNN and Transformer architectures exhibit inherent l...	[ "Yinchao Ma", "Dengqing Yang", "Zhangyu He", "Wenfei Yang", "Tianzhu Zhang" ]	[ "cs.CV" ]	[ "Computer Science", "Medicine" ]	2026-02-02T00:00:00	https://arxiv.org/abs/2602.01677	https://arxiv.org/pdf/2602.01677v1	2602.01677	10.1109/TIP.2026.3661393	1	0	false	null	IEEE Transactions on Image Processing	0.4618
d221e22a7187331bae5c32c3b16a8f47ad53a6cc0002ee31ae476f25eb8baee8	[ "arxiv", "semantic_scholar" ]	Omni-directional attention mechanism based on Mamba for speech separation	Mamba, a selective state-space model (SSM), has emerged as an efficient alternative to Transformers for speech modeling, enabling long-sequence processing with linear complexity. While effective in speech separation, existing approaches, whether in the time or time-frequency domain, typically decompose the input along ...	[ "Ke Xue", "Chang Sun", "Rongfei Fan", "Jing Wang", "Han Hu" ]	[ "cs.SD", "eess.AS" ]	[ "Computer Science", "Engineering" ]	2026-01-23T00:00:00	https://arxiv.org/abs/2601.16603	https://arxiv.org/pdf/2601.16603v1	2601.16603	10.48550/arXiv.2601.16603	1	0	false	null	arXiv.org	0.4503
612a01518eeeec02e4653bc03f86c4a740fc146ba89603ab5a6846cd461a5a9b	[ "arxiv", "semantic_scholar" ]	ConvMambaNet: A Hybrid CNN-Mamba State Space Architecture for Accurate and Real-Time EEG Seizure Detection	Epilepsy is a chronic neurological disorder marked by recurrent seizures that can severely impact quality of life. Electroencephalography (EEG) remains the primary tool for monitoring neural activity and detecting seizures, yet automated analysis remains challenging due to the temporal complexity of EEG signals. This s...	[ "Md. Nishan Khan", "Kazi Shahriar Sanjid", "Md. Tanzim Hossain", "Asib Mostakim Fony", "Istiak Ahmed", "M. Monir Uddin" ]	[ "cs.CV" ]	[ "Computer Science" ]	2026-01-19T00:00:00	https://arxiv.org/abs/2601.13234	https://arxiv.org/pdf/2601.13234v1	2601.13234	10.48550/arXiv.2601.13234	1	0	false	null	arXiv.org	0.4457
d68f11446890746afbbfe2a6e754ec1414ffcfbe1946e8f5bd7d61da449ce9e7	[ "arxiv", "semantic_scholar" ]	On the Relation of State Space Models and Hidden Markov Models	State Space Models (SSMs) and Hidden Markov Models (HMMs) are foundational frameworks for modeling sequential data with latent variables and are widely used in signal processing, control theory, and machine learning. Despite their shared temporal structure, they differ fundamentally in the nature of their latent states...	[ "Aydin Ghojogh", "M. Hadi Sepanj", "Benyamin Ghojogh" ]	[ "cs.LG", "cs.CL", "eess.AS", "eess.SY" ]	[ "Computer Science", "Engineering" ]	2026-01-19T00:00:00	https://arxiv.org/abs/2601.13357	https://arxiv.org/pdf/2601.13357v1	2601.13357	10.48550/arXiv.2601.13357	0	0	false	null	arXiv.org	0.4457
33a67c3d1418ad5c0b7f6f6acd02575d5cf7982c2c38dbe6dce5a217bf76450f	[ "arxiv", "semantic_scholar" ]	Hidden State Poisoning Attacks against Mamba-based Language Models	State space models (SSMs) like Mamba offer efficient alternatives to Transformer-based language models, with linear time complexity. Yet, their adversarial robustness remains critically unexplored. This paper studies the phenomenon whereby specific short input phrases induce a partial amnesia effect in such models, by ...	[ "Alexandre Le Mercier", "Chris Develder", "Thomas Demeester" ]	[ "cs.CL", "cs.AI", "cs.LG" ]	[ "Computer Science" ]	2026-01-05T00:00:00	https://arxiv.org/abs/2601.01972	https://arxiv.org/pdf/2601.01972v4	2601.01972	10.48550/arXiv.2601.01972	3	0	false	null	arXiv.org	0.4297
2172c19e05add74729c932759de915d7d2a3ff61ec2320acf086d8e158f321d7	[ "arxiv", "semantic_scholar" ]	A Mamba-Based Model for Automatic Chord Recognition	In this work, we propose a new efficient solution, which is a Mamba-based model named BMACE (Bidirectional Mamba-based network, for Automatic Chord Estimation), which utilizes selective structured state-space models in a bidirectional Mamba layer to effectively model temporal dependencies. Our model achieves high predi...	[ "Chunyu Yuan", "Johanna Devaney" ]	[ "cs.SD" ]	[ "Computer Science" ]	2026-01-05T00:00:00	https://arxiv.org/abs/2601.02101	https://arxiv.org/pdf/2601.02101v1	2601.02101	10.48550/arXiv.2601.02101	0	0	false	null	arXiv.org	0.4297
c03ab901ac1428900f7f73c9243c15cf4982c78120c730a3e07b7cb747ab1ec2	[ "arxiv", "semantic_scholar" ]	Benchmarking the Computational and Representational Efficiency of State Space Models against Transformers on Long-Context Dyadic Sessions	State Space Models (SSMs) have emerged as a promising alternative to Transformers for long-context sequence modeling, offering linear $O(N)$ computational complexity compared to the Transformer's quadratic $O(N^2)$ scaling. This paper presents a comprehensive benchmarking study comparing the Mamba SSM against the LLaMA...	[ "Abidemi Koledoye", "Chinemerem Unachukwu", "Gold Nwobu", "Hasin Rana" ]	[ "cs.LG", "cs.AI" ]	[ "Computer Science" ]	2026-01-03T00:00:00	https://arxiv.org/abs/2601.01237	https://arxiv.org/pdf/2601.01237v1	2601.01237	10.48550/arXiv.2601.01237	0	0	false	null	arXiv.org	0.4274
3cd572841b728a6dd90e4dc59c9cd1f0b3645561a9e9e1e82b55d6e27db29377	[ "arxiv", "semantic_scholar" ]	MS-SSM: A Multi-Scale State Space Model for Efficient Sequence Modeling	State-space models (SSMs) have recently attention as an efficient alternative to computationally expensive attention-based models for sequence modeling. They rely on linear recurrences to integrate information over time, enabling fast inference, parallelizable training, and control over recurrence stability. However, t...	[ "Mahdi Karami", "Ali Behrouz", "Peilin Zhong", "Razvan Pascanu", "Vahab Mirrokni" ]	[ "cs.LG" ]	[ "Computer Science" ]	2025-12-29T00:00:00	https://arxiv.org/abs/2512.23824	https://arxiv.org/pdf/2512.23824v1	2512.23824	10.48550/arXiv.2512.23824	3	0	false	null	arXiv.org	0.4217
a79e9fe871e85b32068470810bfbda68825625d5acadf9cc4b3105dfec1ec405	[ "arxiv", "semantic_scholar" ]	Lag Operator SSMs: A Geometric Framework for Structured State Space Modeling	Structured State Space Models (SSMs), which are at the heart of the recently popular Mamba architecture, are powerful tools for sequence modeling. However, their theoretical foundation relies on a complex, multi-stage process of continuous-time modeling and subsequent discretization, which can obscure intuition. We int...	[ "Sutashu Tomonaga", "Kenji Doya", "Noboru Murata" ]	[ "cs.LG" ]	[ "Computer Science" ]	2025-12-22T00:00:00	https://arxiv.org/abs/2512.18965	https://arxiv.org/pdf/2512.18965v1	2512.18965	10.48550/arXiv.2512.18965	1	0	false	null	arXiv.org	0.4136
95cad977cbb27cc6f7000beb109fe6287c2d71e2348f4956280d9e341bb41306	[ "arxiv", "semantic_scholar" ]	How Many Heads Make an SSM? A Unified Framework for Attention and State Space Models	Sequence modeling has produced diverse architectures -- from classical recurrent neural networks to modern Transformers and state space models (SSMs) -- yet a unified theoretical understanding of expressivity and trainability trade-offs remains limited. We introduce a unified framework that represents a broad class of ...	[ "Ali Ghodsi" ]	[ "cs.LG", "cs.AI" ]	[ "Computer Science" ]	2025-12-17T00:00:00	https://arxiv.org/abs/2512.15115	https://arxiv.org/pdf/2512.15115v1	2512.15115	10.48550/arXiv.2512.15115	1	0	false	null	arXiv.org	0.4079
1d99b43cb4c7ba6431d6c01f5a54c63dd4c8cbadfacdec6a0c9ef7855505b3d5	[ "arxiv", "semantic_scholar" ]	Characterizing Mamba's Selective Memory using Auto-Encoders	State space models (SSMs) are a promising alternative to transformers for language modeling because they use fixed memory during inference. However, this fixed memory usage requires some information loss in the hidden state when processing long sequences. While prior work has studied the sequence length at which this i...	[ "Tamanna Hossain", "Robert L. Logan", "Ganesh Jagadeesan", "Sameer Singh", "Joel Tetreault", "Alejandro Jaimes" ]	[ "cs.CL" ]	[ "Computer Science" ]	2025-12-17T00:00:00	https://arxiv.org/abs/2512.15653	https://arxiv.org/pdf/2512.15653v1	2512.15653	10.48550/arXiv.2512.15653	2	0	false	null	null	0.2596
3e9593febf6b879410c1cac2fc07ef20511567171021ed24fae2fb4d2ff93d86	[ "arxiv", "semantic_scholar" ]	Kinetic-Mamba: Mamba-Assisted Predictions of Stiff Chemical Kinetics	Accurate chemical kinetics modeling is essential for combustion simulations, as it governs the evolution of complex reaction pathways and thermochemical states. In this work, we introduce Kinetic-Mamba, a Mamba-based neural operator framework that integrates the expressive power of neural operators with the efficient t...	[ "Additi Pandey", "Liang Wei", "Hessam Babaee", "George Em Karniadakis" ]	[ "cs.LG" ]	[ "Computer Science" ]	2025-12-16T00:00:00	https://arxiv.org/abs/2512.14471	https://arxiv.org/pdf/2512.14471v2	2512.14471	10.48550/arXiv.2512.14471	0	0	false	null	arXiv.org	0.4068
91f6c280dda68adec01d8ba533661387cf17705a87d75c55630292e0e9ca64e2	[ "arxiv", "semantic_scholar" ]	TSkel-Mamba: Temporal Dynamic Modeling via State Space Model for Human Skeleton-based Action Recognition	Skeleton-based action recognition has garnered significant attention in the computer vision community. Inspired by the recent success of the selective state-space model (SSM) Mamba in modeling 1D temporal sequences, we propose TSkel-Mamba, a hybrid Transformer-Mamba framework that effectively captures both spatial and ...	[ "Yanan Liu", "Jun Liu", "Hao Zhang", "Dan Xu", "Hossein Rahmani", "Mohammed Bennamoun", "Qiuhong Ke" ]	[ "cs.CV" ]	[ "Computer Science" ]	2025-12-12T00:00:00	https://arxiv.org/abs/2512.11503	https://arxiv.org/pdf/2512.11503v1	2512.11503	10.48550/arXiv.2512.11503	1	0	false	null	arXiv.org	0.4022
f8908c355d6518e3b96981be3ca4cac346ed5f064ce03e240c29ff5aa59b8aeb	[ "arxiv", "semantic_scholar" ]	DF-Mamba: Deformable State Space Modeling for 3D Hand Pose Estimation in Interactions	Modeling daily hand interactions often struggles with severe occlusions, such as when two hands overlap, which highlights the need for robust feature learning in 3D hand pose estimation (HPE). To handle such occluded hand images, it is vital to effectively learn the relationship between local image features (e.g., for ...	[ "Yifan Zhou", "Takehiko Ohkawa", "Guwenxiao Zhou", "Kanoko Goto", "Takumi Hirose", "Yusuke Sekikawa", "Nakamasa Inoue" ]	[ "cs.CV", "cs.AI", "cs.LG" ]	[ "Computer Science" ]	2025-12-02T00:00:00	https://arxiv.org/abs/2512.02727	https://arxiv.org/pdf/2512.02727v1	2512.02727	10.1109/WACV61042.2026.00519	1	0	false	null	IEEE Workshop/Winter Conference on Applications of Computer Vision	0.3907
11714547a02644b81947501fb7c600cd00f1dda2a9edff2fa48f4c1063f10506	[ "arxiv", "semantic_scholar" ]	PerfMamba: Performance Analysis and Pruning of Selective State Space Models	Recent advances in sequence modeling have introduced selective SSMs as promising alternatives to Transformer architectures, offering theoretical computational efficiency and sequence processing advantages. A comprehensive understanding of selective SSMs in runtime behavior, resource utilization patterns, and scaling ch...	[ "Abdullah Al Asif", "Mobina Kashaniyan", "Sixing Yu", "Juan Pablo Muñoz", "Ali Jannesari" ]	[ "cs.LG" ]	[ "Computer Science" ]	2025-11-28T00:00:00	https://arxiv.org/abs/2511.22849	https://arxiv.org/pdf/2511.22849v1	2511.22849	10.48550/arXiv.2511.22849	1	0	false	null	arXiv.org	0.3861
9357ddfb28e97723e8f3bcac2528438756f24eae3448d19ed9689d4adda3c02f	[ "arxiv", "semantic_scholar" ]	MMA: A Momentum Mamba Architecture for Human Activity Recognition with Inertial Sensors	Human activity recognition (HAR) from inertial sensors is essential for ubiquitous computing, mobile health, and ambient intelligence. Conventional deep models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and transformers have advanced HAR but remain limited by vanishing or exloding g...	[ "Thai-Khanh Nguyen", "Uyen Vo", "Tan M. Nguyen", "Thieu N. Vo", "Trung-Hieu Le", "Cuong Pham" ]	[ "cs.HC", "cs.LG" ]	[ "Computer Science" ]	2025-11-26T00:00:00	https://arxiv.org/abs/2511.21550	https://arxiv.org/pdf/2511.21550v1	2511.21550	10.48550/arXiv.2511.21550	0	0	false	null	arXiv.org	0.3839
648c7836adbac4d3dda328933616b6230b005a4e7a6a7ded7a7ca58af56af581	[ "arxiv", "semantic_scholar" ]	RNN as Linear Transformer: A Closer Investigation into Representational Potentials of Visual Mamba Models	Mamba has recently garnered attention as an effective backbone for vision tasks. However, its underlying mechanism in visual domains remains poorly understood. In this work, we systematically investigate Mamba's representational properties and make three primary contributions. First, we theoretically analyze Mamba's re...	[ "Timing Yang", "Guoyizhe Wei", "Alan Yuille", "Feng Wang" ]	[ "cs.CV" ]	[ "Computer Science" ]	2025-11-23T00:00:00	https://arxiv.org/abs/2511.18380	https://arxiv.org/pdf/2511.18380v1	2511.18380	10.48550/arXiv.2511.18380	1	0	false	null	arXiv.org	0.3804
daefc8500bb1b41abaf07b7520ddcceb2fe9fac035babbf4e3b42f89df4b3708	[ "arxiv", "semantic_scholar" ]	Controllability Analysis of State Space-based Language Model	State-space models (SSMs), particularly Mamba, have become powerful architectures for sequence modeling, yet their internal dynamics remain poorly understood compared to attention-based models. We introduce and validate the Influence Score, a controllability-based metric derived from the discretized state-space paramet...	[ "Mohamed Mabrok", "Yalda Zafari" ]	[ "cs.LG" ]	[ "Computer Science" ]	2025-11-22T00:00:00	https://arxiv.org/abs/2511.17970	https://arxiv.org/pdf/2511.17970v1	2511.17970	10.48550/arXiv.2511.17970	1	0	false	null	arXiv.org	0.3793
b2e22fd01bb7fb14131d9911f85eefeb7d2d0aee468e5420cfb3a197ebd795e0	[ "arxiv", "semantic_scholar" ]	Fourier-KAN-Mamba: A Novel State-Space Equation Approach for Time-Series Anomaly Detection	Time-series anomaly detection plays a critical role in numerous real-world applications, including industrial monitoring and fault diagnosis. Recently, Mamba-based state-space models have shown remarkable efficiency in long-sequence modeling. However, directly applying Mamba to anomaly detection tasks still faces chall...	[ "Xiancheng Wang", "Lin Wang", "Rui Wang", "Zhibo Zhang", "Minghang Zhao" ]	[ "cs.LG", "eess.SP" ]	[ "Computer Science", "Engineering" ]	2025-11-19T00:00:00	https://arxiv.org/abs/2511.15083	https://arxiv.org/pdf/2511.15083v2	2511.15083	10.48550/arXiv.2511.15083	0	0	false	null	arXiv.org	0.3758
70db4f54c92397eb2104769d0d40584104ecb67f45af91a408f371ffeb565ba3	[ "arxiv", "semantic_scholar" ]	X-VMamba: Explainable Vision Mamba	State Space Models (SSMs), particularly the Mamba architecture, have recently emerged as powerful alternatives to Transformers for sequence modeling, offering linear computational complexity while achieving competitive performance. Yet, despite their effectiveness, understanding how these Vision SSMs process spatial in...	[ "Mohamed A. Mabrok", "Yalda Zafari" ]	[ "cs.CV", "cs.LG", "math.DS" ]	[ "Computer Science", "Mathematics" ]	2025-11-16T00:00:00	https://arxiv.org/abs/2511.12694	https://arxiv.org/pdf/2511.12694v1	2511.12694	10.48550/arXiv.2511.12694	1	0	false	null	arXiv.org	0.3724
c35b1edfa96493179e448995d32ae41a1c145edb55f18780e8c786b084e54985	[ "arxiv", "semantic_scholar" ]	DensePercept-NCSSD: Vision Mamba towards Real-time Dense Visual Perception with Non-Causal State Space Duality	In this work, we propose an accurate and real-time optical flow and disparity estimation model by fusing pairwise input images in the proposed non-causal selective state space for dense perception tasks. We propose a non-causal Mamba block-based model that is fast and efficient and aptly manages the constraints present...	[ "Tushar Anand", "Advik Sinha", "Abhijit Das" ]	[ "cs.CV" ]	[ "Computer Science" ]	2025-11-16T00:00:00	https://arxiv.org/abs/2511.12671	https://arxiv.org/pdf/2511.12671v1	2511.12671	10.48550/arXiv.2511.12671	0	0	true	https://github.com/vimstereo/DensePerceptNCSSD	arXiv.org	0.5755
2e608561403dadf7907f84134dbe27882c4bd0c97e0105d465805cde88eb4e49	[ "arxiv", "semantic_scholar" ]	Arcee: Differentiable Recurrent State Chain for Generative Vision Modeling with Mamba SSMs	State-space models (SSMs), Mamba in particular, are increasingly adopted for long-context sequence modeling, providing linear-time aggregation via an input-dependent, causal selective-scan operation. Along this line, recent "Mamba-for-vision" variants largely explore multiple scan orders to relax strict causality for n...	[ "Jitesh Chavan", "Rohit Lal", "Anand Kamat", "Mengjia Xu" ]	[ "cs.CV" ]	[ "Computer Science" ]	2025-11-14T00:00:00	https://arxiv.org/abs/2511.11243	https://arxiv.org/pdf/2511.11243v2	2511.11243	10.48550/arXiv.2511.11243	0	0	false	null	arXiv.org	0.3701
f595ce57a6d8c730b9854fe6e43a9bed049f0b04b8a580cbc89c51399709766e	[ "arxiv", "semantic_scholar" ]	Teaching Pretrained Language Models to Think Deeper with Retrofitted Recurrence	Recent advances in depth-recurrent language models show that recurrence can decouple train-time compute and parameter count from test-time compute. In this work, we study how to convert existing pretrained non-recurrent language models into depth-recurrent models. We find that using a curriculum of recurrences to incre...	[ "Sean McLeish", "Ang Li", "John Kirchenbauer", "Dayal Singh Kalra", "Brian R. Bartoldson", "Bhavya Kailkhura", "Avi Schwarzschild", "Jonas Geiping", "Tom Goldstein", "Micah Goldblum" ]	[ "cs.CL", "cs.AI", "cs.LG" ]	[ "Computer Science" ]	2025-11-10T00:00:00	https://arxiv.org/abs/2511.07384	https://arxiv.org/pdf/2511.07384v1	2511.07384	10.48550/arXiv.2511.07384	22	3	true	https://github.com/mcleish7/retrofitting-recurrence	arXiv.org	0.5649
6235c27604fc8609ddb44fed140863ff22aefcfa56391ebfcb635bffc438594c	[ "arxiv", "semantic_scholar" ]	Dual Mamba for Node-Specific Representation Learning: Tackling Over-Smoothing with Selective State Space Modeling	Over-smoothing remains a fundamental challenge in deep Graph Neural Networks (GNNs), where repeated message passing causes node representations to become indistinguishable. While existing solutions, such as residual connections and skip layers, alleviate this issue to some extent, they fail to explicitly model how node...	[ "Xin He", "Yili Wang", "Yiwei Dai", "Xin Wang" ]	[ "cs.LG" ]	[ "Computer Science" ]	2025-11-10T00:00:00	https://arxiv.org/abs/2511.06756	https://arxiv.org/pdf/2511.06756v3	2511.06756	10.1609/aaai.v40i26.39317	0	0	false	null	AAAI Conference on Artificial Intelligence	0.3655
dfe569eeadb992762b6d692f35e0bf6308ee1186389827575c1b99c2165f8605	[ "arxiv", "semantic_scholar" ]	Mamba-driven multi-perspective structural understanding for molecular ground-state conformation prediction	A comprehensive understanding of molecular structures is important for the prediction of molecular ground-state conformation involving property information. Meanwhile, state space model (e.g., Mamba) has recently emerged as a promising mechanism for long sequence modeling and has achieved remarkable results in various ...	[ "Yuxin Gou", "Aming Wu", "Richang Hong", "Meng Wang" ]	[ "physics.chem-ph", "cs.AI" ]	[ "Physics", "Computer Science" ]	2025-11-10T00:00:00	https://arxiv.org/abs/2511.09564	https://arxiv.org/pdf/2511.09564v1	2511.09564	10.48550/arXiv.2511.09564	0	0	false	null	arXiv.org	0.3655
c3a40b96beb755558cbf16078591afdafd21f4aa7bd713ed620ca3b6fe2a8738	[ "arxiv", "semantic_scholar" ]	GTR-Mamba: Geometry-to-Tangent Routing Mamba for Hyperbolic POI Recommendation	Next Point-of-Interest (POI) recommendation is a critical task in modern Location-Based Social Networks (LBSNs), aiming to model the complex decision-making process of human mobility to provide personalized recommendations for a user's next check-in location. Existing hyperbolic POI recommendation models, predominantly...	[ "Zhuoxuan Li", "Jieyuan Pei", "Tangwei Ye", "Zhongyuan Lai", "Zihan Liu", "Fengyuan Xu", "Qi Zhang", "Liang Hu" ]	[ "cs.AI", "cs.IR" ]	[ "Computer Science" ]	2025-10-27T00:00:00	https://arxiv.org/abs/2510.22942	https://arxiv.org/pdf/2510.22942v2	2510.22942	null	2	0	false	null	null	0.2224
41afe911eb885dd665143e3fc16377ec18664f8d7e036907656d3c3dd8900158	[ "arxiv", "semantic_scholar" ]	StretchySnake: Flexible SSM Training Unlocks Action Recognition Across Spatio-Temporal Scales	State space models (SSMs) have emerged as a competitive alternative to transformers in various tasks. Their linear complexity and hidden-state recurrence make them particularly attractive for modeling long sequences, whereas attention becomes quadratically expensive. However, current training methods for video understa...	[ "Nyle Siddiqui", "Rohit Gupta", "Sirnam Swetha", "Mubarak Shah" ]	[ "cs.CV" ]	[ "Computer Science" ]	2025-10-17T00:00:00	https://arxiv.org/abs/2510.16209	https://arxiv.org/pdf/2510.16209v1	2510.16209	10.48550/arXiv.2510.16209	0	0	false	null	arXiv.org	0.338
5474e5559e4b428897945a0a615c20903927347a7368fb59e67f5f4c21d90379	[ "arxiv", "semantic_scholar" ]	State-Space Models for Tabular Prior-Data Fitted Networks	Recent advancements in foundation models for tabular data, such as TabPFN, demonstrated that pretrained Transformer architectures can approximate Bayesian inference with high predictive performance. However, Transformers suffer from quadratic complexity with respect to sequence length, motivating the exploration of mor...	[ "Felix Koch", "Marcel Wever", "Fabian Raisch", "Benjamin Tischler" ]	[ "cs.LG" ]	[ "Computer Science" ]	2025-10-16T00:00:00	https://arxiv.org/abs/2510.14573	https://arxiv.org/pdf/2510.14573v2	2510.14573	10.48550/arXiv.2510.14573	0	0	false	null	arXiv.org	0.3369
b30264a3f43079551cb9bed0adbbcdf0a0453499e2ad26ee65d3d1c40677eda1	[ "arxiv", "semantic_scholar" ]	MSF-Mamba: Motion-aware State Fusion Mamba for Efficient Micro-Gesture Recognition	Micro-gesture recognition (MGR) targets the identification of subtle and fine-grained human motions and requires accurate modeling of both long-range and local spatiotemporal dependencies. While CNNs are effective at capturing local patterns, they struggle with long-range dependencies due to their limited receptive fie...	[ "Deng Li", "Jun Shao", "Bohao Xing", "Rong Gao", "Bihan Wen", "Heikki Kälviäinen", "Xin Liu" ]	[ "cs.CV" ]	[ "Computer Science" ]	2025-10-12T00:00:00	https://arxiv.org/abs/2510.10478	https://arxiv.org/pdf/2510.10478v2	2510.10478	10.48550/arXiv.2510.10478	1	0	false	null	IEEE transactions on multimedia	0.3323
6e5cf0753e113586abf26188053315378bf820ea2df0698163f45a17815c3f58	[ "arxiv", "semantic_scholar" ]	SSM-CGM: Interpretable State-Space Forecasting Model of Continuous Glucose Monitoring for Personalized Diabetes Management	Continuous glucose monitoring (CGM) generates dense data streams critical for diabetes management, but most used forecasting models lack interpretability for clinical use. We present SSM-CGM, a Mamba-based neural state-space forecasting model that integrates CGM and wearable activity signals from the AI-READI cohort. S...	[ "Shakson Isaac", "Yentl Collin", "Chirag Patel" ]	[ "cs.LG" ]	[ "Computer Science" ]	2025-10-05T00:00:00	https://arxiv.org/abs/2510.04386	https://arxiv.org/pdf/2510.04386v1	2510.04386	10.48550/arXiv.2510.04386	1	0	false	null	arXiv.org	0.3243
960483979f5000802897195c1e8f51a02ac7b5329b061be59be983083201fab9	[ "arxiv", "semantic_scholar" ]	Gather-Scatter Mamba: Accelerating Propagation with Efficient State Space Model	State Space Models (SSMs)-most notably RNNs-have historically played a central role in sequential modeling. Although attention mechanisms such as Transformers have since dominated due to their ability to model global context, their quadratic complexity and limited scalability make them less suited for long sequences. V...	[ "Hyun-kyu Ko", "Youbin Kim", "Jihyeon Park", "Dongheok Park", "Gyeongjin Kang", "Wonjun Cho", "Hyung Yi", "Eunbyung Park" ]	[ "cs.CV", "cs.AI" ]	[ "Computer Science" ]	2025-10-01T00:00:00	https://arxiv.org/abs/2510.00862	https://arxiv.org/pdf/2510.00862v1	2510.00862	10.48550/arXiv.2510.00862	0	0	true	https://github.com/Ko-Lani/GSMamba}	arXiv.org	0.4941
437432f7e7c702519932af6e0e9611ae3617f782127d9d29531dc1c93dc1ae27	[ "arxiv", "semantic_scholar" ]	Wavelet-Assisted Mamba for Satellite-Derived Sea Surface Temperature Super-Resolution	Sea surface temperature (SST) is an essential indicator of global climate change and one of the most intuitive factors reflecting ocean conditions. Obtaining high-resolution SST data remains challenging due to limitations in physical imaging, and super-resolution via deep neural networks is a promising solution. Recent...	[ "Wankun Chen", "Feng Gao", "Yanhai Gan", "Jingchao Cao", "Junyu Dong", "Qian Du" ]	[ "eess.IV", "cs.CV" ]	[ "Computer Science", "Engineering" ]	2025-09-29T00:00:00	https://arxiv.org/abs/2509.24334	https://arxiv.org/pdf/2509.24334v1	2509.24334	10.1109/TGRS.2025.3616324	1	0	true	https://github.com/oucailab/WMSR	IEEE Transactions on Geoscience and Remote Sensing	0.4905
fc6854ea06b2101eab232841e4138f48e39c547e9160e531ea8bfb56b8994ded	[ "arxiv", "semantic_scholar" ]	Trained Mamba Emulates Online Gradient Descent in In-Context Linear Regression	State-space models (SSMs), particularly Mamba, emerge as an efficient Transformer alternative with linear complexity for long-sequence modeling. Recent empirical works demonstrate Mamba's in-context learning (ICL) capabilities competitive with Transformers, a critical capacity for large foundation models. However, theo...	[ "Jiarui Jiang", "Wei Huang", "Miao Zhang", "Taiji Suzuki", "Liqiang Nie" ]	[ "cs.LG" ]	[ "Computer Science" ]	2025-09-28T00:00:00	https://arxiv.org/abs/2509.23779	https://arxiv.org/pdf/2509.23779v1	2509.23779	10.48550/arXiv.2509.23779	1	0	false	null	arXiv.org	0.3162
606229849b680d7e3f8344e0ebf9bba11f63964015c58133a5fdd9ebe215bf07	[ "arxiv", "semantic_scholar" ]	HyMaTE: A Hybrid Mamba and Transformer Model for EHR Representation Learning	Electronic health Records (EHRs) have become a cornerstone in modern-day healthcare. They are a crucial part for analyzing the progression of patient health; however, their complexity, characterized by long, multivariate sequences, sparsity, and missing values poses significant challenges in traditional deep learning m...	[ "Md Mozaharul Mottalib", "Thao-Ly T. Phan", "Rahmatollah Beheshti" ]	[ "cs.LG" ]	[ "Computer Science" ]	2025-09-28T00:00:00	https://arxiv.org/abs/2509.24118	https://arxiv.org/pdf/2509.24118v1	2509.24118	10.1145/3765612.3767245	2	0	true	https://github.com/healthylaife/HyMaTE	ACM International Conference on Bioinformatics, Computational Biology and Biomedicine	0.4887

End of preview. Expand in Data Studio

State Space Models & Mamba Papers — FineSet

A research-paper dataset on State Space Models & Mamba Papers, assembled, deduplicated, and quality-scored by FineSet from arXiv and Semantic Scholar.

📸 This is a dated snapshot — generated 2026-06-19. It is not auto-updated. Research on State Space Models & Mamba Papers moves fast — new papers land on arXiv every week. Want this same dataset refreshed daily, on a topic you choose? See the bottom. ↓

Why this dataset

Quality-scored: quality_score float (0–1), blends citations with recency + code/venue signals — filter out the noise
Papers with code: 135 flagged via has_code — find reproducible work fast
Deduplicated: arXiv + Semantic Scholar cross-referenced, duplicate records merged
Clean JSONL: 433 records, one per line, normalized fields — no encoding garbage

Dataset details

Records: 433
Date range: 2020–2026
Snapshot date: 2026-06-19 (frozen — see note above)
Sources: arXiv, Semantic Scholar (cross-referenced, duplicates merged)
arXiv categories: cs.LG, cs.CL, cs.CV
Quality scoring: citations + recency + code/venue blend, 0–1 (p50=0.325, p90=0.564)
Format: JSONL, one record per line

Fields

Field	Type	Description
id	string	Deterministic SHA256 record id
sources	list	Which sources contributed (`arxiv`, `semantic_scholar`)
title	string	Paper title
abstract	string	Full abstract
authors	list	Author names
categories	list	arXiv category codes
fields_of_study	list	Semantic Scholar field tags
published_date	string	ISO 8601 date
url	string	arXiv abstract URL
pdf_url	string\|null	Open-access PDF if available
arxiv_id	string\|null	arXiv identifier
doi	string\|null	DOI if available
citation_count	int	Citation count (Semantic Scholar)
influential_citation_count	int	Influential citations (Semantic Scholar)
has_code	bool	Code repo detected in the arXiv comment
code_url	string\|null	GitHub URL if detected
venue	string\|null	Publication venue
quality_score	float	0–1, blended (citations + recency + code/venue)

Quality score methodology

quality_score = max(impact, freshness), clamped to [0, 1], where:

impact = max( log10(citations+1)/4 , log10(influential_citations+1)/2 ) — realized impact (0.5 at 100 citations, ~0.75 at 1,000, 1.0 at 10,000+).
freshness = recency × (0.35 + 0.30·has_code + 0.20·has_venue) — a baseline for recent papers (so a strong paper published this week isn't scored 0 just for lacking citations), where recency is 1.0 for papers ≤60 days old and decays linearly to 0 by ~18 months.

Old highly-cited papers score on impact; brand-new papers score on freshness; old uncited papers score ~0. Useful for filtering training data by quality, not just age.

👉 Want this on YOUR topic, updated daily?

This snapshot is frozen at 2026-06-19. The live FineSet pipeline keeps a dataset like this refreshed every day on whatever topic you describe — new papers in, dedup and quality scoring automatic, export as JSONL/Parquet or push straight to the Hub.

Tell me the topic you'd want and I'll run the pipeline on it — open a discussion on this dataset, it's free and it's how I decide what to build next.

→ fineset.io — describe what you want to train on, get a dataset. Early-access waitlist open (referral skip available).

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