{"id": "aeaf83abc516eb6a5a39fd49f5063a479468a00f591416b968e28cf4a998d5af", "sources": ["arxiv"], "title": "Ternary Mamba: Grouped Quantization-Aware Training of W1.58A16 State Space Models", "abstract": "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 training (QAT) with knowledge distillation from a frozen FP16 teacher, we compress Mamba-2 1.3B to 3.61x (2,687 to 744 MB) and achieve 48.1% zero-shot accuracy (7-task average) in just 102M tokens (4 GPU-hours, single H100) -- approaching Bi-Mamba's 48.4% (within +/-0.9pp CI). This QAT-from-pretrained setting reveals zero-ratio collapse, a novel instability caused by learnable quantization scales that does not arise in from-scratch training. We further show that post-hoc correction strategies effective for Transformers fail for SSMs due to error accumulation through the recurrence. These results demonstrate that ternary SSMs do not require expensive from-scratch training: QAT from pretrained checkpoints with KD is a data-efficient alternative.", "authors": ["Ramprasath Ganesaraja", "Sahil Dilip Panse", "Swathika N"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": [], "published_date": "2026-06-16", "url": "https://arxiv.org/abs/2606.18114", "pdf_url": "https://arxiv.org/pdf/2606.18114v1", "arxiv_id": "2606.18114", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "a9da9090e15f2acbf70dd9f926ea0f53ce40b2b9c9df5f291a652cdd0ca3d51d", "sources": ["arxiv"], "title": "Reload-Mamba: Hierarchical Anti-Dilution State-Space Modeling for Multi-Class Semantic Segmentation", "abstract": "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 framework that addresses this propagation-induced response dilution through three segmentation-specific designs: (i) a boundary-supervised local detail prior that is explicitly trained with ground-truth boundary masks to identify regions requiring response restoration; (ii) a class-uncertainty-aware Reload Gate that incorporates per-pixel class entropy from a pre-reload auxiliary head as an additional gating signal, a formulation that is informative only under multi-class dense prediction; and (iii) a hierarchical multi-level Reload mechanism that applies anti-dilution refinement at three decoder levels and fuses the restored representations top-down. Built upon a ConvNeXt-Tiny encoder with a multi-scale decoder and four-directional Mamba scanning with pixel-wise directional attention, Reload-Mamba achieves 47.9% single-scale (48.9% multi-scale) mIoU on ADE20K and 83.2% single-scale mIoU on Cityscapes. With ResNet-101 + COCO pre-training under the standard DeepLab-style protocol, Reload-Mamba reaches 87.8% mIoU on PASCAL VOC 2012 val. Controlled ablations show that each of the three segmentation-specific designs contributes beyond a direct port of the prior anti-dilution architecture proposed for binarization, cumulatively improving over the direct-port baseline by +2.2 mIoU on ADE20K.", "authors": ["Sheng-Wei Chan", "Hsin-Jui Pan", "Jen-Shiun Chiang"], "categories": ["cs.CV"], "fields_of_study": [], "published_date": "2026-06-16", "url": "https://arxiv.org/abs/2606.17966", "pdf_url": "https://arxiv.org/pdf/2606.17966v1", "arxiv_id": "2606.17966", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "28890da352d2d2348f1e45e05f8bb8bd71ab5fd0755926ee200db6c818f73759", "sources": ["arxiv"], "title": "Task-Restricted Symmetries in Recurrent Weight Space", "abstract": "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 coordinates. The Schur form separates spectral blocks from directed nonnormal couplings, giving a diagnostic basis for structured ablations that keep the input and readout maps fixed. In a fixed-length copy task, selected nonnormal Schur couplings can be removed with little loss in some trained solutions, whereas other couplings are necessary for accurate autonomous replay. Across flip-flop, sine generation, and context-dependent integration, the loss-preserving ablation profile varies across tasks and trained solutions. These results identify candidate approximate functional invariances, not universal symmetries of recurrent weight space. Schur-coordinate ablations provide a practical diagnostic for which structured perturbations preserve a trained recurrent solution and which ones disrupt its computation.", "authors": ["Simon Dräger"], "categories": ["cs.LG"], "fields_of_study": [], "published_date": "2026-06-16", "url": "https://arxiv.org/abs/2606.18457", "pdf_url": "https://arxiv.org/pdf/2606.18457v1", "arxiv_id": "2606.18457", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "83f36ca26767a9fe5efc32e4a75cce5c1f5b5f49b77b7877a7b7210049ee1671", "sources": ["arxiv", "semantic_scholar"], "title": "DeepMine-Mamba: Mitigating Information Dilution in Mamba-Based State Space Models for Document Image Binarization", "abstract": "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 Mamba-based state space models remain largely unexplored for this task. In this work, we investigate Mamba-based feature propagation and observe that direct state-space propagation may dilute weak foreground cues during long-range modeling, especially faint ink traces, fragmented characters, and boundary-sensitive stroke details. To address this problem, we propose DeepMine-Mamba, a Mamba-based binarization framework equipped with a novel Anti-Dilution Gate that estimates propagation-induced feature changes and selectively restores stroke-sensitive local responses while suppressing unnecessary background enhancement. Experiments on DIBCO/H-DIBCO benchmarks under a strict leave-one-year-out protocol show that DeepMine-Mamba achieves competitive overall performance, with strong average FM and Fps across benchmark years. Ablation results further show that the Anti-Dilution Gate is the key component for mitigating propagation-induced foreground dilution and improving stroke preservation.", "authors": ["Sheng-Wei Chan", "Yung-Che Wang", "Hsin-Jui Pan", "Chia-Min Lin", "Jen-Shiun Chiang"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2026-06-07", "url": "https://arxiv.org/abs/2606.08781", "pdf_url": "https://arxiv.org/pdf/2606.08781v2", "arxiv_id": "2606.08781", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/henrychan0719/Deep-Mine-Mamba", "venue": null, "quality_score": 0.65}
{"id": "e5958c80e2f110d394e2e03639bf45e36990796fcc3336897801fc8566cb4997", "sources": ["arxiv", "semantic_scholar"], "title": "Advancing Heliophysics and Space Weather Modeling through Open Science", "abstract": "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 regarding open data and open software guidelines, as scientific models differ significantly from data analysis software. We gathered feedback from modeling teams worldwide through a living survey and discussion sessions at the 2024 Open Science Workshop in College Park, USA, and at the 2025 COSPAR ISWAT Working Meeting in Cape Canaveral, USA. We complement these findings with lessons learned from almost 25 years of experience at the Community Coordinated Modeling Center in enabling open use of models. We identify key roadblocks in current open science practices and guidelines and offer recommendations for future progress across four overlapping themes: open use of models and simulation results, open validation, open development, and open collaboration. An essential outcome of the discussion is the need for model developers and model users to speak with a united voice and promote the role of models in future open science efforts. We introduce a new cross-domain community initiative called the Heliophysics Open Modeling Environment (HOME), which will be integrated as an overarching activity within COSPAR ISWAT. HOME will serve as a platform for modelers and model users to work together, facilitate community modeling, improve the scientific return on modeling investment, and advance understanding, modeling, and forecasting in heliophysics and space weather.", "authors": ["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. Linker", "H. Liu", "J. G. Luhmann", "S. Markidis", "P. Mayank", "V. Merkin", "N. Moens", "D. Odstrcil", "Y. A. Omelchenko", "M. Palmroth", "S. Poedts", "A. J. Ridley", "Y. Shou", "V. Tenishev", "D. R. Themens", "G. Toth", "W. Wang", "R. -P. Wilhelm", "M. A. Young", "B. Cecconi", "M. -Y. Chou", "D. De Zeeuw", "G. L. Delzanno", "C. Didigu", "M. El Alaoui", "S. Fung", "J. Green", "Z. Huang", "L. K. Jian", "L. J. Landwer", "M. Lesko", "P. MacNeice", "A. Masson", "M. L. Mays", "P. M. Mehta", "M. S. Miesch", "E. Palmerio", "M. Petrenko", "E. Provornikova", "L. Rastätter", "L. Rusaitis", "N. Sachdeva", "E. Samara", "D. Sur", "A. Taktakishvili", "J. Topper", "T. Tsui", "C. Verbeke", "J. Wang", "C. Wiegand", "M. Wiltberger", "Y. Zheng", "M. M. Bisi", "M. K. Georgoulis", "T. Kodikara", "T. Pulkkinen", "A. Chartier", "D. da Silva", "A. Faturahman", "K. Garcia-Sage", "D. Kondrashov", "V. E. Ledvina", "W. Liu", "C. Pandey", "E. Resnick", "C. Shi", "R. S. Weigel", "K. Whitman", "I. Zakharenkova", "K. Zhang"], "categories": ["physics.space-ph", "astro-ph.IM", "astro-ph.SR", "physics.comp-ph"], "fields_of_study": ["Physics"], "published_date": "2026-05-28", "url": "https://arxiv.org/abs/2605.30626", "pdf_url": "https://arxiv.org/pdf/2605.30626v1", "arxiv_id": "2605.30626", "doi": null, "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "fa77155c03cbe72999517c78d920b1d7c0a1f565fbcf60f87a08d2efe99f601b", "sources": ["arxiv", "semantic_scholar"], "title": "SO-Mamba: State-Ownership Mamba for Unrolled MRI Reconstruction", "abstract": "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-consistency-coupled unrolled solver, different stages operate on different reconstruction iterates, where the resident carrier should preserve coherent reconstruction content across stages while stage-dependent non-resident evidence is tied to the current update. Treating these roles uniformly can place persistent resident-carrier evidence and update-dependent non-resident evidence into the same recurrent content route. We therefore propose SO-Mamba, a state-ownership Mamba regularizer that assigns reconstruction evidence within each Mamba stage to recurrent residency, state-interface access, and non-state output correction. SO-Mamba implements this ownership rule with a State-Ownership Router (SOR), which constructs a resident carrier for recurrent content and routes non-resident evidence to affine modulation of the B/C state interfaces and an output correction outlet. The resident carrier supplies the Mamba content route, while the non-resident evidence stream adapts the state interfaces and contributes through the output outlet without entering the recurrent content route. We further introduce a two-level outer-band leakage diagnostic that separates hidden-state storage from readout expression by measuring outer-band energy in the selective-scan state trajectory and the post-scan Mamba readout. Experiments on five public MRI reconstruction benchmarks spanning diverse anatomies, sampling patterns, and coil configurations show that SO-Mamba consistently improves over CNN-, Transformer-, and Mamba-based baselines with competitive computational efficiency.", "authors": ["Pengcheng Fang", "Hongli Chen", "Fangfang Tang", "Feng Liu", "Xiaohao Cai", "Shanshan Shan"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-21", "url": "https://arxiv.org/abs/2605.22031", "pdf_url": "https://arxiv.org/pdf/2605.22031v1", "arxiv_id": "2605.22031", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "f57e7632a973cf7ec297b40929e3789b5538de80b6e0bc2e4223860173d75cde", "sources": ["arxiv", "semantic_scholar"], "title": "Linear-DPO: Linear Direct Preference Optimization for Diffusion and Flow-Matching Generative Models", "abstract": "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 generative tasks.\\ In this paper, we derive a generalized DPO objective that covers both diffusion and flow-matching via a unified reverse-time SDE framework, and point out from a gradient perspective that the standard DPO objective is suboptimal for text-to-image generation. Consequently, we propose Linear-DPO, which replaces the aggressive sigmoid-based utility function with a sustained linear utility and incorporates an EMA-updated reference model. Qualitative and quantitative experiments on diffusion models (SD1.5, SDXL) and flow-matching model (SD3-Medium) demonstrate the superiority of our approach over existing baselines.", "authors": ["Kesong Li", "Yixuan Xu", "Kuo-kun Tseng", "Weiyi Lu", "Kan Liu", "Tao Lan"], "categories": ["cs.CV", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-20", "url": "https://arxiv.org/abs/2605.21123", "pdf_url": "https://arxiv.org/pdf/2605.21123v1", "arxiv_id": "2605.21123", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Whynot0101/Linear-DPO", "venue": null, "quality_score": 0.65}
{"id": "25482b6fc67d8efc0decb862ef004cd0d8e55ac33740811c594dd0720a784318", "sources": ["arxiv", "semantic_scholar"], "title": "Flash PD-SSM: Memory-Optimized Structured Sparse State-Space Models", "abstract": "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 a prohibitively high compute and memory cost. In contrast, most structured transition matrix forms are highly efficient both in runtime and memory consumption, but suffer from limited expressivity. Building on recent work on structured sparse SSMs, we propose Flash PD-SSM, a novel SSM that achieves comparable throughput to widely-used structured SSMs with significantly better expressivity guarantees. Flash PD-SSM maintains a trainable set of structured sparse matrices, a single one of which is discretely selected at each time-step, enabling FSA expressiveness at the level of unstructured matrices while maintaining the efficiency required for training models at scale. First, we validate Flash PD-SSM against a suite of alternative models on synthetic mechanistic and state-tracking tasks, finding that its theoretical expressivity is achieved in practice. Second, on multivariate time-series tasks involving sequences of length over 17,000, we find that Flash PD-SSM defines a new state-of-the-art (SoTA) accuracy among competing SSM methods. Finally, we demonstrate that Flash PD-SSM is an effective drop-in replacement for hybrid LLMs, yielding improvements both in natural language state-tracking and in common language modeling scenarios. The model exhibits increased throughput and decreased memory consumption compared to SSMs widely used in frontier language models.", "authors": ["Aleksandar Terzić", "Francesco Carzaniga", "Nicolas Menet", "Yannick Biehl", "Michael Hersche", "Thomas Hofmann", "Abbas Rahimi"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-18", "url": "https://arxiv.org/abs/2605.19150", "pdf_url": "https://arxiv.org/pdf/2605.19150v1", "arxiv_id": "2605.19150", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "9aad1a1f88d08918aef24be68aaa7542d54d171526810d85b405ddae86827bf7", "sources": ["arxiv", "semantic_scholar"], "title": "Patch-MoE Mamba: A Patch-Ordered Mixture-of-Experts State Space Architecture for Medical Image Segmentation", "abstract": "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 alternative with linear sequence complexity. However, existing Mamba segmentation models still face two limitations: pixel-wise directional scanning can disrupt local 2D spatial structure, and simple summation-based fusion of scan directions cannot adapt well to diverse object sizes, shapes, and boundaries. To address these issues, we propose \\textit{Patch-MoE Mamba}, a patch-ordered mixture-of-experts state space architecture for medical image segmentation. It introduces a hierarchical patch-ordered scanning mechanism that preserves local spatial neighborhoods while capturing multi-scale context, and an MoE-based directional fusion module that adaptively combines multiple Mamba scanner outputs using four directional experts, a learnable concatenation expert, and residual directional aggregation. Experiments on five public polyp segmentation benchmarks and the ISIC 2017/2018 skin lesion segmentation datasets demonstrate the effectiveness and generality of Patch-MoE Mamba.", "authors": ["Diego Adame", "Fabian Vazquez", "Jose A. Nunez", "Huimin Li", "Jinghao Yang", "Erik Enriquez", "DongChul Kim", "Haoteng Tang", "Bin Fu", "Pengfei Gu"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-18", "url": "https://arxiv.org/abs/2605.17719", "pdf_url": "https://arxiv.org/pdf/2605.17719v1", "arxiv_id": "2605.17719", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "f58fff1ef4bb3bf84c6e1eabee858c75a884f30f49fe73e66f23ba21196c6ad0", "sources": ["arxiv", "semantic_scholar"], "title": "Looped SSMs: Depth-Recurrence and Input Reshaping for Time Series Classification", "abstract": "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 closely matches or outperforms a standard SSM with $k \\cdot L$ independent parameters across four architectures (LRU, S5, LinOSS, LrcSSM) and six time series classification benchmarks, despite operating within a strictly smaller hypothesis space, as we formally establish. Since the larger model contains the looped model as a special case, this dominance cannot be explained by expressivity and instead points to parameter sharing across depth as a beneficial inductive bias that simplifies optimization. These results demonstrate that depth-recurrence is orthogonal to sequence-recurrence and independently beneficial. We further show that input reshaping is an equally neglected design axis: concatenating timesteps for low-dimensional inputs, or flattening and rechunking the joint feature-time dimension for high-dimensional ones, yields accuracy gains of 1-6% across all models, confirmed over 5 random seeds. Both techniques provide standalone improvements that compound when combined, suggesting that depth and input reshaping are two independent and underexplored design axes for SSMs on time series.", "authors": ["Mónika Farsang", "Ramin Hasani", "Daniela Rus", "Radu Grosu"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-15", "url": "https://arxiv.org/abs/2605.16048", "pdf_url": "https://arxiv.org/pdf/2605.16048v1", "arxiv_id": "2605.16048", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "9097a3ea41fde59a2e4438f05c7dcd24580ad283c3b1d8f8e661981d8924452e", "sources": ["arxiv", "semantic_scholar"], "title": "MHMamba: Multi-Head Mamba for 3D Brain Tumor Segmentation", "abstract": "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 computational and memory overhead and inter-block contextual in coherence of Transformers in 3D MRI, this paper proposes Multi-Head Mamba (MHMamba). This method combines a U-shaped architecture with a multi-head state-space model (Mamba), splitting the channel dimension into parallel SSM heads and aggregating them with residuals. This enhances long-range representation and improves the stability of multimodal training while maintaining linear complexity. To further align statistics and enhance lesion response, we designed a channel-space calibration module for multi-head outputs and introduced an adaptive fusion mechanism at skip connections to dynamically connect global semantics with local details, thereby improving boundary consistency and the detection of small-volume lesions. We conducted experiments and ablations on BraTS2021 and BraTS2023. The results showed that MHMamba achieved stable and significant improvements in overall accuracy, boundary smoothness, and sensitivity to tumor core and small-volume enhancement areas, while preserving the linear-complexity advantage of Mamba-based modeling, thus verifying the effectiveness and versatility of the method.", "authors": ["Hanjun Tao", "Hua Wang", "Fan Zhang"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-15", "url": "https://arxiv.org/abs/2605.16464", "pdf_url": "https://arxiv.org/pdf/2605.16464v1", "arxiv_id": "2605.16464", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "f2d90579d72846888f37d3a46dd82e962fcd75ea58155852d07980ff5838da3c", "sources": ["arxiv", "semantic_scholar"], "title": "3DTMDet: A Dual-Path Synergy Network of Transformer and SSM for 3D Object Detection in Point Clouds", "abstract": "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 and reduces detection of distant and small objects. To address this issue, we propose 3DTMDet, a novel detection network that synergistically combines state space models (Mamba) with Transformers. The core idea is to utilize SSM's linear complexity and advantages in long sequence modeling to effectively capture global interactions between sparse and distant points, while using Transformer modules with local attention to encode fine-grained geometric structures in local point sets, preserving accurate shape information. We propose the 3D Hybrid Mamba Transformer (3DHMT) block, which uses an SSM-Attention-SSM pipeline to balance global context understanding and local detail preservation, effectively alleviating the tension between receptive field enlargement and geometric preservation in remote detection. In addition, we introduced a voxel generation block inspired by LiDAR physics, which diffuses features along the sensor observation direction to reconstruct the complete object structure of occlusion and distant areas. Extensive experiments conducted on the KITTI and ONCE datasets have shown that 3DTMDet outperforms state-of-the-art detectors. The code is available at https://github.com/QiuBingwen/3DTMDet.", "authors": ["Bingwen Qiu", "Yuan Liu", "Junqi Bai", "Tong Jiang", "Ben Liang", "Fangzhou Chen", "Xiubao Sui", "Qian Chen"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-15", "url": "https://arxiv.org/abs/2605.15546", "pdf_url": "https://arxiv.org/pdf/2605.15546v1", "arxiv_id": "2605.15546", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/QiuBingwen/3DTMDet", "venue": null, "quality_score": 0.65}
{"id": "2d068a9aab4d33193928a82a875a930ca44ed7577e290825e309d78dc793137b", "sources": ["arxiv", "semantic_scholar"], "title": "Social-Mamba: Socially-Aware Trajectory Forecasting with State-Space Models", "abstract": "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 capturing complex dependencies, but incur quadratic computational costs that scale poorly with the growing number of neighbors. Recently, Selective State-Space Models have provided a linear-time alternative; however, their inherently sequential design is misaligned with the unstructured and dynamic nature of social interactions. To address this challenge, we propose Social-Mamba, a forecasting architecture that reformulates social interactions as structured sequential processes. At its core is the Cycle Mamba block, a novel module that enables continuous bidirectional information flow. Social-Mamba organizes agents on an egocentric grid and introduces social triplet factorization, which decomposes interactions into temporal, egocentric, and goal-centric scans. These are dynamically integrated through a learnable social gate and global scan to generate accurate and efficient trajectory predictions. Extensive experiments on five trajectory forecasting benchmarks show that Social-Mamba achieves state-of-the-art accuracy while offering superior parameter efficiency and computational scalability. Furthermore, embedding Social-Mamba into a flow-matching framework further enhances both accuracy and efficiency, establishing it as a flexible and robust foundation for future trajectory forecasting research. The code is publicly available: https://github.com/vita-epfl/Social-Mamba", "authors": ["Po-Chien Luan", "Wuyang Li", "Yang Gao", "Alexandre Alahi"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-14", "url": "https://arxiv.org/abs/2605.15424", "pdf_url": "https://arxiv.org/pdf/2605.15424v1", "arxiv_id": "2605.15424", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/vita-epfl/Social-Mamba", "venue": null, "quality_score": 0.65}
{"id": "d39f2970790f69f67237ef438a9efc95ae312f638b5ce680614a3123f3ef27b0", "sources": ["arxiv", "semantic_scholar"], "title": "TCP-SSM: Efficient Vision State Space Models with Token-Conditioned Poles", "abstract": "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 the recurrent dynamics implicit. Consequently, the model's state-dependent memory behavior is difficult to control, particularly in compact backbones where long scan paths can exceed the effective memory horizon. We propose Token-Conditioned Poles SSM (TCP-SSM), a structured selective SSM framework that improves efficiency while making recurrence dynamics explicit and interpretable through stable poles. TCP-SSM builds each scan operator with 1) real poles that model monotone or sign-alternating decay, and 2) complex-conjugate poles that capture damped oscillatory responses. Using bounded radius and angle modulation, TCP-SSM converts shared base poles into token-dependent poles, allowing each scan step to adapt its memory behavior to the current visual token while preserving pole stability. For practical scalability, we integrate grouped pole sharing with a lightweight low-rank input pathway, yielding an efficient scan operator that preserves linear-time scan complexity. Across image classification, semantic segmentation, and object detection, TCP-SSM reduces SSM computation complexity up to 44% in Vision Mamba-style models while maintaining or surpassing baseline accuracy.", "authors": ["Sara Shoouri", "Morteza Tavakoli Taba", "Hun-Seok Kim"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-12", "url": "https://arxiv.org/abs/2605.11563", "pdf_url": "https://arxiv.org/pdf/2605.11563v1", "arxiv_id": "2605.11563", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "f27662f53c3ef332a49ee0c4c74e1c96f43760bcb5b844397e1b6d46e0dcdc12", "sources": ["arxiv", "semantic_scholar"], "title": "Can Graphs Help Vision SSMs See Better?", "abstract": "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 to dynamic coordinate-based samplers that reroute tokens through predicted offsets and interpolation. While effective, these mechanisms primarily adapt paths or sampling locations, rather than explicitly modeling which local patches should exchange information before global state-space mixing. This motivates a simple question: \\emph{can graphs help vision state space models see better?} We introduce \\textbf{GraphScan}, a graph-induced dynamic scanning operator for Vision SSMs. For each token, GraphScan constructs a spatially bounded local graph, learns feature-conditioned affinities with relative positional bias, and produces the output token by one-step message passing over its semantic neighborhood. The resulting tokens are locally grounded before being processed by the selective SSM for global aggregation. GraphScan preserves token count and linear scaling in image size, while replacing coordinate-conditioned interpolation with feature-conditioned semantic routing. Integrated into a hierarchical backbone, \\textbf{GraphScan-Mamba} achieves state-of-the-art performance among Vision SSMs across image classification, object detection, instance segmentation, and semantic segmentation, with modest computational overhead. Our analysis further shows that GraphScan induces interpretable displacement fields over the token lattice, providing a semantic and spatially grounded view of dynamic scanning. These results suggest that future Vision SSMs should treat scanning not merely as geometric serialization, but as learned local semantic routing before global state-space modeling.", "authors": ["Dhruv Parikh", "Anvitha Ramachandran", "Haoyang Fan", "Mustafa Munir", "Rajgopal Kannan", "Viktor Prasanna"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-11", "url": "https://arxiv.org/abs/2605.11300", "pdf_url": "https://arxiv.org/pdf/2605.11300v1", "arxiv_id": "2605.11300", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "11310f8553106552033d2a7d1cb5801464f2793fd51a26c275c5954449306bb8", "sources": ["arxiv", "semantic_scholar"], "title": "Polygon-mamba: Retinal vessel segmentation using polygon scanning mamba and space-frequency collaborative attention", "abstract": "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 space-frequency collaborative attention mechanism for the detection of small vessels. Considering that the traditional mamba architecture with horizontal-vertical scanning may compromise the topological integrity of target structures and result in local discontinuities in small retinal vessels, we present a polygon scanning visual state space model (PS-VSS) to identify small vessel structural features by multi-layer reverse scanning way. Which effectively preserves pixels connectivity, thereby substantially mitigating the loss of information pertaining to small vessels. Furthermore, as we all known that the spatial domain prioritizes positional and structural information, while the frequency domain emphasizes global perception and local detail components, a space-frequency collaborative attention mechanism (SFCAM) is introduced within the skip connection to extract efficient features from the spatial and frequency domains. This strategy empowers the model to dynamically enhance the key features while effectively suppressing clutters. To assess the efficacy of our model, it was tested on three publicly available datasets: DRIVE, STARE, and CHASE_DB1. Compared to manual annotations, our model demonstrated F1 scores of 0.8283, 0.8282, and 0.8251, Area Under Curve (AUC) values of 0.9806, 0.9840, and 0.9866, and Sensitivity (SE) values of of 0.8268, 0.8314, and 0.8484 across three datasets, respectively. The effectiveness of our model was validated through both visual inspection and quantitative analysis.", "authors": ["Yuanyuan Peng", "Wen Li"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-11", "url": "https://arxiv.org/abs/2605.10581", "pdf_url": "https://arxiv.org/pdf/2605.10581v2", "arxiv_id": "2605.10581", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "4e84681a8ae26f97bdcfa335aec54b0607a97f6cc824987189a21e738c7045e7", "sources": ["arxiv", "semantic_scholar"], "title": "TIDES: Implicit Time-Awareness in Selective State Space Models", "abstract": "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-time SSMs, such as S5, preserve the physical meaning of $\\TildeΔ$ and handle irregular timestamps natively ($\\TildeΔ\\equivΔ)$, but their dynamics remain linear time-invariant (LTI), limiting per-token expressivity. We propose \\textbf{TIDES}, a selective SSM variant that reconciles selective and continuous architectures by moving input-dependence off the step size and onto the diagonal state matrix. As a result, $\\TildeΔ$ retains its physical meaning, tied to the state discretization, allowing the model to handle irregular timestamps natively without sacrificing the per-token expressivity that makes selective SSMs effective. We show this on a novel \\emph{Fading Flash} experimental benchmark, a compact controlled diagnostic for sequence models that jointly tests input-dependence and extrapolation to out-of-distribution $Δ$ values, and isolates the distinct failure modes of current state-of-the-art architectures that TIDES avoids by construction. On large-scale benchmarks, TIDES sets the new state-of-the-art average rank on UEA time-series classification and the Physiome-ODE regression benchmark. Code available at: https://github.com/TaylanSoydan/TIDES.", "authors": ["Taylan Soydan", "Miguel A. Bessa", "Dirk Mohr", "Rui Barreira"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-10", "url": "https://arxiv.org/abs/2605.09742", "pdf_url": "https://arxiv.org/pdf/2605.09742v1", "arxiv_id": "2605.09742", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/TaylanSoydan/TIDES", "venue": null, "quality_score": 0.65}
{"id": "04eecf3d30d2064968ee7896d77da14d89fef198b6548ae7de91f37c1d10999d", "sources": ["arxiv", "semantic_scholar"], "title": "mHC-SSM: Manifold-Constrained Hyper-Connections for State Space Language Models with Stream-Specialized Adapters", "abstract": "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 topology transfers effectively to state space model (SSM) language modeling. We implement a static mHC mechanism around an SSM block by expanding the residual stream into multiple parallel streams, aggregating streams into a single SSM input through simplex-constrained pre-mixing, scattering the SSM output back to streams through simplex-constrained post-mixing, and applying Sinkhorn-projected residual stream mixing at each layer. We further introduce stream-specialized adapters that add lightweight stream-specific capacity through a shared bottleneck with per-stream scaling, applied both before stream aggregation and after the SSM output prior to scattering. We evaluate baseline single-stream SSM, static mHC SSM, and mHC SSM with adapters on WikiText-2 using identical training settings and report checkpoint-based validation loss, perplexity, throughput, and peak GPU memory. Under the reported fair checkpoint evaluation, static mHC improves validation loss from 6.3507 to 6.2448 and reduces perplexity from 572.91 to 515.35, while mHC with adapters further improves validation loss to 6.1353 and perplexity to 461.88. These gains are accompanied by modest throughput reductions from 1025.52 to 964.81 and 938.90 tokens per second, and increased peak memory from 2365 MB to 2568 MB and 3092 MB. The results suggest that mHC-inspired constrained multi-stream residual mixing can yield measurable quality improvements in SSM language models and that stream-specialized adapter capacity can further enhance performance with predictable efficiency tradeoffs.", "authors": ["Abdulvahap Mutlu", "Şengül Doğan", "Türker Tuncer"], "categories": ["cs.LG", "cs.AI", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-08", "url": "https://arxiv.org/abs/2605.08300", "pdf_url": "https://arxiv.org/pdf/2605.08300v1", "arxiv_id": "2605.08300", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/abdulvahapmutlu/mhc-slm", "venue": null, "quality_score": 0.65}
{"id": "207a3159e503655d2408ea4e412754892c1f4abd72b52a11d9714a376e576a4b", "sources": ["arxiv", "semantic_scholar"], "title": "A Simple State Space Model Excels at Multivariate Time Series Classification", "abstract": "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 been largely limited to Mamba-style architectures, leaving the broader SSM design space underexplored. We present the first systematic study spanning diagonal SSMs (S4D) and input-dependent SSMs (Mamba family) on large-scale TSC benchmarks, asking whether such complexity is necessary for top performance. Our results reveal a surprising finding: S4D consistently outperforms Mamba-based variants in both accuracy and efficiency, challenging the assumption that increased complexity translates to meaningful gains in TSC. Building on this, we introduce MS4, lightweight modifications to S4D via a linear input projection and channel-mixing mechanism, and MS4N, a normalized variant that stabilizes state dynamics with negligible overhead. Evaluated on 59 datasets across MONSTER (up to 60 million samples, 50K timesteps, 82 classes) and the UEA benchmark, against 15 baselines, MS4 and MS4N consistently outperform Mamba-based models while remaining more efficient, and MS4N matches or surpasses competing deep learning models that are roughly 2x and 10x larger in parameters. These results position lightweight structured SSMs as a compelling alternative to scaling complexity for TSC.", "authors": ["Hassan Saadatmand", "Geoffrey I. Webb", "Hamid Rezatofighi", "Mahsa Salehi"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-07", "url": "https://arxiv.org/abs/2605.27406", "pdf_url": "https://arxiv.org/pdf/2605.27406v1", "arxiv_id": "2605.27406", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "22af2ffff627a47e743e15060be4a6f0218e539bc37b49831dc693d9c225e578", "sources": ["arxiv", "semantic_scholar"], "title": "ViM-Q: Scalable Algorithm-Hardware Co-Design for Vision Mamba Model Inference on FPGA", "abstract": "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 uniform quantization fails to capture the weight distribution at low bit-widths. Furthermore, while associative scan accelerates SSMs on GPUs, its memory access patterns are misaligned with the streaming dataflow required by FPGAs. To address these challenges, we present ViM-Q, a scalable algorithm-hardware co-design for end-to-end ViM inference on the edge. We introduce a hardware-aware quantization scheme combining dynamic per-token activation quantization and per-channel smoothing to mitigate outliers, alongside a custom 4-bit per-block Additive Power-of-Two (APoT) weight quantization. The models are deployed on a runtime-parameterizable FPGA accelerator featuring a linear engine employing a Lookup-Table (LUT) unit to replace multiplications with shift-add operations, and a fine-grained pipelined SSM engine that parallelizes the state dimension while preserving sequential recurrence. Crucially, the hardware supports runtime configuration, adapting to diverse dimensions and input resolutions across the ViM family. Implemented on an AMD ZCU102 FPGA, ViM-Q achieves an average 4.96x speedup and 59.8x energy efficiency gain over a quantized NVIDIA RTX 3090 GPU baseline for low-batch inference on ViM-tiny. This co-design shows a viable path for deploying ViM models on resource-constrained edge devices.", "authors": ["Shengzhe Lyu", "Yuhan She", "Patrick S. Y. Hung", "Ray C. C. Cheung", "Weitao Xu"], "categories": ["cs.AR", "cs.CV", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-03", "url": "https://arxiv.org/abs/2605.01935", "pdf_url": "https://arxiv.org/pdf/2605.01935v1", "arxiv_id": "2605.01935", "doi": "10.1109/FCCM68464.2026.00025", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/shengzhelyu65/ViM-Q-FCCM-2026", "venue": "IEEE Symposium on Field-Programmable Custom Computing Machines", "quality_score": 0.85}
{"id": "2c9231848a4656d3dde34a1ac58cf43330438ded4278f8a068492e1fcfcba018", "sources": ["arxiv", "semantic_scholar"], "title": "Lost in State Space: Probing Frozen Mamba Representations", "abstract": "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 hypothesis carefully. Across five benchmarks (SST-2, CoLA, MRPC, STS-B, IMDb), we compare four strategies for extracting frozen sentence representations from a pretrained Mamba-130M backbone under a strict frozen-feature probing protocol, using three random seeds where computationally feasible. The results do not support the hypothesis: patch boundary readouts do not consistently outperform simple mean pooling. We identify and quantify two structural pathologies: severe anisotropy (mean pairwise cosine similarity 0.9999, std 0.000044) and representational collapse in the raw final SSM state (MCC = 0.000 on CoLA across all three seeds, confirmed via confusion matrix). We further propose orthogonal injection, a modified recurrence that constrains new information per", "authors": ["Bhagyashree Wagh", "Akash Singh"], "categories": ["cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-04-30", "url": "https://arxiv.org/abs/2605.00253", "pdf_url": "https://arxiv.org/pdf/2605.00253v1", "arxiv_id": "2605.00253", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "282886064f18972324c569eb7d2725e7b7a59ef0d54134c2ae824869b46bb667", "sources": ["arxiv", "semantic_scholar"], "title": "Density Field State Space Models: 1-Bit Distillation, Efficient Inference, and Knowledge Organization in Mamba-2", "abstract": "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 reference implementation) while maintaining downstream task performance within 2-4 percentage points of BitMamba-2, a 1.58-bit model trained from scratch on 150B tokens. The distillation itself requires only 32M tokens and 6 hours on a single A100 GPU, though it presupposes a pretrained FP16 teacher. We develop an optimized inference pipeline combining cuBLAS INT8 tensor cores for the scaffold matmul, custom CUDA kernels for stateful SSM and convolution operations, and an AVX-512 CPU backend for efficient deployment on both GPU and CPU. Beyond compression, we investigate the internal knowledge organization of the resulting model, discovering three distinct processing phases: intent classification (layers 0-3, operating in an abstract space with no vocabulary alignment), knowledge retrieval (layers 25-35, where factual associations localize to a 5-layer window), and output formatting (layers 36-47, where category structure dissolves). Through systematic analysis of 445 factual prompts across 19 categories, we find that early-layer classification is syntactic (driven by template structure) rather than semantic, and that the model exhibits well-organized knowledge representations despite weak factual recall--suggesting that representational structure may precede factual strength.", "authors": ["Chirag Shinde"], "categories": ["cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-04-28", "url": "https://arxiv.org/abs/2606.10932", "pdf_url": "https://arxiv.org/pdf/2606.10932v1", "arxiv_id": "2606.10932", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/cs-cmyk/df-ssm", "venue": null, "quality_score": 0.65}
{"id": "c4c95a83b35cdd9adcba64ffdf1d61c50045034abe98956a446c27982841543a", "sources": ["arxiv", "semantic_scholar"], "title": "BVI-Mamba: Video Enhancement Using a Visual State-Space Model for Low-Light and Underwater Environments", "abstract": "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 enhancement also demand significantly more computational resources compared to image-based methods. This paper introduces a novel framework, Visual Mamba, designed to reduce memory usage and computational time by leveraging the Visual State Space (VSS) model. The framework consists of two modules: (i) a feature alignment module, where spatio-temporal displacement between input frames is registered in the feature space, and (ii) an enhancement module, where noise removal and brightness adjustment are performed using a UNet-like architecture, with all convolutional layers replaced by VSS blocks. Experimental results show that the Visual Mamba technique outperforms Transformer and convolution-based models in both low-light and underwater video enhancement tasks. Code is available on line at https://github.com/russellllaputa/BVI-Mamba.", "authors": ["Guoxi Huang", "Ruirui Lin", "Yini Li", "David R. Bull", "Nantheera Anantrasirichai"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2026-04-26", "url": "https://arxiv.org/abs/2604.23655", "pdf_url": "https://arxiv.org/pdf/2604.23655v1", "arxiv_id": "2604.23655", "doi": "10.1117/12.3053998", "citation_count": 5, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/russellllaputa/BVI-Mamba", "venue": null, "quality_score": 0.65}
{"id": "9ed4cf0afdc497b7d3c44ca3fbd57c124509216f831589cc45a8a551fe113a59", "sources": ["arxiv", "semantic_scholar"], "title": "MambaCSP: Hybrid-Attention State Space Models for Hardware-Efficient Channel State Prediction", "abstract": "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 in sequence length, leading to substantial computational cost, memory consumption, and inference latency, which limits their applicability in real-time and resource-constrained wireless deployments. In this paper, we investigate whether selective state space models (SSMs) can serve as a hardware-efficient alternative for CSI prediction. We propose MambaCSP, a hybrid-attention SSM architecture that replaces LLM-based prediction backbones with a linear-time Mamba model. To overcome the local-only dependencies of pure SSMs, we introduce lightweight patch-mixer attention layers that periodically inject cross-token attentions, helping with long-context CSI prediction. Extensive MISO-OFDM simulations show that MambaCSP improves prediction accuracy over LLM-based approaches by 9-12%, while delivering up to 3.0x higher throughput, 2.6x lower VRAM usage, and 2.9x faster inference. Our results demonstrate that hybrid state space architectures provide a promising direction for scalable and hardware-efficient AI-native CSI prediction in future wireless networks.", "authors": ["Aladin Djuhera", "Haris Gacanin", "Holger Boche"], "categories": ["cs.IT", "cs.AI", "cs.LG", "eess.SP"], "fields_of_study": ["Computer Science", "Engineering", "Mathematics"], "published_date": "2026-04-23", "url": "https://arxiv.org/abs/2604.21957", "pdf_url": "https://arxiv.org/pdf/2604.21957v1", "arxiv_id": "2604.21957", "doi": "10.48550/arXiv.2604.21957", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.55}
{"id": "4723aaa044d53350fbf2665faa3f9beeb268caccb1292eaff7b832b3b960e8f6", "sources": ["arxiv", "semantic_scholar"], "title": "Beyond ZOH: Advanced Discretization Strategies for Vision Mamba", "abstract": "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 this paper, we present a systematic and controlled comparison of six discretization schemes instantiated within the Vision Mamba framework: ZOH, first-order hold (FOH), bilinear/Tustin transform (BIL), polynomial interpolation (POL), higher-order hold (HOH), and the fourth-order Runge-Kutta method (RK4). We evaluate each method on standard visual benchmarks to quantify its influence in image classification, semantic segmentation, and object detection. Our results demonstrate that POL and HOH yield the largest gains in accuracy at the cost of higher training-time computation. In contrast, the BIL provides consistent improvements over ZOH with modest additional overhead, offering the most favorable trade-off between precision and efficiency. These findings elucidate the pivotal role of discretization in SSM-based vision architectures and furnish empirically grounded justification for adopting BIL as the default discretization baseline for state-of-the-art SSM models.", "authors": ["Fady Ibrahim", "Guangjun Liu", "Guanghui Wang"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-04-22", "url": "https://arxiv.org/abs/2604.20606", "pdf_url": "https://arxiv.org/pdf/2604.20606v1", "arxiv_id": "2604.20606", "doi": "10.48550/arXiv.2604.20606", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.55}
{"id": "8a1d65a771c45ce3be136b67315c8dc66fa5ab29c7eeacbc8add94e061e7236c", "sources": ["arxiv", "semantic_scholar"], "title": "Preconditioned DeltaNet: Curvature-aware Sequence Modeling for Linear Recurrences", "abstract": "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 to taxonomize them. One compelling view is the test-time regression (TTR) framework, which interprets recurrences as performing online least squares updates that learn a linear map from the keys to values. Existing delta-rule recurrences can be seen as first-order approximations to this objective, but notably ignore the curvature of the least-squares loss during optimization. In this work, we address this by introducing preconditioning to these recurrences. Starting from the theory of online least squares, we derive equivalences between linear attention and the delta rule in the exactly preconditioned case. Next, we realize this theory in practice by proposing a diagonal approximation: this enables us to introduce preconditioned variants of DeltaNet, GDN, and KDA alongside efficient chunkwise parallel algorithms for computing them. Empirically, we find that our preconditioned delta-rule recurrences yield consistent performance improvements across synthetic recall benchmarks and language modeling at the 340M and 1B scale.", "authors": ["Neehal Tumma", "Noel Loo", "Daniela Rus"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-04-22", "url": "https://arxiv.org/abs/2604.21100", "pdf_url": "https://arxiv.org/pdf/2604.21100v1", "arxiv_id": "2604.21100", "doi": "10.48550/arXiv.2604.21100", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.55}
{"id": "e6e96a498a30d7070139ffd4662426a13c9cb528fab964ea50cac47fc7c64320", "sources": ["arxiv", "semantic_scholar"], "title": "DGSSM: Diffusion guided state-space models for multimodal salient object detection", "abstract": "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 struggle to recover precise object boundaries. In contrast, diffusion models capture strong structural priors through iterative denoising, but their use in discriminative dense prediction is still limited due to computational cost and integration challenges. In this work, we propose DGSSM, a diffusion-guided state space (Mamba) framework that formulates multimodal salient object detection as a progressive denoising process. The framework integrates diffusion structural priors with multi-scale state space encoding, adaptive saliency prompting, and an iterative Mamba diffusion refinement mechanism to improve boundary accuracy. A boundary-aware refinement head and self-distillation strategy further enhance spatial coherence and feature consistency. Extensive experiments on 13 public benchmarks across RGB, RGB-D, and RGB-T settings demonstrate that DGSSM consistently outperforms state-of-the-art methods across multiple evaluation metrics while maintaining a compact model size. These results suggest that diffusion-guided state space modeling is an effective and generalizable paradigm for multimodal dense prediction tasks.", "authors": ["Suklav Ghosh", "Arijit Sur", "Pinaki Mitra"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-04-19", "url": "https://arxiv.org/abs/2604.17585", "pdf_url": "https://arxiv.org/pdf/2604.17585v1", "arxiv_id": "2604.17585", "doi": "10.48550/arXiv.2604.17585", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.5489}
{"id": "44f2e0b33b57dc2f163d6649493ab822338148e1a75cc36b21e94bb2764b91f8", "sources": ["arxiv", "semantic_scholar"], "title": "MambaSL: Exploring Single-Layer Mamba for Time Series Classification", "abstract": "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, guided by four TSC-specific hypotheses. To address benchmarking limitations -- restricted configurations, partial University of East Anglia (UEA) dataset coverage, and insufficiently reproducible setups -- we re-evaluate 20 strong baselines across all 30 UEA datasets under a unified protocol. As a result, MambaSL achieves state-of-the-art performance with statistically significant average improvements, while ensuring reproducibility via public checkpoints for all evaluated models. Together with visualizations, these results demonstrate the potential of Mamba-based architectures as a TSC backbone.", "authors": ["Yoo-Min Jung", "Leekyung Kim"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-04-16", "url": "https://arxiv.org/abs/2604.15174", "pdf_url": "https://arxiv.org/pdf/2604.15174v2", "arxiv_id": "2604.15174", "doi": "10.48550/arXiv.2604.15174", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.5454}
{"id": "d6dc4765a7404c59fa40edd03415dbb417f1b3e19a5f803167be1fdf634b3d1e", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba-SSM with LLM Reasoning for Feature Selection: Faithfulness-Aware Biomarker Discovery", "abstract": "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 is associated with downstream performance. We train a Mamba SSM on TCGA-BRCA RNA-seq and extract the top-50 genes by gradient saliency; DeepSeek-R1 evaluates every candidate with structured CoT to produce a final 17-gene set. On the held-out test split, the raw 50-gene saliency set (no LLM) performs worse than a 5,000-gene variance baseline (AUC 0.832 vs. 0.903), while the LLM-filtered set surpasses it (AUC 0.927), using 294x fewer features. A faithfulness audit (COSMIC CGC, OncoKB, PAM50) shows that 6 of 17 selected genes (35.3%) are validated BRCA biomarkers, while 10 of 16 known BRCA genes present in the input were missed - including FOXA1. This divergence between downstream performance and reasoning faithfulness suggests selective faithfulness in this setting: targeted confounder removal can improve predictive performance without comprehensive recall.", "authors": ["Pushpa Kumar Balan", "Aijing Feng"], "categories": ["q-bio.QM", "cs.AI"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2026-04-15", "url": "https://arxiv.org/abs/2604.14334", "pdf_url": "https://arxiv.org/pdf/2604.14334v2", "arxiv_id": "2604.14334", "doi": "10.48550/arXiv.2604.14334", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.5443}
{"id": "a9798221836864414913566137ab949ec37448bc7ccb7757577c3ba7b689faa5", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba Sequence Modeling meets Model Predictive Control", "abstract": "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 description of the Mamba neural network is provided. Then, adjustments and optimizations are made to construct a decoder-only Mamba multi-step predictor for MPC and an input-output formulation is given for sequence-to-sequence modeling of dynamical systems. The performance of Mamba-MPC is evaluated on several numerical examples and compared to a Long-Short-Term-Memory based MPC (LSTM-MPC) equivalent. First, a Single-Input-Single-Output (SISO) Van der Pol oscillator is considered, where stability, reference tracking, and noise robustness are evaluated. Then, a MIMO Four Tank setup is introduced where Multiple-Input-Multiple-Output (MIMO) reference tracking is evaluated. Lastly, Mamba-MPC is implemented on a physical Quanser Aero2 setup for closed-loop reference tracking. The results demonstrate that Mamba-MPC is able to stabilize and track a reference for SISO and MIMO systems, both in simulation and on a physical setup. Moreover, Mamba-MPC consistently outperforms LSTM-MPC in predictive control and is significantly computationally faster.", "authors": ["Michiel Cevaal", "Thomas de Jong", "Mircea Lazar"], "categories": ["math.OC"], "fields_of_study": ["Mathematics"], "published_date": "2026-04-15", "url": "https://arxiv.org/abs/2604.13857", "pdf_url": "https://arxiv.org/pdf/2604.13857v1", "arxiv_id": "2604.13857", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3464}
{"id": "6b7f69467454bf6cfcc06e21a824aacbaa38d296f8205b9b88431900f244f9a8", "sources": ["arxiv", "semantic_scholar"], "title": "Structured State-Space Regularization for Generation-Friendly Image Tokenization", "abstract": "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 components. In this work, we introduce structured state-space regularization, a principled approach to inducing spectral structure in latent spaces. We derive a regularization objective by revisiting state-space models (SSMs) as systems mimicking a basis function's behavior. This perspective reveals that hidden states of SSMs are induced to capture the frequency components, resulting in a novel regularizer that enforces the latent space to capture spectral structure of images. Experiments demonstrate that our regularizer improves the generative performance of image tokenizers while incurring only minimal loss in their reconstruction fidelity.", "authors": ["Jinsung Lee", "Jaemin Oh", "Namhun Kim", "Dongwon Kim", "Byung-Jun Yoon", "Suha Kwak"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2026-04-13", "url": "https://arxiv.org/abs/2604.11089", "pdf_url": "https://arxiv.org/pdf/2604.11089v2", "arxiv_id": "2604.11089", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3449}
{"id": "0f070942f03c8b34f9c3b2ea86ab61c84dfa3f28e845145a287035061e865565", "sources": ["arxiv", "semantic_scholar"], "title": "Beyond Mamba: Enhancing State-space Models with Deformable Dilated Convolutions for Multi-scale Traffic Object Detection", "abstract": "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, which hinders joint modeling of local structures and global semantics. Moreover, state-space models exhibit limited hierarchical feature representation and weak cross-scale interaction due to flat sequential modeling and insufficient spatial inductive biases, leading to sub-optimal performance in complex scenes. To address these issues, we propose a Mamba with Deformable Dilated Convolutions Network (MDDCNet) for accurate traffic object detection in this study. In MDDCNet, a well-designed hybrid backbone with successive Multi-Scale Deformable Dilated Convolution (MSDDC) blocks and Mamba blocks enables hierarchical feature representation from local details to global semantics. Meanwhile, a Channel-Enhanced Feed-Forward Network (CE-FFN) is further devised to overcome the limited channel interaction capability of conventional feed-forward networks, whilst a Mamba-based Attention-Aggregating Feature Pyramid Network (A^2FPN) is constructed to achieve enhanced multi-scale feature fusion and interaction. Extensive experimental results on public benchmark and real-world datasets demonstrate the superiority of our method over various advanced detectors. The code is available at https://github.com/Bettermea/MDDCNet.", "authors": ["Jun Li", "Yingying Shi", "Zhixuan Ruan", "Nan Guo", "Jianhua Xu"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2026-04-09", "url": "https://arxiv.org/abs/2604.08038", "pdf_url": "https://arxiv.org/pdf/2604.08038v1", "arxiv_id": "2604.08038", "doi": "10.48550/arXiv.2604.08038", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Bettermea/MDDCNet", "venue": "arXiv.org", "quality_score": 0.8305}
{"id": "43d7dbaad9981a59551aa04d38c09951248022bf4b3e448dba35cf8c24e6bc3d", "sources": ["arxiv", "semantic_scholar"], "title": "Optimal Decay Spectra for Linear Recurrences", "abstract": "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 collapse but degrades to $\\exp(-O(N/\\sqrt{T}))$, practically algebraic over long contexts. We introduce Position-Adaptive Spectral Tapering (PoST), an architecture-agnostic framework combining two mechanisms: (1) Spectral Reparameterization, which structurally enforces geometrically spaced log-decay rates, proven minimax optimal at rate $O(\\exp(-cN/\\log T))$; and (2) Position-Adaptive Scaling, the provably unique mechanism that eliminates the scale mismatch of static spectra (where only $N\\log t/\\log T$ of $N$ channels are effective at position $t$) by stretching the spectrum to the actual dependency range, sharpening the rate to $O(\\exp(-cN/\\log t))$. This scaling natively induces fractional invariance: the impulse response becomes scale-free, with channels interpolating between relative and absolute temporal coordinates. PoST integrates into any diagonal linear recurrence without overhead. We instantiate it across Mamba-2, RWKV-7, Gated DeltaNet, Gated Linear Attention, and RetNet. Pre-training at 180M-440M scales shows consistent zero-shot language modeling improvements, significant long-context retrieval gains for Mamba-2 (MQAR and NIAH), and competitive or improved performance across other architectures. Code: https://github.com/SiLifen/PoST.", "authors": ["Yang Cao"], "categories": ["cs.LG", "cs.AI", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2026-04-08", "url": "https://arxiv.org/abs/2604.07658", "pdf_url": "https://arxiv.org/pdf/2604.07658v1", "arxiv_id": "2604.07658", "doi": "10.48550/arXiv.2604.07658", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/SiLifen/PoST", "venue": "arXiv.org", "quality_score": 0.8287}
{"id": "93315a74a417dbacf3614cd84ad347a59e2029746cfef4e5e9e1aa743f05f90c", "sources": ["arxiv", "semantic_scholar"], "title": "StateSMix: Online Lossless Compression via Mamba State Space Models and Sparse N-gram Context Mixing", "abstract": "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 GPU, and no external dependencies. The SSM (DM=32, NL=2, approximately 120K active parameters per file) provides a continuously-updated probability estimate over BPE tokens, while nine sparse n-gram hash tables (bigram through 32-gram, 16M slots each) add exact local and long-range pattern memorisation via a softmax-invariant logit-bias mechanism that updates only non-zero-count tokens. An entropy-adaptive scaling mechanism modulates the n-gram contribution based on the SSM's predictive confidence, preventing over-correction when the neural model is already well-calibrated. On the standard enwik8 benchmark, StateSMix achieves 2.123 bpb on 1 MB, 2.149 bpb on 3 MB, and 2.162 bpb on 10 MB, beating xz -9e (LZMA2) by 8.7%, 5.4%, and 0.7% respectively. Ablation experiments establish the SSM as the dominant compression engine: it alone accounts for a 46.6% size reduction over a frequency-count baseline and beats xz without any n-gram component, while n-gram tables provide a complementary 4.1% gain through exact context memorisation. OpenMP parallelisation of the training loop yields 1.9x speedup on 4 cores. The system is implemented in pure C with AVX2 SIMD and processes approximately 2,000 tokens per second on commodity x86-64 hardware.", "authors": ["Roberto Tacconelli"], "categories": ["cs.LG", "cs.IT"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2026-04-05", "url": "https://arxiv.org/abs/2605.02904", "pdf_url": "https://arxiv.org/pdf/2605.02904v1", "arxiv_id": "2605.02904", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3391}
{"id": "f60562b1504d3ed5cb95bc02897ba005ed8fdaf8f74f86f08730c4fcd0d70658", "sources": ["arxiv", "semantic_scholar"], "title": "Attention to Mamba: A Recipe for Cross-Architecture Distillation", "abstract": "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 Transformers, and many pretrained Transformer models are readily available. To facilitate the adoption of SSMs while leveraging existing pretrained Transformers, we aim to identify an effective recipe to distill an Attention-based model into a Mamba-like architecture. In prior work on cross-architecture distillation, however, it has been shown that a naïve distillation procedure from Transformers to Mamba fails to preserve the original teacher performance, a limitation often overcome with hybrid solutions combining Attention and SSM blocks. The key argument from our work is that, by equipping Mamba with a principled initialization, we can recover an overall better recipe for cross-architectural distillation. To this end, we propose a principled two-stage approach: first, we distill knowledge from a traditional Transformer into a linearized version of Attention, using an adaptation of the kernel trick. Then, we distill the linearized version into an adapted Mamba model that does not use any Attention block. Overall, the distilled Mamba model is able to preserve the original Pythia-1B Transformer performance in downstream tasks, maintaining a perplexity of 14.11 close to the teacher's 13.86. To show the efficacy of our recipe, we conduct thorough ablations at 1B scale with 10B tokens varying sequence mixer architecture, scaling analysis on model sizes and total distillation tokens, and a sensitivity analysis on tokens allocation between stages.", "authors": ["Abhinav Moudgil", "Ningyuan Huang", "Eeshan Gunesh Dhekane", "Pau Rodríguez", "Luca Zappella", "Federico Danieli"], "categories": ["cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-04-01", "url": "https://arxiv.org/abs/2604.14191", "pdf_url": "https://arxiv.org/pdf/2604.14191v1", "arxiv_id": "2604.14191", "doi": "10.48550/arXiv.2604.14191", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.5282}
{"id": "223f5e38c10f6c8b9478e49bd2a0ae47eadd3deb037c98188b9052ea32954a98", "sources": ["arxiv", "semantic_scholar"], "title": "RS-SSM: Refining Forgotten Specifics in State Space Model for Video Semantic Segmentation", "abstract": "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 models can preserve common semantic information during state space compression, the fixed-size state space inevitably forgets specific information, which limits the models' capability for pixel-level segmentation. To tackle the above issue, we proposed a Refining Specifics State Space Model approach (RS-SSM) for video semantic segmentation, which performs complementary refining of forgotten spatiotemporal specifics. Specifically, a Channel-wise Amplitude Perceptron (CwAP) is designed to extract and align the distribution characteristics of specific information in the state space. Besides, a Forgetting Gate Information Refiner (FGIR) is proposed to adaptively invert and refine the forgetting gate matrix in the state space model based on the specific information distribution. Consequently, our RS-SSM leverages the inverted forgetting gate to complementarily refine the specific information forgotten during state space compression, thereby enhancing the model's capability for spatiotemporal pixel-level segmentation. Extensive experiments on four VSS benchmarks demonstrate that our RS-SSM achieves state-of-the-art performance while maintaining high computational efficiency. The code is available at https://github.com/zhoujiahuan1991/CVPR2026-RS-SSM.", "authors": ["Kai Zhu", "Zhenyu Cui", "Zehua Zang", "Jiahuan Zhou"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-25", "url": "https://arxiv.org/abs/2603.24295", "pdf_url": "https://arxiv.org/pdf/2603.24295v2", "arxiv_id": "2603.24295", "doi": "10.48550/arXiv.2603.24295", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/zhoujiahuan1991/CVPR2026-RS-SSM", "venue": "arXiv.org", "quality_score": 0.804}
{"id": "66cf824fe6a9d17f7e92d12bc06bfaa74aa64fbfbb4d2d7f9a4fde32574fedb4", "sources": ["arxiv", "semantic_scholar"], "title": "MFil-Mamba: Multi-Filter Scanning for Spatial Redundancy-Aware Visual State Space Models", "abstract": "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 explored adapting selective SSMs for vision applications, most approaches primarily depend on employing various traversal strategies over the same input. This introduces redundancy and distorts the intricate spatial relationships within images. To address these challenges, we propose MFil-Mamba, a novel visual state space architecture built on a multi-filter scanning backbone. Unlike fixed multi-directional traversal methods, our design enables each scan to capture unique and contextually relevant spatial information while minimizing redundancy. Furthermore, we incorporate an adaptive weighting mechanism to effectively fuse outputs from multiple scans in addition to architectural enhancements. MFil-Mamba achieves superior performance over existing state-of-the-art models across various benchmarks that include image classification, object detection, instance segmentation, and semantic segmentation. For example, our tiny variant attains 83.2% top-1 accuracy on ImageNet-1K, 47.3% box AP and 42.7% mask AP on MS COCO, and 48.5% mIoU on the ADE20K dataset. Code and models are available at https://github.com/puskal-khadka/MFil-Mamba.", "authors": ["Puskal Khadka", "KC Santosh"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-20", "url": "https://arxiv.org/abs/2603.20074", "pdf_url": "https://arxiv.org/pdf/2603.20074v1", "arxiv_id": "2603.20074", "doi": "10.48550/arXiv.2603.20074", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/puskal-khadka/MFil-Mamba", "venue": "arXiv.org", "quality_score": 0.7951}
{"id": "d97aa5b4a62eb3291348d369d37bbb3de67224fe78b19e4eca09c757aef61107", "sources": ["arxiv", "semantic_scholar"], "title": "CS-MUNet: A Channel-Spatial Dual-Stream Mamba Network for Multi-Organ Segmentation", "abstract": "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-Aware State Mamba module employs a Bayesian-attention framework to generate pixel-level boundary posterior maps, injected directly into Mamba's core scan parameters to embed boundary awareness into the SSM state transition mechanism, while dual-branch weight allocation enables complementary modulation between global and local structural representations. The Channel Mamba State Aggregation module redefines the channel dimension as the SSM sequence dimension to explicitly model cross-channel anatomical semantic collaboration in a data-driven manner. Experiments on two public benchmarks demonstrate that CS-MUNet consistently outperforms state-of-the-art methods across multiple metrics, establishing a new SSM modeling paradigm that jointly addresses channel semantic collaboration and boundary-aware feature fusion for abdominal multi-organ segmentation.", "authors": ["Yuyang Zheng", "Mingda Zhang", "Jianglong Qin", "Qi Mo", "Jingdan Pan", "Haozhe Hu", "Hongyi Huang"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-20", "url": "https://arxiv.org/abs/2603.19659", "pdf_url": "https://arxiv.org/pdf/2603.19659v1", "arxiv_id": "2603.19659", "doi": "10.48550/arXiv.2603.19659", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.5145}
{"id": "45d4e32f06a07f0296bd606cc1e3aa891fd64284dd5631f8efc311fa3a883c32", "sources": ["arxiv", "semantic_scholar"], "title": "Do VLMs Need Vision Transformers? Evaluating State Space Models as Vision Encoders", "abstract": "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 systematically evaluate SSM vision backbones for VLMs in a controlled setting. Under matched ImageNet-1K initialization, the SSM backbone achieves the strongest overall performance across both VQA and grounding/localization. We further adapt both SSM and ViT-family backbones with detection or segmentation training and find that dense-task tuning generally improves performance across families; after this adaptation, the SSM backbone remains competitive while operating at a substantially smaller model scale. We further observe that (i) higher ImageNet accuracy or larger backbones do not reliably translate into better VLM performance, and (ii) some visual backbones are unstable in localization. Based on these findings, we propose stabilization strategies that improve robustness for both backbone families and highlight SSM backbones as a strong alternative to transformer-based vision encoders in VLMs.", "authors": ["Shang-Jui Ray Kuo", "Paola Cascante-Bonilla"], "categories": ["cs.CV", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-19", "url": "https://arxiv.org/abs/2603.19209", "pdf_url": "https://arxiv.org/pdf/2603.19209v1", "arxiv_id": "2603.19209", "doi": "10.48550/arXiv.2603.19209", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/raykuo18/vlm-ssm-vision-encoders", "venue": "arXiv.org", "quality_score": 0.7933}
{"id": "0b065f743e8ab44334c534a6073f8b29affd014be63936d1ff611743fa2ccb9b", "sources": ["arxiv", "semantic_scholar"], "title": "DA-Mamba: Learning Domain-Aware State Space Model for Global-Local Alignment in Domain Adaptive Object Detection", "abstract": "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 restricts alignment to local regions, failing to extract global domain-invariant features. Although transformer-based DAOD methods capture global dependencies via attention mechanisms, their quadratic computational cost hinders practical deployment. To solve this, we propose DA-Mamba, a hybrid CNN-State Space Models (SSMs) architecture that combines the efficiency of CNNs with the linear-time long-range modeling capability of State Space Models (SSMs) to capture both global and local domain-invariant features. Specifically, we introduce two novel modules: Image-Aware SSM (IA-SSM) and Object-Aware SSM (OA-SSM). IA-SSM is integrated into the backbone to enhance global domain awareness, enabling image-level global and local alignment. OA-SSM is inserted into the detection head to model spatial and semantic dependencies among objects, enhancing instance-level alignment. Comprehensive experiments demonstrate that the proposed method can efficiently improve the cross-domain performance of the object detector.", "authors": ["Haochen Li", "Rui Zhang", "Hantao Yao", "Xin Zhang", "Yifan Hao", "Shaohui Peng", "Yongwei Zhao", "Ling Li"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-19", "url": "https://arxiv.org/abs/2603.18757", "pdf_url": "https://arxiv.org/pdf/2603.18757v1", "arxiv_id": "2603.18757", "doi": "10.48550/arXiv.2603.18757", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.5133}
{"id": "52d6681e0bdf75e522d49be49d0951948e677bbe40e050770e6124042cf7d955", "sources": ["arxiv", "semantic_scholar"], "title": "SF-Mamba: Rethinking State Space Model for Vision", "abstract": "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. Prior works have attempted to address this limitation through various multi-scan strategies; however, these approaches suffer from inefficiencies due to suboptimal scan designs and frequent data rearrangement. Moreover, Mamba exhibits relatively slow computational speed under short token lengths, commonly used in visual tasks. In pursuit of a truly efficient vision encoder, we rethink the scan operation for vision and the computational efficiency of Mamba. To this end, we propose SF-Mamba, a novel visual Mamba with two key proposals: auxiliary patch swapping for encoding bidirectional information flow under an unidirectional scan and batch folding with periodic state reset for advanced GPU parallelism. Extensive experiments on image classification, object detection, and instance and semantic segmentation consistently demonstrate that our proposed SF-Mamba significantly outperforms state-of-the-art baselines while improving throughput across different model sizes. We will release the source code after publication.", "authors": ["Masakazu Yoshimura", "Teruaki Hayashi", "Yuki Hoshino", "Wei-Yao Wang", "Takeshi Ohashi"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-17", "url": "https://arxiv.org/abs/2603.16423", "pdf_url": "https://arxiv.org/pdf/2603.16423v1", "arxiv_id": "2603.16423", "doi": "10.48550/arXiv.2603.16423", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.511}
{"id": "c3408a88afa47a847054f9661d1b393ee33c504cf1df849623adee92c4887c08", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba-3: Improved Sequence Modeling using State Space Principles", "abstract": "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 spurred the development of sub-quadratic models with reduced linear compute and constant memory requirements. However, many recent linear models trade off model quality and capability for algorithmic efficiency, failing on tasks such as state tracking. Moreover, their theoretically linear inference remains hardware-inefficient in practice. Guided by an inference-first perspective, we introduce three core methodological improvements inspired by the state space model (SSM) viewpoint of linear models. We combine: (1) a more expressive recurrence derived from SSM discretization, (2) a complex-valued state update rule that enables richer state tracking, and (3) a multi-input, multi-output (MIMO) formulation for better model performance without increasing decode latency. Together with architectural refinements, our Mamba-3 model achieves significant gains across retrieval, state-tracking, and downstream language modeling tasks. At the 1.5B scale, Mamba-3 improves average downstream accuracy by 0.6 percentage points compared to the next best model (Gated DeltaNet), with Mamba-3's MIMO variant further improving accuracy by another 1.2 points for a total 1.8 point gain. Across state-size experiments, Mamba-3 achieves comparable perplexity to Mamba-2 despite using half of its predecessor's state size. Our evaluations demonstrate Mamba-3's ability to advance the performance-efficiency Pareto frontier.", "authors": ["Aakash Lahoti", "Kevin Y. Li", "Berlin Chen", "Caitlin Wang", "Aviv Bick", "J. Zico Kolter", "Tri Dao", "Albert Gu"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-16", "url": "https://arxiv.org/abs/2603.15569", "pdf_url": "https://arxiv.org/pdf/2603.15569v1", "arxiv_id": "2603.15569", "doi": "10.48550/arXiv.2603.15569", "citation_count": 52, "influential_citation_count": 5, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.5099}
{"id": "960c97f1c73793b7c469e5605601eab4b70dafe5b7d6a3b72cde424775abdb0c", "sources": ["arxiv", "semantic_scholar"], "title": "PDE-SSM: A Spectral State Space Approach to Spatial Mixing in Diffusion Transformers", "abstract": "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 encodes a strong spatial prior by modeling information flow via physically grounded dynamics rather than all-to-all token interactions. Solving the PDE in the Fourier domain yields global coupling with near-linear complexity of $O(N \\log N)$, delivering a principled and scalable alternative to attention. We integrate PDE-SSM into a flow-matching generative model to obtain the PDE-based Diffusion Transformer PDE-SSM-DiT. Empirically, PDE-SSM-DiT matches or exceeds the performance of state-of-the-art Diffusion Transformers while substantially reducing compute. Our results show that, analogous to 1D settings where SSMs supplant attention, multi-dimensional PDE operators provide an efficient, inductive-bias-rich foundation for next-generation vision models.", "authors": ["Eshed Gal", "Moshe Eliasof", "Siddharth Rout", "Eldad Haber"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-14", "url": "https://arxiv.org/abs/2603.13663", "pdf_url": "https://arxiv.org/pdf/2603.13663v1", "arxiv_id": "2603.13663", "doi": "10.48550/arXiv.2603.13663", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.5076}
{"id": "cba157c3c29f4b4d1dfa726cff45615f8f8300dac4c8fe44a71592c18fc3bc2e", "sources": ["arxiv", "semantic_scholar"], "title": "SpectralGuard: Detecting Memory Collapse Attacks in State Space Models", "abstract": "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 rho toward zero through gradient-based Hidden State Poisoning, memory collapses from millions of tokens to mere dozens, silently destroying reasoning capacity without triggering output-level alarms. We prove an Evasion Existence Theorem showing that for any output-only defense, adversarial inputs exist that simultaneously induce spectral collapse and evade detection, then introduce SpectralGuard, a real-time monitor that tracks spectral stability across all model layers. SpectralGuard achieves F1=0.961 against non-adaptive attackers and retains F1=0.842 under the strongest adaptive setting, with sub-15ms per-token latency. Causal interventions and cross-architecture transfer to hybrid SSM-Attention systems confirm that spectral monitoring provides a principled, deployable safety layer for recurrent foundation models.", "authors": ["Davi Bonetto"], "categories": ["cs.LG", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-12", "url": "https://arxiv.org/abs/2603.12414", "pdf_url": "https://arxiv.org/pdf/2603.12414v1", "arxiv_id": "2603.12414", "doi": "10.48550/arXiv.2603.12414", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/DaviBonetto/spectralguard", "venue": "arXiv.org", "quality_score": 0.7809}
{"id": "d7ca0a25811e381e486053528643c4341c3584899415a7d15de10d419669b286", "sources": ["arxiv", "semantic_scholar"], "title": "Progressive Split Mamba: Effective State Space Modelling for Image Restoration", "abstract": "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 an appealing linear-time alternative for long-range dependency modelling. However, naively extending Mamba to 2D images exposes two intrinsic shortcomings. First, flattening 2D feature maps into 1D sequences disrupts spatial topology, leading to locality distortion that hampers precise structural recovery. Second, the stability-driven recurrent dynamics of SSMs induce long-range decay, progressively attenuating information across distant spatial positions and weakening global consistency. Together, these effects limit the effectiveness of state-space modelling in high-fidelity restoration. We propose Progressive Split-Mamba (PS-Mamba), a topology-aware hierarchical state-space framework designed to reconcile locality preservation with efficient global propagation. Instead of sequentially flattening entire feature maps, PS-Mamba performs geometry-consistent partitioning, maintaining neighbourhood integrity prior to state-space processing. A progressive split hierarchy (halves, quadrants, octants) enables structured multi-scale modelling while retaining linear complexity. To counteract long-range decay, we introduce symmetric cross-scale shortcut pathways that directly transmit low-frequency global context across hierarchical levels, stabilising information flow over large spatial extents. Extensive experiments on super-resolution, denoising, and JPEG artifact reduction show consistent improvements over recent Mamba-based and attention-based models with a clear margin.", "authors": ["Mohammed Hassanin", "Nour Moustafa", "Weijian Deng", "Ibrahim Radwan"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-10", "url": "https://arxiv.org/abs/2603.09171", "pdf_url": "https://arxiv.org/pdf/2603.09171v1", "arxiv_id": "2603.09171", "doi": "10.48550/arXiv.2603.09171", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.503}
{"id": "e7d7b4ca04a0ea236814215c2f73616e152d5b3f8d112aab3ec9aad5a940a801", "sources": ["arxiv", "semantic_scholar"], "title": "InfoMamba: An Attention-Free Hybrid Mamba-Transformer Model", "abstract": "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 often struggle to capture high-rank and synchronous global interactions. We present a consistency boundary analysis that characterizes when diagonal short-memory SSMs can approximate causal attention and identifies structural gaps that remain. Motivated by this analysis, we propose InfoMamba, an attention-free hybrid architecture. InfoMamba replaces token-level self-attention with a concept bottleneck linear filtering layer that serves as a minimal-bandwidth global interface and integrates it with a selective recurrent stream through information-maximizing fusion (IMF). IMF dynamically injects global context into the SSM dynamics and encourages complementary information usage through a mutual-information-inspired objective. Extensive experiments on classification, dense prediction, and non-vision tasks show that InfoMamba consistently outperforms strong Transformer and SSM baselines, achieving competitive accuracy-efficiency trade-offs while maintaining near-linear scaling.", "authors": ["Youjin Wang", "Jiaqiao Zhao", "Rong Fu", "Run Zhou", "Ruizhe Zhang", "Jiani Liang", "Suisuai Cao", "Feng Zhou"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-08", "url": "https://arxiv.org/abs/2603.18031", "pdf_url": "https://arxiv.org/pdf/2603.18031v1", "arxiv_id": "2603.18031", "doi": "10.48550/arXiv.2603.18031", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.5007}
{"id": "9d026685368f958cba1f26590dabc7d8987376f288d9157c41a505cb5db4a860", "sources": ["arxiv", "semantic_scholar"], "title": "Swimba: Switch Mamba Model Scales State Space Models", "abstract": "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--SSM can refer to two designs: (1) MoE over separated SSMs, which maintains multiple state trajectories and thus scales compute with the number of experts; and (2) MoE-parameterized SSM, which mixes experts in parameter space, maintains a single state trajectory, and evaluates the recurrence once. Our method, Switch Mamba (Swimba), follows the second design by routing over expert-produced SSM streams. Theoretically, we establish well-definedness and stability for MoE-parameterized SSMs and characterize the relationship between the two designs. Empirically, we evaluate Swimba on standard benchmark tasks and measure real-time throughput and latency. Under matched FLOPs, Swimba achieves slightly better average performance than the baseline, with a small slowdown in real-time latency and throughput. Overall, these results suggest that parameter-space MoE can increase SSM capacity while keeping the dominant recurrence cost fixed.", "authors": ["Zhixu Du", "Krishna Teja Chitty-Venkata", "Murali Emani", "Venkatram Vishwanath", "Hai Helen Li", "Yiran Chen"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-06", "url": "https://arxiv.org/abs/2603.06938", "pdf_url": "https://arxiv.org/pdf/2603.06938v1", "arxiv_id": "2603.06938", "doi": "10.48550/arXiv.2603.06938", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4984}
{"id": "261fb99af4d97653451ec44d6c9ad14a1c5d339690c575d7932032deb0388b7f", "sources": ["arxiv", "semantic_scholar"], "title": "Mask-aware inference with State-Space Models", "abstract": "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) like Mamba have emerged, offering high performance with linear complexity. However, these architectures lack an inherent mechanism for handling such arbitrarily shaped invalid data at inference time. To bridge this gap, we introduce Partial Vision Mamba (PVM), a novel architectural component that ports the principles of partial operations to the Mamba backbone. We also define a series of rules to design architectures using PVM. We show the efficacy and generalizability of our approach in the tasks of depth completion, image inpainting, and classification with invalid data.", "authors": ["Ignasi Mas", "Ramon Morros", "Javier-Ruiz Hidalgo", "Ivan Huerta"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-04", "url": "https://arxiv.org/abs/2603.04568", "pdf_url": "https://arxiv.org/pdf/2603.04568v1", "arxiv_id": "2603.04568", "doi": "10.48550/arXiv.2603.04568", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4961}
{"id": "6a42333ad1023e1f46932c87fc1b1c33afb4096e056235f6cbc70d6d90a3d40e", "sources": ["arxiv", "semantic_scholar"], "title": "The Expressive Limits of Diagonal SSMs for State-Tracking", "abstract": "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 input-Dependent Complex-valued Diagonal (DCD) SSMs on sequential state-tracking tasks. We show that single-layer DCD SSMs cannot express state-tracking of any non-Abelian group at finite precision. More generally, we show that $k$-layer DCD SSMs can express state-tracking of a group if and only if that group has a subnormal series of length $k$, with Abelian factors. That is, we identify the precise expressivity range of $k$-layer DCD SSMs within the solvable groups. Empirically, we find that multi-layer models often fail to learn state-tracking for non-Abelian groups, highlighting a gap between expressivity and learnability.", "authors": ["Mehran Shakerinava", "Behnoush Khavari", "Siamak Ravanbakhsh", "Sarath Chandar"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-02", "url": "https://arxiv.org/abs/2603.01959", "pdf_url": "https://arxiv.org/pdf/2603.01959v1", "arxiv_id": "2603.01959", "doi": "10.48550/arXiv.2603.01959", "citation_count": 4, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4939}
{"id": "ae41dbaa6a4037110a3ff587d8a68bf300208769ac7a4d8c8b204e74af10119e", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba-CAD: State Space Model For 3D Computer-Aided Design Generative Modeling", "abstract": "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, requiring a longer parametric CAD sequence to define. To address the problem, we introduce Mamba-CAD, a self-supervised generative modeling for complex CAD models in the industry, which can model on a longer parametric CAD sequence. Specifically, we first design an encoder-decoder framework based on a Mamba architecture and pair it with a CAD reconstruction task for pre-training to model the latent representation of CAD models; and then we utilize the learned representation to guide a generative adversarial network to produce the fake representation of CAD models, which would be finally recovered into parametric CAD sequences via the decoder of MambaCAD. To train Mamba-CAD, we further create a new dataset consisting of 77,078 CAD models with longer parametric CAD sequences. Comprehensive experiments are conducted to demonstrate the effectiveness of our model under various evaluation metrics, especially in the generation length of valid parametric CAD sequences. The code and dataset can be achieved from https://github.com/Sunny-Hack/Code-for-Mamba-CAD-AAAI-2025-.", "authors": ["Xueyang Li", "Yunzhong Lou", "Yu Song", "Xiangdong Zhou"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-02-28", "url": "https://arxiv.org/abs/2603.00439", "pdf_url": "https://arxiv.org/pdf/2603.00439v1", "arxiv_id": "2603.00439", "doi": "10.1609/aaai.v39i5.32531", "citation_count": 10, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/Sunny-Hack/Code-for-Mamba-CAD-AAAI-2025-", "venue": "AAAI Conference on Artificial Intelligence", "quality_score": 0.7597}
{"id": "9c6cc95019dce56a7d872503ab65f51bfafe72f1c7894aea5f18726fd475140d", "sources": ["arxiv", "semantic_scholar"], "title": "Scaling State-Space Models on Multiple GPUs with Tensor Parallelism", "abstract": "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 necessary. Although tensor parallelism (TP) is widely used to scale Transformer inference, applying it to selective SSM blocks is non-trivial because the SSM mixer couples large projections with a sequence-wise recurrent state update and local mixing whose efficiency depends on preserving locality and avoiding synchronization in the critical path. This paper presents a communication-efficient TP design for selective SSM inference that addresses three practical engineering challenges: enabling TTFT improvements via an SSM state cache across prefill and decode, partitioning the mixer's packed parameter tensor so that recurrent updates remain local while minimizing communication, and reducing TP aggregation overhead with quantized AllReduce. We evaluate on three representative SSM-based LLMs spanning pure-SSM and hybrid architectures - Mamba, Falcon-Mamba, and Zamba - on NVIDIA A6000 and A100 clusters. Our experiments show substantial throughput gains from tensor-parallel SSM inference, improving batch-request throughput by ~1.6-2.1x on 2 GPUs and ~2.6-4.0x on 4 GPUs for Mamba, with the largest benefits at long context lengths, and achieving a further ~10-18% throughput improvement from quantized all-reduce by lowering synchronization bandwidth overhead.", "authors": ["Anurag Dutt", "Nimit Shah", "Hazem Masarani", "Anshul Gandhi"], "categories": ["cs.DC", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-02-24", "url": "https://arxiv.org/abs/2602.21144", "pdf_url": "https://arxiv.org/pdf/2602.21144v1", "arxiv_id": "2602.21144", "doi": "10.48550/arXiv.2602.21144", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.487}
{"id": "b6cbae13fbba620d54259a89bc536696708ea6d6e6d1cec05b82d6215f2bce98", "sources": ["arxiv", "semantic_scholar"], "title": "CrossLLM-Mamba: Multimodal State Space Fusion of LLMs for RNA Interaction Prediction", "abstract": "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 the dynamic, context-dependent nature of molecular binding. We introduce CrossLLM-Mamba, a novel framework that reformulates interaction prediction as a state-space alignment problem. By leveraging bidirectional Mamba encoders, our approach enables deep ``crosstalk'' between modality-specific embeddings through hidden state propagation, modeling interactions as dynamic sequence transitions rather than static feature overlaps. The framework maintains linear computational complexity, making it scalable to high-dimensional BioLLM embeddings. We further incorporate Gaussian noise injection and Focal Loss to enhance robustness against hard-negative samples. Comprehensive experiments across three interaction categories, RNA-protein, RNA-small molecule, and RNA-RNA demonstrate that CrossLLM-Mamba achieves state-of-the-art performance. On the RPI1460 benchmark, our model attains an MCC of 0.892, surpassing the previous best by 5.2\\%. For binding affinity prediction, we achieve Pearson correlations exceeding 0.95 on riboswitch and repeat RNA subtypes. These results establish state-space modeling as a powerful paradigm for multi-modal biological interaction prediction.", "authors": ["Rabeya Tus Sadia", "Qiang Ye", "Qiang Cheng"], "categories": ["q-bio.GN", "cs.CV", "cs.LG"], "fields_of_study": ["Medicine", "Biology", "Computer Science"], "published_date": "2026-02-23", "url": "https://arxiv.org/abs/2602.22236", "pdf_url": "https://arxiv.org/pdf/2602.22236v1", "arxiv_id": "2602.22236", "doi": "10.48550/arXiv.2602.22236", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4858}
{"id": "477be606565f92863206ae9f8944832befd9b98258f63e736f90b7a0fad45288", "sources": ["arxiv", "semantic_scholar"], "title": "A Theoretical Analysis of Mamba's Training Dynamics: Filtering Relevant Features for Generalization in State Space Models", "abstract": "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 representative Mamba block: a single-layer, single-head selective SSM with input-dependent gating, followed by a two-layer MLP trained via gradient descent (GD). Our study adopts a structured data model with tokens that include both class-relevant and class-irrelevant patterns under token-level noise and examines two canonical regimes: majority-voting and locality-structured data sequences. We prove that the model achieves guaranteed generalization by establishing non-asymptotic sample complexity and convergence rate bounds, which improve as the effective signal increases and the noise decreases. Furthermore, we show that the gating vector aligns with class-relevant features while ignoring irrelevant ones, thereby formalizing a feature-selection role similar to attention but realized through selective recurrence. Numerical experiments on synthetic data justify our theoretical results. Overall, our results provide principled insight into when and why Mamba-style selective SSMs learn efficiently, offering a theoretical counterpoint to Transformer-centric explanations.", "authors": ["Mugunthan Shandirasegaran", "Hongkang Li", "Songyang Zhang", "Meng Wang", "Shuai Zhang"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-02-13", "url": "https://arxiv.org/abs/2602.12499", "pdf_url": "https://arxiv.org/pdf/2602.12499v1", "arxiv_id": "2602.12499", "doi": "10.48550/arXiv.2602.12499", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4744}
{"id": "e41af85f7a45985621644ff6f13638929ef3d784f549a9614496628d68a5805c", "sources": ["arxiv", "semantic_scholar"], "title": "Introduction to High-Temperature Superconductivity for Solid State Chemists", "abstract": "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 chemists have made significant contributions to the field by discovering new compounds and producing high-quality samples for physical measurements. However, superconductivity research remains challenging for most solid state chemists because it requires knowledge of complicated solid state physics. This manuscript aims to provide a simple, intuitive introduction to superconductivity using only fundamental physics concepts that solid state chemists are familiar with. The author investigates a wide range of materials and classifies them according to the superconductivity mechanisms that may drive them. Specifically focusing on a series of copper oxide superconductors with the highest Tc at ambient conditions, the remarkable material dependence of Tc and the underlying, unconventional superconductivity mechanism that leads to the high Tc are thoroughly examined. Although our understanding of cuprate superconductivity is still fragmented, the author believes that once the branches and leaves are removed, the story will be fairly simple, similar to the phonon-based superconductivity mechanism revealed by the BCS theory. Furthermore, potential strategies for raising the Tc of cuprates and other superconductors are discussed. The author hopes that this article will pique interest in superconductors in young solid state chemists and encourage them to pursue the discovery of still unknown and unexplored room-temperature superconductors in the future.", "authors": ["Zenji Hiroi"], "categories": ["cond-mat.supr-con"], "fields_of_study": ["Physics"], "published_date": "2026-02-13", "url": "https://arxiv.org/abs/2602.12608", "pdf_url": "https://arxiv.org/pdf/2602.12608v2", "arxiv_id": "2602.12608", "doi": "10.1016/j.progsolidstchem.2026.100574", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Progress in Solid State Chemistry", "quality_score": 0.4744}
{"id": "0b2dfcc9b6b35bfb9ebf9ac89f1a8db00e87dbd1bcfdd7d45148d6b6592dae78", "sources": ["arxiv", "semantic_scholar"], "title": "Improved state mixing in higher-order and block diagonal linear recurrent networks", "abstract": "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 costly. Here, we explore how expressivity in LRNNs can be increased via richer state mixing across time and channels while maintaining competitive efficiency. Specifically, we introduce two structured LRNN architectures: (i) Higher-order Linear Recurrent Units (H-LRU), which generalize first-order recurrence to higher order, mixing multiple past states, and (ii) Block-Diagonal LRUs (BD-LRU), which enable dense intra-block channel mixing. Per-channel (H-LRU) or per-row (BD-LRU) L1-normalization of selective gates stabilizes training and allows for scaling window/block sizes. A parallel-scan implementation of the proposed architectures keeps the throughput competitive with diagonal LRNNs for moderate orders (H-LRU) and block sizes (BD-LRU). In synthetic sequence modeling tasks, the performance of BD-LRU matches or exceeds those of linear SSMs (Mamba), low-rank LRNNs (DeltaNet) and LSTM baselines, while H-LRU is found to be the most parameter-efficient in compression task. In both synthetic sequence modeling and language modeling, our results indicate that the structure of state mixing rather than width alone shapes expressivity of LRNNs, offering a practical route to closing the efficiency-expressivity gap in linear sequence models.", "authors": ["Igor Dubinin", "Antonio Orvieto", "Felix Effenberger"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-02-12", "url": "https://arxiv.org/abs/2602.12021", "pdf_url": "https://arxiv.org/pdf/2602.12021v2", "arxiv_id": "2602.12021", "doi": "10.48550/arXiv.2602.12021", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4732}
{"id": "f638c2432b95a5be9d4522433254dc226f270639a2cb96dfcc013403f5b7d1fc", "sources": ["arxiv", "semantic_scholar"], "title": "Retrieval-Aware Distillation for Transformer-SSM Hybrids", "abstract": "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 converts a pretrained Transformer into a hybrid student by preserving only these retrieval-critical heads and distilling the rest into recurrent heads. We identify the essential heads via ablation on a synthetic retrieval task, producing a hybrid with sparse, non-uniform attention placement. We show that preserving **just 2% of attention heads recovers over 95% of teacher performance on retrieval-heavy tasks** (10 heads in a 1B model), requiring far fewer heads than hybrids that retain at least 25%. We further find that large recurrent states often compensate for missing retrieval: once retrieval is handled by these heads, the SSM backbone can be simplified with limited loss, even with an $8\\times$ reduction in state dimension. By reducing both the attention cache and the SSM state, the resulting hybrid is $5$--$6\\times$ more memory-efficient than comparable hybrids, closing the Transformer--SSM gap at a fraction of the memory cost.", "authors": ["Aviv Bick", "Eric P. Xing", "Albert Gu"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-02-11", "url": "https://arxiv.org/abs/2602.11374", "pdf_url": "https://arxiv.org/pdf/2602.11374v1", "arxiv_id": "2602.11374", "doi": "10.48550/arXiv.2602.11374", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4721}
{"id": "53839903a7aa68cce1ce14ff5708ede456232b7d895249cf375e9f5a15316bb4", "sources": ["arxiv", "semantic_scholar"], "title": "Kalman Linear Attention: Parallel Bayesian Filtering For Efficient Language Modelling and State Tracking", "abstract": "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 with the Kalman filter as the core primitive. Classical Kalman filters give principled state and uncertainty estimates but are viewed as inherently sequential; we show that reparameterising them in information form turns their updates into an associative scan - so the per-token recurrent update is non-linear (a Möbius/precision recursion) yet remains temporally parallel. The resulting Kalman Linear Attention (KLA) layer is a drop-in sequence mixer that performs time-parallel probabilistic inference, carries an explicit belief-state uncertainty, and is strictly more expressive than GLA-style linear updates at the same computational cost. This expressivity translates directly into stronger state tracking: KLA solves permutation-composition ($A_5$) tasks that linear SSMs and attention cannot, while staying scan-parallel. As a drop-in primitive it also matches or improves on modern SSMs and GLAs across synthetic token-manipulation and zero-shot commonsense benchmarks, and is among the first stacked Bayesian-filtering primitives trained at the billion-token scale.", "authors": ["Vaisakh Shaj", "Cameron Barker", "Aidan Scannell", "Andras Szecsenyi", "Elliot J. Crowley", "Amos Storkey"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-02-11", "url": "https://arxiv.org/abs/2602.10743", "pdf_url": "https://arxiv.org/pdf/2602.10743v2", "arxiv_id": "2602.10743", "doi": "10.48550/arXiv.2602.10743", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4721}
{"id": "12c342082013f4e0a81ed523bb9f0a3da77c24c9159ba56a92380729f5c36bd3", "sources": ["arxiv", "semantic_scholar"], "title": "DMamba: Decomposition-enhanced Mamba for Time Series Forecasting", "abstract": "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 relationships differs fundamentally between the trend and seasonal components of a decomposed series. Trend relationships are often driven by a few common stochastic factors or long-run equilibria, suggesting that they reside on a lower-dimensional manifold. In contrast, seasonal relationships involve dynamic, high-dimensional interactions like phase shifts and amplitude co-movements, requiring more expressive modeling. In this paper, we propose DMamba, a novel forecasting model that explicitly aligns architectural complexity with this component-specific characteristic. DMamba employs seasonal-trend decomposition and processes the components with specialized, differentially complex modules: a variable-direction Mamba encoder captures the rich, cross-variable dynamics within the seasonal component, while a simple Multi-Layer Perceptron (MLP) suffices to learn from the lower-dimensional inter-variable relationships in the trend component. Extensive experiments on diverse datasets demonstrate that DMamba sets a new state-of-the-art (SOTA), consistently outperforming both recent Mamba-based architectures and leading decomposition-based models.", "authors": ["Ruxuan Chen", "Fang Sun"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-02-09", "url": "https://arxiv.org/abs/2602.09081", "pdf_url": "https://arxiv.org/pdf/2602.09081v1", "arxiv_id": "2602.09081", "doi": "10.48550/arXiv.2602.09081", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4698}
{"id": "3516f452b9282a7c6b18fd347ee735884c9e01c6c0b357920b053ff0426aeb16", "sources": ["arxiv", "semantic_scholar"], "title": "AS-Mamba: Asymmetric Self-Guided Mamba Decoupled Iterative Network for Metal Artifact Reduction", "abstract": "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 address these limitations, we propose the Asymmetric Self-Guided Mamba (AS-Mamba) for metal artifact reduction. Specifically, the linear propagation of metal-induced streak artifacts aligns well with the sequential modeling capability of State Space Models (SSMs). Consequently, the Mamba architecture is leveraged to explicitly capture and suppress these directional artifacts. Simultaneously, a frequency domain correction mechanism is incorporated to rectify the global amplitude spectrum, thereby mitigating intensity inhomogeneity caused by beam hardening. Furthermore, to bridge the distribution gap across diverse clinical scenarios, we introduce a self-guided contrastive regularization strategy. Extensive experiments on public andclinical dental CBCT datasets demonstrate that AS-Mamba achieves superior performance in suppressing directional streaks and preserving structural details, validating the effectiveness of integrating physical geometric priors into deep network design.", "authors": ["Bowen Ning", "Zekun Zhou", "Xinyi Zhong", "Zhongzhen Wang", "HongXin Wu", "HaiTao Wang", "Liu Shi", "Qiegen Liu"], "categories": ["eess.IV", "cs.CV"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2026-02-06", "url": "https://arxiv.org/abs/2602.06350", "pdf_url": "https://arxiv.org/pdf/2602.06350v1", "arxiv_id": "2602.06350", "doi": "10.48550/arXiv.2602.06350", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4664}
{"id": "14d27dff8f63e3bb99fd3f11838e084b1ccd18bb3a1d8b8437127dfd2a84eae4", "sources": ["arxiv", "semantic_scholar"], "title": "SMTrack: State-Aware Mamba for Efficient Temporal Modeling in Visual Tracking", "abstract": "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 limitations in modeling long-range temporal dependencies in visual tracking, often necessitating either complex customized modules or substantial computational costs to integrate temporal cues. Inspired by the success of the state space model, we propose a novel temporal modeling paradigm for visual tracking, termed State-aware Mamba Tracker (SMTrack), providing a neat pipeline for training and tracking without needing customized modules or substantial computational costs to build long-range temporal dependencies. It enjoys several merits. First, we propose a novel selective state-aware space model with state-wise parameters to capture more diverse temporal cues for robust tracking. Second, SMTrack facilitates long-range temporal interactions with linear computational complexity during training. Third, SMTrack enables each frame to interact with previously tracked frames via hidden state propagation and updating, which releases computational costs of handling temporal cues during tracking. Extensive experimental results demonstrate that SMTrack achieves promising performance with low computational costs.", "authors": ["Yinchao Ma", "Dengqing Yang", "Zhangyu He", "Wenfei Yang", "Tianzhu Zhang"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science", "Medicine"], "published_date": "2026-02-02", "url": "https://arxiv.org/abs/2602.01677", "pdf_url": "https://arxiv.org/pdf/2602.01677v1", "arxiv_id": "2602.01677", "doi": "10.1109/TIP.2026.3661393", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Image Processing", "quality_score": 0.4618}
{"id": "d221e22a7187331bae5c32c3b16a8f47ad53a6cc0002ee31ae476f25eb8baee8", "sources": ["arxiv", "semantic_scholar"], "title": "Omni-directional attention mechanism based on Mamba for speech separation", "abstract": "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 a single dimension into short one-dimensional sequences before processing them with Mamba, which restricts it to local 1D modeling and limits its ability to capture global dependencies across the 2D spectrogram. In this work, we propose an efficient omni-directional attention (OA) mechanism built upon unidirectional Mamba, which models global dependencies from ten different directions on the spectrogram. We expand the proposed mechanism into two baseline separation models and evaluate on three public datasets. Experimental results show that our approach consistently achieves significant performance gains over the baselines while preserving linear complexity, outperforming existing state-of-the-art (SOTA) systems.", "authors": ["Ke Xue", "Chang Sun", "Rongfei Fan", "Jing Wang", "Han Hu"], "categories": ["cs.SD", "eess.AS"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2026-01-23", "url": "https://arxiv.org/abs/2601.16603", "pdf_url": "https://arxiv.org/pdf/2601.16603v1", "arxiv_id": "2601.16603", "doi": "10.48550/arXiv.2601.16603", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4503}
{"id": "612a01518eeeec02e4653bc03f86c4a740fc146ba89603ab5a6846cd461a5a9b", "sources": ["arxiv", "semantic_scholar"], "title": "ConvMambaNet: A Hybrid CNN-Mamba State Space Architecture for Accurate and Real-Time EEG Seizure Detection", "abstract": "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 study introduces ConvMambaNet, a hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) with the Mamba Structured State Space Model (SSM) to enhance temporal feature extraction. By embedding the Mamba-SSM block within a CNN framework, the model effectively captures both spatial and long-range temporal dynamics. Evaluated on the CHB-MIT Scalp EEG dataset, ConvMambaNet achieved a 99% accuracy and demonstrated robust performance under severe class imbalance. These results underscore the model's potential for precise and efficient seizure detection, offering a viable path toward real-time, automated epilepsy monitoring in clinical environments.", "authors": ["Md. Nishan Khan", "Kazi Shahriar Sanjid", "Md. Tanzim Hossain", "Asib Mostakim Fony", "Istiak Ahmed", "M. Monir Uddin"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2026-01-19", "url": "https://arxiv.org/abs/2601.13234", "pdf_url": "https://arxiv.org/pdf/2601.13234v1", "arxiv_id": "2601.13234", "doi": "10.48550/arXiv.2601.13234", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4457}
{"id": "d68f11446890746afbbfe2a6e754ec1414ffcfbe1946e8f5bd7d61da449ce9e7", "sources": ["arxiv", "semantic_scholar"], "title": "On the Relation of State Space Models and Hidden Markov Models", "abstract": "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, probabilistic assumptions, inference procedures, and training paradigms. Recently, deterministic state space models have re-emerged in natural language processing through architectures such as S4 and Mamba, raising new questions about the relationship between classical probabilistic SSMs, HMMs, and modern neural sequence models. In this paper, we present a unified and systematic comparison of HMMs, linear Gaussian state space models, Kalman filtering, and contemporary NLP state space models. We analyze their formulations through the lens of probabilistic graphical models, examine their inference algorithms -- including forward-backward inference and Kalman filtering -- and contrast their learning procedures via Expectation-Maximization and gradient-based optimization. By highlighting both structural similarities and semantic differences, we clarify when these models are equivalent, when they fundamentally diverge, and how modern NLP SSMs relate to classical probabilistic models. Our analysis bridges perspectives from control theory, probabilistic modeling, and modern deep learning.", "authors": ["Aydin Ghojogh", "M. Hadi Sepanj", "Benyamin Ghojogh"], "categories": ["cs.LG", "cs.CL", "eess.AS", "eess.SY"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2026-01-19", "url": "https://arxiv.org/abs/2601.13357", "pdf_url": "https://arxiv.org/pdf/2601.13357v1", "arxiv_id": "2601.13357", "doi": "10.48550/arXiv.2601.13357", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4457}
{"id": "33a67c3d1418ad5c0b7f6f6acd02575d5cf7982c2c38dbe6dce5a217bf76450f", "sources": ["arxiv", "semantic_scholar"], "title": "Hidden State Poisoning Attacks against Mamba-based Language Models", "abstract": "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 irreversibly overwriting information in their hidden states, referred to as a Hidden State Poisoning Attack (HiSPA). Our benchmark RoBench-25 allows evaluating a model's information retrieval capabilities when subject to HiSPAs, and confirms the vulnerability of SSMs against such attacks. Even the recent Jamba-1.7-Mini SSM--Transformer (a 52B hybrid model) collapses on RoBench-25 under some HiSPA triggers, whereas pure Transformers do not. We also observe that HiSPA triggers significantly weaken the Jamba model on the popular Open-Prompt-Injections benchmark, unlike pure Transformers. We further show that the theoretical and empirical findings extend to Mamba-2, and also analyse a Mamba-2-based hybrid (Nemotron-3-Nano). Finally, our interpretability study reveals patterns in Mamba's hidden layers during HiSPAs that could be used to build a HiSPA mitigation system. The full code and data to reproduce the experiments can be found at https://anonymous.4open.science/r/hispa_anonymous-5DB0.", "authors": ["Alexandre Le Mercier", "Chris Develder", "Thomas Demeester"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-01-05", "url": "https://arxiv.org/abs/2601.01972", "pdf_url": "https://arxiv.org/pdf/2601.01972v4", "arxiv_id": "2601.01972", "doi": "10.48550/arXiv.2601.01972", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4297}
{"id": "2172c19e05add74729c932759de915d7d2a3ff61ec2320acf086d8e158f321d7", "sources": ["arxiv", "semantic_scholar"], "title": "A Mamba-Based Model for Automatic Chord Recognition", "abstract": "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 prediction performance comparable to state-of-the-art models, with the advantage of requiring fewer parameters and lower computational resources", "authors": ["Chunyu Yuan", "Johanna Devaney"], "categories": ["cs.SD"], "fields_of_study": ["Computer Science"], "published_date": "2026-01-05", "url": "https://arxiv.org/abs/2601.02101", "pdf_url": "https://arxiv.org/pdf/2601.02101v1", "arxiv_id": "2601.02101", "doi": "10.48550/arXiv.2601.02101", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4297}
{"id": "c03ab901ac1428900f7f73c9243c15cf4982c78120c730a3e07b7cb747ab1ec2", "sources": ["arxiv", "semantic_scholar"], "title": "Benchmarking the Computational and Representational Efficiency of State Space Models against Transformers on Long-Context Dyadic Sessions", "abstract": "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 Transformer on long-context sequences, using dyadic therapy sessions as a representative test case. We evaluate both architectures across two dimensions: (1) computational efficiency, where we measure memory usage and inference speed from 512 to 8,192 tokens, and (2) representational efficiency, where we analyze hidden state dynamics and attention patterns. Our findings provide actionable insights for practitioners working with long-context applications, establishing precise conditions under which SSMs offer advantages over Transformers.", "authors": ["Abidemi Koledoye", "Chinemerem Unachukwu", "Gold Nwobu", "Hasin Rana"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-01-03", "url": "https://arxiv.org/abs/2601.01237", "pdf_url": "https://arxiv.org/pdf/2601.01237v1", "arxiv_id": "2601.01237", "doi": "10.48550/arXiv.2601.01237", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4274}
{"id": "3cd572841b728a6dd90e4dc59c9cd1f0b3645561a9e9e1e82b55d6e27db29377", "sources": ["arxiv", "semantic_scholar"], "title": "MS-SSM: A Multi-Scale State Space Model for Efficient Sequence Modeling", "abstract": "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, traditional SSMs often suffer from limited effective memory, requiring larger state sizes for improved recall. Moreover, existing SSMs struggle to capture multi-scale dependencies, which are essential for modeling complex structures in time series, images, and natural language. This paper introduces a multi-scale SSM framework that addresses these limitations by representing sequence dynamics across multiple resolution and processing each resolution with specialized state-space dynamics. By capturing both fine-grained, high-frequency patterns and coarse, global trends, MS-SSM enhances memory efficiency and long-range modeling. We further introduce an input-dependent scale-mixer, enabling dynamic information fusion across resolutions. The proposed approach significantly improves sequence modeling, particularly in long-range and hierarchical tasks, while maintaining computational efficiency. Extensive experiments on benchmarks, including Long Range Arena, hierarchical reasoning, time series classification, and image recognition, demonstrate that MS-SSM consistently outperforms prior SSM-based models, highlighting the benefits of multi-resolution processing in state-space architectures.", "authors": ["Mahdi Karami", "Ali Behrouz", "Peilin Zhong", "Razvan Pascanu", "Vahab Mirrokni"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-12-29", "url": "https://arxiv.org/abs/2512.23824", "pdf_url": "https://arxiv.org/pdf/2512.23824v1", "arxiv_id": "2512.23824", "doi": "10.48550/arXiv.2512.23824", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4217}
{"id": "a79e9fe871e85b32068470810bfbda68825625d5acadf9cc4b3105dfec1ec405", "sources": ["arxiv", "semantic_scholar"], "title": "Lag Operator SSMs: A Geometric Framework for Structured State Space Modeling", "abstract": "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 introduce a direct, first-principles framework for constructing discrete-time SSMs that is both flexible and modular. Our approach is based on a novel lag operator, which geometrically derives the discrete-time recurrence by measuring how the system's basis functions \"slide\" and change from one timestep to the next. The resulting state matrices are computed via a single inner product involving this operator, offering a modular design space for creating novel SSMs by flexibly combining different basis functions and time-warping schemes. To validate our approach, we demonstrate that a specific instance exactly recovers the recurrence of the influential HiPPO model. Numerical simulations confirm our derivation, providing new theoretical tools for designing flexible and robust sequence models.", "authors": ["Sutashu Tomonaga", "Kenji Doya", "Noboru Murata"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-12-22", "url": "https://arxiv.org/abs/2512.18965", "pdf_url": "https://arxiv.org/pdf/2512.18965v1", "arxiv_id": "2512.18965", "doi": "10.48550/arXiv.2512.18965", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4136}
{"id": "95cad977cbb27cc6f7000beb109fe6287c2d71e2348f4956280d9e341bb41306", "sources": ["arxiv", "semantic_scholar"], "title": "How Many Heads Make an SSM? A Unified Framework for Attention and State Space Models", "abstract": "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 sequence maps via an input-dependent effective interaction operator $W_{ij}(X)$, making explicit two recurring construction patterns: (i) the Unified Factorized Framework (Explicit) (attention-style mixing), in which $W_{ij}(X)$ varies through scalar coefficients applied to shared value maps, and (ii) Structured Dynamics (Implicit) (state-space recurrences), in which $W_{ij}$ is induced by a latent dynamical system. Using this framework, we derive three theoretical results. First, we establish the Interaction Rank Gap: models in the Unified Factorized Framework, such as single-head attention, are constrained to a low-dimensional operator span and cannot represent certain structured dynamical maps. Second, we prove an Equivalence (Head-Count) Theorem showing that, within our multi-head factorized class, representing a linear SSM whose lag operators span a $k$-dimensional subspace on length-$n$ sequences requires and is achievable with $H=k$ heads. Third, we prove a Gradient Highway Result, showing that attention layers admit inputs with distance-independent gradient paths, whereas stable linear dynamics exhibit distance-dependent gradient attenuation. Together, these results formalize a fundamental trade-off between algebraic expressivity (interaction/operator span) and long-range gradient propagation, providing theoretical grounding for modern sequence architecture design.", "authors": ["Ali Ghodsi"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-12-17", "url": "https://arxiv.org/abs/2512.15115", "pdf_url": "https://arxiv.org/pdf/2512.15115v1", "arxiv_id": "2512.15115", "doi": "10.48550/arXiv.2512.15115", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4079}
{"id": "1d99b43cb4c7ba6431d6c01f5a54c63dd4c8cbadfacdec6a0c9ef7855505b3d5", "sources": ["arxiv", "semantic_scholar"], "title": "Characterizing Mamba's Selective Memory using Auto-Encoders", "abstract": "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 information loss occurs, it does not characterize the types of information SSM language models (LMs) tend to forget. In this paper, we address this knowledge gap by identifying the types of tokens (e.g., parts of speech, named entities) and sequences (e.g., code, math problems) that are more frequently forgotten by SSM LMs. We achieve this by training an auto-encoder to reconstruct sequences from the SSM's hidden state, and measure information loss by comparing inputs with their reconstructions. We perform experiments using the Mamba family of SSM LMs (130M--1.4B) on sequences ranging from 4--256 tokens. Our results show significantly higher rates of information loss on math-related tokens (e.g., numbers, variables), mentions of organization entities, and alternative dialects to Standard American English. We then examine the frequency that these tokens appear in Mamba's pretraining data and find that less prevalent tokens tend to be the ones Mamba is most likely to forget. By identifying these patterns, our work provides clear direction for future research to develop methods that better control Mamba's ability to retain important information.", "authors": ["Tamanna Hossain", "Robert L. Logan", "Ganesh Jagadeesan", "Sameer Singh", "Joel Tetreault", "Alejandro Jaimes"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-12-17", "url": "https://arxiv.org/abs/2512.15653", "pdf_url": "https://arxiv.org/pdf/2512.15653v1", "arxiv_id": "2512.15653", "doi": "10.48550/arXiv.2512.15653", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2596}
{"id": "3e9593febf6b879410c1cac2fc07ef20511567171021ed24fae2fb4d2ff93d86", "sources": ["arxiv", "semantic_scholar"], "title": "Kinetic-Mamba: Mamba-Assisted Predictions of Stiff Chemical Kinetics", "abstract": "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 temporal modeling capabilities of Mamba architectures. The framework comprises three complementary models: (i) a standalone Mamba model that predicts the time evolution of thermochemical state variables from given initial conditions; (ii) a constrained Mamba model that enforces mass conservation while learning the state dynamics; and (iii) a regime-informed architecture employing two standalone Mamba models to capture dynamics across temperature-dependent regimes. We additionally develop a latent Kinetic-Mamba variant that evolves dynamics in a reduced latent space and reconstructs the full state on the physical manifold. The accuracy and robustness of Kinetic-Mamba was evaluated using both time-decomposition and recursive-prediction strategies. We further assess the extrapolation capabilities of the model on varied out-of-distribution datasets. Computational experiments on Syngas and GRI-Mech 3.0 reaction mechanisms demonstrate that our framework achieves high fidelity in predicting complex kinetic behavior using only the initial conditions of the state variables.", "authors": ["Additi Pandey", "Liang Wei", "Hessam Babaee", "George Em Karniadakis"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-12-16", "url": "https://arxiv.org/abs/2512.14471", "pdf_url": "https://arxiv.org/pdf/2512.14471v2", "arxiv_id": "2512.14471", "doi": "10.48550/arXiv.2512.14471", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4068}
{"id": "91f6c280dda68adec01d8ba533661387cf17705a87d75c55630292e0e9ca64e2", "sources": ["arxiv", "semantic_scholar"], "title": "TSkel-Mamba: Temporal Dynamic Modeling via State Space Model for Human Skeleton-based Action Recognition", "abstract": "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 temporal dynamics. In particular, our approach leverages Spatial Transformer for spatial feature learning while utilizing Mamba for temporal modeling. Mamba, however, employs separate SSM blocks for individual channels, which inherently limits its ability to model inter-channel dependencies. To better adapt Mamba for skeleton data and enhance Mamba`s ability to model temporal dependencies, we introduce a Temporal Dynamic Modeling (TDM) block, which is a versatile plug-and-play component that integrates a novel Multi-scale Temporal Interaction (MTI) module. The MTI module employs multi-scale Cycle operators to capture cross-channel temporal interactions, a critical factor in action recognition. Extensive experiments on NTU-RGB+D 60, NTU-RGB+D 120, NW-UCLA and UAV-Human datasets demonstrate that TSkel-Mamba achieves state-of-the-art performance while maintaining low inference time, making it both efficient and highly effective.", "authors": ["Yanan Liu", "Jun Liu", "Hao Zhang", "Dan Xu", "Hossein Rahmani", "Mohammed Bennamoun", "Qiuhong Ke"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-12-12", "url": "https://arxiv.org/abs/2512.11503", "pdf_url": "https://arxiv.org/pdf/2512.11503v1", "arxiv_id": "2512.11503", "doi": "10.48550/arXiv.2512.11503", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4022}
{"id": "f8908c355d6518e3b96981be3ca4cac346ed5f064ce03e240c29ff5aa59b8aeb", "sources": ["arxiv", "semantic_scholar"], "title": "DF-Mamba: Deformable State Space Modeling for 3D Hand Pose Estimation in Interactions", "abstract": "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 occluded joints) and global context (e.g., cues from inter-joints, inter-hands, or the scene). However, most current 3D HPE methods still rely on ResNet for feature extraction, and such CNN's inductive bias may not be optimal for 3D HPE due to its limited capability to model the global context. To address this limitation, we propose an effective and efficient framework for visual feature extraction in 3D HPE using recent state space modeling (i.e., Mamba), dubbed Deformable Mamba (DF-Mamba). DF-Mamba is designed to capture global context cues beyond standard convolution through Mamba's selective state modeling and the proposed deformable state scanning. Specifically, for local features after convolution, our deformable scanning aggregates these features within an image while selectively preserving useful cues that represent the global context. This approach significantly improves the accuracy of structured 3D HPE, with comparable inference speed to ResNet-50. Our experiments involve extensive evaluations on five divergent datasets including single-hand and two-hand scenarios, hand-only and hand-object interactions, as well as RGB and depth-based estimation. DF-Mamba outperforms the latest image backbones, including VMamba and Spatial-Mamba, on all datasets and achieves state-of-the-art performance.", "authors": ["Yifan Zhou", "Takehiko Ohkawa", "Guwenxiao Zhou", "Kanoko Goto", "Takumi Hirose", "Yusuke Sekikawa", "Nakamasa Inoue"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-12-02", "url": "https://arxiv.org/abs/2512.02727", "pdf_url": "https://arxiv.org/pdf/2512.02727v1", "arxiv_id": "2512.02727", "doi": "10.1109/WACV61042.2026.00519", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Workshop/Winter Conference on Applications of Computer Vision", "quality_score": 0.3907}
{"id": "11714547a02644b81947501fb7c600cd00f1dda2a9edff2fa48f4c1063f10506", "sources": ["arxiv", "semantic_scholar"], "title": "PerfMamba: Performance Analysis and Pruning of Selective State Space Models", "abstract": "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 characteristics still remains unexplored, thus obstructing their optimal deployment and further architectural improvements. This paper presents a thorough empirical study of Mamba-1 and Mamba-2, systematically profiled for performance to assess the design principles that contribute to their efficiency in state-space modeling. A detailed analysis of computation patterns, memory access, I/O characteristics, and scaling properties was performed for sequence lengths ranging from 64 to 16384 tokens. Our findings show that the SSM component, a central part of the selective SSM architecture, demands a significant portion of computational resources compared to other components in the Mamba block. Based on these insights, we propose a pruning technique that selectively removes low-activity states within the SSM component, achieving measurable throughput and memory gains while maintaining accuracy within a moderate pruning regime. This approach results in performance improvements across varying sequence lengths, achieving a 1.14x speedup and reducing memory usage by 11.50\\%. These results offer valuable guidance for designing more efficient SSM architectures that can be applied to a wide range of real-world applications.", "authors": ["Abdullah Al Asif", "Mobina Kashaniyan", "Sixing Yu", "Juan Pablo Muñoz", "Ali Jannesari"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-28", "url": "https://arxiv.org/abs/2511.22849", "pdf_url": "https://arxiv.org/pdf/2511.22849v1", "arxiv_id": "2511.22849", "doi": "10.48550/arXiv.2511.22849", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3861}
{"id": "9357ddfb28e97723e8f3bcac2528438756f24eae3448d19ed9689d4adda3c02f", "sources": ["arxiv", "semantic_scholar"], "title": "MMA: A Momentum Mamba Architecture for Human Activity Recognition with Inertial Sensors", "abstract": "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 gradients, high computational cost, and difficulty in capturing long-range dependencies. Structured state-space models (SSMs) like Mamba address these challenges with linear complexity and effective temporal modeling, yet they are restricted to first-order dynamics without stable longterm memory mechanisms. We introduce Momentum Mamba, a momentum-augmented SSM that incorporates second-order dynamics to improve stability of information flow across time steps, robustness, and long-sequence modeling. Two extensions further expand its capacity: Complex Momentum Mamba for frequency-selective memory scaling. Experiments on multiple HAR benchmarks demonstrate consistent gains over vanilla Mamba and Transformer baselines in accuracy, robustness, and convergence speed. With only moderate increases in training cost, momentum-augmented SSMs offer a favorable accuracy-efficiency balance, establishing them as a scalable paradigm for HAR and a promising principal framework for broader sequence modeling applications.", "authors": ["Thai-Khanh Nguyen", "Uyen Vo", "Tan M. Nguyen", "Thieu N. Vo", "Trung-Hieu Le", "Cuong Pham"], "categories": ["cs.HC", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-26", "url": "https://arxiv.org/abs/2511.21550", "pdf_url": "https://arxiv.org/pdf/2511.21550v1", "arxiv_id": "2511.21550", "doi": "10.48550/arXiv.2511.21550", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3839}
{"id": "648c7836adbac4d3dda328933616b6230b005a4e7a6a7ded7a7ca58af56af581", "sources": ["arxiv", "semantic_scholar"], "title": "RNN as Linear Transformer: A Closer Investigation into Representational Potentials of Visual Mamba Models", "abstract": "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 relationship to Softmax and Linear Attention, confirming that it can be viewed as a low-rank approximation of Softmax Attention and thereby bridging the representational gap between Softmax and Linear forms. Second, we introduce a novel binary segmentation metric for activation map evaluation, extending qualitative assessments to a quantitative measure that demonstrates Mamba's capacity to model long-range dependencies. Third, by leveraging DINO for self-supervised pretraining, we obtain clearer activation maps than those produced by standard supervised approaches, highlighting Mamba's potential for interpretability. Notably, our model also achieves a 78.5 percent linear probing accuracy on ImageNet, underscoring its strong performance. We hope this work can provide valuable insights for future investigations of Mamba-based vision architectures.", "authors": ["Timing Yang", "Guoyizhe Wei", "Alan Yuille", "Feng Wang"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-23", "url": "https://arxiv.org/abs/2511.18380", "pdf_url": "https://arxiv.org/pdf/2511.18380v1", "arxiv_id": "2511.18380", "doi": "10.48550/arXiv.2511.18380", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3804}
{"id": "daefc8500bb1b41abaf07b7520ddcceb2fe9fac035babbf4e3b42f89df4b3708", "sources": ["arxiv", "semantic_scholar"], "title": "Controllability Analysis of State Space-based Language Model", "abstract": "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 parameters of Mamba and computed through a backward recurrence analogous to system observability. The score quantifies how strongly a token at position k affects all later states and outputs. We evaluate this measure across three Mamba variants: mamba-130m, mamba-2.8b, and mamba-2.8b-slimpj, using six experiments that test its sensitivity to temperature, prompt complexity, token type, layer depth, token position, and input perturbations. The results show three main insights: (1) the Influence Score increases with model size and training data, reflecting model capacity; (2) Mamba exhibits consistent architectural patterns, including recency bias and concentrated influence in mid-to-late layers; and (3) emergent behaviors appear only at scale, with mamba-2.8b-slimpj uniquely prioritizing content words and reducing internal influence in the presence of noise. These findings establish the Influence Score as a practical diagnostic tool for interpreting and comparing SSM-based language models.", "authors": ["Mohamed Mabrok", "Yalda Zafari"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-22", "url": "https://arxiv.org/abs/2511.17970", "pdf_url": "https://arxiv.org/pdf/2511.17970v1", "arxiv_id": "2511.17970", "doi": "10.48550/arXiv.2511.17970", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3793}
{"id": "b2e22fd01bb7fb14131d9911f85eefeb7d2d0aee468e5420cfb3a197ebd795e0", "sources": ["arxiv", "semantic_scholar"], "title": "Fourier-KAN-Mamba: A Novel State-Space Equation Approach for Time-Series Anomaly Detection", "abstract": "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 challenges in capturing complex temporal patterns and nonlinear dynamics. In this paper, we propose Fourier-KAN-Mamba, a novel hybrid architecture that integrates Fourier layer, Kolmogorov-Arnold Networks (KAN), and Mamba selective state-space model. The Fourier layer extracts multi-scale frequency features, KAN enhances nonlinear representation capability, and a temporal gating control mechanism further improves the model's ability to distinguish normal and anomalous patterns. Extensive experiments on MSL, SMAP, and SWaT datasets demonstrate that our method significantly outperforms existing state-of-the-art approaches. Keywords: time-series anomaly detection, state-space model, Mamba, Fourier transform, Kolmogorov-Arnold Network", "authors": ["Xiancheng Wang", "Lin Wang", "Rui Wang", "Zhibo Zhang", "Minghang Zhao"], "categories": ["cs.LG", "eess.SP"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2025-11-19", "url": "https://arxiv.org/abs/2511.15083", "pdf_url": "https://arxiv.org/pdf/2511.15083v2", "arxiv_id": "2511.15083", "doi": "10.48550/arXiv.2511.15083", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3758}
{"id": "70db4f54c92397eb2104769d0d40584104ecb67f45af91a408f371ffeb565ba3", "sources": ["arxiv", "semantic_scholar"], "title": "X-VMamba: Explainable Vision Mamba", "abstract": "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 information remains challenging due to the lack of transparent, attention-like mechanisms. To address this gap, we introduce a controllability-based interpretability framework that quantifies how different parts of the input sequence (tokens or patches) influence the internal state dynamics of SSMs. We propose two complementary formulations: a Jacobian-based method applicable to any SSM architecture that measures influence through the full chain of state propagation, and a Gramian-based approach for diagonal SSMs that achieves superior speed through closed-form analytical solutions. Both methods operate in a single forward pass with linear complexity, requiring no architectural modifications or hyperparameter tuning. We validate our framework through experiments on three diverse medical imaging modalities, demonstrating that SSMs naturally implement hierarchical feature refinement from diffuse low-level textures in early layers to focused, clinically meaningful patterns in deeper layers. Our analysis reveals domain-specific controllability signatures aligned with diagnostic criteria, progressive spatial selectivity across the network hierarchy, and the substantial influence of scanning strategies on attention patterns. Beyond medical imaging, we articulate applications spanning computer vision, natural language processing, and cross-domain tasks. Our framework establishes controllability analysis as a unified, foundational interpretability paradigm for SSMs across all domains. Code and analysis tools will be made available upon publication", "authors": ["Mohamed A. Mabrok", "Yalda Zafari"], "categories": ["cs.CV", "cs.LG", "math.DS"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2025-11-16", "url": "https://arxiv.org/abs/2511.12694", "pdf_url": "https://arxiv.org/pdf/2511.12694v1", "arxiv_id": "2511.12694", "doi": "10.48550/arXiv.2511.12694", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3724}
{"id": "c35b1edfa96493179e448995d32ae41a1c145edb55f18780e8c786b084e54985", "sources": ["arxiv", "semantic_scholar"], "title": "DensePercept-NCSSD: Vision Mamba towards Real-time Dense Visual Perception with Non-Causal State Space Duality", "abstract": "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 in a real-time applications. Our proposed model reduces inference times while maintaining high accuracy and low GPU usage for optical flow and disparity map generation. The results and analysis, and validation in real-life scenario justify that our proposed model can be used for unified real-time and accurate 3D dense perception estimation tasks. The code, along with the models, can be found at https://github.com/vimstereo/DensePerceptNCSSD", "authors": ["Tushar Anand", "Advik Sinha", "Abhijit Das"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-16", "url": "https://arxiv.org/abs/2511.12671", "pdf_url": "https://arxiv.org/pdf/2511.12671v1", "arxiv_id": "2511.12671", "doi": "10.48550/arXiv.2511.12671", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/vimstereo/DensePerceptNCSSD", "venue": "arXiv.org", "quality_score": 0.5755}
{"id": "2e608561403dadf7907f84134dbe27882c4bd0c97e0105d465805cde88eb4e49", "sources": ["arxiv", "semantic_scholar"], "title": "Arcee: Differentiable Recurrent State Chain for Generative Vision Modeling with Mamba SSMs", "abstract": "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 non-sequential signals (e.g., images). Rather than preserving cross-block memory, the conventional formulation of the selective-scan operation in Mamba reinitializes each block's state-space dynamics from zero, discarding the terminal state-space representation (SSR) from the previous block. Arcee, a cross-block recurrent state chain, reuses each block's terminal state-space representation as the initial condition for the next block. Handoff across blocks is constructed as a differentiable boundary map whose Jacobian enables end-to-end gradient flow across terminal boundaries. Key to practicality, Arcee is compatible with all prior \"vision-mamba\" variants, parameter-free, and incurs constant, negligible cost. As a modeling perspective, we view terminal SSR as a mild directional prior induced by a causal pass over the input, rather than an estimator of the non-sequential signal itself. To quantify the impact, for unconditional generation on CelebA-HQ (256$\\times$256) with Flow Matching, Arcee reduces FID$\\downarrow$ from $82.81$ to $15.33$ ($5.4\\times$ lower) on a single scan-order Zigzag Mamba baseline. Efficient CUDA kernels and training code will be released to support rigorous and reproducible research.", "authors": ["Jitesh Chavan", "Rohit Lal", "Anand Kamat", "Mengjia Xu"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-14", "url": "https://arxiv.org/abs/2511.11243", "pdf_url": "https://arxiv.org/pdf/2511.11243v2", "arxiv_id": "2511.11243", "doi": "10.48550/arXiv.2511.11243", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3701}
{"id": "f595ce57a6d8c730b9854fe6e43a9bed049f0b04b8a580cbc89c51399709766e", "sources": ["arxiv", "semantic_scholar"], "title": "Teaching Pretrained Language Models to Think Deeper with Retrofitted Recurrence", "abstract": "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 increase the effective depth of the model over the course of training preserves performance while reducing total computational cost. In our experiments, on mathematics, we observe that converting pretrained models to recurrent ones results in better performance at a given compute budget than simply post-training the original non-recurrent language model.", "authors": ["Sean McLeish", "Ang Li", "John Kirchenbauer", "Dayal Singh Kalra", "Brian R. Bartoldson", "Bhavya Kailkhura", "Avi Schwarzschild", "Jonas Geiping", "Tom Goldstein", "Micah Goldblum"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-10", "url": "https://arxiv.org/abs/2511.07384", "pdf_url": "https://arxiv.org/pdf/2511.07384v1", "arxiv_id": "2511.07384", "doi": "10.48550/arXiv.2511.07384", "citation_count": 22, "influential_citation_count": 3, "has_code": true, "code_url": "https://github.com/mcleish7/retrofitting-recurrence", "venue": "arXiv.org", "quality_score": 0.5649}
{"id": "6235c27604fc8609ddb44fed140863ff22aefcfa56391ebfcb635bffc438594c", "sources": ["arxiv", "semantic_scholar"], "title": "Dual Mamba for Node-Specific Representation Learning: Tackling Over-Smoothing with Selective State Space Modeling", "abstract": "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 representations evolve in a node-specific and progressive manner across layers. Moreover, these methods do not take global information into account, which is also crucial for mitigating the over-smoothing problem. To address the aforementioned issues, in this work, we propose a Dual Mamba-enhanced Graph Convolutional Network (DMbaGCN), which is a novel framework that integrates Mamba into GNNs to address over-smoothing from both local and global perspectives. DMbaGCN consists of two modules: the Local State-Evolution Mamba (LSEMba) for local neighborhood aggregation and utilizing Mamba's selective state space modeling to capture node-specific representation dynamics across layers, and the Global Context-Aware Mamba (GCAMba) that leverages Mamba's global attention capabilities to incorporate global context for each node. By combining these components, DMbaGCN enhances node discriminability in deep GNNs, thereby mitigating over-smoothing. Extensive experiments on multiple benchmarks demonstrate the effectiveness and efficiency of our method.", "authors": ["Xin He", "Yili Wang", "Yiwei Dai", "Xin Wang"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-10", "url": "https://arxiv.org/abs/2511.06756", "pdf_url": "https://arxiv.org/pdf/2511.06756v3", "arxiv_id": "2511.06756", "doi": "10.1609/aaai.v40i26.39317", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "AAAI Conference on Artificial Intelligence", "quality_score": 0.3655}
{"id": "dfe569eeadb992762b6d692f35e0bf6308ee1186389827575c1b99c2165f8605", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba-driven multi-perspective structural understanding for molecular ground-state conformation prediction", "abstract": "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 language and vision tasks. However, towards molecular ground-state conformation prediction, exploiting Mamba to understand molecular structure is underexplored. To this end, we strive to design a generic and efficient framework with Mamba to capture critical components. In general, molecular structure could be considered to consist of three elements, i.e., atom types, atom positions, and connections between atoms. Thus, considering the three elements, an approach of Mamba-driven multi-perspective structural understanding (MPSU-Mamba) is proposed to localize molecular ground-state conformation. Particularly, for complex and diverse molecules, three different kinds of dedicated scanning strategies are explored to construct a comprehensive perception of corresponding molecular structures. And a bright-channel guided mechanism is defined to discriminate the critical conformation-related atom information. Experimental results on QM9 and Molecule3D datasets indicate that MPSU-Mamba significantly outperforms existing methods. Furthermore, we observe that for the case of few training samples, MPSU-Mamba still achieves superior performance, demonstrating that our method is indeed beneficial for understanding molecular structures.", "authors": ["Yuxin Gou", "Aming Wu", "Richang Hong", "Meng Wang"], "categories": ["physics.chem-ph", "cs.AI"], "fields_of_study": ["Physics", "Computer Science"], "published_date": "2025-11-10", "url": "https://arxiv.org/abs/2511.09564", "pdf_url": "https://arxiv.org/pdf/2511.09564v1", "arxiv_id": "2511.09564", "doi": "10.48550/arXiv.2511.09564", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3655}
{"id": "c3a40b96beb755558cbf16078591afdafd21f4aa7bd713ed620ca3b6fe2a8738", "sources": ["arxiv", "semantic_scholar"], "title": "GTR-Mamba: Geometry-to-Tangent Routing Mamba for Hyperbolic POI Recommendation", "abstract": "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 based on rotations and graph representations, have been extensively investigated. Although hyperbolic geometry has proven superior in representing hierarchical data with low distortion, current hyperbolic sequence models typically rely on performing recurrence via expensive Möbius operations directly on the manifold. This incurs prohibitive computational costs and numerical instability, rendering them ill-suited for trajectory modeling. To resolve this conflict between geometric representational power and sequential efficiency, we propose GTR-Mamba, a novel framework featuring Geometry-to-Tangent Routing. GTR-Mamba strategically routes complex state transitions to the computationally efficient Euclidean tangent space. Crucially, instead of a static approximation, we introduce a Parallel Transport (PT) mechanism that dynamically aligns tangent spaces along the trajectory. This ensures geometric consistency across recursive updates, effectively bridging the gap between the curved manifold and linear tangent operations. This process is orchestrated by an exogenous spatio-temporal channel, which explicitly modulates the SSM discretization parameters. Extensive experiments on three real-world datasets demonstrate that GTR-Mamba consistently outperforms state-of-the-art baselines in next POI recommendation.", "authors": ["Zhuoxuan Li", "Jieyuan Pei", "Tangwei Ye", "Zhongyuan Lai", "Zihan Liu", "Fengyuan Xu", "Qi Zhang", "Liang Hu"], "categories": ["cs.AI", "cs.IR"], "fields_of_study": ["Computer Science"], "published_date": "2025-10-27", "url": "https://arxiv.org/abs/2510.22942", "pdf_url": "https://arxiv.org/pdf/2510.22942v2", "arxiv_id": "2510.22942", "doi": null, "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2224}
{"id": "41afe911eb885dd665143e3fc16377ec18664f8d7e036907656d3c3dd8900158", "sources": ["arxiv", "semantic_scholar"], "title": "StretchySnake: Flexible SSM Training Unlocks Action Recognition Across Spatio-Temporal Scales", "abstract": "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 understanding are tailored towards transformers and fail to fully leverage the unique attributes of SSMs. For example, video models are often trained at a fixed resolution and video length to balance the quadratic scaling of attention cost against performance. Consequently, these models suffer from degraded performance when evaluated on videos with spatial and temporal resolutions unseen during training; a property we call spatio-temporal inflexibility. In the context of action recognition, this severely limits a model's ability to retain performance across both short- and long-form videos. Therefore, we propose a flexible training method that leverages and improves the inherent adaptability of SSMs. Our method samples videos at varying temporal and spatial resolutions during training and dynamically interpolates model weights to accommodate any spatio-temporal scale. This instills our SSM, which we call StretchySnake, with spatio-temporal flexibility and enables it to seamlessly handle videos ranging from short, fine-grained clips to long, complex activities. We introduce and compare five different variants of flexible training, and identify the most effective strategy for video SSMs. On short-action (UCF-101, HMDB-51) and long-action (COIN, Breakfast) benchmarks, StretchySnake outperforms transformer and SSM baselines alike by up to 28%, with strong adaptability to fine-grained actions (SSV2, Diving-48). Therefore, our method provides a simple drop-in training recipe that makes video SSMs more robust, resolution-agnostic, and efficient across diverse action recognition scenarios.", "authors": ["Nyle Siddiqui", "Rohit Gupta", "Sirnam Swetha", "Mubarak Shah"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-10-17", "url": "https://arxiv.org/abs/2510.16209", "pdf_url": "https://arxiv.org/pdf/2510.16209v1", "arxiv_id": "2510.16209", "doi": "10.48550/arXiv.2510.16209", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.338}
{"id": "5474e5559e4b428897945a0a615c20903927347a7368fb59e67f5f4c21d90379", "sources": ["arxiv", "semantic_scholar"], "title": "State-Space Models for Tabular Prior-Data Fitted Networks", "abstract": "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 more efficient sequence models. In this work, we investigate the potential of using Hydra, a bidirectional linear-time structured state space model (SSM), as an alternative to Transformers in TabPFN. A key challenge lies in SSM's inherent sensitivity to the order of input tokens - an undesirable property for tabular datasets where the row order is semantically meaningless. We investigate to what extent a bidirectional approach can preserve efficiency and enable symmetric context aggregation. Our experiments show that this approach reduces the order-dependence, achieving predictive performance competitive to the original TabPFN model.", "authors": ["Felix Koch", "Marcel Wever", "Fabian Raisch", "Benjamin Tischler"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-10-16", "url": "https://arxiv.org/abs/2510.14573", "pdf_url": "https://arxiv.org/pdf/2510.14573v2", "arxiv_id": "2510.14573", "doi": "10.48550/arXiv.2510.14573", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3369}
{"id": "b30264a3f43079551cb9bed0adbbcdf0a0453499e2ad26ee65d3d1c40677eda1", "sources": ["arxiv", "semantic_scholar"], "title": "MSF-Mamba: Motion-aware State Fusion Mamba for Efficient Micro-Gesture Recognition", "abstract": "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 fields. Transformer-based models address this limitation through self-attention mechanisms but suffer from high computational costs. Recently, Mamba has shown promise as an efficient model, leveraging state space models (SSMs) to enable linear-time processing However, directly applying the vanilla Mamba to MGR may not be optimal. This is because Mamba processes inputs as 1D sequences, with state updates relying solely on the previous state, and thus lacks the ability to model local spatiotemporal dependencies. In addition, previous methods lack a design of motion-awareness, which is crucial in MGR. To overcome these limitations, we propose motion-aware state fusion mamba (MSF-Mamba), which enhances Mamba with local spatiotemporal modeling by fusing local contextual neighboring states. Our design introduces a motion-aware state fusion module based on central frame difference (CFD). Furthermore, a multiscale version named MSF-Mamba+ has been proposed. Specifically, MSF-Mamba supports multiscale motion-aware state fusion, as well as an adaptive scale weighting module that dynamically weighs the fused states across different scales. These enhancements explicitly address the limitations of vanilla Mamba by enabling motion-aware local spatiotemporal modeling, allowing MSF-Mamba and MSF-Mamba to effectively capture subtle motion cues for MGR. Experiments on two public MGR datasets demonstrate that even the lightweight version, namely, MSF-Mamba, achieves SoTA performance, outperforming existing CNN-, Transformer-, and SSM-based models while maintaining high efficiency.", "authors": ["Deng Li", "Jun Shao", "Bohao Xing", "Rong Gao", "Bihan Wen", "Heikki Kälviäinen", "Xin Liu"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-10-12", "url": "https://arxiv.org/abs/2510.10478", "pdf_url": "https://arxiv.org/pdf/2510.10478v2", "arxiv_id": "2510.10478", "doi": "10.48550/arXiv.2510.10478", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE transactions on multimedia", "quality_score": 0.3323}
{"id": "6e5cf0753e113586abf26188053315378bf820ea2df0698163f45a17815c3f58", "sources": ["arxiv", "semantic_scholar"], "title": "SSM-CGM: Interpretable State-Space Forecasting Model of Continuous Glucose Monitoring for Personalized Diabetes Management", "abstract": "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. SSM-CGM improves short-term accuracy over a Temporal Fusion Transformer baseline, adds interpretability through variable selection and temporal attribution, and enables counterfactual forecasts simulating how planned changes in physiological signals (e.g., heart rate, respiration) affect near-term glucose. Together, these features make SSM-CGM an interpretable, physiologically grounded framework for personalized diabetes management.", "authors": ["Shakson Isaac", "Yentl Collin", "Chirag Patel"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-10-05", "url": "https://arxiv.org/abs/2510.04386", "pdf_url": "https://arxiv.org/pdf/2510.04386v1", "arxiv_id": "2510.04386", "doi": "10.48550/arXiv.2510.04386", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3243}
{"id": "960483979f5000802897195c1e8f51a02ac7b5329b061be59be983083201fab9", "sources": ["arxiv", "semantic_scholar"], "title": "Gather-Scatter Mamba: Accelerating Propagation with Efficient State Space Model", "abstract": "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. Video super-resolution (VSR) methods have traditionally relied on recurrent architectures to propagate features across frames. However, such approaches suffer from well-known issues including vanishing gradients, lack of parallelism, and slow inference speed. Recent advances in selective SSMs like Mamba offer a compelling alternative: by enabling input-dependent state transitions with linear-time complexity, Mamba mitigates these issues while maintaining strong long-range modeling capabilities. Despite this potential, Mamba alone struggles to capture fine-grained spatial dependencies due to its causal nature and lack of explicit context aggregation. To address this, we propose a hybrid architecture that combines shifted window self-attention for spatial context aggregation with Mamba-based selective scanning for efficient temporal propagation. Furthermore, we introduce Gather-Scatter Mamba (GSM), an alignment-aware mechanism that warps features toward a center anchor frame within the temporal window before Mamba propagation and scatters them back afterward, effectively reducing occlusion artifacts and ensuring effective redistribution of aggregated information across all frames. The official implementation is provided at: https://github.com/Ko-Lani/GSMamba.", "authors": ["Hyun-kyu Ko", "Youbin Kim", "Jihyeon Park", "Dongheok Park", "Gyeongjin Kang", "Wonjun Cho", "Hyung Yi", "Eunbyung Park"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-10-01", "url": "https://arxiv.org/abs/2510.00862", "pdf_url": "https://arxiv.org/pdf/2510.00862v1", "arxiv_id": "2510.00862", "doi": "10.48550/arXiv.2510.00862", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Ko-Lani/GSMamba}", "venue": "arXiv.org", "quality_score": 0.4941}
{"id": "437432f7e7c702519932af6e0e9611ae3617f782127d9d29531dc1c93dc1ae27", "sources": ["arxiv", "semantic_scholar"], "title": "Wavelet-Assisted Mamba for Satellite-Derived Sea Surface Temperature Super-Resolution", "abstract": "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. Recently, Mamba-based approaches leveraging State Space Models (SSM) have demonstrated significant potential for long-range dependency modeling with linear complexity. However, their application to SST data super-resolution remains largely unexplored. To this end, we propose the Wavelet-assisted Mamba Super-Resolution (WMSR) framework for satellite-derived SST data. The WMSR includes two key components: the Low-Frequency State Space Module (LFSSM) and High-Frequency Enhancement Module (HFEM). The LFSSM uses 2D-SSM to capture global information of the input data, and the robust global modeling capabilities of SSM are exploited to preserve the critical temperature information in the low-frequency component. The HFEM employs the pixel difference convolution to match and correct the high-frequency feature, achieving accurate and clear textures. Through comprehensive experiments on three SST datasets, our WMSR demonstrated superior performance over state-of-the-art methods. Our codes and datasets will be made publicly available at https://github.com/oucailab/WMSR.", "authors": ["Wankun Chen", "Feng Gao", "Yanhai Gan", "Jingchao Cao", "Junyu Dong", "Qian Du"], "categories": ["eess.IV", "cs.CV"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2025-09-29", "url": "https://arxiv.org/abs/2509.24334", "pdf_url": "https://arxiv.org/pdf/2509.24334v1", "arxiv_id": "2509.24334", "doi": "10.1109/TGRS.2025.3616324", "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/oucailab/WMSR", "venue": "IEEE Transactions on Geoscience and Remote Sensing", "quality_score": 0.4905}
{"id": "fc6854ea06b2101eab232841e4138f48e39c547e9160e531ea8bfb56b8994ded", "sources": ["arxiv", "semantic_scholar"], "title": "Trained Mamba Emulates Online Gradient Descent in In-Context Linear Regression", "abstract": "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, theoretical understanding of Mamba's ICL remains limited, restricting deeper insights into its underlying mechanisms. Even fundamental tasks such as linear regression ICL, widely studied as a standard theoretical benchmark for Transformers, have not been thoroughly analyzed in the context of Mamba. To address this gap, we study the training dynamics of Mamba on the linear regression ICL task. By developing novel techniques tackling non-convex optimization with gradient descent related to Mamba's structure, we establish an exponential convergence rate to ICL solution, and derive a loss bound that is comparable to Transformer's. Importantly, our results reveal that Mamba can perform a variant of \\textit{online gradient descent} to learn the latent function in context. This mechanism is different from that of Transformer, which is typically understood to achieve ICL through gradient descent emulation. The theoretical results are verified by experimental simulation.", "authors": ["Jiarui Jiang", "Wei Huang", "Miao Zhang", "Taiji Suzuki", "Liqiang Nie"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-28", "url": "https://arxiv.org/abs/2509.23779", "pdf_url": "https://arxiv.org/pdf/2509.23779v1", "arxiv_id": "2509.23779", "doi": "10.48550/arXiv.2509.23779", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3162}
{"id": "606229849b680d7e3f8344e0ebf9bba11f63964015c58133a5fdd9ebe215bf07", "sources": ["arxiv", "semantic_scholar"], "title": "HyMaTE: A Hybrid Mamba and Transformer Model for EHR Representation Learning", "abstract": "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 modeling. While Transformer-based models have demonstrated success in modeling EHR data and predicting clinical outcomes, their quadratic computational complexity and limited context length hinder their efficiency and practical applications. On the other hand, State Space Models (SSMs) like Mamba present a promising alternative offering linear-time sequence modeling and improved efficiency for handling long sequences, but focus mostly on mixing sequence-level information rather than channel-level data. To overcome these challenges, we propose HyMaTE (A Hybrid Mamba and Transformer Model for EHR Representation Learning), a novel hybrid model tailored for representing longitudinal data, combining the strengths of SSMs with advanced attention mechanisms. By testing the model on predictive tasks on multiple clinical datasets, we demonstrate HyMaTE's ability to capture an effective, richer, and more nuanced unified representation of EHR data. Additionally, the interpretability of the outcomes achieved by self-attention illustrates the effectiveness of our model as a scalable and generalizable solution for real-world healthcare applications. Codes are available at: https://github.com/healthylaife/HyMaTE.", "authors": ["Md Mozaharul Mottalib", "Thao-Ly T. Phan", "Rahmatollah Beheshti"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-28", "url": "https://arxiv.org/abs/2509.24118", "pdf_url": "https://arxiv.org/pdf/2509.24118v1", "arxiv_id": "2509.24118", "doi": "10.1145/3765612.3767245", "citation_count": 2, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/healthylaife/HyMaTE", "venue": "ACM International Conference on Bioinformatics, Computational Biology and Biomedicine", "quality_score": 0.4887}
{"id": "4345a7e92ac6363983bba5edf96203684826fe99ba3ad91bf9881bb4688c2e2f", "sources": ["arxiv", "semantic_scholar"], "title": "TRUST: Test-Time Refinement using Uncertainty-Guided SSM Traverses", "abstract": "State Space Models (SSMs) have emerged as efficient alternatives to Vision Transformers (ViTs), with VMamba standing out as a pioneering architecture designed for vision tasks. However, their generalization performance degrades significantly under distribution shifts. To address this limitation, we propose TRUST (Test-Time Refinement using Uncertainty-Guided SSM Traverses), a novel test-time adaptation (TTA) method that leverages diverse traversal permutations to generate multiple causal perspectives of the input image. Model predictions serve as pseudo-labels to guide updates of the Mamba-specific parameters, and the adapted weights are averaged to integrate the learned information across traversal scans. Altogether, TRUST is the first approach that explicitly leverages the unique architectural properties of SSMs for adaptation. Experiments on seven benchmarks show that TRUST consistently improves robustness and outperforms existing TTA methods.", "authors": ["Sahar Dastani", "Ali Bahri", "Gustavo Adolfo Vargas Hakim", "Moslem Yazdanpanah", "Mehrdad Noori", "David Osowiechi", "Samuel Barbeau", "Ismail Ben Ayed", "Herve Lombaert", "Christian Desrosiers"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-26", "url": "https://arxiv.org/abs/2509.22813", "pdf_url": "https://arxiv.org/pdf/2509.22813v2", "arxiv_id": "2509.22813", "doi": "10.48550/arXiv.2509.22813", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.314}
{"id": "7203b4bef93b76271f9d0185b18b50acd1b85871caf275c1a80acb5267e9ad50", "sources": ["arxiv", "semantic_scholar"], "title": "Structured Sparse Transition Matrices to Enable State Tracking in State-Space Models", "abstract": "Modern state-space models (SSMs) often utilize transition matrices which enable efficient computation but pose restrictions on the model's expressivity, as measured in terms of the ability to emulate finite-state automata (FSA). While unstructured transition matrices are optimal in terms of expressivity, they come at a prohibitively high compute and memory cost even for moderate state sizes. We propose a structured sparse parametrization of transition matrices in SSMs that enables FSA state tracking with optimal state size and depth, while keeping the computational cost of the recurrence comparable to that of diagonal SSMs. Our method, PD-SSM, parametrizes the transition matrix as the product of a column one-hot matrix ($P$) and a complex-valued diagonal matrix ($D$). Consequently, the computational cost of parallel scans scales linearly with the state size. Theoretically, the model is BIBO-stable and can emulate any $N$-state FSA with one layer of dimension $N$ and a linear readout of size $N \\times N$, significantly improving on all current structured SSM guarantees. Experimentally, the model significantly outperforms a wide collection of modern SSM variants on various FSA state tracking tasks. On multiclass time-series classification, the performance is comparable to that of neural controlled differential equations, a paradigm explicitly built for time-series analysis. Finally, we integrate PD-SSM into a hybrid Transformer-SSM architecture and demonstrate that the model can effectively track the states of a complex FSA in which transitions are encoded as a set of variable-length English sentences. The code is available at https://github.com/IBM/expressive-sparse-state-space-model", "authors": ["Aleksandar Terzić", "Nicolas Menet", "Michael Hersche", "Thomas Hofmann", "Abbas Rahimi"], "categories": ["cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-26", "url": "https://arxiv.org/abs/2509.22284", "pdf_url": "https://arxiv.org/pdf/2509.22284v3", "arxiv_id": "2509.22284", "doi": "10.48550/arXiv.2509.22284", "citation_count": 12, "influential_citation_count": 3, "has_code": true, "code_url": "https://github.com/IBM/expressive-sparse-state-space-model", "venue": "arXiv.org", "quality_score": 0.4852}
{"id": "ad9c0d380cd671f34f669c66ce4e8491b51ea016c658e69440c0729be00810de", "sources": ["arxiv", "semantic_scholar"], "title": "SpecMamba: Accelerating Mamba Inference on FPGA with Speculative Decoding", "abstract": "The growing demand for efficient long-sequence modeling on edge devices has propelled widespread adoption of State Space Models (SSMs) like Mamba, due to their superior computational efficiency and scalability. As its autoregressive generation process remains memory-bound, speculative decoding has been proposed that incorporates draft model generation and target model verification. However, directly applying speculative decoding to SSMs faces three key challenges: (1) hidden state backtracking difficulties, (2) tree-based parallel verification incompatibility, and (3) hardware workload mismatch. To address these challenges, we propose SpecMamba, the first FPGA-based accelerator for Mamba with speculative decoding, which features system, algorithm, and hardware co-design. At the system level, we present a memory-aware hybrid backtracking strategy to coordinate both models. At the algorithm level, we propose first-in-first-out (FIFO)-based tree verification with tiling to minimize memory access. At the hardware level, we customize a dataflow that computes linear layers in parallel and SSM layers in series to enable maximal overlapping. Implemented on AMD FPGA platforms (VHK158 and VCK190), SpecMamba achieves a 2.27x speedup over GPU baselines and a 2.85x improvement compared to prior FPGA solutions, while demonstrating 5.41x and 1.26x higher energy efficiency, respectively.", "authors": ["Linfeng Zhong", "Songqiang Xu", "Huifeng Wen", "Tong Xie", "Qingyu Guo", "Yuan Wang", "Meng Li"], "categories": ["cs.AR"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-24", "url": "https://arxiv.org/abs/2509.19873", "pdf_url": "https://arxiv.org/pdf/2509.19873v1", "arxiv_id": "2509.19873", "doi": "10.1109/ICCAD66269.2025.11240945", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1983}
{"id": "a409faa02aebcf779ce5e8a27a623adad4f22c9955c23e392f285598bbafcc31", "sources": ["arxiv", "semantic_scholar"], "title": "LEAF-Mamba: Local Emphatic and Adaptive Fusion State Space Model for RGB-D Salient Object Detection", "abstract": "RGB-D salient object detection (SOD) aims to identify the most conspicuous objects in a scene with the incorporation of depth cues. Existing methods mainly rely on CNNs, limited by the local receptive fields, or Vision Transformers that suffer from the cost of quadratic complexity, posing a challenge in balancing performance and computational efficiency. Recently, state space models (SSM), Mamba, have shown great potential for modeling long-range dependency with linear complexity. However, directly applying SSM to RGB-D SOD may lead to deficient local semantics as well as the inadequate cross-modality fusion. To address these issues, we propose a Local Emphatic and Adaptive Fusion state space model (LEAF-Mamba) that contains two novel components: 1) a local emphatic state space module (LE-SSM) to capture multi-scale local dependencies for both modalities. 2) an SSM-based adaptive fusion module (AFM) for complementary cross-modality interaction and reliable cross-modality integration. Extensive experiments demonstrate that the LEAF-Mamba consistently outperforms 16 state-of-the-art RGB-D SOD methods in both efficacy and efficiency. Moreover, our method can achieve excellent performance on the RGB-T SOD task, proving a powerful generalization ability.", "authors": ["Lanhu Wu", "Zilin Gao", "Hao Fei", "Mong-Li Lee", "Wynne Hsu"], "categories": ["cs.CV", "cs.AI", "cs.MM"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-23", "url": "https://arxiv.org/abs/2509.18683", "pdf_url": "https://arxiv.org/pdf/2509.18683v1", "arxiv_id": "2509.18683", "doi": "10.1145/3746027.3754863", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "ACM Multimedia", "quality_score": 0.3105}
{"id": "45ed0cbfc69fc6e307b7099068bf0ee8db104c9cfcf5d75c9b508baf470158bc", "sources": ["arxiv", "semantic_scholar"], "title": "Achilles' Heel of Mamba: Essential difficulties of the Mamba architecture demonstrated by synthetic data", "abstract": "State Space Models (SSMs) have emerged as promising alternatives to attention mechanisms, with the Mamba architecture demonstrating impressive performance and linear complexity for processing long sequences. However, the fundamental differences between Mamba and Transformer architectures remain incompletely understood. In this work, we use carefully designed synthetic tasks to reveal Mamba's inherent limitations. Through experiments, we identify that Mamba's nonlinear convolution introduces an asymmetry bias that significantly impairs its ability to recognize symmetrical patterns and relationships. Using composite function and inverse sequence matching tasks, we demonstrate that Mamba strongly favors compositional solutions over symmetrical ones and struggles with tasks requiring the matching of reversed sequences. We show these limitations stem not from the SSM module itself but from the nonlinear convolution preceding it, which fuses token information asymmetrically. These insights provide a new understanding of Mamba's constraints and suggest concrete architectural improvements for future sequence models.", "authors": ["Tianyi Chen", "Pengxiao Lin", "Zhiwei Wang", "Zhi-Qin John Xu"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-22", "url": "https://arxiv.org/abs/2509.17514", "pdf_url": "https://arxiv.org/pdf/2509.17514v2", "arxiv_id": "2509.17514", "doi": "10.48550/arXiv.2509.17514", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3094}
{"id": "6ce4f07e621828fdd82a08718ff59a973755d697a27ed73f3b461f7ecb94405b", "sources": ["arxiv", "semantic_scholar"], "title": "SAM: A Mamba-2 State-Space Audio-Language Model", "abstract": "We present SAM, a State-space Audio-language Model that integrates an audio encoder with a Mamba-2 backbone. SAM-2.7B achieves 21.1 mAP on AudioSet and 17.6 SPICE on AudioCaps, matching or surpassing larger 7B transformer-based models with fewer parameters. We further provide the first systematic, representation-level analysis of how SSMs interact with audio encoder outputs: (1) joint audio encoder finetuning is essential, supported by accuracy gains and observed adaptation of token representation rank and similarity across different SSM sizes; (2) despite linear scaling, SSMs benefit more from compact, information-rich audio token representations than from excessively long token sequences; and (3) incorporating instruction-following supervision substantially improves reasoning ability, boosting MMAU-Sound accuracy from 22.8 to 56.8. Through comprehensive experiments and analysis, we establish practical design principles for SSMs as strong, scalable backbones for audio-language models.", "authors": ["Taehan Lee", "Jaehan Jung", "Hyukjun Lee"], "categories": ["cs.SD", "eess.AS"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2025-09-19", "url": "https://arxiv.org/abs/2509.15680", "pdf_url": "https://arxiv.org/pdf/2509.15680v3", "arxiv_id": "2509.15680", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1947}
{"id": "d8e7d7299a95e103c20ae66b07784f29056d21c3fea710a651a1a430a4f51a39", "sources": ["arxiv", "semantic_scholar"], "title": "First-order State Space Model for Lightweight Image Super-resolution", "abstract": "State space models (SSMs), particularly Mamba, have shown promise in NLP tasks and are increasingly applied to vision tasks. However, most Mamba-based vision models focus on network architecture and scan paths, with little attention to the SSM module. In order to explore the potential of SSMs, we modified the calculation process of SSM without increasing the number of parameters to improve the performance on lightweight super-resolution tasks. In this paper, we introduce the First-order State Space Model (FSSM) to improve the original Mamba module, enhancing performance by incorporating token correlations. We apply a first-order hold condition in SSMs, derive the new discretized form, and analyzed cumulative error. Extensive experimental results demonstrate that FSSM improves the performance of MambaIR on five benchmark datasets without additionally increasing the number of parameters, and surpasses current lightweight SR methods, achieving state-of-the-art results.", "authors": ["Yujie Zhu", "Xinyi Zhang", "Yekai Lu", "Guang Yang", "Faming Fang", "Guixu Zhang"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-10", "url": "https://arxiv.org/abs/2509.08458", "pdf_url": "https://arxiv.org/pdf/2509.08458v2", "arxiv_id": "2509.08458", "doi": "10.1109/ICASSP49660.2025.10887656", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE International Conference on Acoustics, Speech, and Signal Processing", "quality_score": 0.2956}
{"id": "c29daf23902b08f2dbc721dc3a17254c25e7bec7b75e605a6a2a072044d46277", "sources": ["arxiv", "semantic_scholar"], "title": "Rethinking the long-range dependency in Mamba/SSM and transformer models", "abstract": "Long-range dependency is one of the most desired properties of recent sequence models such as state-space models (particularly Mamba) and transformer models. New model architectures are being actively developed and benchmarked for prediction tasks requiring long-range dependency. However, the capability of modeling long-range dependencies of these models has not been investigated from a theoretical perspective, which hinders a systematic improvement on this aspect. In this work, we mathematically define long-range dependency using the derivative of hidden states with respect to past inputs and compare the capability of SSM and transformer models of modeling long-range dependency based on this definition. We showed that the long-range dependency of SSM decays exponentially with the sequence length, which aligns with the exponential decay of memory function in RNN. But the attention mechanism used in transformers is more flexible and is not constrained to exponential decay, which could in theory perform better at modeling long-range dependency with sufficient training data, computing resources, and proper training. To combine the flexibility of long-range dependency of attention mechanism and computation efficiency of SSM, we propose a new formulation for hidden state update in SSM and prove its stability under a standard Gaussian distribution of the input data.", "authors": ["Cong Ma", "Kayvan Najarian"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-04", "url": "https://arxiv.org/abs/2509.04226", "pdf_url": "https://arxiv.org/pdf/2509.04226v1", "arxiv_id": "2509.04226", "doi": "10.48550/arXiv.2509.04226", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2888}
{"id": "3474d0293794ff54010704a21d2128a12acc064d5699217c7abd19011db2bc88", "sources": ["arxiv", "semantic_scholar"], "title": "VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation", "abstract": "Recent advances in Vision Transformers (ViTs) and State Space Models (SSMs) have challenged the dominance of Convolutional Neural Networks (CNNs) in computer vision. ViTs excel at capturing global context, and SSMs like Mamba offer linear complexity for long sequences, yet they do not capture fine-grained local features as effectively as CNNs. Conversely, CNNs possess strong inductive biases for local features but lack the global reasoning capabilities of transformers and Mamba. To bridge this gap, we introduce \\textit{VCMamba}, a novel vision backbone that integrates the strengths of CNNs and multi-directional Mamba SSMs. VCMamba employs a convolutional stem and a hierarchical structure with convolutional blocks in its early stages to extract rich local features. These convolutional blocks are then processed by later stages incorporating multi-directional Mamba blocks designed to efficiently model long-range dependencies and global context. This hybrid design allows for superior feature representation while maintaining linear complexity with respect to image resolution. We demonstrate VCMamba's effectiveness through extensive experiments on ImageNet-1K classification and ADE20K semantic segmentation. Our VCMamba-B achieves 82.6% top-1 accuracy on ImageNet-1K, surpassing PlainMamba-L3 by 0.3% with 37% fewer parameters, and outperforming Vision GNN-B by 0.3% with 64% fewer parameters. Furthermore, VCMamba-B obtains 47.1 mIoU on ADE20K, exceeding EfficientFormer-L7 by 2.0 mIoU while utilizing 62% fewer parameters. Code is available at https://github.com/Wertyuui345/VCMamba.", "authors": ["Mustafa Munir", "Alex Zhang", "Radu Marculescu"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-04", "url": "https://arxiv.org/abs/2509.04669", "pdf_url": "https://arxiv.org/pdf/2509.04669v1", "arxiv_id": "2509.04669", "doi": "10.1109/ICCVW69036.2025.00319", "citation_count": 3, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Wertyuui345/VCMamba", "venue": null, "quality_score": 0.3412}
{"id": "69f6da6b1666d53a5fce53eb03bb626ec035eeddfa8b2204de75299fbd50a3b0", "sources": ["arxiv", "semantic_scholar"], "title": "Echo State Networks as State-Space Models: A Systems Perspective", "abstract": "Echo State Networks (ESNs) are typically presented as efficient, readout-trained recurrent models, yet their dynamics and design are often guided by heuristics rather than first principles. We recast ESNs explicitly as state-space models (SSMs), providing a unified systems-theoretic account that links reservoir computing with classical identification and modern kernelized SSMs. First, we show that the echo-state property is an instance of input-to-state stability for a contractive nonlinear SSM and derive verifiable conditions in terms of leak, spectral scaling, and activation Lipschitz constants. Second, we develop two complementary mappings: (i) small-signal linearizations that yield locally valid LTI SSMs with interpretable poles and memory horizons; and (ii) lifted/Koopman random-feature expansions that render the ESN a linear SSM in an augmented state, enabling transfer-function and convolutional-kernel analyses. This perspective yields frequency-domain characterizations of memory spectra and clarifies when ESNs emulate structured SSM kernels. Third, we cast teacher forcing as state estimation and propose Kalman/EKF-assisted readout learning, together with EM for hyperparameters (leak, spectral radius, process/measurement noise) and a hybrid subspace procedure for spectral shaping under contraction constraints.", "authors": ["Pradeep Singh", "Balasubramanian Raman"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-04", "url": "https://arxiv.org/abs/2509.04422", "pdf_url": "https://arxiv.org/pdf/2509.04422v1", "arxiv_id": "2509.04422", "doi": "10.48550/arXiv.2509.04422", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2888}
{"id": "3cfe5d63b7b29c5ab6c270e01c5fec7ebd2c1311886cad4ec533c69323c7ad7d", "sources": ["arxiv", "semantic_scholar"], "title": "S2M2ECG: Spatio-temporal bi-directional State Space Model Enabled Multi-branch Mamba for ECG", "abstract": "As one of the most effective methods for cardiovascular disease (CVD) diagnosis, multi-lead Electrocardiogram (ECG) signals present a characteristic multi-sensor information fusion challenge that has been continuously researched in deep learning domains. Despite the numerous algorithms proposed with different DL architectures, maintaining a balance among performance, computational complexity, and multi-source ECG feature fusion remains challenging. Recently, state space models (SSMs), particularly Mamba, have demonstrated remarkable effectiveness across various fields. Their inherent design for high-efficiency computation and linear complexity makes them particularly suitable for low-dimensional data like ECGs. This work proposes S2M2ECG, an SSM architecture featuring three-level fusion mechanisms: (1) Spatio-temporal bi-directional SSMs with segment tokenization for low-level signal fusion, (2) Intra-lead temporal information fusion with bi-directional scanning to enhance recognition accuracy in both forward and backward directions, (3) Cross-lead feature interaction modules for spatial information fusion. To fully leverage the ECG-specific multi-lead mechanisms inherent in ECG signals, a multi-branch design and lead fusion modules are incorporated, enabling individual analysis of each lead while ensuring seamless integration with others. Experimental results reveal that S2M2ECG achieves superior performance in the rhythmic, morphological, and clinical scenarios. Moreover, its lightweight architecture ensures it has nearly the fewest parameters among existing models, making it highly suitable for efficient inference and convenient deployment. Collectively, S2M2ECG offers a promising alternative that strikes an excellent balance among performance, computational complexity, and ECG-specific characteristics, paving the way for high-performance, lightweight computations in CVD diagnosis.", "authors": ["Huaicheng Zhang", "Ruoxin Wang", "Chenlian Zhou", "Jiguang Shi", "Yue Ge", "Zhoutong Li", "Sheng Chang", "Hao Wang", "Jin He", "Qijun Huang"], "categories": ["eess.SP", "cs.AI", "cs.LG"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2025-09-03", "url": "https://arxiv.org/abs/2509.03066", "pdf_url": "https://arxiv.org/pdf/2509.03066v1", "arxiv_id": "2509.03066", "doi": "10.48550/arXiv.2509.03066", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2876}
{"id": "a79c5f0620496b8dab63973f3d005622035229a81aec0201a8c3f3403f416563", "sources": ["arxiv", "semantic_scholar"], "title": "MV-SSM: Multi-View State Space Modeling for 3D Human Pose Estimation", "abstract": "While significant progress has been made in single-view 3D human pose estimation, multi-view 3D human pose estimation remains challenging, particularly in terms of generalizing to new camera configurations. Existing attention-based transformers often struggle to accurately model the spatial arrangement of keypoints, especially in occluded scenarios. Additionally, they tend to overfit specific camera arrangements and visual scenes from training data, resulting in substantial performance drops in new settings. In this study, we introduce a novel Multi-View State Space Modeling framework, named MV-SSM, for robustly estimating 3D human keypoints. We explicitly model the joint spatial sequence at two distinct levels: the feature level from multi-view images and the person keypoint level. We propose a Projective State Space (PSS) block to learn a generalized representation of joint spatial arrangements using state space modeling. Moreover, we modify Mamba's traditional scanning into an effective Grid Token-guided Bidirectional Scanning (GTBS), which is integral to the PSS block. Multiple experiments demonstrate that MV-SSM achieves strong generalization, outperforming state-of-the-art methods: +10.8 on AP25 (+24%) on the challenging three-camera setting in CMU Panoptic, +7.0 on AP25 (+13%) on varying camera arrangements, and +15.3 PCP (+38%) on Campus A1 in cross-dataset evaluations. Project Website: https://aviralchharia.github.io/MV-SSM", "authors": ["Aviral Chharia", "Wenbo Gou", "Haoye Dong"], "categories": ["cs.CV", "cs.RO"], "fields_of_study": ["Computer Science"], "published_date": "2025-08-31", "url": "https://arxiv.org/abs/2509.00649", "pdf_url": "https://arxiv.org/pdf/2509.00649v1", "arxiv_id": "2509.00649", "doi": "10.1109/CVPR52734.2025.01082", "citation_count": 9, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Computer Vision and Pattern Recognition", "quality_score": 0.2842}
{"id": "c0e6ac2829d3f78a4d1c68c20e70ad640720c11ecbc35a926c217de8c337accb", "sources": ["arxiv", "semantic_scholar"], "title": "CSFMamba: Cross State Fusion Mamba Operator for Multimodal Remote Sensing Image Classification", "abstract": "Multimodal fusion has made great progress in the field of remote sensing image classification due to its ability to exploit the complementary spatial-spectral information. Deep learning methods such as CNN and Transformer have been widely used in these domains. State Space Models recently highlighted that prior methods suffer from quadratic computational complexity. As a result, modeling longer-range dependencies of spatial-spectral features imposes an overwhelming burden on the network. Mamba solves this problem by incorporating time-varying parameters into ordinary SSM and performing hardware optimization, but it cannot perform feature fusion directly. In order to make full use of Mamba's low computational burden and explore the potential of internal structure in multimodal feature fusion, we propose Cross State Fusion Mamba (CSFMamba) Network. Specifically, we first design the preprocessing module of remote sensing image information for the needs of Mamba structure, and combine it with CNN to extract multi-layer features. Secondly, a cross-state module based on Mamba operator is creatively designed to fully fuse the feature of the two modalities. The advantages of Mamba and CNN are combined by designing a more powerful backbone. We capture the fusion relationship between HSI and LiDAR modalities with stronger full-image understanding. The experimental results on two datasets of MUUFL and Houston2018 show that the proposed method outperforms the experimental results of Transformer under the premise of reducing the network training burden.", "authors": ["Qingyu Wang", "Xue Jiang", "Guozheng Xu"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-08-31", "url": "https://arxiv.org/abs/2509.00677", "pdf_url": "https://arxiv.org/pdf/2509.00677v1", "arxiv_id": "2509.00677", "doi": "10.1109/IGARSS55030.2025.11314008", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE International Geoscience and Remote Sensing Symposium", "quality_score": 0.2842}
{"id": "273b4fa4b802ab540f33e0f6823cbaf5acb4fd9e400fa146cdceb4c68f6d7244", "sources": ["arxiv", "semantic_scholar"], "title": "Characterizing the Behavior of Training Mamba-based State Space Models on GPUs", "abstract": "Mamba-based State Space Models (SSM) have emerged as a promising alternative to the ubiquitous transformers. Despite the expressive power of transformers, the quadratic complexity of computing attention is a major impediment to scaling performance as we increase the sequence length. SSMs provide an alternative path that addresses this problem, reducing the computational complexity requirements of self-attention with novel model architectures for different domains and fields such as video, text generation and graphs. Thus, it is important to characterize the behavior of these emerging workloads on GPUs and understand their requirements during GPU microarchitectural design. In this work we evaluate Mamba-based SSMs and characterize their behavior during training on GPUs. We construct a workload suite that offers representative models that span different model architectures. We then use this suite to analyze the architectural implications of running Mamba-based SSMs on GPUs. Our work sheds new light on potential optimizations to continue scaling the performance for such models.", "authors": ["Trinayan Baruah", "Kaustubh Shivdikar", "Sara Prescott", "David Kaeli"], "categories": ["cs.LG", "cs.AR", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-08-25", "url": "https://arxiv.org/abs/2508.17679", "pdf_url": "https://arxiv.org/pdf/2508.17679v1", "arxiv_id": "2508.17679", "doi": "10.48550/arXiv.2508.17679", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2773}
{"id": "78638157228f1fc545ac2ce0192f2e8db9d9e12ed38d9b576020b87cfa893993", "sources": ["arxiv", "semantic_scholar"], "title": "UST-SSM: Unified Spatio-Temporal State Space Models for Point Cloud Video Modeling", "abstract": "Point cloud videos capture dynamic 3D motion while reducing the effects of lighting and viewpoint variations, making them highly effective for recognizing subtle and continuous human actions. Although Selective State Space Models (SSMs) have shown good performance in sequence modeling with linear complexity, the spatio-temporal disorder of point cloud videos hinders their unidirectional modeling when directly unfolding the point cloud video into a 1D sequence through temporally sequential scanning. To address this challenge, we propose the Unified Spatio-Temporal State Space Model (UST-SSM), which extends the latest advancements in SSMs to point cloud videos. Specifically, we introduce Spatial-Temporal Selection Scanning (STSS), which reorganizes unordered points into semantic-aware sequences through prompt-guided clustering, thereby enabling the effective utilization of points that are spatially and temporally distant yet similar within the sequence. For missing 4D geometric and motion details, Spatio-Temporal Structure Aggregation (STSA) aggregates spatio-temporal features and compensates. To improve temporal interaction within the sampled sequence, Temporal Interaction Sampling (TIS) enhances fine-grained temporal dependencies through non-anchor frame utilization and expanded receptive fields. Experimental results on the MSR-Action3D, NTU RGB+D, and Synthia 4D datasets validate the effectiveness of our method. Our code is available at https://github.com/wangzy01/UST-SSM.", "authors": ["Peiming Li", "Ziyi Wang", "Yulin Yuan", "Hong Liu", "Xiangming Meng", "Junsong Yuan", "Mengyuan Liu"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-08-20", "url": "https://arxiv.org/abs/2508.14604", "pdf_url": "https://arxiv.org/pdf/2508.14604v1", "arxiv_id": "2508.14604", "doi": "10.1109/ICCV51701.2025.00634", "citation_count": 5, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/wangzy01/UST-SSM", "venue": "IEEE International Conference on Computer Vision", "quality_score": 0.4197}
{"id": "80adc5b6834cd2dff360ea9014fc2785fe2918cda9ccff865a204e312d02b183", "sources": ["arxiv", "semantic_scholar"], "title": "eMamba: Efficient Acceleration Framework for Mamba Models in Edge Computing", "abstract": "State Space Model (SSM)-based machine learning architectures have recently gained significant attention for processing sequential data. Mamba, a recent sequence-to-sequence SSM, offers competitive accuracy with superior computational efficiency compared to state-of-the-art transformer models. While this advantage makes Mamba particularly promising for resource-constrained edge devices, no hardware acceleration frameworks are currently optimized for deploying it in such environments. This paper presents eMamba, a comprehensive end-to-end hardware acceleration framework explicitly designed for deploying Mamba models on edge platforms. eMamba maximizes computational efficiency by replacing complex normalization layers with lightweight hardware-aware alternatives and approximating expensive operations, such as SiLU activation and exponentiation, considering the target applications. Then, it performs an approximation-aware neural architecture search (NAS) to tune the learnable parameters used during approximation. Evaluations with Fashion-MNIST, CIFAR-10, and MARS, an open-source human pose estimation dataset, show eMamba achieves comparable accuracy to state-of-the-art techniques using 1.63-19.9$\\times$ fewer parameters. In addition, it generalizes well to large-scale natural language tasks, demonstrating stable perplexity across varying sequence lengths on the WikiText2 dataset. We also quantize and implement the entire eMamba pipeline on an AMD ZCU102 FPGA and ASIC using GlobalFoundries (GF) 22 nm technology. Experimental results show 4.95-5.62$\\times$ lower latency and 2.22-9.95$\\times$ higher throughput, with 4.77$\\times$ smaller area, 9.84$\\times$ lower power, and 48.6$\\times$ lower energy consumption than baseline solutions while maintaining competitive accuracy.", "authors": ["Jiyong Kim", "Jaeho Lee", "Jiahao Lin", "Alish Kanani", "Miao Sun", "Umit Y. Ogras", "Jaehyun Park"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-08-14", "url": "https://arxiv.org/abs/2508.10370", "pdf_url": "https://arxiv.org/pdf/2508.10370v1", "arxiv_id": "2508.10370", "doi": "10.1145/3762190", "citation_count": 8, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "ACM Transactions on Embedded Computing Systems", "quality_score": 0.4091}
{"id": "9ecbac42c7f04d05e689b83bc5064d1fa95adaf265e55110529468859dce07c6", "sources": ["arxiv", "semantic_scholar"], "title": "Trajectory-aware Shifted State Space Models for Online Video Super-Resolution", "abstract": "Online video super-resolution (VSR) is an important technique for many real-world video processing applications, which aims to restore the current high-resolution video frame based on temporally previous frames. Most of the existing online VSR methods solely employ one neighboring previous frame to achieve temporal alignment, which limits long-range temporal modeling of videos. Recently, state space models (SSMs) have been proposed with linear computational complexity and a global receptive field, which significantly improve computational efficiency and performance. In this context, this paper presents a novel online VSR method based on Trajectory-aware Shifted SSMs (TS-Mamba), leveraging both long-term trajectory modeling and low-complexity Mamba to achieve efficient spatio-temporal information aggregation. Specifically, TS-Mamba first constructs the trajectories within a video to select the most similar tokens from the previous frames. Then, a Trajectory-aware Shifted Mamba Aggregation (TSMA) module consisting of proposed shifted SSMs blocks is employed to aggregate the selected tokens. The shifted SSMs blocks are designed based on Hilbert scannings and corresponding shift operations to compensate for scanning losses and strengthen the spatial continuity of Mamba. Additionally, we propose a trajectory-aware loss function to supervise the trajectory generation, ensuring the accuracy of token selection when training our model. Extensive experiments on three widely used VSR test datasets demonstrate that compared with six online VSR benchmark models, our TS-Mamba achieves state-of-the-art performance in most cases and over 22.7% complexity reduction (in MACs).", "authors": ["Qiang Zhu", "Xiandong Meng", "Yuxian Jiang", "Fan Zhang", "David Bull", "Shuyuan Zhu", "Bing Zeng", "Ronggang Wang"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-08-14", "url": "https://arxiv.org/abs/2508.10453", "pdf_url": "https://arxiv.org/pdf/2508.10453v2", "arxiv_id": "2508.10453", "doi": "10.48550/arXiv.2508.10453", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2647}
{"id": "6a8b4f0f95ff61827141fc635cf12887ad70cbc999b60337f75f7db175d80c59", "sources": ["arxiv", "semantic_scholar"], "title": "Keyword Mamba: Spoken Keyword Spotting with State Space Models", "abstract": "Keyword spotting (KWS) is an essential task in speech processing. It is widely used in voice assistants and smart devices. Deep learning models like CNNs, RNNs, and Transformers have performed well in KWS. However, they often struggle to handle long-term patterns and stay efficient at the same time. In this work, we present Keyword Mamba, a new architecture for KWS. It uses a neural state space model (SSM) called Mamba. We apply Mamba along the time axis and also explore how it can replace the self-attention part in Transformer models. We test our model on the Google Speech Commands datasets. The results show that Keyword Mamba reaches strong accuracy with fewer parameters and lower computational cost. To our knowledge, this is the first time a state space model has been used for KWS. These results suggest that Mamba has strong potential in speech-related tasks.", "authors": ["Hanyu Ding", "Wenlong Dong", "Qirong Mao"], "categories": ["cs.SD", "eess.AS"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2025-08-10", "url": "https://arxiv.org/abs/2508.07363", "pdf_url": "https://arxiv.org/pdf/2508.07363v1", "arxiv_id": "2508.07363", "doi": "10.48550/arXiv.2508.07363", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Computer Speech and Language", "quality_score": 0.2601}
{"id": "d4abc5018eee2fc37547b5d3d74f4054cf0203c24984638dbcf5f360b693e356", "sources": ["arxiv", "semantic_scholar"], "title": "Parity Requires Unified Input Dependence and Negative Eigenvalues in SSMs", "abstract": "Recent work has shown that LRNN models such as S4D, Mamba, and DeltaNet lack state-tracking capability due to either time-invariant transition matrices or restricted eigenvalue ranges. To address this, input-dependent transition matrices, particularly those that are complex or non-triangular, have been proposed to enhance SSM performance on such tasks. While existing theorems demonstrate that both input-independent and non-negative SSMs are incapable of solving simple state-tracking tasks, such as parity, regardless of depth, they do not explore whether combining these two types in a multilayer SSM could help. We investigate this question for efficient SSMs with diagonal transition matrices and show that such combinations still fail to solve parity. This implies that a recurrence layer must both be input-dependent and include negative eigenvalues. Our experiments support this conclusion by analyzing an SSM model that combines S4D and Mamba layers.", "authors": ["Behnoush Khavari", "Mehran Shakerinava", "Jayesh Khullar", "Jerry Huang", "François Rivest", "Siamak Ravanbakhsh", "Sarath Chandar"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-08-10", "url": "https://arxiv.org/abs/2508.07395", "pdf_url": "https://arxiv.org/pdf/2508.07395v1", "arxiv_id": "2508.07395", "doi": "10.48550/arXiv.2508.07395", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2601}
{"id": "0eeac542a6917604459eec76d84544aaab4fb8f5922d7b6d1c6e302cdae56c27", "sources": ["arxiv", "semantic_scholar"], "title": "A unified framework for the analysis, numerical approximation and model reduction of linear operator equations, Part I: Well-posedness in space and time", "abstract": "We present a unified framework to construct well-posed formulations for large classes of linear operator equations including elliptic, parabolic and hyperbolic partial differential equations. This general approach incorporates known weak variational formulations as well as novel space-time variational forms of the hyperbolic wave equation. The main concept is completion and extension of operators starting from the strong form of the problem. This paper lays the theoretical foundation for a unified approach towards numerical approximation methods and also model reduction of parameterized linear operator equations which will be the subject of the following parts.", "authors": ["Moritz Feuerle", "Richard Löscher", "Olaf Steinbach", "Karsten Urban"], "categories": ["math.NA"], "fields_of_study": ["Mathematics", "Computer Science"], "published_date": "2025-08-07", "url": "https://arxiv.org/abs/2508.05407", "pdf_url": "https://arxiv.org/pdf/2508.05407v1", "arxiv_id": "2508.05407", "doi": "10.48550/arXiv.2508.05407", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2567}
{"id": "5219fd9af2ad20d77e4530812071f3e22a93fd75495e9d7c3cd0e9e0a2b6cc47", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba-X: An End-to-End Vision Mamba Accelerator for Edge Computing Devices", "abstract": "Transformers have proven effective in language modeling but are limited by high computational and memory demands that grow quadratically with input sequence length. State space models (SSMs) offer a promising alternative by reducing attention complexity from $O(L^2)$ to $O(L)$ while also lowering overall memory consumption. Vision Mamba adapts the SSM approach for computer vision tasks, achieving lower latency and memory consumption than traditional transformer models. However, deploying Vision Mamba on edge devices is challenging due to its sequential scan operations, which hinder GPU efficiency. We propose Mamba-X, an end-to-end Vision Mamba accelerator that includes a systolic scan array to maximize parallelism and minimize memory traffic, along with a hybrid, hardware-friendly quantization technique to reduce memory usage and improve hardware efficiency without sacrificing accuracy.", "authors": ["Dongho Yoon", "Gungyu Lee", "Jaewon Chang", "Yunjae Lee", "Dongjae Lee", "Minsoo Rhu"], "categories": ["cs.AR"], "fields_of_study": ["Computer Science"], "published_date": "2025-08-05", "url": "https://arxiv.org/abs/2508.02977", "pdf_url": "https://arxiv.org/pdf/2508.02977v1", "arxiv_id": "2508.02977", "doi": "10.1109/ICCAD66269.2025.11240777", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1619}
{"id": "ae4f1c8e8936dd71dabbfc378564e2d31b28a9048117a0e7721931fb15920a49", "sources": ["arxiv", "semantic_scholar"], "title": "Content-Aware Mamba for Learned Image Compression", "abstract": "Recent learned image compression (LIC) leverages Mamba-style state-space models (SSMs) for global receptive fields with linear complexity. However, the standard Mamba adopts content-agnostic, predefined raster (or multi-directional) scans under strict causality. This rigidity hinders its ability to effectively eliminate redundancy between tokens that are content-correlated but spatially distant. We introduce Content-Aware Mamba (CAM), an SSM that dynamically adapts its processing to the image content. Specifically, CAM overcomes prior limitations with two novel mechanisms. First, it replaces the rigid scan with a content-adaptive token permutation strategy to prioritize interactions between content-similar tokens regardless of their location. Second, it overcomes the sequential dependency by injecting sample-specific global priors into the state-space model, which effectively mitigates the strict causality without multi-directional scans. These innovations enable CAM to better capture global redundancy while preserving computational efficiency. Our Content-Aware Mamba-based LIC model (CMIC) achieves state-of-the-art rate-distortion performance, surpassing VTM-21.0 by 15.91%, 21.34%, and 17.58% in BD-rate on the Kodak, Tecnick, and CLIC datasets, respectively. Code will be released at https://github.com/UnoC-727/CMIC.", "authors": ["Yunuo Chen", "Zezheng Lyu", "Bing He", "Hongwei Hu", "Qi Wang", "Yuan Tian", "Li Song", "Wenjun Zhang", "Guo Lu"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-08-04", "url": "https://arxiv.org/abs/2508.02192", "pdf_url": "https://arxiv.org/pdf/2508.02192v6", "arxiv_id": "2508.02192", "doi": null, "citation_count": 3, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/UnoC-727/CMIC", "venue": null, "quality_score": 0.2993}
{"id": "23022690c259ed193e7a7a63b487366c8b89146aa196f717e4ecfa3f68b66c53", "sources": ["arxiv", "semantic_scholar"], "title": "UIS-Mamba: Exploring Mamba for Underwater Instance Segmentation via Dynamic Tree Scan and Hidden State Weaken", "abstract": "Underwater Instance Segmentation (UIS) tasks are crucial for underwater complex scene detection. Mamba, as an emerging state space model with inherently linear complexity and global receptive fields, is highly suitable for processing image segmentation tasks with long sequence features. However, due to the particularity of underwater scenes, there are many challenges in applying Mamba to UIS. The existing fixed-patch scanning mechanism cannot maintain the internal continuity of scanned instances in the presence of severely underwater color distortion and blurred instance boundaries, and the hidden state of the complex underwater background can also inhibit the understanding of instance objects. In this work, we propose the first Mamba-based underwater instance segmentation model UIS-Mamba, and design two innovative modules, Dynamic Tree Scan (DTS) and Hidden State Weaken (HSW), to migrate Mamba to the underwater task. DTS module maintains the continuity of the internal features of the instance objects by allowing the patches to dynamically offset and scale, thereby guiding the minimum spanning tree and providing dynamic local receptive fields. HSW module suppresses the interference of complex backgrounds and effectively focuses the information flow of state propagation to the instances themselves through the Ncut-based hidden state weakening mechanism. Experimental results show that UIS-Mamba achieves state-of-the-art performance on both UIIS and USIS10K datasets, while maintaining a low number of parameters and computational complexity. Code is available at https://github.com/Maricalce/UIS-Mamba.", "authors": ["Runmin Cong", "Zongji Yu", "Hao Fang", "Haoyan Sun", "Sam Kwong"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-08-01", "url": "https://arxiv.org/abs/2508.00421", "pdf_url": "https://arxiv.org/pdf/2508.00421v1", "arxiv_id": "2508.00421", "doi": "10.1145/3746027.3755131", "citation_count": 11, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Maricalce/UIS-Mamba", "venue": "ACM Multimedia", "quality_score": 0.386}
{"id": "77b1c927a5796667ffacf4c2cfb51e122fb117e6af8c919ae664fe630954816e", "sources": ["arxiv", "semantic_scholar"], "title": "Online Fine-Tuning of Carbon Emission Predictions using Real-Time Recurrent Learning for State Space Models", "abstract": "This paper introduces a new approach for fine-tuning the predictions of structured state space models (SSMs) at inference time using real-time recurrent learning. While SSMs are known for their efficiency and long-range modeling capabilities, they are typically trained offline and remain static during deployment. Our method enables online adaptation by continuously updating model parameters in response to incoming data. We evaluate our approach for linear-recurrent-unit SSMs using a small carbon emission dataset collected from embedded automotive hardware. Experimental results show that our method consistently reduces prediction error online during inference, demonstrating its potential for dynamic, resource-constrained environments.", "authors": ["Julian Lemmel", "Manuel Kranzl", "Adam Lamine", "Philipp Neubauer", "Radu Grosu", "Sophie Neubauer"], "categories": ["cs.CE", "cs.LG", "eess.SY"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2025-08-01", "url": "https://arxiv.org/abs/2508.00804", "pdf_url": "https://arxiv.org/pdf/2508.00804v1", "arxiv_id": "2508.00804", "doi": "10.1109/SMC58881.2025.11342456", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE International Conference on Systems, Man and Cybernetics", "quality_score": 0.2498}
{"id": "194a29100eb38a6d423bd8a348c354c04c75b69b0fd5c33358aeaf7f76d473d9", "sources": ["arxiv", "semantic_scholar"], "title": "Verification of the NOAA Space Weather Prediction Center solar flare forecast (1998-2024)", "abstract": "The NOAA Space Weather Prediction Center (SWPC) issues the official U.S. government forecast for M-class and X-class solar flares, yet the skill of these forecasts has never been comprehensively verified. In this study, we evaluate the SWPC probabilistic flare forecasts over a 26-year period (1998-2024), comparing them to several zero-cost and statistical baselines including persistence, climatology, Naive Bayes, and logistic regression. We find that the SWPC model does not outperform these baselines across key classification and probabilistic metrics and exhibits severe calibration issues and high false alarm rates, especially in high-stakes scenarios such as detecting the first flare after extended quiet periods. These findings demonstrate the need for more accurate and reliable eruption forecasting models which we suggest should be based on modern data-driven methods. The findings also provide a standard against which any proposed eruption prediction system should be compared. We suggest that space weather forecasters regularly update and publish analyses like the one demonstrated here to provide up-to-date standards of accuracy and reliability against which to compare new methods.", "authors": ["Enrico Camporeale", "Thomas E. Berger"], "categories": ["physics.space-ph"], "fields_of_study": ["Physics"], "published_date": "2025-08-01", "url": "https://arxiv.org/abs/2508.01114", "pdf_url": "https://arxiv.org/pdf/2508.01114v1", "arxiv_id": "2508.01114", "doi": "10.1029/2025SW004546", "citation_count": 2, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.159}
{"id": "8971c9553a6bb2e9ace72a1b7a1b3e3a27d3688a116cc26cc2a47d62263aad72", "sources": ["arxiv", "semantic_scholar"], "title": "MambaVesselNet++: A Hybrid CNN-Mamba Architecture for Medical Image Segmentation", "abstract": "Medical image segmentation plays an important role in computer-aided diagnosis. Traditional convolution-based U-shape segmentation architectures are usually limited by the local receptive field. Existing vision transformers have been widely applied to diverse medical segmentation frameworks due to their superior capabilities of capturing global contexts. Despite the advantage, the real-world application of vision transformers is challenged by their non-linear self-attention mechanism, requiring huge computational costs. To address this issue, the selective state space model (SSM) Mamba has gained recognition for its adeptness in modeling long-range dependencies in sequential data, particularly noted for its efficient memory costs. In this paper, we propose MambaVesselNet++, a Hybrid CNN-Mamba framework for medical image segmentation. Our MambaVesselNet++ is comprised of a hybrid image encoder (Hi-Encoder) and a bifocal fusion decoder (BF-Decoder). In Hi-Encoder, we first devise the texture-aware layer to capture low-level semantic features by leveraging convolutions. Then, we utilize Mamba to effectively model long-range dependencies with linear complexity. The Bi-Decoder adopts skip connections to combine local and global information of the Hi-Encoder for the accurate generation of segmentation masks. Extensive experiments demonstrate that MambaVesselNet++ outperforms current convolution-based, transformer-based, and Mamba-based state-of-the-arts across diverse medical 2D, 3D, and instance segmentation tasks. The code is available at https://github.com/CC0117/MambaVesselNet.", "authors": ["Qing Xu", "Yanming Chen", "Yue Li", "Ziyu Liu", "Zhenye Lou", "Yixuan Zhang", "Xiangjian He"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-26", "url": "https://arxiv.org/abs/2507.19931", "pdf_url": "https://arxiv.org/pdf/2507.19931v1", "arxiv_id": "2507.19931", "doi": "10.1145/3757324", "citation_count": 13, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/CC0117/MambaVesselNet", "venue": "ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)", "quality_score": 0.3754}
{"id": "e66d50e2e97e4f9a1c05380ef7c9471f5aa215ec1bf35d2001a22816856090c9", "sources": ["arxiv", "semantic_scholar"], "title": "SP-Mamba: Spatial-Perception State Space Model for Unsupervised Medical Anomaly Detection", "abstract": "Radiography imaging protocols target on specific anatomical regions, resulting in highly consistent images with recurrent structural patterns across patients. Recent advances in medical anomaly detection have demonstrated the effectiveness of CNN- and transformer-based approaches. However, CNNs exhibit limitations in capturing long-range dependencies, while transformers suffer from quadratic computational complexity. In contrast, Mamba-based models, leveraging superior long-range modeling, structural feature extraction, and linear computational efficiency, have emerged as a promising alternative. To capitalize on the inherent structural regularity of medical images, this study introduces SP-Mamba, a spatial-perception Mamba framework for unsupervised medical anomaly detection. The window-sliding prototype learning and Circular-Hilbert scanning-based Mamba are introduced to better exploit consistent anatomical patterns and leverage spatial information for medical anomaly detection. Furthermore, we excavate the concentration and contrast characteristics of anomaly maps for improving anomaly detection. Extensive experiments on three diverse medical anomaly detection benchmarks confirm the proposed method's state-of-the-art performance, validating its efficacy and robustness. The code is available at https://github.com/Ray-RuiPan/SP-Mamba.", "authors": ["Rui Pan", "Ruiying Lu"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-25", "url": "https://arxiv.org/abs/2507.19076", "pdf_url": "https://arxiv.org/pdf/2507.19076v1", "arxiv_id": "2507.19076", "doi": "10.1145/3746027.3755641", "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Ray-RuiPan/SP-Mamba", "venue": "ACM Multimedia", "quality_score": 0.3736}
{"id": "5804230ecf1777603ecdaa07f9ea7e6da75b90808bb4de1780bf4b9859456005", "sources": ["arxiv", "semantic_scholar"], "title": "A2Mamba: Attention-augmented State Space Models for Visual Recognition", "abstract": "Transformers and Mamba, initially invented for natural language processing, have inspired backbone architectures for visual recognition. Recent studies integrated Local Attention Transformers with Mamba to capture both local details and global contexts. Despite competitive performance, these methods are limited to simple stacking of Transformer and Mamba layers without any interaction mechanism between them. Thus, deep integration between Transformer and Mamba layers remains an open problem. We address this problem by proposing A2Mamba, a powerful Transformer-Mamba hybrid network architecture, featuring a new token mixer termed Multi-scale Attention-augmented State Space Model (MASS), where multi-scale attention maps are integrated into an attention-augmented SSM (A2SSM). A key step of A2SSM performs a variant of cross-attention by spatially aggregating the SSM's hidden states using the multi-scale attention maps, which enhances spatial dependencies pertaining to a two-dimensional space while improving the dynamic modeling capabilities of SSMs. Our A2Mamba outperforms all previous ConvNet-, Transformer-, and Mamba-based architectures in visual recognition tasks. For instance, A2Mamba-L achieves an impressive 86.1% top-1 accuracy on ImageNet-1K. In semantic segmentation, A2Mamba-B exceeds CAFormer-S36 by 2.5% in mIoU, while exhibiting higher efficiency. In object detection and instance segmentation with Cascade Mask R-CNN, A2Mamba-S surpasses MambaVision-B by 1.2%/0.9% in AP^b/AP^m, while having 40% less parameters. Code is publicly available at https://github.com/LMMMEng/A2Mamba.", "authors": ["Meng Lou", "Yunxiang Fu", "Yizhou Yu"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-22", "url": "https://arxiv.org/abs/2507.16624", "pdf_url": "https://arxiv.org/pdf/2507.16624v1", "arxiv_id": "2507.16624", "doi": "10.48550/arXiv.2507.16624", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/LMMMEng/A2Mamba", "venue": "arXiv.org", "quality_score": 0.3683}
{"id": "2ecd10d733bb5152776cde794e06f1dee147ca6d6ffddae58572c928539d5d99", "sources": ["arxiv", "semantic_scholar"], "title": "QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models", "abstract": "State space models (SSMs) reduce the quadratic complexity of transformers by leveraging linear recurrence. Recently, VMamba has emerged as a strong SSM-based vision backbone, yet remains bottlenecked by spatial redundancy in its four-directional scan. We propose QuarterMap, a post-training activation pruning method that removes redundant spatial activations before scanning and restores dimensions via nearest-neighbor upsampling. Our method improves throughput without retraining. On ImageNet-1K, QuarterMap achieves up to 11% speedup on VMamba with less than 0.9% accuracy drop, and yields similar gains on ADE20K segmentation. Beyond VMamba, we validate QuarterMap on MedMamba, a domain-specific model that shares the same four-directional scanning structure, where it consistently improves throughput while preserving accuracy across multiple medical imaging tasks. Compared to token merging methods like ToMe, QuarterMap is tailored for SSMs and avoids costly merge-unmerge operations. Our method offers a plug-and-play tool for deployment-time efficiency without compromising transferability.", "authors": ["Tien-Yu Chi", "Hung-Yueh Chiang", "Diana Marculescu", "Kai-Chiang Wu"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-13", "url": "https://arxiv.org/abs/2507.09514", "pdf_url": "https://arxiv.org/pdf/2507.09514v1", "arxiv_id": "2507.09514", "doi": "10.48550/arXiv.2507.09514", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.228}
{"id": "8b683d0a908131c338e440c10ae0be24938dc9a2e24dd814b0805e7732ca9683", "sources": ["arxiv", "semantic_scholar"], "title": "A forecasting framework for galactic cosmic ray flux in space weather applications", "abstract": "The intensity and energy spectrum of galactic cosmic rays in the heliosphere are significantly influenced by the 11-year solar cycle, a phenomenon known as solar modulation. Understanding this effect and its underlying physical mechanisms is essential for assessing radiation exposure and associated risks during space missions. Starting from a previously developed effective predictive model of solar modulation, validated using cosmic ray flux measurements from space-based detectors such as PAMELA and AMS-02, we build a generalizable forecasting strategy for the long-term evolution of cosmic ray fluxes. This strategy is based on identifying delayed cross-correlation relationships between solar proxies and the model's parameters. It integrates recent findings on time lags between cosmic ray fluxes and solar activity, and incorporates advanced time-series signal processing techniques. The framework not only performs well in reproducing observed data, but also shows strong potential for applications in space radiation monitoring and forecasting. By efficiently capturing the long-term variability of galactic cosmic rays, our approach contributes valuable insights for evaluating radiation risks, ultimately supporting safer and more effective space exploration.", "authors": ["David Pelosi", "Fernando Barão", "Bruna Bertucci", "Francesco Faldi", "Emanuele Fiandrini", "Alejandro Reina Conde", "Miguel Orcinha", "Nicola Tomassetti"], "categories": ["astro-ph.IM", "astro-ph.HE", "astro-ph.SR", "physics.space-ph"], "fields_of_study": ["Physics"], "published_date": "2025-07-10", "url": "https://arxiv.org/abs/2507.07616", "pdf_url": "https://arxiv.org/pdf/2507.07616v2", "arxiv_id": "2507.07616", "doi": "10.1016/j.asr.2025.08.022", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1429}
{"id": "d65d513064e4a46aca7b763e029a586c9da6cd87654f2b1fe6b6ff02bd781299", "sources": ["arxiv", "semantic_scholar"], "title": "Differential Mamba", "abstract": "Sequence models like Transformers and RNNs often overallocate attention to irrelevant context, leading to noisy intermediate representations. This degrades LLM capabilities by promoting hallucinations, weakening long-range and retrieval abilities, and reducing robustness. Recent work has shown that differential design can mitigate this issue in Transformers, improving their effectiveness across various applications. In this paper, we explore whether these techniques, originally developed for Transformers, can be applied to Mamba, a recent architecture based on selective state-space layers that achieves Transformer-level performance with greater efficiency. We show that a naive adaptation of differential design to Mamba is insufficient and requires careful architectural modifications. To address this, we introduce a novel differential mechanism for Mamba, empirically validated on language modeling benchmarks, demonstrating improved retrieval capabilities and superior performance over vanilla Mamba. Finally, we conduct extensive ablation studies and empirical analyses to justify our design choices and provide evidence that our approach effectively mitigates the overallocation problem in Mamba-based models. Our code is publicly available: https://github.com/NadavSc/Diff-Mamba", "authors": ["Nadav Schneider", "Itamar Zimerman", "Eliya Nachmani"], "categories": ["cs.LG", "cs.AI", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-08", "url": "https://arxiv.org/abs/2507.06204", "pdf_url": "https://arxiv.org/pdf/2507.06204v2", "arxiv_id": "2507.06204", "doi": "10.48550/arXiv.2507.06204", "citation_count": 3, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/NadavSc/Diff-Mamba", "venue": null, "quality_score": 0.2627}
{"id": "1c1b963e2957b27021b0a24ffb2b3ed5ba0fb1ae9dc648a01910d1b196b7baf6", "sources": ["arxiv", "semantic_scholar"], "title": "Bridging Expressivity and Scalability with Adaptive Unitary SSMs", "abstract": "Recent work has revealed that state space models (SSMs), while efficient for long-sequence processing, are fundamentally limited in their ability to represent formal languages-particularly due to time-invariant and real-valued recurrence structures. In this work, we draw inspiration from adaptive and structured dynamics observed in biological neural systems and introduce the Adaptive Unitary State Space Model (AUSSM): a novel class of SSMs that leverages skew-symmetric, input-dependent recurrence to achieve unitary evolution and high expressive power. Using algebraic automata theory, we prove that AUSSM can perform modulo counting and simulate solvable group automata at precision logarithmically bounded in the input length, enabling SSMs to model a broad class of regular languages out of reach for other SSM architectures. To overcome the practical inefficiencies of adaptive recurrence, we develop a separable convolution formulation and a CUDA implementation that enables scalable parallel training. Empirically, we show that AUSSM and its hybrid variant-interleaved with Mamba-outperform prior SSMs on formal algorithmic tasks such as parity and modular arithmetic, and achieve competent performance on real-world long time-series classification benchmarks. Our results demonstrate that adaptive unitary recurrence provides a powerful and efficient inductive bias for both symbolic and continuous sequence modeling. The code is available at https://github.com/arjunkaruvally/AUSSM", "authors": ["Arjun Karuvally", "Franz Nowak", "Anderson T. Keller", "Carmen Amo Alonso", "Terrence J. Sejnowski", "Hava T. Siegelmann"], "categories": ["cs.NE"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-07", "url": "https://arxiv.org/abs/2507.05238", "pdf_url": "https://arxiv.org/pdf/2507.05238v3", "arxiv_id": "2507.05238", "doi": "10.48550/arXiv.2507.05238", "citation_count": 5, "influential_citation_count": 2, "has_code": true, "code_url": "https://github.com/arjunkaruvally/AUSSM", "venue": "arXiv.org", "quality_score": 0.3418}
{"id": "233e289ebaea717acd5ec4bff2e916f46cd806408a202a2e8eddca7e86496d49", "sources": ["arxiv", "semantic_scholar"], "title": "DC-Mamber: A Dual Channel Prediction Model based on Mamba and Linear Transformer for Multivariate Time Series Forecasting", "abstract": "In multivariate time series forecasting (MTSF), existing strategies for processing sequences are typically categorized as channel-independent and channel-mixing. The former treats all temporal information of each variable as a token, focusing on capturing local temporal features of individual variables, while the latter constructs a token from the multivariate information at each time step, emphasizing the modeling of global temporal dependencies. Current mainstream models are mostly based on Transformer and the emerging Mamba. Transformers excel at modeling global dependencies through self-attention mechanisms but exhibit limited sensitivity to local temporal patterns and suffer from quadratic computational complexity, restricting their efficiency in long-sequence processing. In contrast, Mamba, based on state space models (SSMs), achieves linear complexity and efficient long-range modeling but struggles to aggregate global contextual information in parallel. To overcome the limitations of both models, we propose DC-Mamber, a dual-channel forecasting model based on Mamba and linear Transformer for time series forecasting. Specifically, the Mamba-based channel employs a channel-independent strategy to extract intra-variable features, while the Transformer-based channel adopts a channel-mixing strategy to model cross-timestep global dependencies. DC-Mamber first maps the raw input into two distinct feature representations via separate embedding layers. These representations are then processed by a variable encoder (built on Mamba) and a temporal encoder (built on linear Transformer), respectively. Finally, a fusion layer integrates the dual-channel features for prediction. Extensive experiments on eight public datasets confirm DC-Mamber's superior accuracy over existing models.", "authors": ["Bing Fan", "Shusen Ma", "Yun-Bo Zhao", "Yu Kang"], "categories": ["cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-06", "url": "https://arxiv.org/abs/2507.04381", "pdf_url": "https://arxiv.org/pdf/2507.04381v1", "arxiv_id": "2507.04381", "doi": "10.48550/arXiv.2507.04381", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.22}
{"id": "c43ef72391bece4d3e1b671c082e47d2e8ac6124c23dc9a0d4ed558d186e4be6", "sources": ["arxiv", "semantic_scholar"], "title": "MCST-Mamba: Multivariate Mamba-Based Model for Traffic Prediction", "abstract": "Accurate traffic prediction plays a vital role in intelligent transportation systems by enabling efficient routing, congestion mitigation, and proactive traffic control. However, forecasting is challenging due to the combined effects of dynamic road conditions, varying traffic patterns across different locations, and external influences such as weather and accidents. Traffic data often consists of several interrelated measurements - such as speed, flow and occupancy - yet many deep-learning approaches either predict only one of these variables or require a separate model for each. This limits their ability to capture joint patterns across channels. To address this, we introduce the Multi-Channel Spatio-Temporal (MCST) Mamba model, a forecasting framework built on the Mamba selective state-space architecture that natively handles multivariate inputs and simultaneously models all traffic features. The proposed MCST-Mamba model integrates adaptive spatio-temporal embeddings and separates the modeling of temporal sequences and spatial sensor interactions into two dedicated Mamba blocks, improving representation learning. Unlike prior methods that evaluate on a single channel, we assess MCST-Mamba across all traffic features at once, aligning more closely with how congestion arises in practice. Our results show that MCST-Mamba achieves strong predictive performance with a lower parameter count compared to baseline models.", "authors": ["Mohamed Hamad", "Mohamed Mabrok", "Nizar Zorba"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-05", "url": "https://arxiv.org/abs/2507.03927", "pdf_url": "https://arxiv.org/pdf/2507.03927v1", "arxiv_id": "2507.03927", "doi": "10.1109/GLOBECOM59602.2025.11432511", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Global Communications Conference", "quality_score": 0.2189}
{"id": "c4486bcc610bbe3596505f717f590e87a56a445448b2f9768d9831044bc9cbba", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking", "abstract": "Combining traditional RGB cameras with bio-inspired event cameras for robust object tracking has garnered increasing attention in recent years. However, most existing multimodal tracking algorithms depend heavily on high-complexity Vision Transformer architectures for feature extraction and fusion across modalities. This not only leads to substantial computational overhead but also limits the effectiveness of cross-modal interactions. In this paper, we propose an efficient RGB-Event object tracking framework based on the linear-complexity Vision Mamba network, termed Mamba-FETrack V2. Specifically, we first design a lightweight Prompt Generator that utilizes embedded features from each modality, together with a shared prompt pool, to dynamically generate modality-specific learnable prompt vectors. These prompts, along with the modality-specific embedded features, are then fed into a Vision Mamba-based FEMamba backbone, which facilitates prompt-guided feature extraction, cross-modal interaction, and fusion in a unified manner. Finally, the fused representations are passed to the tracking head for accurate target localization. Extensive experimental evaluations on multiple RGB-Event tracking benchmarks, including short-term COESOT dataset and long-term datasets, i.e., FE108 and FELT V2, demonstrate the superior performance and efficiency of the proposed tracking framework. The source code and pre-trained models will be released on https://github.com/Event-AHU/Mamba_FETrack", "authors": ["Shiao Wang", "Ju Huang", "Qingchuan Ma", "Jinfeng Gao", "Chunyi Xu", "Xiao Wang", "Lan Chen", "Bo Jiang"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-30", "url": "https://arxiv.org/abs/2506.23783", "pdf_url": "https://arxiv.org/pdf/2506.23783v1", "arxiv_id": "2506.23783", "doi": "10.48550/arXiv.2506.23783", "citation_count": 6, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Event-AHU/Mamba_FETrack", "venue": "arXiv.org", "quality_score": 0.3294}
{"id": "646f75fed8567a3c0f3b896caf1d7874ae42a67558e68a3049b82a5beac20842", "sources": ["arxiv", "semantic_scholar"], "title": "The Effect of Depth on the Expressivity of Deep Linear State-Space Models", "abstract": "Deep state-space models (SSMs) have gained increasing popularity in sequence modelling. While there are numerous theoretical investigations of shallow SSMs, how the depth of the SSM affects its expressiveness remains a crucial problem. In this paper, we systematically investigate the role of depth and width in deep linear SSMs, aiming to characterize how they influence the expressive capacity of the architecture. First, we rigorously prove that in the absence of parameter constraints, increasing depth and increasing width are generally equivalent, provided that the parameter count remains within the same order of magnitude. However, under the assumption that the parameter norms are constrained, the effects of depth and width differ significantly. We show that a shallow linear SSM with large parameter norms can be represented by a deep linear SSM with smaller norms using a constructive method. In particular, this demonstrates that deep SSMs are more capable of representing targets with large norms than shallow SSMs under norm constraints. Finally, we derive upper bounds on the minimal depth required for a deep linear SSM to represent a given shallow linear SSM under constrained parameter norms. We also validate our theoretical results with numerical experiments", "authors": ["Zeyu Bao", "Penghao Yu", "Haotian Jiang", "Qianxiao Li"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-24", "url": "https://arxiv.org/abs/2506.19296", "pdf_url": "https://arxiv.org/pdf/2506.19296v1", "arxiv_id": "2506.19296", "doi": "10.48550/arXiv.2506.19296", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2063}
{"id": "8171df4777dc5ad784dda7db09db6b3abf3160023fe777b1b91166ed18b2834c", "sources": ["arxiv", "semantic_scholar"], "title": "Diffusion Transformer-to-Mamba Distillation for High-Resolution Image Generation", "abstract": "The quadratic computational complexity of self-attention in diffusion transformers (DiT) introduces substantial computational costs in high-resolution image generation. While the linear-complexity Mamba model emerges as a potential alternative, direct Mamba training remains empirically challenging. To address this issue, this paper introduces diffusion transformer-to-mamba distillation (T2MD), forming an efficient training pipeline that facilitates the transition from the self-attention-based transformer to the linear complexity state-space model Mamba. We establish a diffusion self-attention and Mamba hybrid model that simultaneously achieves efficiency and global dependencies. With the proposed layer-level teacher forcing and feature-based knowledge distillation, T2MD alleviates the training difficulty and high cost of a state space model from scratch. Starting from the distilled 512$\\times$512 resolution base model, we push the generation towards 2048$\\times$2048 images via lightweight adaptation and high-resolution fine-tuning. Experiments demonstrate that our training path leads to low overhead but high-quality text-to-image generation. Importantly, our results also justify the feasibility of using sequential and causal Mamba models for generating non-causal visual output, suggesting the potential for future exploration.", "authors": ["Yuan Yao", "Yicong Hong", "Difan Liu", "Long Mai", "Feng Liu", "Jiebo Luo"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-23", "url": "https://arxiv.org/abs/2506.18999", "pdf_url": "https://arxiv.org/pdf/2506.18999v1", "arxiv_id": "2506.18999", "doi": "10.48550/arXiv.2506.18999", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2051}
{"id": "70975b8c57bd95b226981727407b06668d67bc171c6a48aca965f3802035deb4", "sources": ["arxiv", "semantic_scholar"], "title": "Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection", "abstract": "Linear State Space Models (SSMs) offer remarkable performance gains in efficient sequence modeling, with constant inference-time computation and memory complexity. Recent advances, such as Mamba, further enhance SSMs with input-dependent gating and hardware-aware implementations, positioning them as strong alternatives to Transformers for long sequence modeling. However, efficiently scaling the expressive power of SSMs, particularly with Mixture of Experts (MoE), remains challenging, as naive integration attempts often falter or degrade performance. In this work, we introduce Routing Mamba (RoM), a novel approach that scales SSM parameters using sparse mixtures of linear projection experts. By sharing routing decisions between projection layers and lightweight sub-modules within Mamba across experts, RoM leverages synergies among linear projection experts for effective and efficient sparse scaling of Mamba layers. At a scale of 1.3B active parameters (10B total) and 16K training sequence length, RoM achieves language modeling performance equivalent to a dense Mamba model requiring over 2.3x more active parameters, and demonstrates consistent perplexity across context lengths. Experimental results further show RoM effectively scales hybrid language models, yielding a 23% FLOPS saving compared to dense Mamba scaling for similar performance.", "authors": ["Zheng Zhan", "Liliang Ren", "Shuohang Wang", "Liyuan Liu", "Yang Liu", "Yeyun Gong", "Yanzhi Wang", "Yelong Shen"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-22", "url": "https://arxiv.org/abs/2506.18145", "pdf_url": "https://arxiv.org/pdf/2506.18145v1", "arxiv_id": "2506.18145", "doi": "10.48550/arXiv.2506.18145", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.204}
{"id": "3751b2e785d871796df33c2703e6bc118396b0fa82c630e25e01b85420a8bee8", "sources": ["arxiv", "semantic_scholar"], "title": "Memba: Membrane-driven Parameter-Efficient Fine-Tuning for Mamba", "abstract": "State Space Models (SSMs) have emerged as powerful alternatives to attention-based Transformers, with Mamba demonstrating impressive efficiency and scalability. As these models grow increasingly larger, the need for Parameter-Efficient Fine-Tuning (PEFT) methods becomes critical to adapt pre-trained Mamba to downstream tasks without prohibitive computational costs. However, previous approaches simply apply traditional Transformer-tailored PEFT methods without addressing the unique temporal processing dynamics of SSMs. To address this limitation, we propose Memba, a membrane-driven PEFT approach specifically designed for Mamba. Memba introduces Leaky Integrate Membrane (LIM) neurons as bio-inspired gating mechanisms that naturally accumulate membrane potentials over time, enhancing selective information retention. By strategically combining LIM neurons with Low-Rank Adaptations (LoRA) and cross-layer membrane transfer, our approach significantly improves Mamba's temporal modeling capabilities. Extensive experiments across language and vision tasks demonstrate that Memba achieves substantial improvements over existing PEFT methods. The code is available at https://github.com/Intelligent-Computing-Lab-Yale/Memba.", "authors": ["Donghyun Lee", "Yuhang Li", "Ruokai Yin", "Shiting Xiao", "Priyadarshini Panda"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-22", "url": "https://arxiv.org/abs/2506.18184", "pdf_url": "https://arxiv.org/pdf/2506.18184v2", "arxiv_id": "2506.18184", "doi": "10.48550/arXiv.2506.18184", "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Intelligent-Computing-Lab-Yale/Memba", "venue": "arXiv.org", "quality_score": 0.3152}
{"id": "c9e62b931f2a1adeeee4ad3cc35797a2c4f26e716c03f548ef47288f4c65f116", "sources": ["arxiv", "semantic_scholar"], "title": "LBMamba: Locally Bi-directional Mamba", "abstract": "Mamba, a State Space Model (SSM) that accelerates training by recasting recurrence as a parallel scan, has recently emerged as a linearly-scaling alternative to self-attention. Because of its unidirectional nature, each state in Mamba only has information of its previous states and is blind to states after. Current Mamba-based computer-vision methods typically overcome this by augmenting Mamba's global forward scan with a global backward scan, forming a bi-directional scan to restore a full receptive field. However, this operation doubles the computational load, eroding much of the efficiency advantage that originally Mamba have. To eliminate this extra scans, we introduce LBMamba, a locally bi-directional SSM block that embeds a lightweight locally backward scan inside the forward scan and executes it in per-thread registers. Building on LBMamba, we present LBVim, a backbone that alternates scan directions every two layers to recover a global receptive field without extra backward sweeps. We validate our approach on both natural images and whole slide images (WSIs) and show that it constantly offers a superior performance-throughput trade-off. Under the same throughput, LBVim achieves 0.8% to 1.6% higher top-1 accuracy on the ImageNet-1K classification dataset, 0.6% to 2.7% higher mIoU on the ADE20K semantic segmentation dataset, 0.9% higher APb and 1.1% higher APm on the COCO detection dataset. Our method also boosts the accuracy of four SOTA Mamba models, namely VMamba, LocalVim, PlainMamba and Adventurer, by 0.5% to 3.4%. We integrate LBMamba into the SOTA pathology multiple instance learning (MIL) model, MambaMIL, which is unidirectional. Experiments on 3 public WSI classification datasets show that our method achieves a relative improvement of up to 3.06% better AUC, 3.39% better F1, 1.67% better accuracy. Our code is available at https://github.com/cvlab-stonybrook/LBMamba.", "authors": ["Jingwei Zhang", "Xi Han", "Hong Qin", "Mahdi S. Hosseini", "Dimitris Samaras"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-19", "url": "https://arxiv.org/abs/2506.15976", "pdf_url": "https://arxiv.org/pdf/2506.15976v2", "arxiv_id": "2506.15976", "doi": "10.48550/arXiv.2506.15976", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/cvlab-stonybrook/LBMamba", "venue": null, "quality_score": 0.237}
{"id": "7d0cad8ce4ee64cc4b0013c56c7e0996310a69dd5c334ecbab17997295f9bad1", "sources": ["arxiv", "semantic_scholar"], "title": "An Exploration of Mamba for Speech Self-Supervised Models", "abstract": "While Mamba has demonstrated strong performance in language modeling, its potential as a speech self-supervised learning (SSL) model remains underexplored, with prior studies limited to isolated tasks. To address this, we explore Mamba-based HuBERT models as alternatives to Transformer-based SSL architectures. Leveraging the linear-time Selective State Space, these models enable fine-tuning on long-context ASR with significantly lower compute. Moreover, they show superior performance when fine-tuned for streaming ASR. Beyond fine-tuning, these models show competitive performance on SUPERB probing benchmarks, particularly in causal settings. Our analysis shows that they yield higher-quality quantized representations and capture speaker-related features more distinctly than Transformer-based models. These findings highlight Mamba-based SSL as a promising and complementary direction for long-sequence modeling, real-time speech modeling, and speech unit extraction. The codebase is available at https://github.com/hckuo145/Mamba-based-HuBERT.", "authors": ["Tzu-Quan Lin", "Heng-Cheng Kuo", "Tzu-Chieh Wei", "Hsi-Chun Cheng", "Chun Wei Chen", "Hsien-Fu Hsiao", "Yu Tsao", "Hung-yi Lee"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-14", "url": "https://arxiv.org/abs/2506.12606", "pdf_url": "https://arxiv.org/pdf/2506.12606v2", "arxiv_id": "2506.12606", "doi": "10.48550/arXiv.2506.12606", "citation_count": 2, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/hckuo145/Mamba-based-HuBERT", "venue": "arXiv.org", "quality_score": 0.301}
{"id": "ac867d5ab0a78e085cc1d25e2674768afaae9b1197bb2e87fe40c4dd2970f91d", "sources": ["arxiv", "semantic_scholar"], "title": "Understanding Input Selectivity in Mamba: Impact on Approximation Power, Memorization, and Associative Recall Capacity", "abstract": "State-Space Models (SSMs), and particularly Mamba, have recently emerged as a promising alternative to Transformers. Mamba introduces input selectivity to its SSM layer (S6) and incorporates convolution and gating into its block definition. While these modifications do improve Mamba's performance over its SSM predecessors, it remains largely unclear how Mamba leverages the additional functionalities provided by input selectivity, and how these interact with the other operations in the Mamba architecture. In this work, we demystify the role of input selectivity in Mamba, investigating its impact on function approximation power, long-term memorization, and associative recall capabilities. In particular: (i) we prove that the S6 layer of Mamba can represent projections onto Haar wavelets, providing an edge over its Diagonal SSM (S4D) predecessor in approximating discontinuous functions commonly arising in practice; (ii) we show how the S6 layer can dynamically counteract memory decay; (iii) we provide analytical solutions to the MQAR associative recall task using the Mamba architecture with different mixers -- Mamba, Mamba-2, and S4D. We demonstrate the tightness of our theoretical constructions with empirical results on concrete tasks. Our findings offer a mechanistic understanding of Mamba and reveal opportunities for improvement.", "authors": ["Ningyuan Huang", "Miguel Sarabia", "Abhinav Moudgil", "Pau Rodriguez", "Luca Zappella", "Federico Danieli"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-13", "url": "https://arxiv.org/abs/2506.11891", "pdf_url": "https://arxiv.org/pdf/2506.11891v1", "arxiv_id": "2506.11891", "doi": "10.48550/arXiv.2506.11891", "citation_count": 3, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "International Conference on Machine Learning", "quality_score": 0.1936}
{"id": "776cf931974ccf4083f689998c17a975ce9e6ec83de6edf7e6791edfc0b09d6f", "sources": ["arxiv", "semantic_scholar"], "title": "SparseSSM: Efficient Selective Structured State Space Models Can Be Pruned in One-Shot", "abstract": "State-space language models such as Mamba match Transformer quality while permitting linear complexity inference, yet still comprise billions of parameters that hinder deployment. Existing one-shot pruning methods are tailored to attention blocks and fail to account for the time-shared and discretized state-transition matrix at the heart of the selective state-space module (SSM). In this paper, we introduce SparseSSM, the first training-free pruning framework that extends the classic optimal brain surgeon (OBS) framework to state space architectures. Our layer-wise algorithm (i) derives an approximate second-order saliency score that aggregates Hessian-trace information across time steps, (ii) incorporates a component sensitivity analysis to guide feed-forward network (FFN) pruning, which also sheds light on where redundancy resides in mamba architecture, (iii) can be easily extended to semi-structured and structured sparsity. Empirically, we prune 50% of SSM weights without fine-tuning and observe no zero-shot accuracy loss, achieving the current state-of-the-art pruning algorithm for Mamba-based LLMs.", "authors": ["Kaiwen Tuo", "Huan Wang"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-11", "url": "https://arxiv.org/abs/2506.09613", "pdf_url": "https://arxiv.org/pdf/2506.09613v1", "arxiv_id": "2506.09613", "doi": "10.48550/arXiv.2506.09613", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2113}
{"id": "72c5813de7876fa8a4b3817e682637510fba18eb6a42416b7e6de6ba7bbfc873", "sources": ["arxiv", "semantic_scholar"], "title": "DM-SegNet: Dual-Mamba Architecture for 3D Medical Image Segmentation with Global Context Modeling", "abstract": "Accurate 3D medical image segmentation demands architectures capable of reconciling global context modeling with spatial topology preservation. While State Space Models (SSMs) like Mamba show potential for sequence modeling, existing medical SSMs suffer from encoder-decoder incompatibility: the encoder's 1D sequence flattening compromises spatial structures, while conventional decoders fail to leverage Mamba's state propagation. We present DM-SegNet, a Dual-Mamba architecture integrating directional state transitions with anatomy-aware hierarchical decoding. The core innovations include a quadri-directional spatial Mamba module employing four-directional 3D scanning to maintain anatomical spatial coherence, a gated spatial convolution layer that enhances spatially sensitive feature representation prior to state modeling, and a Mamba-driven decoding framework enabling bidirectional state synchronization across scales. Extensive evaluation on two clinically significant benchmarks demonstrates the efficacy of DM-SegNet: achieving state-of-the-art Dice Similarity Coefficient (DSC) of 85.44% on the Synapse dataset for abdominal organ segmentation and 90.22% on the BraTS2023 dataset for brain tumor segmentation.", "authors": ["Hangyu Ji"], "categories": ["eess.IV", "cs.CV"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2025-06-05", "url": "https://arxiv.org/abs/2506.05297", "pdf_url": "https://arxiv.org/pdf/2506.05297v1", "arxiv_id": "2506.05297", "doi": "10.48550/arXiv.2506.05297", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1845}
{"id": "264b5875e1b9c0da2f9b372840650b739d7088522daca4f6333f6765297610e1", "sources": ["arxiv", "semantic_scholar"], "title": "ss-Mamba: Semantic-Spline Selective State-Space Model", "abstract": "We propose ss-Mamba, a novel foundation model that enhances time series forecasting by integrating semantic-aware embeddings and adaptive spline-based temporal encoding within a selective state-space modeling framework. Building upon the recent success of Transformer architectures, ss-Mamba adopts the Mamba selective state space model as an efficient alternative that achieves comparable performance while significantly reducing computational complexity from quadratic to linear time. Semantic index embeddings, initialized from pretrained language models, allow effective generalization to previously unseen series through meaningful semantic priors. Additionally, spline-based Kolmogorov-Arnold Networks (KAN) dynamically and interpretably capture complex seasonalities and non-stationary temporal effects, providing a powerful enhancement over conventional temporal feature encodings. Extensive experimental evaluations confirm that ss-Mamba delivers superior accuracy, robustness, and interpretability, demonstrating its capability as a versatile and computationally efficient alternative to traditional Transformer-based models in time-series forecasting.", "authors": ["Zuochen Ye"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-03", "url": "https://arxiv.org/abs/2506.14802", "pdf_url": "https://arxiv.org/pdf/2506.14802v1", "arxiv_id": "2506.14802", "doi": "10.48550/arXiv.2506.14802", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1822}
{"id": "8b6b439af8f4b5badea95970a8b193439f1fe006f0598961a9e50bd380411f4f", "sources": ["arxiv", "semantic_scholar"], "title": "ECP-Mamba: An Efficient Multi-scale Self-supervised Contrastive Learning Method with State Space Model for PolSAR Image Classification", "abstract": "Recently, polarimetric synthetic aperture radar (PolSAR) image classification has been greatly promoted by deep neural networks. However,current deep learning-based PolSAR classification methods encounter difficulties due to its dependence on extensive labeled data and the computational inefficiency of architectures like Transformers. This paper presents ECP-Mamba, an efficient framework integrating multi-scale self-supervised contrastive learning with a state space model (SSM) backbone. Specifically, ECP-Mamba addresses annotation scarcity through a multi-scale predictive pretext task based on local-to-global feature correspondences, which uses a simplified self-distillation paradigm without negative sample pairs. To enhance computational efficiency,the Mamba architecture (a selective SSM) is first tailored for pixel-wise PolSAR classification task by designing a spiral scan strategy. This strategy prioritizes causally relevant features near the central pixel, leveraging the localized nature of pixel-wise classification tasks. Additionally, the lightweight Cross Mamba module is proposed to facilitates complementary multi-scale feature interaction with minimal overhead. Extensive experiments across four benchmark datasets demonstrate ECP-Mamba's effectiveness in balancing high accuracy with resource efficiency. On the Flevoland 1989 dataset, ECP-Mamba achieves state-of-the-art performance with an overall accuracy of 99.70%, average accuracy of 99.64% and Kappa coefficient of 99.62e-2. Our code will be available at https://github.com/HaixiaBi1982/ECP_Mamba.", "authors": ["Zuzheng Kuang", "Haixia Bi", "Chen Xu", "Jian Sun"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-01", "url": "https://arxiv.org/abs/2506.01040", "pdf_url": "https://arxiv.org/pdf/2506.01040v1", "arxiv_id": "2506.01040", "doi": "10.1109/TGRS.2025.3601583", "citation_count": 5, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/HaixiaBi1982/ECP_Mamba", "venue": "IEEE Transactions on Geoscience and Remote Sensing", "quality_score": 0.278}
{"id": "8c1867cb7daa2ac00c7a0804951b628f0024457c8d8f453e9299284e817571e5", "sources": ["arxiv", "semantic_scholar"], "title": "Weight-Space Linear Recurrent Neural Networks", "abstract": "We introduce WARP (Weight-space Adaptive Recurrent Prediction), a simple yet powerful model that unifies weight-space learning with linear recurrence to redefine sequence modeling. Unlike conventional recurrent neural networks (RNNs) which collapse temporal dynamics into fixed-dimensional hidden states, WARP explicitly parametrizes its hidden state as the weights and biases of a distinct auxiliary neural network, and uses input differences to drive its recurrence. This brain-inspired formulation enables efficient gradient-free adaptation of the auxiliary network at test-time, in-context learning abilities, and seamless integration of domain-specific physical priors. Empirical validation shows that WARP matches or surpasses state-of-the-art baselines on diverse classification tasks, featuring in the top three in 4 out of 6 real-world challenging datasets. Furthermore, extensive experiments across sequential image completion, multivariate time series forecasting, and dynamical system reconstruction demonstrate its expressiveness and generalisation capabilities. Remarkably, a physics-informed variant of our model outperforms the next best model by more than 10x. Ablation studies confirm the architectural necessity of key components, solidifying weight-space linear RNNs as a transformative paradigm for adaptive machine intelligence.", "authors": ["Roussel Desmond Nzoyem", "Nawid Keshtmand", "Enrique Crespo Fernandez", "Idriss Tsayem", "Raul Santos-Rodriguez", "David A. W. Barton", "Tom Deakin"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-01", "url": "https://arxiv.org/abs/2506.01153", "pdf_url": "https://arxiv.org/pdf/2506.01153v3", "arxiv_id": "2506.01153", "doi": "10.48550/arXiv.2506.01153", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1945}
{"id": "874e52828838f468316cc8e891a356c6678045d5a9b238734eee6674c6a960db", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba Drafters for Speculative Decoding", "abstract": "Speculative decoding has emerged as a promising approach to accelerating large language model (LLM) generation using a fast drafter while maintaining alignment with the target model's distribution. However, existing approaches face a trade-off: external drafters offer flexibility but can suffer from slower drafting, while self-speculation methods use drafters tailored to the target model but require re-training. In this paper, we introduce novel drafters based on Mamba, a state-of-the-art state space model (SSM), as a solution that combines the best aspects of both approaches. By leveraging the linear structure of SSMs, our approach avoids the quadratic complexity inherent in traditional Transformer-based methods, enabling faster drafting and lower memory usage while maintaining the flexibility to work across different target models. We further enhance efficiency with a novel test-time tree search algorithm for generating high-quality draft candidates. Our empirical evaluation demonstrates that Mamba-based drafters not only outperform existing external drafting methods but are also comparable to state-of-the-art self-speculation approaches while using less memory and maintaining their cross-model adaptability.", "authors": ["Daewon Choi", "Seunghyuk Oh", "Saket Dingliwal", "Jihoon Tack", "Kyuyoung Kim", "Woomin Song", "Seojin Kim", "Insu Han", "Jinwoo Shin", "Aram Galstyan", "Shubham Katiyar", "Sravan Babu Bodapati"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-01", "url": "https://arxiv.org/abs/2506.01206", "pdf_url": "https://arxiv.org/pdf/2506.01206v1", "arxiv_id": "2506.01206", "doi": "10.48550/arXiv.2506.01206", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Conference on Empirical Methods in Natural Language Processing", "quality_score": 0.1799}
{"id": "1e0fca43cba6b1b6b7a0f95327ffde506dec122031b8b508cc7b27340e9ee335", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba Knockout for Unraveling Factual Information Flow", "abstract": "This paper investigates the flow of factual information in Mamba State-Space Model (SSM)-based language models. We rely on theoretical and empirical connections to Transformer-based architectures and their attention mechanisms. Exploiting this relationship, we adapt attentional interpretability techniques originally developed for Transformers--specifically, the Attention Knockout methodology--to both Mamba-1 and Mamba-2. Using them we trace how information is transmitted and localized across tokens and layers, revealing patterns of subject-token information emergence and layer-wise dynamics. Notably, some phenomena vary between mamba models and Transformer based models, while others appear universally across all models inspected--hinting that these may be inherent to LLMs in general. By further leveraging Mamba's structured factorization, we disentangle how distinct \"features\" either enable token-to-token information exchange or enrich individual tokens, thus offering a unified lens to understand Mamba internal operations.", "authors": ["Nir Endy", "Idan Daniel Grosbard", "Yuval Ran-Milo", "Yonatan Slutzky", "Itay Tshuva", "Raja Giryes"], "categories": ["cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-30", "url": "https://arxiv.org/abs/2505.24244", "pdf_url": "https://arxiv.org/pdf/2505.24244v1", "arxiv_id": "2505.24244", "doi": "10.48550/arXiv.2505.24244", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Annual Meeting of the Association for Computational Linguistics", "quality_score": 0.1776}
{"id": "a7d2f6a217f7e4e78b804fb00996bb629932f8184fbeca04d03baf1e7a66bb4b", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba-Driven Topology Fusion for Monocular 3D Human Pose Estimation", "abstract": "Transformer-based methods for 3D human pose estimation face significant computational challenges due to the quadratic growth of self-attention mechanism complexity with sequence length. Recently, the Mamba model has substantially reduced computational overhead and demonstrated outstanding performance in modeling long sequences by leveraging state space model (SSM). However, the ability of SSM to process sequential data is not suitable for 3D joint sequences with topological structures, and the causal convolution structure in Mamba also lacks insight into local joint relationships. To address these issues, we propose the Mamba-Driven Topology Fusion framework in this paper. Specifically, the proposed Bone Aware Module infers the direction and length of bone vectors in the spherical coordinate system, providing effective topological guidance for the Mamba model in processing joint sequences. Furthermore, we enhance the convolutional structure within the Mamba model by integrating forward and backward graph convolutional network, enabling it to better capture local joint dependencies. Finally, we design a Spatiotemporal Refinement Module to model both temporal and spatial relationships within the sequence. Through the incorporation of skeletal topology, our approach effectively alleviates Mamba's limitations in capturing human structural relationships. We conduct extensive experiments on the Human3.6M and MPI-INF-3DHP datasets for testing and comparison, and the results show that the proposed method greatly reduces computational cost while achieving higher accuracy. Ablation studies further demonstrate the effectiveness of each proposed module. The code and models will be released.", "authors": ["Zenghao Zheng", "Lianping Yang", "Jinshan Pan", "Hegui Zhu"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-27", "url": "https://arxiv.org/abs/2505.20611", "pdf_url": "https://arxiv.org/pdf/2505.20611v2", "arxiv_id": "2505.20611", "doi": "10.48550/arXiv.2505.20611", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Image and Vision Computing", "quality_score": 0.1742}
{"id": "771a7cf504ed8349c9466a01f6435851f6899d8730fd2459373793dfbca2aa3b", "sources": ["arxiv", "semantic_scholar"], "title": "Revisiting Bi-Linear State Transitions in Recurrent Neural Networks", "abstract": "The role of hidden units in recurrent neural networks is typically seen as modeling memory, with research focusing on enhancing information retention through gating mechanisms. A less explored perspective views hidden units as active participants in the computation performed by the network, rather than passive memory stores. In this work, we revisit bilinear operations, which involve multiplicative interactions between hidden units and input embeddings. We demonstrate theoretically and empirically that they constitute a natural inductive bias for representing the evolution of hidden states in state tracking tasks. These are the simplest type of tasks that require hidden units to actively contribute to the behavior of the network. We also show that bilinear state updates form a natural hierarchy corresponding to state tracking tasks of increasing complexity, with popular linear recurrent networks such as Mamba residing at the lowest-complexity center of that hierarchy.", "authors": ["M. Reza Ebrahimi", "Roland Memisevic"], "categories": ["cs.LG", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-27", "url": "https://arxiv.org/abs/2505.21749", "pdf_url": "https://arxiv.org/pdf/2505.21749v2", "arxiv_id": "2505.21749", "doi": "10.48550/arXiv.2505.21749", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1742}
{"id": "b06eee5a7a0ae1fff8ab8ed5567160a5dd1aeb2c577d0d3019b1774bbad2445a", "sources": ["arxiv", "semantic_scholar"], "title": "Parallelization of Non-linear State-Space Models: Scaling Up Liquid-Resistance Liquid-Capacitance Networks for Efficient Sequence Modeling", "abstract": "We present LrcSSM, a $\\textit{non-linear}$ recurrent model that processes long sequences as fast as today's linear state-space layers. By forcing its Jacobian matrix to be diagonal, the full sequence can be solved in parallel, giving $\\mathcal{O}(TD)$ computational work and memory and only $\\mathcal{O}(\\log T)$ sequential depth, for input-sequence length $T$ and a state dimension $D$. Moreover, LrcSSM offers a formal gradient-stability guarantee that other input-varying systems such as Liquid-S4 and Mamba do not provide. Importantly, the diagonal Jacobian structure of our model results in no performance loss compared to the original model with dense Jacobian, and the approach can be generalized to other non-linear recurrent models, demonstrating broader applicability. On a suite of long-range forecasting tasks, we demonstrate that LrcSSM outperforms Transformers, LRU, S5, and Mamba.", "authors": ["Mónika Farsang", "Ramin Hasani", "Daniela Rus", "Radu Grosu"], "categories": ["cs.LG", "cs.AI", "cs.NE"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-27", "url": "https://arxiv.org/abs/2505.21717", "pdf_url": "https://arxiv.org/pdf/2505.21717v6", "arxiv_id": "2505.21717", "doi": null, "citation_count": 10, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2603}
{"id": "7f9dfc0aae27f0287c543e9729dc832e91878ac7fffc8070bf43fa8a19499f7c", "sources": ["arxiv", "semantic_scholar"], "title": "Sparsified State-Space Models are Efficient Highway Networks", "abstract": "State-space models (SSMs) offer a promising architecture for sequence modeling, providing an alternative to Transformers by replacing expensive self-attention with linear recurrences. In this paper, we propose a simple yet effective trick to enhance SSMs within given computational budgets by sparsifying them. Our intuition is that tokens in SSMs are highly redundant due to gradual recurrent updates, and dense recurrence operations block the delivery of past information. In particular, we observe that upper layers of SSMs tend to be more redundant as they encode global information, while lower layers encode local information. Motivated by this, we introduce Simba, a hierarchical sparsification method for SSMs based on token pruning. Simba sparsifies upper layers more than lower layers, encouraging the upper layers to behave like highways. To achieve this, we propose a novel token pruning criterion for SSMs, measuring the global impact of tokens on the final output by accumulating local recurrences. We demonstrate that Simba outperforms the baseline model, Mamba, with the same FLOPS in various natural language tasks. Moreover, we illustrate the effect of highways, showing that Simba not only enhances efficiency but also improves the information flow across long sequences. Code is available at https://github.com/woominsong/Simba.", "authors": ["Woomin Song", "Jihoon Tack", "Sangwoo Mo", "Seunghyuk Oh", "Jinwoo Shin"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-27", "url": "https://arxiv.org/abs/2505.20698", "pdf_url": "https://arxiv.org/pdf/2505.20698v1", "arxiv_id": "2505.20698", "doi": "10.48550/arXiv.2505.20698", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/woominsong/Simba", "venue": null, "quality_score": 0.2058}
{"id": "07c1d358ec5034e4d7ebb6698e4bc63fe2933f72630aec3f65f8ba1b5b423efa", "sources": ["arxiv", "semantic_scholar"], "title": "Geometric SSM: LTI State Space Models for Selective Tasks", "abstract": "A key claim in recent work on Selective State Space Models is that selectivity, the ability to focus on relevant information while filtering irrelevant inputs, requires breaking the Linear Time-Invariant (LTI) property through time-varying dynamics. We challenge this claim by demonstrating that LTI systems can achieve selectivity when designed using principles from geometric control. We introduce the Geometric SSM, in which different input patterns excite distinct invariant subspaces of the dynamics. Unlike Mamba's memoryless selection mechanism, our approach employs a dynamic residual generator that maintains temporal memory, enabling recognition of multi-token patterns without time-varying system matrices. The Geometric SSM achieves near-perfect performance on a novel extended induction head task where Mamba fails, while preserving efficient FFT-based training. Our results demonstrate that geometric control theory can inform the design of novel selective sequence models that combine theoretical rigor with practical efficiency.", "authors": ["Umberto Casti", "Giacomo Baggio", "Sandro Zampieri", "Fabio Pasqualetti"], "categories": ["eess.SY", "cs.LG"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2025-05-23", "url": "https://arxiv.org/abs/2505.17932", "pdf_url": "https://arxiv.org/pdf/2505.17932v2", "arxiv_id": "2505.17932", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1079}
{"id": "2ee64f156722738d59846a03a627518d6db6ac45dc5aba499adf81a789e83fe9", "sources": ["arxiv", "semantic_scholar"], "title": "FR-Mamba: Time-Series Physical Field Reconstruction Based on State Space Model", "abstract": "Physical field reconstruction (PFR) aims to predict the state distribution of physical quantities (e.g., velocity, pressure, and temperature) based on limited sensor measurements. It plays a critical role in domains such as fluid dynamics and thermodynamics. However, existing deep learning methods often fail to capture long-range temporal dependencies, resulting in suboptimal performance on time-evolving physical systems. To address this, we propose FR-Mamba, a novel spatiotemporal flow field reconstruction framework based on state space modeling. Specifically, we design a hybrid neural network architecture that combines Fourier Neural Operator (FNO) and State Space Model (SSM) to capture both global spatial features and long-range temporal dependencies. We adopt Mamba, a recently proposed efficient SSM architecture, to model long-range temporal dependencies with linear time complexity. In parallel, the FNO is employed to capture non-local spatial features by leveraging frequency-domain transformations. The spatiotemporal representations extracted by these two components are then fused to reconstruct the full-field distribution of the physical system. Extensive experiments demonstrate that our approach significantly outperforms existing PFR methods in flow field reconstruction tasks, achieving high-accuracy performance on long sequences.", "authors": ["Jiahuan Long", "Wenzhe Zhang", "Ning Wang", "Tingsong Jiang", "Wen Yao"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-21", "url": "https://arxiv.org/abs/2505.16083", "pdf_url": "https://arxiv.org/pdf/2505.16083v1", "arxiv_id": "2505.16083", "doi": "10.48550/arXiv.2505.16083", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1673}
{"id": "d750eab7f146b41d00c7fd0463a6c5fbab0a861a8307d83a0a8144098e48861b", "sources": ["arxiv", "semantic_scholar"], "title": "Mechanistic evaluation of Transformers and state space models", "abstract": "State space models (SSMs) for language modelling promise an efficient and performant alternative to quadratic-attention Transformers, yet show variable performance on recalling basic information from the context. While performance on synthetic tasks like Associative Recall (AR) can point to this deficiency, behavioural metrics provide little information as to \\textit{why} -- on a mechanistic level -- certain architectures fail and others succeed. To address this, we conduct experiments on AR, and find that only Transformers and Based SSM models fully succeed at AR, with Mamba and DeltaNet close behind, while the other SSMs (H3, Hyena) fail. We then use causal interventions to explain why. We find that Transformers and Based learn to store key-value associations in-context using induction. By contrast, the SSMs seem to compute these associations only at the last state using a single layer. We further investigate the mechanism underlying the success of Mamba, and find novel evidence that Mamba \\textit{does} implement induction: not via the SSM, but instead via short convolutions. Further experiments on a new hierarchical retrieval task, Associative Treecall (ATR), show that all architectures learn the same mechanism as they did for AR. Furthermore, we show that Mamba can learn Attention-like induction on ATR when short convolutions are removed. These results reveal that architectures with similar accuracy may still have substantive differences, motivating the adoption of mechanistic evaluations.", "authors": ["Aryaman Arora", "Neil Rathi", "Nikil Roashan Selvam", "Róbert Csordás", "Dan Jurafsky", "Christopher Potts"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-21", "url": "https://arxiv.org/abs/2505.15105", "pdf_url": "https://arxiv.org/pdf/2505.15105v3", "arxiv_id": "2505.15105", "doi": "10.48550/arXiv.2505.15105", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2113}
{"id": "7c1b2a5943c83fbd710b3e3e56eab974e6927db2a7766f7454bfc981370b3d30", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba-Adaptor: State Space Model Adaptor for Visual Recognition", "abstract": "Recent State Space Models (SSM), especially Mamba, have demonstrated impressive performance in visual modeling and possess superior model efficiency. However, the application of Mamba to visual tasks suffers inferior performance due to three main constraints existing in the sequential model: 1) Casual computing is incapable of accessing global context; 2) Long-range forgetting when computing the current hidden states; 3) Weak spatial structural modeling due to the transformed sequential input. To address these issues, we investigate a simple yet powerful vision task Adaptor for Mamba models, which consists of two functional modules: Adaptor-T and Adaptor-S. When solving the hidden states for SSM, we apply a lightweight prediction module Adaptor-T to select a set of learnable locations as memory augmentations to ease long-range forgetting issues. Moreover, we leverage Adapator-S, composed of multi-scale dilated convolutional kernels, to enhance the spatial modeling and introduce the image inductive bias into the feature output. Both modules can enlarge the context modeling in casual computing, as the output is enhanced by the inaccessible features. We explore three usages of Mamba-Adaptor: A general visual backbone for various vision tasks; A booster module to raise the performance of pretrained backbones; A highly efficient fine-tuning module that adapts the base model for transfer learning tasks. Extensive experiments verify the effectiveness of Mamba-Adaptor in three settings. Notably, our Mamba-Adaptor achieves state-of the-art performance on the ImageNet and COCO benchmarks.", "authors": ["Fei Xie", "Jiahao Nie", "Yujin Tang", "Wenkang Zhang", "Hongshen Zhao"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-19", "url": "https://arxiv.org/abs/2505.12685", "pdf_url": "https://arxiv.org/pdf/2505.12685v1", "arxiv_id": "2505.12685", "doi": "10.1109/CVPR52734.2025.01874", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Computer Vision and Pattern Recognition", "quality_score": 0.165}
{"id": "0153eb4b507a83dfcb4ff47ad0548ba4e57051620916ea89de7f7e2b2e4a3205", "sources": ["arxiv", "semantic_scholar"], "title": "WaLRUS: Wavelets for Long-range Representation Using SSMs", "abstract": "State-Space Models (SSMs) have proven to be powerful tools for modeling long-range dependencies in sequential data. While the recent method known as HiPPO has demonstrated strong performance, and formed the basis for machine learning models S4 and Mamba, it remains limited by its reliance on closed-form solutions for a few specific, well-behaved bases. The SaFARi framework generalized this approach, enabling the construction of SSMs from arbitrary frames, including non-orthogonal and redundant ones, thus allowing an infinite diversity of possible \"species\" within the SSM family. In this paper, we introduce WaLRUS (Wavelets for Long-range Representation Using SSMs), a new implementation of SaFARi built from Daubechies wavelets.", "authors": ["Hossein Babaei", "Mel White", "Sina Alemohammad", "Richard G. Baraniuk"], "categories": ["eess.IV", "cs.LG", "eess.AS", "eess.SP", "eess.SY"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2025-05-17", "url": "https://arxiv.org/abs/2505.12161", "pdf_url": "https://arxiv.org/pdf/2505.12161v1", "arxiv_id": "2505.12161", "doi": "10.48550/arXiv.2505.12161", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1627}
{"id": "a57c874c38bc7ba8e661278350169ae2dcd22298930259ea7984fc8ebd1264ea", "sources": ["arxiv", "semantic_scholar"], "title": "Learning to Dissipate Energy in Oscillatory State-Space Models", "abstract": "State-space models (SSMs) are a class of networks for sequence learning that benefit from fixed state size and linear complexity with respect to sequence length, contrasting the quadratic scaling of typical attention mechanisms. Inspired from observations in neuroscience, Linear Oscillatory State-Space models (LinOSS) are a recently proposed class of SSMs constructed from layers of discretized forced harmonic oscillators. Although these models perform competitively, leveraging fast parallel scans over diagonal recurrent matrices and achieving state-of-the-art performance on tasks with sequence length up to 50k, LinOSS models rely on rigid energy dissipation (\"forgetting\") mechanisms that are inherently coupled to the time scale of state evolution. As forgetting is a crucial mechanism for long-range reasoning, we demonstrate the representational limitations of these models and introduce Damped Linear Oscillatory State-Space models (D-LinOSS), a more general class of oscillatory SSMs that learn to dissipate latent state energy on arbitrary time scales. We analyze the spectral distribution of the model's recurrent matrices and prove that the SSM layers exhibit stable dynamics under a simple, flexible parameterization. Without introducing additional complexity, D-LinOSS consistently outperforms previous LinOSS methods on long-range learning tasks, achieves faster convergence, and reduces the hyperparameter search space by 50%.", "authors": ["Jared Boyer", "T. Konstantin Rusch", "Daniela Rus"], "categories": ["cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2025-05-17", "url": "https://arxiv.org/abs/2505.12171", "pdf_url": "https://arxiv.org/pdf/2505.12171v2", "arxiv_id": "2505.12171", "doi": "10.48550/arXiv.2505.12171", "citation_count": 4, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1747}
{"id": "0e8f60d9a1707d145b9eeed544a5bcf37de70abe6490defdd0c38e1f67af2a34", "sources": ["arxiv", "semantic_scholar"], "title": "HMamba: Hyperbolic Mamba for Sequential Recommendation", "abstract": "Sequential recommendation systems have become a cornerstone of personalized services, adept at modeling the temporal evolution of user preferences by capturing dynamic interaction sequences. Existing approaches predominantly rely on traditional models, including RNNs and Transformers. Despite their success in local pattern recognition, Transformer-based methods suffer from quadratic computational complexity and a tendency toward superficial attention patterns, limiting their ability to infer enduring preference hierarchies in sequential recommendation data. Recent advances in Mamba-based sequential models introduce linear-time efficiency but remain constrained by Euclidean geometry, failing to leverage the intrinsic hyperbolic structure of recommendation data. To bridge this gap, we propose Hyperbolic Mamba, a novel architecture that unifies the efficiency of Mamba's selective state space mechanism with hyperbolic geometry's hierarchical representational power. Our framework introduces (1) a hyperbolic selective state space that maintains curvature-aware sequence modeling and (2) stabilized Riemannian operations to enable scalable training. Experiments across four benchmarks demonstrate that Hyperbolic Mamba achieves 3-11% improvement while retaining Mamba's linear-time efficiency, enabling real-world deployment. This work establishes a new paradigm for efficient, hierarchy-aware sequential modeling.", "authors": ["Qianru Zhang", "Honggang Wen", "Wei Yuan", "Crystal Chen", "Menglin Yang", "Siu-Ming Yiu", "Hongzhi Yin"], "categories": ["cs.IR"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-14", "url": "https://arxiv.org/abs/2505.09205", "pdf_url": "https://arxiv.org/pdf/2505.09205v1", "arxiv_id": "2505.09205", "doi": "10.1145/3811405", "citation_count": 10, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2603}
{"id": "6095be62d43d60e31072f634932fd99484526b5f62a70effcb32daa4c69885fa", "sources": ["arxiv", "semantic_scholar"], "title": "Efficient Unstructured Pruning of Mamba State-Space Models for Resource-Constrained Environments", "abstract": "State-space models (SSMs), particularly the Mamba architecture, have emerged as powerful alternatives to Transformers for sequence modeling, offering linear-time complexity and competitive performance across diverse tasks. However, their large parameter counts pose significant challenges for deployment in resource-constrained environments. We propose a novel unstructured pruning framework tailored for Mamba models that achieves up to 70\\% parameter reduction while retaining over 95\\% of the original performance. Our approach integrates three key innovations: (1) a gradient-aware magnitude pruning technique that combines weight magnitude and gradient information to identify less critical parameters, (2) an iterative pruning schedule that gradually increases sparsity to maintain model stability, and (3) a global pruning strategy that optimizes parameter allocation across the entire model. Through extensive experiments on WikiText-103, Long Range Arena, and ETT time-series benchmarks, we demonstrate significant efficiency gains with minimal performance degradation. Our analysis of pruning effects on Mamba's components reveals critical insights into the architecture's redundancy and robustness, enabling practical deployment in resource-constrained settings while broadening Mamba's applicability.", "authors": ["Ibne Farabi Shihab", "Sanjeda Akter", "Anuj Sharma"], "categories": ["cs.LG", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-13", "url": "https://arxiv.org/abs/2505.08299", "pdf_url": "https://arxiv.org/pdf/2505.08299v2", "arxiv_id": "2505.08299", "doi": "10.48550/arXiv.2505.08299", "citation_count": 9, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Conference on Empirical Methods in Natural Language Processing", "quality_score": 0.25}
{"id": "81a206cd38404c59c5f3ff511aad7a12b5a8974665c9ef589eae52d4f69a1148", "sources": ["arxiv", "semantic_scholar"], "title": "Block-Biased Mamba for Long-Range Sequence Processing", "abstract": "Mamba extends earlier state space models (SSMs) by introducing input-dependent dynamics, and has demonstrated strong empirical performance across a range of domains, including language modeling, computer vision, and foundation models. However, a surprising weakness remains: despite being built on architectures designed for long-range dependencies, Mamba performs poorly on long-range sequential tasks. Understanding and addressing this gap is important for improving Mamba's universality and versatility. In this work, we analyze Mamba's limitations through three perspectives: expressiveness, inductive bias, and training stability. Our theoretical results show how Mamba falls short in each of these aspects compared to earlier SSMs such as S4D. To address these issues, we propose $\\text{B}_2\\text{S}_6$, a simple extension of Mamba's S6 unit that combines block-wise selective dynamics with a channel-specific bias. We prove that these changes equip the model with a better-suited inductive bias and improve its expressiveness and stability. Empirically, $\\text{B}_2\\text{S}_6$ outperforms S4 and S4D on Long-Range Arena (LRA) tasks while maintaining Mamba's performance on language modeling benchmarks.", "authors": ["Annan Yu", "N. Benjamin Erichson"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2025-05-13", "url": "https://arxiv.org/abs/2505.09022", "pdf_url": "https://arxiv.org/pdf/2505.09022v1", "arxiv_id": "2505.09022", "doi": "10.48550/arXiv.2505.09022", "citation_count": 9, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.25}
{"id": "8bdbb328527a0a305c24e150edd566ddfeea0e097fe1d6f376fbecd17d77cf6c", "sources": ["arxiv", "semantic_scholar"], "title": "PRE-Mamba: A 4D State Space Model for Ultra-High-Frequent Event Camera Deraining", "abstract": "Event cameras excel in high temporal resolution and dynamic range but suffer from dense noise in rainy conditions. Existing event deraining methods face trade-offs between temporal precision, deraining effectiveness, and computational efficiency. In this paper, we propose PRE-Mamba, a novel point-based event camera deraining framework that fully exploits the spatiotemporal characteristics of raw event and rain. Our framework introduces a 4D event cloud representation that integrates dual temporal scales to preserve high temporal precision, a Spatio-Temporal Decoupling and Fusion module (STDF) that enhances deraining capability by enabling shallow decoupling and interaction of temporal and spatial information, and a Multi-Scale State Space Model (MS3M) that captures deeper rain dynamics across dual-temporal and multi-spatial scales with linear computational complexity. Enhanced by frequency-domain regularization, PRE-Mamba achieves superior performance (0.95 SR, 0.91 NR, and 0.4s/M events) with only 0.26M parameters on EventRain-27K, a comprehensive dataset with labeled synthetic and real-world sequences. Moreover, our method generalizes well across varying rain intensities, viewpoints, and even snowy conditions.", "authors": ["Ciyu Ruan", "Ruishan Guo", "Zihang Gong", "Jingao Xu", "Wenhan Yang", "Xinlei Chen"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-08", "url": "https://arxiv.org/abs/2505.05307", "pdf_url": "https://arxiv.org/pdf/2505.05307v2", "arxiv_id": "2505.05307", "doi": "10.1109/ICCV51701.2025.00857", "citation_count": 12, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE International Conference on Computer Vision", "quality_score": 0.2785}
{"id": "43ff19e387095e712b35c64e6842e35f652d36982b109940bf53a470afdd242b", "sources": ["arxiv", "semantic_scholar"], "title": "Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook", "abstract": "Deep learning has profoundly transformed remote sensing, yet prevailing architectures like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) remain constrained by critical trade-offs: CNNs suffer from limited receptive fields, while ViTs grapple with quadratic computational complexity, hindering their scalability for high-resolution remote sensing data. State Space Models (SSMs), particularly the recently proposed Mamba architecture, have emerged as a paradigm-shifting solution, combining linear computational scaling with global context modeling. This survey presents a comprehensive review of Mamba-based methodologies in remote sensing, systematically analyzing about 120 Mamba-based remote sensing studies to construct a holistic taxonomy of innovations and applications. Our contributions are structured across five dimensions: (i) foundational principles of vision Mamba architectures, (ii) micro-architectural advancements such as adaptive scan strategies and hybrid SSM formulations, (iii) macro-architectural integrations, including CNN-Transformer-Mamba hybrids and frequency-domain adaptations, (iv) rigorous benchmarking against state-of-the-art methods in multiple application tasks, such as object detection, semantic segmentation, change detection, etc. and (v) critical analysis of unresolved challenges with actionable future directions. By bridging the gap between SSM theory and remote sensing practice, this survey establishes Mamba as a transformative framework for remote sensing analysis. To our knowledge, this paper is the first systematic review of Mamba architectures in remote sensing. Our work provides a structured foundation for advancing research in remote sensing systems through SSM-based methods. We curate an open-source repository (https://github.com/BaoBao0926/Awesome-Mamba-in-Remote-Sensing) to foster community-driven advancements.", "authors": ["Muyi Bao", "Shuchang Lyu", "Zhaoyang Xu", "Huiyu Zhou", "Jinchang Ren", "Shiming Xiang", "Xiangtai Li", "Guangliang Cheng"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-01", "url": "https://arxiv.org/abs/2505.00630", "pdf_url": "https://arxiv.org/pdf/2505.00630v2", "arxiv_id": "2505.00630", "doi": "10.48550/arXiv.2505.00630", "citation_count": 27, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/BaoBao0926/Awesome-Mamba-in-Remote-Sensing", "venue": "Remote Sensing", "quality_score": 0.3618}
{"id": "b498a7ca0fdd43714016a82f3f3eaaaa94a5e0f430148607a41babb29732c40f", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation", "abstract": "To segment medical images with distribution shifts, domain generalization (DG) has emerged as a promising setting to train models on source domains that can generalize to unseen target domains. Existing DG methods are mainly based on CNN or ViT architectures. Recently, advanced state space models, represented by Mamba, have shown promising results in various supervised medical image segmentation. The success of Mamba is primarily owing to its ability to capture long-range dependencies while keeping linear complexity with input sequence length, making it a promising alternative to CNNs and ViTs. Inspired by the success, in the paper, we explore the potential of the Mamba architecture to address distribution shifts in DG for medical image segmentation. Specifically, we propose a novel Mamba-based framework, Mamba-Sea, incorporating global-to-local sequence augmentation to improve the model's generalizability under domain shift issues. Our Mamba-Sea introduces a global augmentation mechanism designed to simulate potential variations in appearance across different sites, aiming to suppress the model's learning of domain-specific information. At the local level, we propose a sequence-wise augmentation along input sequences, which perturbs the style of tokens within random continuous sub-sequences by modeling and resampling style statistics associated with domain shifts. To our best knowledge, Mamba-Sea is the first work to explore the generalization of Mamba for medical image segmentation, providing an advanced and promising Mamba-based architecture with strong robustness to domain shifts. Remarkably, our proposed method is the first to surpass a Dice coefficient of 90% on the Prostate dataset, which exceeds previous SOTA of 88.61%. The code is available at https://github.com/orange-czh/Mamba-Sea.", "authors": ["Zihan Cheng", "Jintao Guo", "Jian Zhang", "Lei Qi", "Luping Zhou", "Yinghuan Shi", "Yang Gao"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science", "Medicine"], "published_date": "2025-04-24", "url": "https://arxiv.org/abs/2504.17515", "pdf_url": "https://arxiv.org/pdf/2504.17515v1", "arxiv_id": "2504.17515", "doi": "10.1109/TMI.2025.3564765", "citation_count": 26, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/orange-czh/Mamba-Sea", "venue": "IEEE Transactions on Medical Imaging", "quality_score": 0.3578}
{"id": "95657b78a4a0284c22fca74b9433f5f995976e75d7efca1740cbf058a0dadd73", "sources": ["arxiv", "semantic_scholar"], "title": "U-Shape Mamba: State Space Model for faster diffusion", "abstract": "Diffusion models have become the most popular approach for high-quality image generation, but their high computational cost still remains a significant challenge. To address this problem, we propose U-Shape Mamba (USM), a novel diffusion model that leverages Mamba-based layers within a U-Net-like hierarchical structure. By progressively reducing sequence length in the encoder and restoring it in the decoder through Mamba blocks, USM significantly lowers computational overhead while maintaining strong generative capabilities. Experimental results against Zigma, which is currently the most efficient Mamba-based diffusion model, demonstrate that USM achieves one-third the GFlops, requires less memory and is faster, while outperforming Zigma in image quality. Frechet Inception Distance (FID) is improved by 15.3, 0.84 and 2.7 points on AFHQ, CelebAHQ and COCO datasets, respectively. These findings highlight USM as a highly efficient and scalable solution for diffusion-based generative models, making high-quality image synthesis more accessible to the research community while reducing computational costs.", "authors": ["Alex Ergasti", "Filippo Botti", "Tomaso Fontanini", "Claudio Ferrari", "Massimo Bertozzi", "Andrea Prati"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-04-18", "url": "https://arxiv.org/abs/2504.13499", "pdf_url": "https://arxiv.org/pdf/2504.13499v2", "arxiv_id": "2504.13499", "doi": "10.1109/CVPRW67362.2025.00307", "citation_count": 8, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/ErgastiAlex/U-Shape-Mamba", "venue": null, "quality_score": 0.2386}
{"id": "91b47170669ee0de9d485dba938397361fc843ad5cf0ce581dd0d2ec122a450a", "sources": ["arxiv", "semantic_scholar"], "title": "RadMamba: Efficient Human Activity Recognition through Radar-based Micro-Doppler-Oriented Mamba State-Space Model", "abstract": "Radar-based Human Activity Recognition (HAR) is an attractive alternative to wearables and cameras because it preserves privacy, and is contactless and robust to occlusions. However, dominant Convolutional Neural Network (CNN)- and Recurrent Neural Network (RNN)-based solutions are computationally intensive at deployment, and recent lightweight Vision Transformer (ViT) and State Space Model (SSM) variants still exhibit substantial complexity. In this paper, we present RadMamba, a parameter-efficient, micro-Doppler-oriented Mamba SSM tailored to radar HAR under on-sensor compute, latency, and energy constraints typical of distributed radar systems. RadMamba combines (i) channel fusion with downsampling, (ii) Doppler-aligned segmentation that preserves the physical continuity of Doppler over time, and (iii) convolutional token projections that better capture Doppler-span variations, thereby retaining temporal-Doppler structure while reducing the number of Floating-point Operations per Inference (#FLOP/Inf.). Evaluated across three datasets with different radars and types of activities, RadMamba matches the prior best 99.8% accuracy of a recent SSM-based model on the Continuous Wave (CW) radar dataset, while requiring only 1/400 of its parameters. On a dataset of non-continuous activities with Frequency Modulated Continuous Wave (FMCW) radar, RadMamba remains competitive with leading 92.0% results using about 1/10 of the parameters, and on a continuous FMCW radar dataset it surpasses methods with far more parameters by at least 3%, using only 6.7k parameters. Code: https://github.com/lab-emi/AIRHAR.", "authors": ["Yizhuo Wu", "Francesco Fioranelli", "Chang Gao"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-04-16", "url": "https://arxiv.org/abs/2504.12039", "pdf_url": "https://arxiv.org/pdf/2504.12039v3", "arxiv_id": "2504.12039", "doi": "10.1109/TRS.2025.3648848", "citation_count": 4, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/lab-emi/AIRHAR", "venue": null, "quality_score": 0.1747}
{"id": "68904a8cea565ede5e135be39d35d5b30abe0eba9510dc04edbd07434aa0033d", "sources": ["arxiv", "semantic_scholar"], "title": "Minitron-SSM: Efficient Hybrid Language Model Compression through Group-Aware SSM Pruning", "abstract": "Hybrid LLM architectures that combine Attention and State Space Models (SSMs) achieve state-of-the-art accuracy and runtime performance. Recent work has demonstrated that applying compression and distillation to Attention-only models yields smaller, more accurate models at a fraction of the training cost. In this work, we explore the effectiveness of compressing Hybrid architectures. We introduce a novel group-aware pruning strategy that preserves the structural integrity of SSM blocks and their sequence modeling capabilities. Furthermore, we demonstrate the necessity of such SSM pruning to achieve improved accuracy and inference speed compared to traditional approaches. Our compression recipe combines SSM, FFN, embedding dimension, and layer pruning, followed by knowledge distillation-based retraining, similar to the MINITRON technique. Using this approach, we compress the Nemotron-H 8B Hybrid model down to 4B parameters with up to 40x fewer training tokens. The resulting model surpasses the accuracy of similarly-sized models while achieving 2x faster inference, significantly advancing the Pareto frontier.", "authors": ["Ali Taghibakhshi", "Sharath Turuvekere Sreenivas", "Saurav Muralidharan", "Marcin Chochowski", "Yashaswi Karnati", "Raviraj Joshi", "Ameya Sunil Mahabaleshwarkar", "Zijia Chen", "Yoshi Suhara", "Oluwatobi Olabiyi", "Daniel Korzekwa", "Mostofa Patwary", "Mohammad Shoeybi", "Jan Kautz", "Bryan Catanzaro", "Ashwath Aithal", "Nima Tajbakhsh", "Pavlo Molchanov"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-04-15", "url": "https://arxiv.org/abs/2504.11409", "pdf_url": "https://arxiv.org/pdf/2504.11409v2", "arxiv_id": "2504.11409", "doi": null, "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2258}
{"id": "6e084a55b60f9ba19abc6c0fd262c042f129e610acb335f01ebdbcb98f55af2b", "sources": ["arxiv", "semantic_scholar"], "title": "Global and Local Mamba Network for Multi-Modality Medical Image Super-Resolution", "abstract": "Convolutional neural networks and Transformer have made significant progresses in multi-modality medical image super-resolution. However, these methods either have a fixed receptive field for local learning or significant computational burdens for global learning, limiting the super-resolution performance. To solve this problem, State Space Models, notably Mamba, is introduced to efficiently model long-range dependencies in images with linear computational complexity. Relying on the Mamba and the fact that low-resolution images rely on global information to compensate for missing details, while high-resolution reference images need to provide more local details for accurate super-resolution, we propose a global and local Mamba network (GLMamba) for multi-modality medical image super-resolution. To be specific, our GLMamba is a two-branch network equipped with a global Mamba branch and a local Mamba branch. The global Mamba branch captures long-range relationships in low-resolution inputs, and the local Mamba branch focuses more on short-range details in high-resolution reference images. We also use the deform block to adaptively extract features of both branches to enhance the representation ability. A modulator is designed to further enhance deformable features in both global and local Mamba blocks. To fully integrate the reference image for low-resolution image super-resolution, we further develop a multi-modality feature fusion block to adaptively fuse features by considering similarities, differences, and complementary aspects between modalities. In addition, a contrastive edge loss (CELoss) is developed for sufficient enhancement of edge textures and contrast in medical images.", "authors": ["Zexin Ji", "Beiji Zou", "Xiaoyan Kui", "Sebastien Thureau", "Su Ruan"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-04-14", "url": "https://arxiv.org/abs/2504.10105", "pdf_url": "https://arxiv.org/pdf/2504.10105v1", "arxiv_id": "2504.10105", "doi": "10.48550/arXiv.2504.10105", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Pattern Recognition", "quality_score": 0.2386}
{"id": "d749b0792a9e62db098cd176174df4d31ad41b09daf8de63389991a6c9b5b081", "sources": ["arxiv", "semantic_scholar"], "title": "ms-Mamba: Multi-scale Mamba for Time-Series Forecasting", "abstract": "The problem of Time-series Forecasting is generally addressed by recurrent, Transformer-based and the recently proposed Mamba-based architectures. However, existing architectures generally process their input at a single temporal scale, which may be sub-optimal for many tasks where information changes over multiple time scales. In this paper, we introduce a novel architecture called Multi-scale Mamba (ms-Mamba) to address this gap. ms-Mamba incorporates multiple temporal scales by using multiple Mamba blocks with different sampling rates ($Δ$s). Our experiments on many benchmarks demonstrate that ms-Mamba outperforms state-of-the-art approaches, including the recently proposed Transformer-based and Mamba-based models. For example, on the Solar-Energy dataset, ms-Mamba outperforms its closest competitor S-Mamba (0.229 vs. 0.240 in terms of mean-squared error) while using fewer parameters (3.53M vs. 4.77M), less memory (13.46MB vs. 18.18MB), and less operations (14.93G vs. 20.53G MACs), averaged across four forecast lengths. Codes and models will be made available.", "authors": ["Yusuf Meric Karadag", "Ismail Talaz", "Ipek Gursel Dino", "Sinan Kalkan"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-04-10", "url": "https://arxiv.org/abs/2504.07654", "pdf_url": "https://arxiv.org/pdf/2504.07654v2", "arxiv_id": "2504.07654", "doi": "10.48550/arXiv.2504.07654", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Neurocomputing", "quality_score": 0.2258}
{"id": "0c42a0d6af0f8d7cb2627d8a23650cdfd8e55ef7047d1d7ef4406db4601a561d", "sources": ["arxiv", "semantic_scholar"], "title": "Dynamic Vision Mamba", "abstract": "Mamba-based vision models have gained extensive attention as a result of being computationally more efficient than attention-based models. However, spatial redundancy still exists in these models, represented by token and block redundancy. For token redundancy, we analytically find that early token pruning methods will result in inconsistency between training and inference or introduce extra computation for inference. Therefore, we customize token pruning to fit the Mamba structure by rearranging the pruned sequence before feeding it into the next Mamba block. For block redundancy, we allow each image to select SSM blocks dynamically based on an empirical observation that the inference speed of Mamba-based vision models is largely affected by the number of SSM blocks. Our proposed method, Dynamic Vision Mamba (DyVM), effectively reduces FLOPs with minor performance drops. We achieve a reduction of 35.2\\% FLOPs with only a loss of accuracy of 1.7\\% on Vim-S. It also generalizes well across different Mamba vision model architectures and different vision tasks. Our code will be made public.", "authors": ["Mengxuan Wu", "Zekai Li", "Zhiyuan Liang", "Moyang Li", "Xuanlei Zhao", "Samir Khaki", "Zheng Zhu", "Xiaojiang Peng", "Konstantinos N. Plataniotis", "Kai Wang", "Wangbo Zhao", "Yang You"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-04-07", "url": "https://arxiv.org/abs/2504.04787", "pdf_url": "https://arxiv.org/pdf/2504.04787v1", "arxiv_id": "2504.04787", "doi": "10.48550/arXiv.2504.04787", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2386}
{"id": "8bb22f03e70172fce8a543c229be4181e4a6a57aa3a213c781fc78aa420efbfb", "sources": ["arxiv", "semantic_scholar"], "title": "Mesh Mamba: A Unified State Space Model for Saliency Prediction in Non-Textured and Textured Meshes", "abstract": "Mesh saliency enhances the adaptability of 3D vision by identifying and emphasizing regions that naturally attract visual attention. To investigate the interaction between geometric structure and texture in shaping visual attention, we establish a comprehensive mesh saliency dataset, which is the first to systematically capture the differences in saliency distribution under both textured and non-textured visual conditions. Furthermore, we introduce mesh Mamba, a unified saliency prediction model based on a state space model (SSM), designed to adapt across various mesh types. Mesh Mamba effectively analyzes the geometric structure of the mesh while seamlessly incorporating texture features into the topological framework, ensuring coherence throughout appearance-enhanced modeling. More importantly, by subgraph embedding and a bidirectional SSM, the model enables global context modeling for both local geometry and texture, preserving the topological structure and improving the understanding of visual details and structural complexity. Through extensive theoretical and empirical validation, our model not only improves performance across various mesh types but also demonstrates high scalability and versatility, particularly through cross validations of various visual features.", "authors": ["Kaiwei Zhang", "Dandan Zhu", "Xiongkuo Min", "Guangtao Zhai"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-04-02", "url": "https://arxiv.org/abs/2504.01466", "pdf_url": "https://arxiv.org/pdf/2504.01466v2", "arxiv_id": "2504.01466", "doi": "10.1109/CVPR52734.2025.01512", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Computer Vision and Pattern Recognition", "quality_score": 0.1505}
{"id": "5834bc19775f346f761e872167fca74fe576bc26158e2896e159c827b4f5c620", "sources": ["arxiv", "semantic_scholar"], "title": "Is Small Language Model the Silver Bullet to Low-Resource Languages Machine Translation?", "abstract": "Low-resource languages (LRLs) lack sufficient linguistic resources and are underrepresented in benchmark datasets, resulting in persistently lower translation quality than high-resource languages, especially in privacy-sensitive and resource-limited contexts. Firstly, this study systematically evaluates state-of-the-art smaller Large Language Models in 200 languages using the FLORES-200 benchmark, highlighting persistent deficiencies and disparities in the translation of LRLs. To mitigate these limitations, we investigate knowledge distillation from large pre-trained teacher models to Small Language Models (SLMs) through supervised fine-tuning. The results show substantial improvements; for example, the translation performance of English to Luxembourgish (EN to LB), measured by the LLM-as-a-Judge score, increases from 0.36 to 0.89 in the validation set for Llama-3.2-3B. We further investigate various fine-tuning configurations and tasks to clarify the trade-offs between data scale and training efficiency, verify that the model retains its general capabilities without significant catastrophic forgetting after training, and explore the distillation benefits to other LRLs on SLMs (Khasi, Assamese, and Ukrainian). In general, this work exposes the limitations and fairness issues of current SLMs in LRL translation and systematically explores the potential of using the distillation of knowledge from large to small models, offering practical, empirically grounded recommendations to improve LRL translation systems", "authors": ["Yewei Song", "Lujun Li", "Cedric Lothritz", "Saad Ezzini", "Lama Sleem", "Niccolo Gentile", "Radu State", "Tegawendé F. Bissyandé", "Jacques Klein"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-31", "url": "https://arxiv.org/abs/2503.24102", "pdf_url": "https://arxiv.org/pdf/2503.24102v3", "arxiv_id": "2503.24102", "doi": null, "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1945}
{"id": "9a7e6f6d2167315b34c580e8835650866eea9472f991b78767be58f40c414cec", "sources": ["arxiv", "semantic_scholar"], "title": "TransMamba: A Sequence-Level Hybrid Transformer-Mamba Language Model", "abstract": "Transformers are the cornerstone of modern large language models, but their quadratic computational complexity limits efficiency in long-sequence processing. Recent advancements in Mamba, a state space model (SSM) with linear complexity, offer promising efficiency gains but suffer from unstable contextual learning and multitask generalization. Some works conduct layer-level hybrid structures that combine Transformer and Mamba layers, aiming to make full use of both advantages. This paper proposes TransMamba, a novel sequence-level hybrid framework that unifies Transformer and Mamba through shared parameter matrices (QKV and CBx), and thus could dynamically switch between attention and SSM mechanisms at different token lengths and layers. We design the Memory Converter to bridge Transformer and Mamba by converting attention outputs into SSM-compatible states, ensuring seamless information flow at TransPoints where the transformation happens. The TransPoint scheduling is also thoroughly explored for balancing effectiveness and efficiency. We conducted extensive experiments demonstrating that TransMamba achieves superior training efficiency and performance compared to single and hybrid baselines, and validated the deeper consistency between Transformer and Mamba paradigms at sequence level, offering a scalable solution for next-generation language modeling. Code and data are available at https://github.com/Yixing-Li/TransMamba", "authors": ["Yixing Li", "Ruobing Xie", "Zhen Yang", "Xingwu Sun", "Shuaipeng Li", "Weidong Han", "Zhanhui Kang", "Yu Cheng", "Chengzhong Xu", "Di Wang", "Jie Jiang"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-31", "url": "https://arxiv.org/abs/2503.24067", "pdf_url": "https://arxiv.org/pdf/2503.24067v2", "arxiv_id": "2503.24067", "doi": "10.1609/aaai.v40i38.40451", "citation_count": 3, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Yixing-Li/TransMamba", "venue": "AAAI Conference on Artificial Intelligence", "quality_score": 0.1682}
{"id": "90c9a6a78411bdcd1bab18ff278261d8ddade570abd3fa045d59832ea96ad770", "sources": ["arxiv", "semantic_scholar"], "title": "SSM-RDU: A Reconfigurable Dataflow Unit for Long-Sequence State-Space Models", "abstract": "Long-sequence state-space models (SSMs) such as Hyena and Mamba replace the quadratic complexity of self-attention with more efficient FFT and scan operations. However, modern accelerators like GPUs are poorly suited to these non-GEMM workloads due to rigid execution models and specialization for dense matrix operations. This paper proposes architectural extensions to a baseline Reconfigurable Dataflow Unit (RDU) that efficiently support FFT-based and scan-based SSMs. By introducing lightweight interconnect enhancements within compute tiles, the extended RDU enables spatial mapping of FFT and scan dataflows with less than 1% area and power overhead. The resulting architecture achieves a 5.95X speedup over the GPU and a 1.95X speedup over the baseline RDU for Hyena, and a 2.12X and 1.75X speedup over the GPU and baseline RDU, respectively, for Mamba.", "authors": ["Sho Ko", "Kunle Olukotun"], "categories": ["cs.AR"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-29", "url": "https://arxiv.org/abs/2503.22937", "pdf_url": "https://arxiv.org/pdf/2503.22937v2", "arxiv_id": "2503.22937", "doi": "10.1109/ICCD65941.2025.00095", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "ICCD", "quality_score": 0.1066}
{"id": "ff0c7bd49ea94a3d3382a247eb7f0df6f9024a7ad666abd862a26b27dd9bc527", "sources": ["arxiv", "semantic_scholar"], "title": "Resona: Improving Context Copying in Linear Recurrence Models with Retrieval", "abstract": "Recent shifts in the space of large language model (LLM) research have shown an increasing focus on novel architectures to compete with prototypical Transformer-based models that have long dominated this space. Linear recurrent models have proven to be a viable competitor due to their computational efficiency. However, such models still demonstrate a sizable gap compared to Transformers in terms of in-context learning among other tasks that require recalling information from a context. In this work, we introduce Resona, a simple and scalable framework for augmenting linear recurrent models with retrieval. Resona augments models with the ability to integrate retrieved information from the provided input context, enabling tailored behavior to diverse task requirements. Experiments on a variety of linear recurrent models demonstrate that Resona-augmented models observe significant performance gains on a variety of synthetic as well as real-world natural language tasks, highlighting its ability to act as a general purpose method to improve the in-context learning and language modeling abilities of linear recurrent LLMs.", "authors": ["Xinyu Wang", "Linrui Ma", "Jerry Huang", "Peng Lu", "Prasanna Parthasarathi", "Xiao-Wen Chang", "Boxing Chen", "Yufei Cui"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-28", "url": "https://arxiv.org/abs/2503.22913", "pdf_url": "https://arxiv.org/pdf/2503.22913v3", "arxiv_id": "2503.22913", "doi": "10.48550/arXiv.2503.22913", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1747}
{"id": "dacb6abf49d439c22f11970e41e9dfdbbd02cacbf65af0ea5250f61120146dfe", "sources": ["arxiv", "semantic_scholar"], "title": "Ancestral Mamba: Enhancing Selective Discriminant Space Model with Online Visual Prototype Learning for Efficient and Robust Discriminant Approach", "abstract": "In the realm of computer graphics, the ability to learn continuously from non-stationary data streams while adapting to new visual patterns and mitigating catastrophic forgetting is of paramount importance. Existing approaches often struggle to capture and represent the essential characteristics of evolving visual concepts, hindering their applicability to dynamic graphics tasks. In this paper, we propose Ancestral Mamba, a novel approach that integrates online prototype learning into a selective discriminant space model for efficient and robust online continual learning. The key components of our approach include Ancestral Prototype Adaptation (APA), which continuously refines and builds upon learned visual prototypes, and Mamba Feedback (MF), which provides targeted feedback to adapt to challenging visual patterns. APA enables the model to continuously adapt its prototypes, building upon ancestral knowledge to tackle new challenges, while MF acts as a targeted feedback mechanism, focusing on challenging classes and refining their representations. Extensive experiments on graphics-oriented datasets, such as CIFAR-10 and CIFAR-100, demonstrate the superior performance of Ancestral Mamba compared to state-of-the-art baselines, achieving significant improvements in accuracy and forgetting mitigation.", "authors": ["Jiahao Qin", "Feng Liu", "Lu Zong"], "categories": ["cs.GR", "cs.AI", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-26", "url": "https://arxiv.org/abs/2503.22729", "pdf_url": "https://arxiv.org/pdf/2503.22729v1", "arxiv_id": "2503.22729", "doi": "10.48550/arXiv.2503.22729", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1031}
{"id": "85db67c534944adc69e35ba88c0601c9a1adbd75c947f3b7379120e9c576555a", "sources": ["arxiv", "semantic_scholar"], "title": "A Comprehensive Analysis of Mamba for 3D Volumetric Medical Image Segmentation", "abstract": "Mamba, with its selective State Space Models (SSMs), offers a more computationally efficient solution than Transformers for long-range dependency modeling. However, there is still a debate about its effectiveness in high-resolution 3D medical image segmentation. In this study, we present a comprehensive investigation into Mamba's capabilities in 3D medical image segmentation by tackling three pivotal questions: Can Mamba replace Transformers? Can it elevate multi-scale representation learning? Is complex scanning necessary to unlock its full potential? We evaluate Mamba's performance across three large public benchmarks-AMOS, TotalSegmentator, and BraTS. Our findings reveal that UlikeMamba, a U-shape Mamba-based network, consistently surpasses UlikeTrans, a U-shape Transformer-based network, particularly when enhanced with custom-designed 3D depthwise convolutions, boosting accuracy and computational efficiency. Further, our proposed multi-scale Mamba block demonstrates superior performance in capturing both fine-grained details and global context, especially in complex segmentation tasks, surpassing Transformer-based counterparts. We also critically assess complex scanning strategies, finding that simpler methods often suffice, while our Tri-scan approach delivers notable advantages in the most challenging scenarios. By integrating these advancements, we introduce a new network for 3D medical image segmentation, positioning Mamba as a transformative force that outperforms leading models such as nnUNet, CoTr, and U-Mamba, offering competitive accuracy with superior computational efficiency. This study provides key insights into Mamba's unique advantages, paving the way for more efficient and accurate approaches to 3D medical imaging.", "authors": ["Chaohan Wang", "Yutong Xie", "Qi Chen", "Yuyin Zhou", "Qi Wu"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-25", "url": "https://arxiv.org/abs/2503.19308", "pdf_url": "https://arxiv.org/pdf/2503.19308v1", "arxiv_id": "2503.19308", "doi": "10.48550/arXiv.2503.19308", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Pattern Recognition", "quality_score": 0.2386}
{"id": "4c0acd52b8eb357ede2ecb4eb38807e6587191670824f5cd9371f36be0709f8a", "sources": ["arxiv", "semantic_scholar"], "title": "Advancing Intelligent Sequence Modeling: Evolution, Trade-offs, and Applications of State- Space Architectures from S4 to Mamba", "abstract": "Structured State Space Models (SSMs) have emerged as a transformative paradigm in sequence modeling, addressing critical limitations of Recurrent Neural Networks (RNNs) and Transformers, namely, vanishing gradients, sequential computation bottlenecks, and quadratic memory complexity. By integrating structured recurrence with state-space representations, SSMs achieve linear or near-linear computational scaling while excelling in long-range dependency tasks. This study systematically traces the evolution of SSMs from the foundational Structured State Space Sequence (S4) model to modern variants like Mamba, Simplified Structured State Space Sequence (S5), and Jamba, analyzing architectural innovations that enhance computational efficiency, memory optimization, and inference speed. We critically evaluate trade-offs inherent to SSM design, such as balancing expressiveness with computational constraints and integrating hybrid architectures for domain-specific performance. Across domains including natural language processing, speech recognition, computer vision, and time-series forecasting, SSMs demonstrate state-of-the-art results in handling ultra-long sequences, outperforming Transformer-based models in both speed and memory utilization. Case studies highlight applications such as real-time speech synthesis and genomic sequence modeling, where SSMs reduce inference latency by up to 60% compared to traditional approaches. However, challenges persist in training dynamics, interpretability, and hardware-aware optimization. We conclude with a forward-looking analysis of SSMs' potential to redefine scalable deep learning, proposing directions for hybrid systems, theoretical guarantees, and broader adoption in resource-constrained environments.", "authors": ["Shriyank Somvanshi", "Md Monzurul Islam", "Mahmuda Sultana Mimi", "Sazzad Bin Bashar Polock", "Gaurab Chhetri", "Anandi Dutta", "Amir Rafe", "Subasish Das"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-22", "url": "https://arxiv.org/abs/2503.18970", "pdf_url": "https://arxiv.org/pdf/2503.18970v3", "arxiv_id": "2503.18970", "doi": null, "citation_count": 17, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3138}
{"id": "91afa4bf5320673db5c2dea2f7b3ed4dcd56ed11cbcc2c90d017d5c8c603dd03", "sources": ["arxiv", "semantic_scholar"], "title": "SaMam: Style-aware State Space Model for Arbitrary Image Style Transfer", "abstract": "Global effective receptive field plays a crucial role for image style transfer (ST) to obtain high-quality stylized results. However, existing ST backbones (e.g., CNNs and Transformers) suffer huge computational complexity to achieve global receptive fields. Recently, the State Space Model (SSM), especially the improved variant Mamba, has shown great potential for long-range dependency modeling with linear complexity, which offers a approach to resolve the above dilemma. In this paper, we develop a Mamba-based style transfer framework, termed SaMam. Specifically, a mamba encoder is designed to efficiently extract content and style information. In addition, a style-aware mamba decoder is developed to flexibly adapt to various styles. Moreover, to address the problems of local pixel forgetting, channel redundancy and spatial discontinuity of existing SSMs, we introduce both local enhancement and zigzag scan. Qualitative and quantitative results demonstrate that our SaMam outperforms state-of-the-art methods in terms of both accuracy and efficiency.", "authors": ["Hongda Liu", "Longguang Wang", "Ye Zhang", "Ziru Yu", "Yulan Guo"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-20", "url": "https://arxiv.org/abs/2503.15934", "pdf_url": "https://arxiv.org/pdf/2503.15934v1", "arxiv_id": "2503.15934", "doi": "10.1109/CVPR52734.2025.02651", "citation_count": 16, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Computer Vision and Pattern Recognition", "quality_score": 0.3076}
{"id": "18f6140dc42c8d435d354b624cd7091626ca7dee91c6c785428f442b45492732", "sources": ["arxiv", "semantic_scholar"], "title": "Technologies on Effectiveness and Efficiency: A Survey of State Spaces Models", "abstract": "State Space Models (SSMs) have emerged as a promising alternative to the popular transformer-based models and have been increasingly gaining attention. Compared to transformers, SSMs excel at tasks with sequential data or longer contexts, demonstrating comparable performances with significant efficiency gains. In this survey, we provide a coherent and systematic overview for SSMs, including their theoretical motivations, mathematical formulations, comparison with existing model classes, and various applications. We divide the SSM series into three main sections, providing a detailed introduction to the original SSM, the structured SSM represented by S4, and the selective SSM typified by Mamba. We put an emphasis on technicality, and highlight the various key techniques introduced to address the effectiveness and efficiency of SSMs. We hope this manuscript serves as an introduction for researchers to explore the theoretical foundations of SSMs.", "authors": ["Xingtai Lv", "Youbang Sun", "Kaiyan Zhang", "Shang Qu", "Xuekai Zhu", "Yuchen Fan", "Yi Wu", "Ermo Hua", "Xinwei Long", "Ning Ding", "Bowen Zhou"], "categories": ["cs.LG", "cs.AI", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-14", "url": "https://arxiv.org/abs/2503.11224", "pdf_url": "https://arxiv.org/pdf/2503.11224v1", "arxiv_id": "2503.11224", "doi": "10.48550/arXiv.2503.11224", "citation_count": 5, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1945}
{"id": "dc46b4995f47bf980723db6d48205782e58e8e95e7c28192c923400a05317771", "sources": ["arxiv", "semantic_scholar"], "title": "Trajectory Mamba: Efficient Attention-Mamba Forecasting Model Based on Selective SSM", "abstract": "Motion prediction is crucial for autonomous driving, as it enables accurate forecasting of future vehicle trajectories based on historical inputs. This paper introduces Trajectory Mamba, a novel efficient trajectory prediction framework based on the selective state-space model (SSM). Conventional attention-based models face the challenge of computational costs that grow quadratically with the number of targets, hindering their application in highly dynamic environments. In response, we leverage the SSM to redesign the self-attention mechanism in the encoder-decoder architecture, thereby achieving linear time complexity. To address the potential reduction in prediction accuracy resulting from modifications to the attention mechanism, we propose a joint polyline encoding strategy to better capture the associations between static and dynamic contexts, ultimately enhancing prediction accuracy. Additionally, to balance prediction accuracy and inference speed, we adopted the decoder that differs entirely from the encoder. Through cross-state space attention, all target agents share the scene context, allowing the SSM to interact with the shared scene representation during decoding, thus inferring different trajectories over the next prediction steps. Our model achieves state-of-the-art results in terms of inference speed and parameter efficiency on both the Argoverse 1 and Argoverse 2 datasets. It demonstrates a four-fold reduction in FLOPs compared to existing methods and reduces parameter count by over 40% while surpassing the performance of the vast majority of previous methods. These findings validate the effectiveness of Trajectory Mamba in trajectory prediction tasks.", "authors": ["Yizhou Huang", "Yihua Cheng", "Kezhi Wang"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-13", "url": "https://arxiv.org/abs/2503.10898", "pdf_url": "https://arxiv.org/pdf/2503.10898v1", "arxiv_id": "2503.10898", "doi": "10.1109/CVPR52734.2025.01126", "citation_count": 30, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Computer Vision and Pattern Recognition", "quality_score": 0.3728}
{"id": "e2022585369758bd68e2bda837c1a932098443093b5f0d82d55d9a8990b38a3a", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba-VA: A Mamba-based Approach for Continuous Emotion Recognition in Valence-Arousal Space", "abstract": "Continuous Emotion Recognition (CER) plays a crucial role in intelligent human-computer interaction, mental health monitoring, and autonomous driving. Emotion modeling based on the Valence-Arousal (VA) space enables a more nuanced representation of emotional states. However, existing methods still face challenges in handling long-term dependencies and capturing complex temporal dynamics. To address these issues, this paper proposes a novel emotion recognition model, Mamba-VA, which leverages the Mamba architecture to efficiently model sequential emotional variations in video frames. First, the model employs a Masked Autoencoder (MAE) to extract deep visual features from video frames, enhancing the robustness of temporal information. Then, a Temporal Convolutional Network (TCN) is utilized for temporal modeling to capture local temporal dependencies. Subsequently, Mamba is applied for long-sequence modeling, enabling the learning of global emotional trends. Finally, a fully connected (FC) layer performs regression analysis to predict continuous valence and arousal values. Experimental results on the Valence-Arousal (VA) Estimation task of the 8th competition on Affective Behavior Analysis in-the-wild (ABAW) demonstrate that the proposed model achieves valence and arousal scores of 0.5362 (0.5036) and 0.4310 (0.4119) on the validation (test) set, respectively, outperforming the baseline. The source code is available on GitHub:https://github.com/FreedomPuppy77/Charon.", "authors": ["Yuheng Liang", "Zheyu Wang", "Feng Liu", "Mingzhou Liu", "Yu Yao"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-13", "url": "https://arxiv.org/abs/2503.10104", "pdf_url": "https://arxiv.org/pdf/2503.10104v1", "arxiv_id": "2503.10104", "doi": "10.1109/CVPRW67362.2025.00562", "citation_count": 11, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/FreedomPuppy77/Charon", "venue": null, "quality_score": 0.2698}
{"id": "db7153449da1f99a7a68d775abe1405e4ce25da28532a1561a8d4740f7c7cdf5", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba base PKD for efficient knowledge compression", "abstract": "Deep neural networks (DNNs) have remarkably succeeded in various image processing tasks. However, their large size and computational complexity present significant challenges for deploying them in resource-constrained environments. This paper presents an innovative approach for integrating Mamba Architecture within a Progressive Knowledge Distillation (PKD) process to address the challenge of reducing model complexity while maintaining accuracy in image classification tasks. The proposed framework distills a large teacher model into progressively smaller student models, designed using Mamba blocks. Each student model is trained using Selective-State-Space Models (S-SSM) within the Mamba blocks, focusing on important input aspects while reducing computational complexity. The work's preliminary experiments use MNIST and CIFAR-10 as datasets to demonstrate the effectiveness of this approach. For MNIST, the teacher model achieves 98% accuracy. A set of seven student models as a group retained 63% of the teacher's FLOPs, approximating the teacher's performance with 98% accuracy. The weak student used only 1% of the teacher's FLOPs and maintained 72% accuracy. Similarly, for CIFAR-10, the students achieved 1% less accuracy compared to the teacher, with the small student retaining 5% of the teacher's FLOPs to achieve 50% accuracy. These results confirm the flexibility and scalability of Mamba Architecture, which can be integrated into PKD, succeeding in the process of finding students as weak learners. The framework provides a solution for deploying complex neural networks in real-time applications with a reduction in computational cost.", "authors": ["José Medina", "Amnir Hadachi", "Paul Honeine", "Abdelaziz Bensrhair"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-03", "url": "https://arxiv.org/abs/2503.01727", "pdf_url": "https://arxiv.org/pdf/2503.01727v2", "arxiv_id": "2503.01727", "doi": "10.48550/arXiv.2503.01727", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0768}
{"id": "46d1846c8ea1b98324446e9cebc49a26f982ecbef246bc8f9cce2867ef0ead2f", "sources": ["arxiv", "semantic_scholar"], "title": "Visual Attention Exploration in Vision-Based Mamba Models", "abstract": "State space models (SSMs) have emerged as an efficient alternative to transformer-based models, offering linear complexity that scales better than transformers. One of the latest advances in SSMs, Mamba, introduces a selective scan mechanism that assigns trainable weights to input tokens, effectively mimicking the attention mechanism. Mamba has also been successfully extended to the vision domain by decomposing 2D images into smaller patches and arranging them as 1D sequences. However, it remains unclear how these patches interact with (or attend to) each other in relation to their original 2D spatial location. Additionally, the order used to arrange the patches into a sequence also significantly impacts their attention distribution. To better understand the attention between patches and explore the attention patterns, we introduce a visual analytics tool specifically designed for vision-based Mamba models. This tool enables a deeper understanding of how attention is distributed across patches in different Mamba blocks and how it evolves throughout a Mamba model. Using the tool, we also investigate the impact of different patch-ordering strategies on the learned attention, offering further insights into the model's behavior.", "authors": ["Junpeng Wang", "Chin-Chia Michael Yeh", "Uday Singh Saini", "Mahashweta Das"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-02-28", "url": "https://arxiv.org/abs/2502.20764", "pdf_url": "https://arxiv.org/pdf/2502.20764v1", "arxiv_id": "2502.20764", "doi": "10.1109/PacificVis64226.2025.00031", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Pacific Visualization Symposium", "quality_score": 0.0753}
{"id": "ca2a30099a828ba8c5b84e8336c745a42804a1997fbcf7d64d952baeabda5f76", "sources": ["arxiv", "semantic_scholar"], "title": "Improving Speech Enhancement by Cross- and Sub-band Processing with State Space Model", "abstract": "Recently, the state space model (SSM) represented by Mamba has shown remarkable performance in long-term sequence modeling tasks, including speech enhancement. However, due to substantial differences in sub-band features, applying the same SSM to all sub-bands limits its inference capability. Additionally, when processing each time frame of the time-frequency representation, the SSM may forget certain high-frequency information of low energy, making the restoration of structure in the high-frequency bands challenging. For this reason, we propose Cross- and Sub-band Mamba (CSMamba). To assist the SSM in handling different sub-band features flexibly, we propose a band split block that splits the full-band into four sub-bands with different widths based on their information similarity. We then allocate independent weights to each sub-band, thereby reducing the inference burden on the SSM. Furthermore, to mitigate the forgetting of low-energy information in the high-frequency bands by the SSM, we introduce a spectrum restoration block that enhances the representation of the cross-band features from multiple perspectives. Experimental results on the DNS Challenge 2021 dataset demonstrate that CSMamba outperforms several state-of-the-art (SOTA) speech enhancement methods in three objective evaluation metrics with fewer parameters.", "authors": ["Jizhen Li", "Weiping Tu", "Yuhong Yang", "Xinmeng Xu", "Yiqun Zhang", "Yanzhen Ren"], "categories": ["cs.SD", "eess.AS"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2025-02-22", "url": "https://arxiv.org/abs/2502.16207", "pdf_url": "https://arxiv.org/pdf/2502.16207v1", "arxiv_id": "2502.16207", "doi": "10.1109/ICASSP49660.2025.10888886", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE International Conference on Acoustics, Speech, and Signal Processing", "quality_score": 0.0753}
{"id": "fd0f24f7bc9e01aec471738e4705271d15a474b731336077fbf6521cbbf4a1ee", "sources": ["arxiv", "semantic_scholar"], "title": "LaTIM: Measuring Latent Token-to-Token Interactions in Mamba Models", "abstract": "State space models (SSMs), such as Mamba, have emerged as an efficient alternative to transformers for long-context sequence modeling. However, despite their growing adoption, SSMs lack the interpretability tools that have been crucial for understanding and improving attention-based architectures. While recent efforts provide insights into Mamba's internal mechanisms, they do not explicitly decompose token-wise contributions, leaving gaps in understanding how Mamba selectively processes sequences across layers. In this work, we introduce LaTIM, a novel token-level decomposition method for both Mamba-1 and Mamba-2 that enables fine-grained interpretability. We extensively evaluate our method across diverse tasks, including machine translation, copying, and retrieval-based generation, demonstrating its effectiveness in revealing Mamba's token-to-token interaction patterns.", "authors": ["Hugo Pitorro", "Marcos Treviso"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-02-21", "url": "https://arxiv.org/abs/2502.15612", "pdf_url": "https://arxiv.org/pdf/2502.15612v2", "arxiv_id": "2502.15612", "doi": "10.48550/arXiv.2502.15612", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Annual Meeting of the Association for Computational Linguistics", "quality_score": 0.1505}
{"id": "96b9f13c8a74de878110547b7b971b1640f74e3b62e3f9800283e47e473c0185", "sources": ["arxiv", "semantic_scholar"], "title": "LightMamba: Efficient Mamba Acceleration on FPGA with Quantization and Hardware Co-design", "abstract": "State space models (SSMs) like Mamba have recently attracted much attention. Compared to Transformer-based large language models (LLMs), Mamba achieves linear computation complexity with the sequence length and demonstrates superior performance. However, Mamba is hard to accelerate due to the scattered activation outliers and the complex computation dependency, rendering existing LLM accelerators inefficient. In this paper, we propose LightMamba that co-designs the quantization algorithm and FPGA accelerator architecture for efficient Mamba inference. We first propose an FPGA-friendly post-training quantization algorithm that features rotation-assisted quantization and power-of-two SSM quantization to reduce the majority of computation to 4-bit. We further design an FPGA accelerator that partially unrolls the Mamba computation to balance the efficiency and hardware costs. Through computation reordering as well as fine-grained tiling and fusion, the hardware utilization and memory efficiency of the accelerator get drastically improved. We implement LightMamba on Xilinx Versal VCK190 FPGA and achieve 4.65x to 6.06x higher energy efficiency over the GPU baseline. When evaluated on Alveo U280 FPGA, LightMamba reaches 93 tokens/s, which is 1.43x that of the GPU baseline. Our code is available at https://github.com/PKU-SEC-Lab/LightMamba.", "authors": ["Renjie Wei", "Songqiang Xu", "Linfeng Zhong", "Zebin Yang", "Qingyu Guo", "Yuan Wang", "Runsheng Wang", "Meng Li"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-02-21", "url": "https://arxiv.org/abs/2502.15260", "pdf_url": "https://arxiv.org/pdf/2502.15260v2", "arxiv_id": "2502.15260", "doi": "10.23919/DATE64628.2025.10993079", "citation_count": 19, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/PKU-SEC-Lab/LightMamba", "venue": "Design, Automation and Test in Europe", "quality_score": 0.3253}
{"id": "9494fa916ce964c8504fbfc4d770eb1c41f041b2afb77cf70828527ad30e14ca", "sources": ["arxiv", "semantic_scholar"], "title": "Multimodal Mamba: Decoder-only Multimodal State Space Model via Quadratic to Linear Distillation", "abstract": "Recent Multimodal Large Language Models (MLLMs) have achieved remarkable performance but face deployment challenges due to their quadratic computational complexity, growing Key-Value cache requirements, and reliance on separate vision encoders. We propose mmMamba, a framework for developing linear-complexity native multimodal state space models through progressive distillation from existing MLLMs using moderate academic computational resources. Our approach enables the direct conversion of trained decoder-only MLLMs to linear-complexity architectures without requiring pre-trained RNN-based LLM or vision encoders. We propose an seeding strategy to carve Mamba from trained Transformer and a three-stage distillation recipe, which can effectively transfer the knowledge from Transformer to Mamba while preserving multimodal capabilities. Our method also supports flexible hybrid architectures that combine Transformer and Mamba layers for customizable efficiency-performance trade-offs. Distilled from the Transformer-based decoder-only HoVLE, mmMamba-linear achieves competitive performance against existing linear and quadratic-complexity VLMs, while mmMamba-hybrid further improves performance significantly, approaching HoVLE's capabilities. At 103K tokens, mmMamba-linear demonstrates 20.6$\\times$ speedup and 75.8% GPU memory reduction compared to HoVLE, while mmMamba-hybrid achieves 13.5$\\times$ speedup and 60.2% memory savings. Code and models are released at https://github.com/hustvl/mmMamba", "authors": ["Bencheng Liao", "Hongyuan Tao", "Qian Zhang", "Tianheng Cheng", "Yingyue Li", "Haoran Yin", "Wenyu Liu", "Xinggang Wang"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-02-18", "url": "https://arxiv.org/abs/2502.13145", "pdf_url": "https://arxiv.org/pdf/2502.13145v2", "arxiv_id": "2502.13145", "doi": "10.48550/arXiv.2502.13145", "citation_count": 12, "influential_citation_count": 3, "has_code": true, "code_url": "https://github.com/hustvl/mmMamba", "venue": "arXiv.org", "quality_score": 0.301}
{"id": "92fb141b2b55bacdf3ddb7046009553f98ff00eced32a369dd24cbd02f245e5f", "sources": ["arxiv", "semantic_scholar"], "title": "From Markov to Laplace: How Mamba In-Context Learns Markov Chains", "abstract": "While transformer-based language models have driven the AI revolution thus far, their computational complexity has spurred growing interest in viable alternatives, such as structured state space sequence models (SSMs) and Selective SSMs. Among these, Mamba (S6) and its variant Mamba-2 have shown remarkable inference speed ups over transformers while achieving comparable or superior performance on complex language modeling tasks. However, despite these architectural innovations and empirical successes, the fundamental learning capabilities of Mamba remain poorly understood. In this paper, we address this gap by studying in-context learning (ICL) on Markov chains and uncovering a surprising phenomenon: unlike transformers, even a single-layer Mamba efficiently learns the in-context Laplacian smoothing estimator, which is both Bayes and minimax optimal, for all Markovian orders. To explain this, we theoretically characterize the representation capacity of Mamba and reveal the fundamental role of convolution in enabling it to represent the optimal Laplacian smoothing. These theoretical insights align strongly with empirical results and, to the best of our knowledge, represent the first formal connection between Mamba and optimal statistical estimators. Finally, we outline promising research directions inspired by these findings.", "authors": ["Marco Bondaschi", "Nived Rajaraman", "Xiuying Wei", "Kannan Ramchandran", "Razvan Pascanu", "Caglar Gulcehre", "Michael Gastpar", "Ashok Vardhan Makkuva"], "categories": ["cs.LG", "cs.AI", "cs.IT"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2025-02-14", "url": "https://arxiv.org/abs/2502.10178", "pdf_url": "https://arxiv.org/pdf/2502.10178v1", "arxiv_id": "2502.10178", "doi": "10.48550/arXiv.2502.10178", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2386}
{"id": "63e8340a6eb754c073a300eb9bd9e44f0dcc844dcd51b8f4b8b8239d88215ec5", "sources": ["arxiv", "semantic_scholar"], "title": "DeltaProduct: Improving State-Tracking in Linear RNNs via Householder Products", "abstract": "Linear Recurrent Neural Networks (linear RNNs) have emerged as competitive alternatives to Transformers for sequence modeling, offering efficient training and linear-time inference. However, existing architectures face a fundamental trade-off between expressivity and efficiency, dictated by the structure of their state-transition matrices. Diagonal matrices, used in models such as Mamba, GLA, or mLSTM, yield fast runtime but have limited expressivity. To address this, recent architectures such as DeltaNet and RWKV-7 adopted a diagonal plus rank--1 structure, which allows simultaneous token and channel mixing, improving associative recall and, as recently shown, state-tracking when allowing state-transition matrices to have negative eigenvalues. Building on the interpretation of DeltaNet's recurrence as performing one step of online gradient descent per token on an associative recall loss, we introduce DeltaProduct, which instead takes multiple ($n_h$) steps per token. This naturally leads to diagonal plus rank--$n_h$ state-transition matrices, formed as products of $n_h$ generalized Householder transformations, providing a tunable mechanism to balance expressivity and efficiency. We provide a detailed theoretical characterization of the state-tracking capability of DeltaProduct in finite precision, showing how it improves by increasing $n_h$. Our extensive experiments demonstrate that DeltaProduct outperforms DeltaNet in both state-tracking and language modeling, while also showing significantly improved length extrapolation capabilities.", "authors": ["Julien Siems", "Timur Carstensen", "Arber Zela", "Frank Hutter", "Massimiliano Pontil", "Riccardo Grazzi"], "categories": ["cs.LG", "cs.CL", "cs.FL"], "fields_of_study": ["Computer Science"], "published_date": "2025-02-14", "url": "https://arxiv.org/abs/2502.10297", "pdf_url": "https://arxiv.org/pdf/2502.10297v7", "arxiv_id": "2502.10297", "doi": null, "citation_count": 50, "influential_citation_count": 7, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.4515}
{"id": "a9ff09ac593f189f4aea6a4cb110f5fd7533d1a4c1de9121ac4bc3eaf17be546", "sources": ["arxiv", "semantic_scholar"], "title": "A Survey on Mamba Architecture for Vision Applications", "abstract": "Transformers have become foundational for visual tasks such as object detection, semantic segmentation, and video understanding, but their quadratic complexity in attention mechanisms presents scalability challenges. To address these limitations, the Mamba architecture utilizes state-space models (SSMs) for linear scalability, efficient processing, and improved contextual awareness. This paper investigates Mamba architecture for visual domain applications and its recent advancements, including Vision Mamba (ViM) and VideoMamba, which introduce bidirectional scanning, selective scanning mechanisms, and spatiotemporal processing to enhance image and video understanding. Architectural innovations like position embeddings, cross-scan modules, and hierarchical designs further optimize the Mamba framework for global and local feature extraction. These advancements position Mamba as a promising architecture in computer vision research and applications.", "authors": ["Fady Ibrahim", "Guangjun Liu", "Guanghui Wang"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-02-11", "url": "https://arxiv.org/abs/2502.07161", "pdf_url": "https://arxiv.org/pdf/2502.07161v1", "arxiv_id": "2502.07161", "doi": "10.48550/arXiv.2502.07161", "citation_count": 14, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.294}
{"id": "036fcedb75e999c23d45b267ee2c7a74488c30812ae88dee773b4fe391b9954f", "sources": ["arxiv", "semantic_scholar"], "title": "Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing", "abstract": "State Space Models (SSMs) with selective scan (Mamba) have been adapted into efficient vision models. Mamba, unlike Vision Transformers, achieves linear complexity for token interactions through a recurrent hidden state process. This sequential processing is enhanced by a parallel scan algorithm, which reduces the computational time of recurrent steps from $L$ sequential steps to $log(L)$ parallel steps with respect to the number of input tokens ($L$). In this work, we propose Fast Vision Mamba (FastVim), that further reduces the computational time of the SSM block by reducing the number of recurrent steps in Vision Mamba models while still retaining model performance. By alternately pooling tokens along image dimensions across Mamba blocks, we obtain a 2$\\times$ reduction in the number of parallel steps in SSM block. Our model offers up to $72.5\\%$ speedup in inference speed compared to baseline Vision Mamba models on high resolution (2048$\\times$2048) images. Our experiments demonstrate state-of-the-art performance with dramatically improved throughput in a range of tasks such as image classification, cell perturbation prediction, segmentation, and object detection. Code is made available at https://github.com/insitro/FastVim", "authors": ["Saarthak Kapse", "Robin Betz", "Srinivasan Sivanandan"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-02-01", "url": "https://arxiv.org/abs/2502.00594", "pdf_url": "https://arxiv.org/pdf/2502.00594v1", "arxiv_id": "2502.00594", "doi": "10.1109/WACV61042.2026.00286", "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/insitro/FastVim", "venue": "IEEE Workshop/Winter Conference on Applications of Computer Vision", "quality_score": 0.0753}
{"id": "ee827f515cf9273fe46e67a0651c62bb3b7def326f097f263fa632028bf7512d", "sources": ["arxiv", "semantic_scholar"], "title": "MatIR: A Hybrid Mamba-Transformer Image Restoration Model", "abstract": "In recent years, Transformers-based models have made significant progress in the field of image restoration by leveraging their inherent ability to capture complex contextual features. Recently, Mamba models have made a splash in the field of computer vision due to their ability to handle long-range dependencies and their significant computational efficiency compared to Transformers. However, Mamba currently lags behind Transformers in contextual learning capabilities. To overcome the limitations of these two models, we propose a Mamba-Transformer hybrid image restoration model called MatIR. Specifically, MatIR cross-cycles the blocks of the Transformer layer and the Mamba layer to extract features, thereby taking full advantage of the advantages of the two architectures. In the Mamba module, we introduce the Image Inpainting State Space (IRSS) module, which traverses along four scan paths to achieve efficient processing of long sequence data. In the Transformer module, we combine triangular window-based local attention with channel-based global attention to effectively activate the attention mechanism over a wider range of image pixels. Extensive experimental results and ablation studies demonstrate the effectiveness of our approach.", "authors": ["Juan Wen", "Weiyan Hou", "Luc Van Gool", "Radu Timofte"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-01-30", "url": "https://arxiv.org/abs/2501.18401", "pdf_url": "https://arxiv.org/pdf/2501.18401v2", "arxiv_id": "2501.18401", "doi": "10.48550/arXiv.2501.18401", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2258}
{"id": "ab707f53533d0c9ec6e64e643a8c7c7dea1f4b72df88819951369a5de0b233bc", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba-Shedder: Post-Transformer Compression for Efficient Selective Structured State Space Models", "abstract": "Large pre-trained models have achieved outstanding results in sequence modeling. The Transformer block and its attention mechanism have been the main drivers of the success of these models. Recently, alternative architectures, such as Selective Structured State Space Models (SSMs), have been proposed to address the inefficiencies of Transformers. This paper explores the compression of SSM-based models, particularly Mamba and its hybrids. We study the sensitivity of these models to the removal of selected components at different granularities to reduce the model size and computational overhead, thus improving their efficiency while maintaining accuracy. The proposed solutions, collectively referred to as Mamba-Shedder, achieve a speedup of up to 1.4x during inference, demonstrating that model efficiency can be improved by eliminating several redundancies with minimal impact on the overall model performance. The code is available at https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning.", "authors": ["J. Pablo Muñoz", "Jinjie Yuan", "Nilesh Jain"], "categories": ["cs.LG", "cs.AI", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-01-28", "url": "https://arxiv.org/abs/2501.17088", "pdf_url": "https://arxiv.org/pdf/2501.17088v1", "arxiv_id": "2501.17088", "doi": "10.48550/arXiv.2501.17088", "citation_count": 6, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning", "venue": "North American Chapter of the Association for Computational Linguistics", "quality_score": 0.2113}
{"id": "c39525a22e3d00fa3b2a8226b80f51d290c75955ba6d45bd3a5315a2cd9d79bb", "sources": ["arxiv", "semantic_scholar"], "title": "Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization", "abstract": "The Mamba model, utilizing a structured state-space model (SSM), offers linear time complexity and demonstrates significant potential. Vision Mamba (ViM) extends this framework to vision tasks by incorporating a bidirectional SSM and patch embedding, surpassing Transformer-based models in performance. While model quantization is essential for efficient computing, existing works have focused solely on the original Mamba model and have not been applied to ViM. Additionally, they neglect quantizing the SSM layer, which is central to Mamba and can lead to substantial error propagation by naive quantization due to its inherent structure. In this paper, we focus on the post-training quantization (PTQ) of ViM. We address the issues with three core techniques: 1) a k-scaled token-wise quantization method for linear and convolutional layers, 2) a reparameterization technique to simplify hidden state quantization, and 3) a factor-determining method that reduces computational overhead by integrating operations. Through these methods, the error caused by PTQ can be mitigated. Experimental results on ImageNet-1k demonstrate only a 0.8-1.2\\% accuracy degradation due to PTQ, highlighting the effectiveness of our approach.", "authors": ["Bo-Yun Shi", "Yi-Cheng Lo", " An-Yeu", " Wu", "Yi-Min Tsai"], "categories": ["eess.IV"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2025-01-28", "url": "https://arxiv.org/abs/2501.16738", "pdf_url": "https://arxiv.org/pdf/2501.16738v2", "arxiv_id": "2501.16738", "doi": "10.1109/MLSP62443.2025.11204250", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Workshop on Machine Learning for Signal Processing", "quality_score": 0.1505}
{"id": "c18b88a445677f0d0821287478d4a952f328fe3376666c79c7e82bfac6868742", "sources": ["arxiv", "semantic_scholar"], "title": "Mixture-of-Mamba: Enhancing Multi-Modal State-Space Models with Modality-Aware Sparsity", "abstract": "State Space Models (SSMs) have emerged as efficient alternatives to Transformers for sequential modeling, but their inability to leverage modality-specific features limits their performance in multi-modal pretraining. Here, we propose Mixture-of-Mamba, a novel SSM architecture that introduces modality-aware sparsity through modality-specific parameterization of the Mamba block. Building on Mixture-of-Transformers (W. Liang et al. arXiv:2411.04996; 2024), we extend the benefits of modality-aware sparsity to SSMs while preserving their computational efficiency. We evaluate Mixture-of-Mamba across three multi-modal pretraining settings: Transfusion (interleaved text and continuous image tokens with diffusion loss), Chameleon (interleaved text and discrete image tokens), and an extended three-modality framework incorporating speech. Mixture-of-Mamba consistently reaches the same loss values at earlier training steps with significantly reduced computational costs. In the Transfusion setting, Mixture-of-Mamba achieves equivalent image loss using only 34.76% of the training FLOPs at the 1.4B scale. In the Chameleon setting, Mixture-of-Mamba reaches similar image loss with just 42.50% of the FLOPs at the 1.4B scale, and similar text loss with just 65.40% of the FLOPs. In the three-modality setting, MoM matches speech loss at 24.80% of the FLOPs at the 1.4B scale. Our ablation study highlights the synergistic effects of decoupling projection components, where joint decoupling yields greater gains than individual modifications. These results establish modality-aware sparsity as a versatile and effective design principle, extending its impact from Transformers to SSMs and setting new benchmarks in multi-modal pretraining. Our code can be accessed at https://github.com/Weixin-Liang/Mixture-of-Mamba", "authors": ["Weixin Liang", "Junhong Shen", "Genghan Zhang", "Ning Dong", "Luke Zettlemoyer", "Lili Yu"], "categories": ["cs.LG", "cs.AI", "cs.CL", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-01-27", "url": "https://arxiv.org/abs/2501.16295", "pdf_url": "https://arxiv.org/pdf/2501.16295v1", "arxiv_id": "2501.16295", "doi": "10.48550/arXiv.2501.16295", "citation_count": 8, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Weixin-Liang/Mixture-of-Mamba", "venue": "arXiv.org", "quality_score": 0.2386}
{"id": "979014108f4595a73a09d8e638eaf733f6b52aabdbd36a59fecd52fec28231d3", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space", "abstract": "Graph Neural Networks (GNNs) have shown great success in various graph-based learning tasks. However, it often faces the issue of over-smoothing as the model depth increases, which causes all node representations to converge to a single value and become indistinguishable. This issue stems from the inherent limitations of GNNs, which struggle to distinguish the importance of information from different neighborhoods. In this paper, we introduce MbaGCN, a novel graph convolutional architecture that draws inspiration from the Mamba paradigm-originally designed for sequence modeling. MbaGCN presents a new backbone for GNNs, consisting of three key components: the Message Aggregation Layer, the Selective State Space Transition Layer, and the Node State Prediction Layer. These components work in tandem to adaptively aggregate neighborhood information, providing greater flexibility and scalability for deep GNN models. While MbaGCN may not consistently outperform all existing methods on each dataset, it provides a foundational framework that demonstrates the effective integration of the Mamba paradigm into graph representation learning. Through extensive experiments on benchmark datasets, we demonstrate that MbaGCN paves the way for future advancements in graph neural network research.", "authors": ["Xin He", "Yili Wang", "Wenqi Fan", "Xu Shen", "Xin Juan", "Rui Miao", "Xin Wang"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-01-26", "url": "https://arxiv.org/abs/2501.15461", "pdf_url": "https://arxiv.org/pdf/2501.15461v4", "arxiv_id": "2501.15461", "doi": "10.24963/ijcai.2025/595", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Joint Conference on Artificial Intelligence", "quality_score": 0.2258}
{"id": "30cb6f81a1959e2818e2a8e78ed4d69ed6d1b2ddcb2146c145a4f5ab5b4cfd0b", "sources": ["arxiv", "semantic_scholar"], "title": "Surface Vision Mamba: Leveraging Bidirectional State Space Model for Efficient Spherical Manifold Representation", "abstract": "Attention-based methods have demonstrated exceptional performance in modelling long-range dependencies on spherical cortical surfaces, surpassing traditional Geometric Deep Learning (GDL) models. However, their extensive inference time and high memory demands pose challenges for application to large datasets with limited computing resources. Inspired by the state space model in computer vision, we introduce the attention-free Vision Mamba (Vim) to spherical surfaces, presenting a domain-agnostic architecture for analyzing data on spherical manifolds. Our method achieves surface patching by representing spherical data as a sequence of triangular patches derived from a subdivided icosphere. The proposed Surface Vision Mamba (SiM) is evaluated on multiple neurodevelopmental phenotype regression tasks using cortical surface metrics from neonatal brains. Experimental results demonstrate that SiM outperforms both attention- and GDL-based methods, delivering 4.8 times faster inference and achieving 91.7% lower memory consumption compared to the Surface Vision Transformer (SiT) under the Ico-4 grid partitioning. Sensitivity analysis further underscores the potential of SiM to identify subtle cognitive developmental patterns. The code is available at https://github.com/Rongzhao-He/surface-vision-mamba.", "authors": ["Rongzhao He", "Weihao Zheng", "Leilei Zhao", "Ying Wang", "Dalin Zhu", "Dan Wu", "Bin Hu"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-01-24", "url": "https://arxiv.org/abs/2501.14679", "pdf_url": "https://arxiv.org/pdf/2501.14679v5", "arxiv_id": "2501.14679", "doi": "10.48550/arXiv.2501.14679", "citation_count": 4, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Rongzhao-He/surface-vision-mamba", "venue": "International Conference on Medical Image Computing and Computer-Assisted Intervention", "quality_score": 0.1747}
{"id": "d6e512a519fb5d72c5fe907efe8192698903728f066d6e4512fe291ce660530c", "sources": ["arxiv", "semantic_scholar"], "title": "Let SSMs be ConvNets: State-space Modeling with Optimal Tensor Contractions", "abstract": "We introduce Centaurus, a class of networks composed of generalized state-space model (SSM) blocks, where the SSM operations can be treated as tensor contractions during training. The optimal order of tensor contractions can then be systematically determined for every SSM block to maximize training efficiency. This allows more flexibility in designing SSM blocks beyond the depthwise-separable configuration commonly implemented. The new design choices will take inspiration from classical convolutional blocks including group convolutions, full convolutions, and bottleneck blocks. We architect the Centaurus network with a mixture of these blocks, to balance between network size and performance, as well as memory and computational efficiency during both training and inference. We show that this heterogeneous network design outperforms its homogeneous counterparts in raw audio processing tasks including keyword spotting, speech denoising, and automatic speech recognition (ASR). For ASR, Centaurus is the first network with competitive performance that can be made fully state-space based, without using any nonlinear recurrence (LSTMs), explicit convolutions (CNNs), or (surrogate) attention mechanism. The source code is available as supplementary material on https://openreview.net/forum?id=PkpNRmBZ32", "authors": ["Yan Ru Pei"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-01-22", "url": "https://arxiv.org/abs/2501.13230", "pdf_url": "https://arxiv.org/pdf/2501.13230v2", "arxiv_id": "2501.13230", "doi": "10.48550/arXiv.2501.13230", "citation_count": 5, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "International Conference on Learning Representations", "quality_score": 0.1945}
{"id": "57b40900c6ac4f922b06f8f315b583a3daa906d3d7eac5df6ef73990f8658039", "sources": ["arxiv", "semantic_scholar"], "title": "SeRpEnt: Selective Resampling for Expressive State Space Models", "abstract": "State Space Models (SSMs) have recently enjoyed a rise to prominence in the field of deep learning for sequence modeling, especially as an alternative to Transformers. Their success stems from avoiding two well-known drawbacks of attention-based models: quadratic complexity with respect to the sequence length and inability to model long-range dependencies. The SSM variant Mamba has demonstrated performance comparable to Transformers without any form of attention, thanks to the use of a selective mechanism for the state parameters. Selectivity, however, is only evaluated empirically and the reasons of its effectiveness remain unclear. In this work, we show how selectivity is related to the sequence processing. Our analysis shows that selective time intervals in Mamba act as linear approximators of information. Then, we propose our SeRpEnt architecture, a SSM that further exploits selectivity to compress sequences in an information-aware fashion. It employs a resampling mechanism that aggregates elements based on their information content. Our empirical results in the Long Range Arena benchmark and other language modeling tasks show benefits of the SeRpEnt's resampling mechanism.", "authors": ["Stefano Rando", "Luca Romani", "Matteo Migliarini", "Luca Franco", "Denis Gudovskiy", "Fabio Galasso"], "categories": ["cs.LG", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-01-20", "url": "https://arxiv.org/abs/2501.11729", "pdf_url": "https://arxiv.org/pdf/2501.11729v1", "arxiv_id": "2501.11729", "doi": "10.48550/arXiv.2501.11729", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "fdeea7006f81268ed91c98c905f33db4d2a2071b8f0b294152be69ea21364368", "sources": ["arxiv", "semantic_scholar"], "title": "DH-Mamba: Exploring Dual-domain Hierarchical State Space Models for MRI Reconstruction", "abstract": "The accelerated MRI reconstruction poses a challenging ill-posed inverse problem due to the significant undersampling in k-space. Deep neural networks, such as CNNs and ViTs, have shown substantial performance improvements for this task while encountering the dilemma between global receptive fields and efficient computation. To this end, this paper explores selective state space models (Mamba), a new paradigm for long-range dependency modeling with linear complexity, for efficient and effective MRI reconstruction. However, directly applying Mamba to MRI reconstruction faces three significant issues: (1) Mamba typically flattens 2D images into distinct 1D sequences along rows and columns, disrupting k-space's unique spectrum and leaving its potential in k-space learning unexplored. (2) Existing approaches adopt multi-directional lengthy scanning to unfold images at the pixel level, leading to long-range forgetting and high computational burden. (3) Mamba struggles with spatially-varying contents, resulting in limited diversity of local representations. To address these, we propose a dual-domain hierarchical Mamba for MRI reconstruction from the following perspectives: (1) We pioneer vision Mamba in k-space learning. A circular scanning is customized for spectrum unfolding, benefiting the global modeling of k-space. (2) We propose a hierarchical Mamba with an efficient scanning strategy in both image and k-space domains. It mitigates long-range forgetting and achieves a better trade-off between efficiency and performance. (3) We develop a local diversity enhancement module to improve the spatially-varying representation of Mamba. Extensive experiments are conducted on three public datasets for MRI reconstruction under various undersampling patterns. Comprehensive results demonstrate that our method significantly outperforms state-of-the-art methods with lower computational cost.", "authors": ["Yucong Meng", "Zhiwei Yang", "Zhijian Song", "Yonghong Shi"], "categories": ["eess.IV", "cs.CV"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2025-01-14", "url": "https://arxiv.org/abs/2501.08163", "pdf_url": "https://arxiv.org/pdf/2501.08163v3", "arxiv_id": "2501.08163", "doi": "10.1109/TCSVT.2025.3614828", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1945}
{"id": "4df06be7e063b7ab6e0d940154d61ad154fbe8e836b7357d82f071d1cc830e84", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba-MOC: A Multicategory Remote Object Counting via State Space Model", "abstract": "Multicategory remote object counting is a fundamental task in computer vision, aimed at accurately estimating the number of objects of various categories in remote images. Existing methods rely on CNNs and Transformers, but CNNs struggle to capture global dependencies, and Transformers are computationally expensive, which limits their effectiveness in remote applications. Recently, Mamba has emerged as a promising solution in the field of computer vision, offering a linear complexity for modeling global dependencies. To this end, we propose Mamba-MOC, a mamba-based network designed for multi-category remote object counting, which represents the first application of Mamba to remote sensing object counting. Specifically, we propose a cross-scale interaction module to facilitate the deep integration of hierarchical features. Then we design a context state space model to capture both global and local contextual information and provide local neighborhood information during the scan process. Experimental results in large-scale realistic scenarios demonstrate that our proposed method achieves state-of-the-art performance compared with some mainstream counting algorithms.", "authors": ["Peng Liu", "Sen Lei", "Heng-Chao Li"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-01-12", "url": "https://arxiv.org/abs/2501.06697", "pdf_url": "https://arxiv.org/pdf/2501.06697v2", "arxiv_id": "2501.06697", "doi": "10.1109/IGARSS55030.2025.11243409", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE International Geoscience and Remote Sensing Symposium", "quality_score": 0.1505}
{"id": "1345633c234f2199440867a735110198484dd5664eed87cb9be3cfd957d98673", "sources": ["arxiv", "semantic_scholar"], "title": "MS-Temba: Multi-Scale Temporal Mamba for Understanding Long Untrimmed Videos", "abstract": "Temporal Action Detection (TAD) in untrimmed videos poses significant challenges, particularly for Activities of Daily Living (ADL) requiring models to (1) process long-duration videos, (2) capture temporal variations in actions, and (3) simultaneously detect dense overlapping actions. Existing CNN and Transformer-based approaches, struggle to jointly capture fine-grained detail and long-range structure at scale. State-space Model (SSM) based Mamba offers powerful long-range modeling, but naive application to TAD collapses fine-grained temporal structure and fails to account for the challenges inherent to TAD. To this end, we propose Multi-Scale Temporal Mamba (MS-Temba), which extends Mamba to TAD with newly introduced dilated SSMs. Each Temba block, comprising dilated SSMs coupled with our proposed additional losses, enables the learning of discriminative representations across temporal scales. A lightweight Multi-scale Mamba Fuser then unifies these multi-scale features via SSM-based aggregation, yielding precise action-boundary localization. With only 17M parameters, MS-Temba achieves state-of-the-art performance on densely labeled ADL benchmarks TSU & Charades, and further generalizes to long-form video summarization, setting new state-of-the-art results on TVSum & SumMe.", "authors": ["Arkaprava Sinha", "Monish Soundar Raj", "Pu Wang", "Ahmed Helmy", "Hieu Le", "Srijan Das"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-01-10", "url": "https://arxiv.org/abs/2501.06138", "pdf_url": "https://arxiv.org/pdf/2501.06138v3", "arxiv_id": "2501.06138", "doi": null, "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1505}
{"id": "7c70705b21198ea570d913add6a45c87eaa8c59a726633cf27e370c9329a19c3", "sources": ["arxiv", "semantic_scholar"], "title": "A Separable Self-attention Inspired by the State Space Model for Computer Vision", "abstract": "Mamba is an efficient State Space Model (SSM) with linear computational complexity. Although SSMs are not suitable for handling non-causal data, Vision Mamba (ViM) methods still demonstrate good performance in tasks such as image classification and object detection. Recent studies have shown that there is a rich theoretical connection between state space models and attention variants. We propose a novel separable self attention method, for the first time introducing some excellent design concepts of Mamba into separable self-attention. To ensure a fair comparison with ViMs, we introduce VMINet, a simple yet powerful prototype architecture, constructed solely by stacking our novel attention modules with the most basic down-sampling layers. Notably, VMINet differs significantly from the conventional Transformer architecture. Our experiments demonstrate that VMINet has achieved competitive results on image classification and high-resolution dense prediction tasks.Code is available at: https://github.com/yws-wxs/VMINet.", "authors": ["Juntao Zhang", "Shaogeng Liu", "Kun Bian", "You Zhou", "Pei Zhang", "Jianning Liu", "Jun Zhou", "Bingyan Liu"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-01-03", "url": "https://arxiv.org/abs/2501.02040", "pdf_url": "https://arxiv.org/pdf/2501.02040v2", "arxiv_id": "2501.02040", "doi": "10.48550/arXiv.2501.02040", "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/yws-wxs/VMINet", "venue": "arXiv.org", "quality_score": 0.0753}
{"id": "89dff4ac38fb0cd922944e50da6884b6e0d152a87492905bac9b50d8176c3f3a", "sources": ["arxiv", "semantic_scholar"], "title": "Exploring Graph Mamba: A Comprehensive Survey on State-Space Models for Graph Learning", "abstract": "Graph Mamba, a powerful graph embedding technique, has emerged as a cornerstone in various domains, including bioinformatics, social networks, and recommendation systems. This survey represents the first comprehensive study devoted to Graph Mamba, to address the critical gaps in understanding its applications, challenges, and future potential. We start by offering a detailed explanation of the original Graph Mamba architecture, highlighting its key components and underlying mechanisms. Subsequently, we explore the most recent modifications and enhancements proposed to improve its performance and applicability. To demonstrate the versatility of Graph Mamba, we examine its applications across diverse domains. A comparative analysis of Graph Mamba and its variants is conducted to shed light on their unique characteristics and potential use cases. Furthermore, we identify potential areas where Graph Mamba can be applied in the future, highlighting its potential to revolutionize data analysis in these fields. Finally, we address the current limitations and open research questions associated with Graph Mamba. By acknowledging these challenges, we aim to stimulate further research and development in this promising area. This survey serves as a valuable resource for both newcomers and experienced researchers seeking to understand and leverage the power of Graph Mamba.", "authors": ["Safa Ben Atitallah", "Chaima Ben Rabah", "Maha Driss", "Wadii Boulila", "Anis Koubaa"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-12-24", "url": "https://arxiv.org/abs/2412.18322", "pdf_url": "https://arxiv.org/pdf/2412.18322v1", "arxiv_id": "2412.18322", "doi": "10.48550/arXiv.2412.18322", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "5479323239357660d47e386d3fd38effe39f59e9cacb14978294e02b649b4974", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba-SEUNet: Mamba UNet for Monaural Speech Enhancement", "abstract": "In recent speech enhancement (SE) research, transformer and its variants have emerged as the predominant methodologies. However, the quadratic complexity of the self-attention mechanism imposes certain limitations on practical deployment. Mamba, as a novel state-space model (SSM), has gained widespread application in natural language processing and computer vision due to its strong capabilities in modeling long sequences and relatively low computational complexity. In this work, we introduce Mamba-SEUNet, an innovative architecture that integrates Mamba with U-Net for SE tasks. By leveraging bidirectional Mamba to model forward and backward dependencies of speech signals at different resolutions, and incorporating skip connections to capture multi-scale information, our approach achieves state-of-the-art (SOTA) performance. Experimental results on the VCTK+DEMAND dataset indicate that Mamba-SEUNet attains a PESQ score of 3.59, while maintaining low computational complexity. When combined with the Perceptual Contrast Stretching technique, Mamba-SEUNet further improves the PESQ score to 3.73.", "authors": ["Junyu Wang", "Zizhen Lin", "Tianrui Wang", "Meng Ge", "Longbiao Wang", "Jianwu Dang"], "categories": ["cs.SD", "cs.AI", "eess.AS"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-12-21", "url": "https://arxiv.org/abs/2412.16626", "pdf_url": "https://arxiv.org/pdf/2412.16626v2", "arxiv_id": "2412.16626", "doi": "10.1109/ICASSP49660.2025.10889525", "citation_count": 16, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "IEEE International Conference on Acoustics, Speech, and Signal Processing", "quality_score": 0.3076}
{"id": "66033d19eed07804aec2658c8afc4f6c05b341ca430d6a4c05303edc4dc525c9", "sources": ["arxiv", "semantic_scholar"], "title": "State Space Models are Strong Text Rerankers", "abstract": "Transformers dominate NLP and IR; but their inference inefficiencies and challenges in extrapolating to longer contexts have sparked interest in alternative model architectures. Among these, state space models (SSMs) like Mamba offer promising advantages, particularly $O(1)$ time complexity in inference. Despite their potential, SSMs' effectiveness at text reranking -- a task requiring fine-grained query-document interaction and long-context understanding -- remains underexplored. This study benchmarks SSM-based architectures (specifically, Mamba-1 and Mamba-2) against transformer-based models across various scales, architectures, and pre-training objectives, focusing on performance and efficiency in text reranking tasks. We find that (1) Mamba architectures achieve competitive text ranking performance, comparable to transformer-based models of similar size; (2) they are less efficient in training and inference compared to transformers with flash attention; and (3) Mamba-2 outperforms Mamba-1 in both performance and efficiency. These results underscore the potential of state space models as a transformer alternative and highlight areas for improvement in future IR applications.", "authors": ["Zhichao Xu", "Jinghua Yan", "Ashim Gupta", "Vivek Srikumar"], "categories": ["cs.CL", "cs.IR"], "fields_of_study": ["Computer Science"], "published_date": "2024-12-18", "url": "https://arxiv.org/abs/2412.14354", "pdf_url": "https://arxiv.org/pdf/2412.14354v3", "arxiv_id": "2412.14354", "doi": "10.48550/arXiv.2412.14354", "citation_count": 10, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Workshop on Representation Learning for NLP", "quality_score": 0.2603}
{"id": "804f511b6e1403d5d488acdb4c2873c9c48c696ee5c18edbb2068d23193831bb", "sources": ["arxiv", "semantic_scholar"], "title": "GG-SSMs: Graph-Generating State Space Models", "abstract": "State Space Models (SSMs) are powerful tools for modeling sequential data in computer vision and time series analysis domains. However, traditional SSMs are limited by fixed, one-dimensional sequential processing, which restricts their ability to model non-local interactions in high-dimensional data. While methods like Mamba and VMamba introduce selective and flexible scanning strategies, they rely on predetermined paths, which fails to efficiently capture complex dependencies. We introduce Graph-Generating State Space Models (GG-SSMs), a novel framework that overcomes these limitations by dynamically constructing graphs based on feature relationships. Using Chazelle's Minimum Spanning Tree algorithm, GG-SSMs adapt to the inherent data structure, enabling robust feature propagation across dynamically generated graphs and efficiently modeling complex dependencies. We validate GG-SSMs on 11 diverse datasets, including event-based eye-tracking, ImageNet classification, optical flow estimation, and six time series datasets. GG-SSMs achieve state-of-the-art performance across all tasks, surpassing existing methods by significant margins. Specifically, GG-SSM attains a top-1 accuracy of 84.9% on ImageNet, outperforming prior SSMs by 1%, reducing the KITTI-15 error rate to 2.77%, and improving eye-tracking detection rates by up to 0.33% with fewer parameters. These results demonstrate that dynamic scanning based on feature relationships significantly improves SSMs' representational power and efficiency, offering a versatile tool for various applications in computer vision and beyond.", "authors": ["Nikola Zubić", "Davide Scaramuzza"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-12-17", "url": "https://arxiv.org/abs/2412.12423", "pdf_url": "https://arxiv.org/pdf/2412.12423v2", "arxiv_id": "2412.12423", "doi": "10.1109/CVPR52734.2025.02688", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Computer Vision and Pattern Recognition", "quality_score": 0.1945}
{"id": "59a53da5fbfcad7aec2aaecc4c403bc3706a842b4778fb833c3ad99683817acd", "sources": ["arxiv", "semantic_scholar"], "title": "BarcodeMamba: State Space Models for Biodiversity Analysis", "abstract": "DNA barcodes are crucial in biodiversity analysis for building automatic identification systems that recognize known species and discover unseen species. Unlike human genome modeling, barcode-based invertebrate identification poses challenges in the vast diversity of species and taxonomic complexity. Among Transformer-based foundation models, BarcodeBERT excelled in species-level identification of invertebrates, highlighting the effectiveness of self-supervised pretraining on barcode-specific datasets. Recently, structured state space models (SSMs) have emerged, with a time complexity that scales sub-quadratically with the context length. SSMs provide an efficient parameterization of sequence modeling relative to attention-based architectures. Given the success of Mamba and Mamba-2 in natural language, we designed BarcodeMamba, a performant and efficient foundation model for DNA barcodes in biodiversity analysis. We conducted a comprehensive ablation study on the impacts of self-supervised training and tokenization methods, and compared both versions of Mamba layers in terms of expressiveness and their capacity to identify \"unseen\" species held back from training. Our study shows that BarcodeMamba has better performance than BarcodeBERT even when using only 8.3% as many parameters, and improves accuracy to 99.2% on species-level accuracy in linear probing without fine-tuning for \"seen\" species. In our scaling study, BarcodeMamba with 63.6% of BarcodeBERT's parameters achieved 70.2% genus-level accuracy in 1-nearest neighbor (1-NN) probing for unseen species. The code repository to reproduce our experiments is available at https://github.com/bioscan-ml/BarcodeMamba.", "authors": ["Tiancheng Gao", "Graham W. Taylor"], "categories": ["cs.LG", "q-bio.GN", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-12-15", "url": "https://arxiv.org/abs/2412.11084", "pdf_url": "https://arxiv.org/pdf/2412.11084v1", "arxiv_id": "2412.11084", "doi": "10.48550/arXiv.2412.11084", "citation_count": 5, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/bioscan-ml/BarcodeMamba", "venue": "arXiv.org", "quality_score": 0.1945}
{"id": "f1896b6af59edebeaea79db208a8666e4854069cfbf5e39e37171a6136a2b1e5", "sources": ["arxiv", "semantic_scholar"], "title": "Image Forgery Localization with State Space Models", "abstract": "Pixel dependency modeling from tampered images is pivotal for image forgery localization. Current approaches predominantly rely on Convolutional Neural Networks (CNNs) or Transformer-based models, which often either lack sufficient receptive fields or entail significant computational overheads. Recently, State Space Models (SSMs), exemplified by Mamba, have emerged as a promising approach. They not only excel in modeling long-range interactions but also maintain a linear computational complexity. In this paper, we propose LoMa, a novel image forgery localization method that leverages the selective SSMs. Specifically, LoMa initially employs atrous selective scan to traverse the spatial domain and convert the tampered image into ordered patch sequences, and subsequently applies multi-directional state space modeling. In addition, an auxiliary convolutional branch is introduced to enhance local feature extraction. Extensive experimental results validate the superiority of LoMa over CNN-based and Transformer-based state-of-the-arts. To our best knowledge, this is the first image forgery localization model constructed based on the SSM-based model. We aim to establish a baseline and provide valuable insights for the future development of more efficient and effective SSM-based forgery localization models. Code is available at https://github.com/multimediaFor/LoMa.", "authors": ["Zijie Lou", "Gang Cao", "Kun Guo", "Shaowei Weng", "Lifang Yu"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-12-15", "url": "https://arxiv.org/abs/2412.11214", "pdf_url": "https://arxiv.org/pdf/2412.11214v2", "arxiv_id": "2412.11214", "doi": "10.1109/LSP.2025.3559429", "citation_count": 8, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/multimediaFor/LoMa", "venue": "IEEE Signal Processing Letters", "quality_score": 0.2386}
{"id": "0470402734e4b1aebdf2bc5823bdd36f9fb9ad40f49a463d09161fba8d3d8169", "sources": ["arxiv", "semantic_scholar"], "title": "XYScanNet: A State Space Model for Single Image Deblurring", "abstract": "Deep state-space models (SSMs), like recent Mamba architectures, are emerging as a promising alternative to CNN and Transformer networks. Existing Mamba-based restoration methods process visual data by leveraging a flatten-and-scan strategy that converts image patches into a 1D sequence before scanning. However, this scanning paradigm ignores local pixel dependencies and introduces spatial misalignment by positioning distant pixels incorrectly adjacent, which reduces local noise-awareness and degrades image sharpness in low-level vision tasks. To overcome these issues, we propose a novel slice-and-scan strategy that alternates scanning along intra- and inter-slices. We further design a new Vision State Space Module (VSSM) for image deblurring, and tackle the inefficiency challenges of the current Mamba-based vision module. Building upon this, we develop XYScanNet, an SSM architecture integrated with a lightweight feature fusion module for enhanced image deblurring. XYScanNet, maintains competitive distortion metrics and significantly improves perceptual performance. Experimental results show that XYScanNet enhances KID by $17\\%$ compared to the nearest competitor.", "authors": ["Hanzhou Liu", "Chengkai Liu", "Jiacong Xu", "Peng Jiang", "Mi Lu"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-12-13", "url": "https://arxiv.org/abs/2412.10338", "pdf_url": "https://arxiv.org/pdf/2412.10338v3", "arxiv_id": "2412.10338", "doi": "10.1109/CVPRW67362.2025.00082", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR) Workshops, 2025", "quality_score": 0.2113}
{"id": "09f5453b9cfbaadf4514202ba7a0d1b597bff603a4110dd9053f70f70061b5e9", "sources": ["arxiv", "semantic_scholar"], "title": "DG-Mamba: Robust and Efficient Dynamic Graph Structure Learning with Selective State Space Models", "abstract": "Dynamic graphs exhibit intertwined spatio-temporal evolutionary patterns, widely existing in the real world. Nevertheless, the structure incompleteness, noise, and redundancy result in poor robustness for Dynamic Graph Neural Networks (DGNNs). Dynamic Graph Structure Learning (DGSL) offers a promising way to optimize graph structures. However, aside from encountering unacceptable quadratic complexity, it overly relies on heuristic priors, making it hard to discover underlying predictive patterns. How to efficiently refine the dynamic structures, capture intrinsic dependencies, and learn robust representations, remains under-explored. In this work, we propose the novel DG-Mamba, a robust and efficient Dynamic Graph structure learning framework with the Selective State Space Models (Mamba). To accelerate the spatio-temporal structure learning, we propose a kernelized dynamic message-passing operator that reduces the quadratic time complexity to linear. To capture global intrinsic dynamics, we establish the dynamic graph as a self-contained system with State Space Model. By discretizing the system states with the cross-snapshot graph adjacency, we enable the long-distance dependencies capturing with the selective snapshot scan. To endow learned dynamic structures more expressive with informativeness, we propose the self-supervised Principle of Relevant Information for DGSL to regularize the most relevant yet least redundant information, enhancing global robustness. Extensive experiments demonstrate the superiority of the robustness and efficiency of our DG-Mamba compared with the state-of-the-art baselines against adversarial attacks.", "authors": ["Haonan Yuan", "Qingyun Sun", "Zhaonan Wang", "Xingcheng Fu", "Cheng Ji", "Yongjian Wang", "Bo Jin", "Jianxin Li"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-12-11", "url": "https://arxiv.org/abs/2412.08160", "pdf_url": "https://arxiv.org/pdf/2412.08160v4", "arxiv_id": "2412.08160", "doi": "10.48550/arXiv.2412.08160", "citation_count": 15, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "AAAI Conference on Artificial Intelligence", "quality_score": 0.301}
{"id": "b72c4346f6b58253bc9014e4cc4c82d3f312ef02ffe2ca46743793c7bbfdee90", "sources": ["arxiv", "semantic_scholar"], "title": "Bidirectional Mamba state-space model for anomalous diffusion", "abstract": "Characterizing anomalous diffusion is crucial in order to understand the evolution of complex stochastic systems, from molecular interactions to cellular dynamics. In this work, we characterize the performances regarding such a task of Bi-Mamba, a novel state-space deep-learning architecture articulated with a bidirectional scan mechanism. Our implementation is tested on the AnDi-2 challenge datasets among others. Designed for regression tasks, the Bi-Mamba architecture infers efficiently the effective diffusion coefficient and anomalous exponent from single, short trajectories. As such, our results indicate the potential practical use of the Bi-Mamba architecture for anomalousdiffusion characterization.", "authors": ["Maxime Lavaud", "Yosef Shokeeb", "Juliette Lacherez", "Yacine Amarouchene", "Thomas Salez"], "categories": ["cond-mat.soft", "physics.bio-ph", "physics.optics", "stat.ML"], "fields_of_study": ["Physics", "Mathematics"], "published_date": "2024-12-10", "url": "https://arxiv.org/abs/2412.07299", "pdf_url": "https://arxiv.org/pdf/2412.07299v1", "arxiv_id": "2412.07299", "doi": "10.1088/2515-7647/add42c", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0753}
{"id": "80aa1b7f3d8b444da13b3347408b90c9a06893e10e4395d0154db3dc68764b28", "sources": ["arxiv", "semantic_scholar"], "title": "The Computational Limits of State-Space Models and Mamba via the Lens of Circuit Complexity", "abstract": "In this paper, we analyze the computational limitations of Mamba and State-space Models (SSMs) by using the circuit complexity framework. Despite Mamba's stateful design and recent attention as a strong candidate to outperform Transformers, we have demonstrated that both Mamba and SSMs with $\\mathrm{poly}(n)$-precision and constant-depth layers reside within the $\\mathsf{DLOGTIME}$-uniform $\\mathsf{TC}^0$ complexity class. This result indicates Mamba has the same computational capabilities as Transformer theoretically, and it cannot solve problems like arithmetic formula problems, boolean formula value problems, and permutation composition problems if $\\mathsf{TC}^0 \\neq \\mathsf{NC}^1$. Therefore, it challenges the assumption Mamba is more computationally expressive than Transformers. Our contributions include rigorous proofs showing that Selective SSM and Mamba architectures can be simulated by $\\mathsf{DLOGTIME}$-uniform $\\mathsf{TC}^0$ circuits, and they cannot solve problems outside $\\mathsf{TC}^0$.", "authors": ["Yifang Chen", "Xiaoyu Li", "Yingyu Liang", "Zhenmei Shi", "Zhao Song"], "categories": ["cs.CC", "cs.AI", "cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-12-09", "url": "https://arxiv.org/abs/2412.06148", "pdf_url": "https://arxiv.org/pdf/2412.06148v2", "arxiv_id": "2412.06148", "doi": "10.48550/arXiv.2412.06148", "citation_count": 28, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3656}
{"id": "a364113b04ffa18a3b300d12bb5bbdb1a421c9948cd95ab97652074f73dcdaf1", "sources": ["arxiv", "semantic_scholar"], "title": "Learning Mamba as a Continual Learner: Meta-learning Selective State Space Models for Efficient Continual Learning", "abstract": "Continual learning (CL) aims to efficiently learn from a non-stationary data stream, without storing or recomputing all seen samples. CL enables prediction on new tasks by incorporating sequential training samples. Building on this connection between CL and sequential modeling, meta-continual learning (MCL) aims to meta-learn an efficient continual learner as a sequence prediction model, with advanced sequence models like Transformers being natural choices. However, despite decent performance, Transformers rely on a linearly growing cache to store all past representations, conflicting with CL's objective of not storing all seen samples and limiting efficiency. In this paper, we focus on meta-learning sequence-prediction-based continual learners without retaining all past representations. While attention-free models with fixed-size hidden states (e.g., Linear Transformers) align with CL's essential goal and efficiency needs, they have shown limited effectiveness in MCL in previous literature. Given Mamba's strong sequence modeling performance and attention-free nature, we explore a key question: Can attention-free models like Mamba perform well on MCL? By formulating Mamba and the SSM for MCL tasks, we propose MambaCL, a meta-learned continual learner. To enhance MambaCL's training, we introduce selectivity regularization, leveraging the connection between Mamba and Transformers to guide its behavior over sequences. Furthermore, we study how Mamba and other models perform across various MCL scenarios through extensive and well-designed experiments. Our results highlight the promising performance and strong generalization of Mamba and attention-free models in MCL, demonstrating its potential for efficient continual learning and adaptation.", "authors": ["Chongyang Zhao", "Dong Gong"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-12-01", "url": "https://arxiv.org/abs/2412.00776", "pdf_url": "https://arxiv.org/pdf/2412.00776v4", "arxiv_id": "2412.00776", "doi": "10.48550/arXiv.2412.00776", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1747}
{"id": "9fb777cfe231521320735c9cddccbd19976404f2f99ae36775d341939835b338", "sources": ["arxiv", "semantic_scholar"], "title": "In-situ observations of resident space objects with the CHEOPS space telescope", "abstract": "The CHaracterising ExOPlanet Satellite (CHEOPS) is a partnership between the European Space Agency and Switzerland with important contributions by 10 additional ESA member States. It is the first S-class mission in the ESA Science Programme. CHEOPS has been flying on a Sun-synchronous low Earth orbit since December 2019, collecting millions of short-exposure images in the visible domain to study exoplanet properties. A small yet increasing fraction of CHEOPS images show linear trails caused by resident space objects crossing the instrument field of view. To characterize the population of satellites and orbital debris observed by CHEOPS, all and every science images acquired over the past 3 years have been scanned with a Hough transform algorithm to identify the characteristic linear features that these objects cause on the images. Thousands of trails have been detected. This statistically significant sample shows interesting trends and features such as an increased occurrence rate over the past years as well as the fingerprint of the Starlink constellation. The cross-matching of individual trails with catalogued objects is underway as we aim to measure their distance at the time of observation and deduce the apparent magnitude of the detected objects. As space agencies and private companies are developing new space-based surveillance and tracking activities to catalogue and characterize the distribution of small debris, the CHEOPS experience is timely and relevant. With the first CHEOPS mission extension currently running until the end of 2026, and a possible second extension until the end of 2029, the longer time coverage will make our dataset even more valuable to the community, especially for characterizing objects with recurrent crossings.", "authors": ["Nicolas Billot", "Stephan Hellmich", "Willy Benz", "Andrea Fortier", "David Ehrenreich", "Christopher Broeg", "Alexis Heitzmann", "Anja Bekkelien", "Alexis Brandeker", "Yann Alibert", "Roi Alonso", "Tamas Bárczy", "David Barrado Navascues", "Susana C. C. Barros", "Wolfgang Baumjohann", "Federico Biondi", "Luca Borsato", "Andrew Collier Cameron", "Carlos Corral van Damme", "Alexandre C. M. Correia", "Szilard Csizmadia", "Patricio E. Cubillos", "Melvyn B. Davies", "Magali Deleuil", "Adrien Deline", "Olivier D. S. Demangeon", "Brice-Olivier Demory", "Aliz Derekas", "Billy Edwards", "Jo Ann Egger", "Anders Erikson", "Luca Fossati", "Malcolm Fridlund", "Davide Gandolfi", "Kosmas Gazeas", "Michaël Gillon", "Manuel Güdel", "Maximilian N. Günther", "Ch. Helling", "Kate G. Isaak", "Laszlo L. Kiss", "Judith Korth", "Kristine W. F. Lam", "Jacques Laskar", "Alain Lecavelier des Etangs", "Monika Lendl", "Demetrio Magrin", "Pierre F. L. Maxted", "Marko Mecina", "Bruno Merín", "Christoph Mordasini", "Valerio Nascimbeni", "Göran Olofsson", "Roland Ottensamer", "Isabella Pagano", "Enric Pallé", "Gisbert Peter", "Daniele Piazza", "Giampaolo Piotto", "Don Pollacco", "Didier Queloz", "Roberto Ragazzoni", "Nicola Rando", "Heike Rauer", "Ignasi Ribas", "Martin Rieder", "Nuno C. Santos", "Gaetano Scandariato", "Damien Ségransan", "Attila E. Simon", "Alexis M. S. Smith", "Sérgio G. Sousa", "Manu Stalport", "Sophia Sulis", "Gyula M. Szabó", "Stéphane Udry", "Bernd Ulmer", "Solène Ulmer-Moll", "Valérie Van Grootel", "Julia Venturini", "Eva Villaver", "Nicholas A. Walton", "Thomas G. Wilson"], "categories": ["astro-ph.EP", "astro-ph.IM", "physics.data-an", "physics.space-ph"], "fields_of_study": ["Physics"], "published_date": "2024-11-27", "url": "https://arxiv.org/abs/2411.18326", "pdf_url": "https://arxiv.org/pdf/2411.18326v1", "arxiv_id": "2411.18326", "doi": "10.1016/j.jsse.2024.08.005", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Journal of Space Safety Engineering", "quality_score": 0.1505}
{"id": "f07dded78933fbf41fdef11fb3ed23e843e8e7c282ee8c3526f5aa559bd1dae7", "sources": ["arxiv", "semantic_scholar"], "title": "Deformable Mamba for Wide Field of View Segmentation", "abstract": "Recent advancements in the Mamba architecture, with its linear computational complexity, being a promising alternative to transformer architectures suffering from quadratic complexity. While existing works primarily focus on adapting Mamba as vision encoders, the critical role of task-specific Mamba decoders remains under-explored, particularly for distortion-prone dense prediction tasks. This paper addresses two interconnected challenges: (1) The design of a Mamba-based decoder that seamlessly adapts to various architectures (e.g., CNN-, Transformer-, and Mamba-based backbones), and (2) The performance degradation in decoders lacking distortion-aware capability when processing wide-FoV images (e.g., 180° fisheye and 360° panoramic settings). We propose the Deformable Mamba Decoder, an efficient distortion-aware decoder that integrates Mamba's computational efficiency with adaptive distortion awareness. Comprehensive experiments on five wide-FoV segmentation benchmarks validate its effectiveness. Notably, our decoder achieves a +2.5% performance improvement on the 360° Stanford2D3D segmentation benchmark while reducing 72% parameters and 97% FLOPs, as compared to the widely-used decoder heads.", "authors": ["Jie Hu", "Junwei Zheng", "Jiale Wei", "Jiaming Zhang", "Rainer Stiefelhagen"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-25", "url": "https://arxiv.org/abs/2411.16481", "pdf_url": "https://arxiv.org/pdf/2411.16481v2", "arxiv_id": "2411.16481", "doi": "10.48550/arXiv.2411.16481", "citation_count": 10, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/JieHu1996/DeformableMamba", "venue": "arXiv.org", "quality_score": 0.2603}
{"id": "9ea46fbfef41aa89eb99eb2d81c1ed1939388185e7224909fac6d458dba918cc", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning", "abstract": "Continual Learning (CL) aims to equip AI models with the ability to learn a sequence of tasks over time, without forgetting previously learned knowledge. Recently, State Space Models (SSMs), particularly the Mamba model, have achieved notable success in computer vision. Building on the strengths of SSMs, this study explores leveraging the Mamba model for CL. Therefore, we introduce Mamba-CL, a framework that continuously fine-tunes the core SSMs of the large-scale Mamba foundation model by updating parameters orthogonal to the feature subspace of previous tasks. This approach theoretically guarantees the consistency objective aiming to preserves consistent output for each SSM module across both previous and current tasks, so as to overcome catastrophic forgetting issue. Specifically, we achieve this goal by deducing the overall consistency constraints on four key time-invariant parameters in the Mamba model, streamlining its recurrent state-space structure and non-linear discretization process in SSM. In practice, we apply the null-space projection to efficiently implement the orthogonality within Mamba model. Extensive experiments on four class-incremental benchmarks demonstrate the effectiveness of Mamba-CL for anti-forgetting, achieving superior performances to state-of-the-art methods. Code is available in the supplementary materials.", "authors": ["De Cheng", "Yue Lu", "Lingfeng He", "Shizhou Zhang", "Xi Yang", "Nannan Wang", "Xinbo Gao"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-23", "url": "https://arxiv.org/abs/2411.15469", "pdf_url": "https://arxiv.org/pdf/2411.15469v2", "arxiv_id": "2411.15469", "doi": "10.48550/arXiv.2411.15469", "citation_count": 5, "influential_citation_count": 1, "has_code": true, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1945}
{"id": "e4b54cfe1481fa04ea0cbb5ccb7d62d0cc210bd476de0fb8afeb4ffe65061a98", "sources": ["arxiv", "semantic_scholar"], "title": "EfficientViM: Efficient Vision Mamba with Hidden State Mixer based State Space Duality", "abstract": "For the deployment of neural networks in resource-constrained environments, prior works have built lightweight architectures with convolution and attention for capturing local and global dependencies, respectively. Recently, the state space model (SSM) has emerged as an effective operation for global interaction with its favorable linear computational cost in the number of tokens. To harness the efficacy of SSM, we introduce Efficient Vision Mamba (EfficientViM), a novel architecture built on hidden state mixer-based state space duality (HSM-SSD) that efficiently captures global dependencies with further reduced computational cost. With the observation that the runtime of the SSD layer is driven by the linear projections on the input sequences, we redesign the original SSD layer to perform the channel mixing operation within compressed hidden states in the HSM-SSD layer. Additionally, we propose multi-stage hidden state fusion to reinforce the representation power of hidden states and provide the design to alleviate the bottleneck caused by the memory-bound operations. As a result, the EfficientViM family achieves a new state-of-the-art speed-accuracy trade-off on ImageNet-1k, offering up to a 0.7% performance improvement over the second-best model SHViT with faster speed. Further, we observe significant improvements in throughput and accuracy compared to prior works, when scaling images or employing distillation training. Code is available at https://github.com/mlvlab/EfficientViM.", "authors": ["Sanghyeok Lee", "Joonmyung Choi", "Hyunwoo J. Kim"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-22", "url": "https://arxiv.org/abs/2411.15241", "pdf_url": "https://arxiv.org/pdf/2411.15241v2", "arxiv_id": "2411.15241", "doi": "10.1109/CVPR52734.2025.01390", "citation_count": 48, "influential_citation_count": 2, "has_code": true, "code_url": "https://github.com/mlvlab/EfficientViM", "venue": "Computer Vision and Pattern Recognition", "quality_score": 0.4225}
{"id": "0d91ad0de8967a714ca4ef8862b516f89d10d15efe602c286e21a14574693763", "sources": ["arxiv", "semantic_scholar"], "title": "Parameter Efficient Mamba Tuning via Projector-targeted Diagonal-centric Linear Transformation", "abstract": "Despite the growing interest in Mamba architecture as a potential replacement for Transformer architecture, parameter-efficient fine-tuning (PEFT) approaches for Mamba remain largely unexplored. In our study, we introduce two key insights-driven strategies for PEFT in Mamba architecture: (1) While state-space models (SSMs) have been regarded as the cornerstone of Mamba architecture, then expected to play a primary role in transfer learning, our findings reveal that Projectors -- not SSMs -- are the predominant contributors to transfer learning. (2) Based on our observation, we propose a novel PEFT method specialized to Mamba architecture: Projector-targeted Diagonal-centric Linear Transformation (ProDiaL). ProDiaL focuses on optimizing only the pretrained Projectors for new tasks through diagonal-centric linear transformation matrices, without directly fine-tuning the Projector weights. This targeted approach allows efficient task adaptation, utilizing less than 1% of the total parameters, and exhibits strong performance across both vision and language Mamba models, highlighting its versatility and effectiveness.", "authors": ["Seokil Ham", "Hee-Seon Kim", "Sangmin Woo", "Changick Kim"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-21", "url": "https://arxiv.org/abs/2411.15224", "pdf_url": "https://arxiv.org/pdf/2411.15224v3", "arxiv_id": "2411.15224", "doi": "10.1109/CVPR52734.2025.02802", "citation_count": 3, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Computer Vision and Pattern Recognition", "quality_score": 0.1505}
{"id": "aee5ad61cb745d34fdba011f574e2a659d3a9eb18d45eed2f615eb8d4c450910", "sources": ["arxiv", "semantic_scholar"], "title": "Bi-Mamba: Towards Accurate 1-Bit State Space Models", "abstract": "The typical Selective State-Space Model (SSM) used in Mamba addresses several limitations of Transformers, such as the quadratic computational complexity with respect to sequence length and the significant memory requirements during inference due to the key-value (KV) cache. However, the increasing size of Mamba models continues to pose challenges for training and deployment, particularly due to their substantial computational demands during both training and inference. In this work, we introduce $\\texttt{Bi-Mamba}$, a scalable and powerful 1-bit Mamba architecture designed to enable more efficient large language models (LLMs), with model sizes of 780M, 1.3B, and 2.7B parameters. $\\texttt{Bi-Mamba}$ models are trained from scratch on a standard LLM-scale dataset using an autoregressive distillation loss. Extensive experiments on language modeling benchmarks demonstrate that $\\texttt{Bi-Mamba}$ achieves performance comparable to its full-precision (FP16 or BF16) counterparts, while outperforming post-training binarization (PTB) Mamba and binarization-aware training (BAT) Transformer baselines. Moreover, $\\texttt{Bi-Mamba}$ drastically reduces memory usage and computational cost compared to the original Mamba. Our work pioneers a new line of linear-complexity LLMs under low-bit representation and provides the way for the design of specialized hardware optimized for efficient 1-bit Mamba-based models. Code and the pre-trained weights are available at https://github.com/Tangshengku/Bi-Mamba.", "authors": ["Shengkun Tang", "Liqun Ma", "Haonan Li", "Mingjie Sun", "Zhiqiang Shen"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-18", "url": "https://arxiv.org/abs/2411.11843", "pdf_url": "https://arxiv.org/pdf/2411.11843v2", "arxiv_id": "2411.11843", "doi": "10.48550/arXiv.2411.11843", "citation_count": 10, "influential_citation_count": 2, "has_code": true, "code_url": "https://github.com/Tangshengku/Bi-Mamba", "venue": "arXiv.org", "quality_score": 0.2603}
{"id": "c8094b1ee4e37bd3f4b21b4c1be350d7d2b120b0ed828b0b1c0ab595dcc7ad7a", "sources": ["arxiv", "semantic_scholar"], "title": "KAN-Mamba FusionNet: Redefining Medical Image Segmentation with Non-Linear Modeling", "abstract": "Medical image segmentation is essential for applications like robotic surgeries, disease diagnosis, and treatment planning. Recently, various deep-learning models have been proposed to enhance medical image segmentation. One promising approach utilizes Kolmogorov-Arnold Networks (KANs), which better capture non-linearity in input data. However, they are unable to effectively capture long-range dependencies, which are required to accurately segment complex medical images and, by that, improve diagnostic accuracy in clinical settings. Neural networks such as Mamba can handle long-range dependencies. However, they have a limited ability to accurately capture non-linearities in the images as compared to KANs. Thus, we propose a novel architecture, the KAN-Mamba FusionNet, which improves segmentation accuracy by effectively capturing the non-linearities from input and handling long-range dependencies with the newly proposed KAMBA block. We evaluated the proposed KAN-Mamba FusionNet on three distinct medical image segmentation datasets: BUSI, Kvasir-Seg, and GlaS - and found it consistently outperforms state-of-the-art methods in IoU and F1 scores. Further, we examined the effects of various components and assessed their contributions to the overall model performance via ablation studies. The findings highlight the effectiveness of this methodology for reliable medical image segmentation, providing a unique approach to address intricate visual data issues in healthcare.", "authors": ["Akansh Agrawal", "Akshan Agrawal", "Shashwat Gupta", "Priyanka Bagade"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-18", "url": "https://arxiv.org/abs/2411.11926", "pdf_url": "https://arxiv.org/pdf/2411.11926v2", "arxiv_id": "2411.11926", "doi": "10.48550/arXiv.2411.11926", "citation_count": 9, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.301}
{"id": "3767d8d16bc568373843622246713a8b277f9ee05271650c27dd883ea497749f", "sources": ["arxiv", "semantic_scholar"], "title": "$\\text{S}^{3}$Mamba: Arbitrary-Scale Super-Resolution via Scaleable State Space Model", "abstract": "Arbitrary scale super-resolution (ASSR) aims to super-resolve low-resolution images to high-resolution images at any scale using a single model, addressing the limitations of traditional super-resolution methods that are restricted to fixed-scale factors (e.g., $\\times2$, $\\times4$). The advent of Implicit Neural Representations (INR) has brought forth a plethora of novel methodologies for ASSR, which facilitate the reconstruction of original continuous signals by modeling a continuous representation space for coordinates and pixel values, thereby enabling arbitrary-scale super-resolution. Consequently, the primary objective of ASSR is to construct a continuous representation space derived from low-resolution inputs. However, existing methods, primarily based on CNNs and Transformers, face significant challenges such as high computational complexity and inadequate modeling of long-range dependencies, which hinder their effectiveness in real-world applications. To overcome these limitations, we propose a novel arbitrary-scale super-resolution method, called $\\text{S}^{3}$Mamba, to construct a scalable continuous representation space. Specifically, we propose a Scalable State Space Model (SSSM) to modulate the state transition matrix and the sampling matrix of step size during the discretization process, achieving scalable and continuous representation modeling with linear computational complexity. Additionally, we propose a novel scale-aware self-attention mechanism to further enhance the network's ability to perceive global important features at different scales, thereby building the $\\text{S}^{3}$Mamba to achieve superior arbitrary-scale super-resolution. Extensive experiments on both synthetic and real-world benchmarks demonstrate that our method achieves state-of-the-art performance and superior generalization capabilities at arbitrary super-resolution scales.", "authors": ["Peizhe Xia", "Long Peng", "Xin Di", "Renjing Pei", "Yang Wang", "Yang Cao", "Zheng-Jun Zha"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-16", "url": "https://arxiv.org/abs/2411.11906", "pdf_url": "https://arxiv.org/pdf/2411.11906v1", "arxiv_id": "2411.11906", "doi": "10.48550/arXiv.2411.11906", "citation_count": 14, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.294}
{"id": "bf1bc416a65a0890cfb02dadc071e2f8fcab1de380e52f8babbcf7982504d27d", "sources": ["arxiv", "semantic_scholar"], "title": "XLSR-Mamba: A Dual-Column Bidirectional State Space Model for Spoofing Attack Detection", "abstract": "Transformers and their variants have achieved great success in speech processing. However, their multi-head self-attention mechanism is computationally expensive. Therefore, one novel selective state space model, Mamba, has been proposed as an alternative. Building on its success in automatic speech recognition, we apply Mamba for spoofing attack detection. Mamba is well-suited for this task as it can capture the artifacts in spoofed speech signals by handling long-length sequences. However, Mamba's performance may suffer when it is trained with limited labeled data. To mitigate this, we propose combining a new structure of Mamba based on a dual-column architecture with self-supervised learning, using the pre-trained wav2vec 2.0 model. The experiments show that our proposed approach achieves competitive results and faster inference on the ASVspoof 2021 LA and DF datasets, and on the more challenging In-the-Wild dataset, it emerges as the strongest candidate for spoofing attack detection. The code has been publicly released in https://github.com/swagshaw/XLSR-Mamba.", "authors": ["Yang Xiao", "Rohan Kumar Das"], "categories": ["eess.AS", "cs.SD"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-11-15", "url": "https://arxiv.org/abs/2411.10027", "pdf_url": "https://arxiv.org/pdf/2411.10027v2", "arxiv_id": "2411.10027", "doi": "10.1109/LSP.2025.3547861", "citation_count": 54, "influential_citation_count": 8, "has_code": true, "code_url": "https://github.com/swagshaw/XLSR-Mamba", "venue": "IEEE Signal Processing Letters", "quality_score": 0.4771}
{"id": "448da04f27c8b3b3b8e8c08a9ec58d18bf68cd5fac834934d4d81e018ea223e2", "sources": ["arxiv", "semantic_scholar"], "title": "CT-Mamba: A Hybrid Convolutional State Space Model for Low-Dose CT Denoising", "abstract": "Low-dose CT (LDCT) significantly reduces the radiation dose received by patients, however, dose reduction introduces additional noise and artifacts. Currently, denoising methods based on convolutional neural networks (CNNs) face limitations in long-range modeling capabilities, while Transformer-based denoising methods, although capable of powerful long-range modeling, suffer from high computational complexity. Furthermore, the denoised images predicted by deep learning-based techniques inevitably exhibit differences in noise distribution compared to normal-dose CT (NDCT) images, which can also impact the final image quality and diagnostic outcomes. This paper proposes CT-Mamba, a hybrid convolutional State Space Model for LDCT image denoising. The model combines the local feature extraction advantages of CNNs with Mamba's strength in capturing long-range dependencies, enabling it to capture both local details and global context. Additionally, we introduce an innovative spatially coherent Z-shaped scanning scheme to ensure spatial continuity between adjacent pixels in the image. We design a Mamba-driven deep noise power spectrum (NPS) loss function to guide model training, ensuring that the noise texture of the denoised LDCT images closely resembles that of NDCT images, thereby enhancing overall image quality and diagnostic value. Experimental results have demonstrated that CT-Mamba performs excellently in reducing noise in LDCT images, enhancing detail preservation, and optimizing noise texture distribution, and exhibits higher statistical similarity with the radiomics features of NDCT images. The proposed CT-Mamba demonstrates outstanding performance in LDCT denoising and holds promise as a representative approach for applying the Mamba framework to LDCT denoising tasks.", "authors": ["Linxuan Li", "Wenjia Wei", "Luyao Yang", "Wenwen Zhang", "Jiashu Dong", "Yahua Liu", "Hongshi Huang", "Wei Zhao"], "categories": ["eess.IV"], "fields_of_study": ["Medicine", "Computer Science", "Engineering"], "published_date": "2024-11-12", "url": "https://arxiv.org/abs/2411.07930", "pdf_url": "https://arxiv.org/pdf/2411.07930v5", "arxiv_id": "2411.07930", "doi": "10.1016/j.compmedimag.2025.102595", "citation_count": 23, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3451}
{"id": "8e4e1f5f8bde36405d7e5993c77f6a313f31b383d4daca5ef489127440f78ff0", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba-based Decoder-Only Approach with Bidirectional Speech Modeling for Speech Recognition", "abstract": "Selective state space models (SSMs) represented by Mamba have demonstrated their computational efficiency and promising outcomes in various tasks, including automatic speech recognition (ASR). Mamba has been applied to ASR task with the attention-based encoder-decoder framework, where the cross-attention mechanism between encoder and decoder remains. This paper explores the capability of Mamba as the decoder-only architecture in ASR task. Our MAmba-based DEcoder-ONly approach (MADEON) consists of a single decoder that takes speech tokens as a condition and predicts text tokens in an autoregressive manner. To enhance MADEON, we further propose speech prefixing that performs bidirectional processing on speech tokens, which enriches the contextual information in the hidden states. Our experiments show that MADEON significantly outperforms a non-selective SSM. The combination of speech prefixing and the recently proposed Mamba-2 yields comparable performance to Transformer-based models on large datasets.", "authors": ["Yoshiki Masuyama", "Koichi Miyazaki", "Masato Murata"], "categories": ["cs.SD", "eess.AS"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-11-11", "url": "https://arxiv.org/abs/2411.06968", "pdf_url": "https://arxiv.org/pdf/2411.06968v1", "arxiv_id": "2411.06968", "doi": "10.1109/SLT61566.2024.10832186", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Spoken Language Technology Workshop", "quality_score": 0.2258}
{"id": "7c238c91ebdaa4abc1ad7d5273bff138dd34d9cd18340b5d5b4cddcff85ba853", "sources": ["arxiv", "semantic_scholar"], "title": "SEM-Net: Efficient Pixel Modelling for image inpainting with Spatially Enhanced SSM", "abstract": "Image inpainting aims to repair a partially damaged image based on the information from known regions of the images. \\revise{Achieving semantically plausible inpainting results is particularly challenging because it requires the reconstructed regions to exhibit similar patterns to the semanticly consistent regions}. This requires a model with a strong capacity to capture long-range dependencies. Existing models struggle in this regard due to the slow growth of receptive field for Convolutional Neural Networks (CNNs) based methods and patch-level interactions in Transformer-based methods, which are ineffective for capturing long-range dependencies. Motivated by this, we propose SEM-Net, a novel visual State Space model (SSM) vision network, modelling corrupted images at the pixel level while capturing long-range dependencies (LRDs) in state space, achieving a linear computational complexity. To address the inherent lack of spatial awareness in SSM, we introduce the Snake Mamba Block (SMB) and Spatially-Enhanced Feedforward Network. These innovations enable SEM-Net to outperform state-of-the-art inpainting methods on two distinct datasets, showing significant improvements in capturing LRDs and enhancement in spatial consistency. Additionally, SEM-Net achieves state-of-the-art performance on motion deblurring, demonstrating its generalizability. Our source code will be released in https://github.com/ChrisChen1023/SEM-Net.", "authors": ["Shuang Chen", "Haozheng Zhang", "Amir Atapour-Abarghouei", "Hubert P. H. Shum"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-10", "url": "https://arxiv.org/abs/2411.06318", "pdf_url": "https://arxiv.org/pdf/2411.06318v1", "arxiv_id": "2411.06318", "doi": "10.1109/WACV61041.2025.00055", "citation_count": 18, "influential_citation_count": 2, "has_code": true, "code_url": "https://github.com/ChrisChen1023/SEM-Net", "venue": "IEEE Workshop/Winter Conference on Applications of Computer Vision", "quality_score": 0.3197}
{"id": "3c8e10cb933c78eb58d2e40ab8e2f8d7db5d5bc764e26f7f189abefbf68bd091", "sources": ["arxiv", "semantic_scholar"], "title": "MambaPEFT: Exploring Parameter-Efficient Fine-Tuning for Mamba", "abstract": "An ecosystem of Transformer-based models has been established by building large models with extensive data. Parameter-efficient fine-tuning (PEFT) is a crucial technology for deploying these models to downstream tasks with minimal cost while achieving effective performance. Recently, Mamba, a State Space Model (SSM)-based model, has attracted attention as a potential alternative to Transformers. While many large-scale Mamba-based models have been proposed, efficiently adapting pre-trained Mamba-based models to downstream tasks remains unexplored. In this paper, we conduct an exploratory analysis of PEFT methods for Mamba. We investigate the effectiveness of existing PEFT methods for Transformers when applied to Mamba. We also modify these methods to better align with the Mamba architecture. Additionally, we propose new Mamba-specific PEFT methods that leverage the distinctive structure of Mamba. Our experiments indicate that PEFT performs more effectively for Mamba than Transformers. Lastly, we demonstrate how to effectively combine multiple PEFT methods and provide a framework that outperforms previous works. To ensure reproducibility, we will release the code after publication.", "authors": ["Masakazu Yoshimura", "Teruaki Hayashi", "Yota Maeda"], "categories": ["cs.CL", "cs.AI", "cs.CV", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-06", "url": "https://arxiv.org/abs/2411.03855", "pdf_url": "https://arxiv.org/pdf/2411.03855v3", "arxiv_id": "2411.03855", "doi": "10.48550/arXiv.2411.03855", "citation_count": 14, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "International Conference on Learning Representations", "quality_score": 0.294}
{"id": "4a3ec255a9bce206f13e97fe58871402bdb038a078869919256f4cf5cfc9b99d", "sources": ["arxiv", "semantic_scholar"], "title": "A Mamba Foundation Model for Time Series Forecasting", "abstract": "Time series foundation models have demonstrated strong performance in zero-shot learning, making them well-suited for predicting rapidly evolving patterns in real-world applications where relevant training data are scarce. However, most of these models rely on the Transformer architecture, which incurs quadratic complexity as input length increases. To address this, we introduce TSMamba, a linear-complexity foundation model for time series forecasting built on the Mamba architecture. The model captures temporal dependencies through both forward and backward Mamba encoders, achieving high prediction accuracy. To reduce reliance on large datasets and lower training costs, TSMamba employs a two-stage transfer learning process that leverages pretrained Mamba LLMs, allowing effective time series modeling with a moderate training set. In the first stage, the forward and backward backbones are optimized via patch-wise autoregressive prediction; in the second stage, the model trains a prediction head and refines other components for long-term forecasting. While the backbone assumes channel independence to manage varying channel numbers across datasets, a channel-wise compressed attention module is introduced to capture cross-channel dependencies during fine-tuning on specific multivariate datasets. Experiments show that TSMamba's zero-shot performance is comparable to state-of-the-art time series foundation models, despite using significantly less training data. It also achieves competitive or superior full-shot performance compared to task-specific prediction models. The code will be made publicly available.", "authors": ["Haoyu Ma", "Yushu Chen", "Wenlai Zhao", "Jinzhe Yang", "Yingsheng Ji", "Xinghua Xu", "Xiaozhu Liu", "Hao Jing", "Shengzhuo Liu", "Guangwen Yang"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-05", "url": "https://arxiv.org/abs/2411.02941", "pdf_url": "https://arxiv.org/pdf/2411.02941v1", "arxiv_id": "2411.02941", "doi": "10.48550/arXiv.2411.02941", "citation_count": 18, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3197}
{"id": "b698d7e572c863c8b8a2f5528ddca2ce02a0b61acd531eac2bc2aeb664a4d1eb", "sources": ["arxiv", "semantic_scholar"], "title": "NIMBA: Towards Robust and Principled Processing of Point Clouds With SSMs", "abstract": "Transformers have become dominant in large-scale deep learning tasks across various domains, including text, 2D and 3D vision. However, the quadratic complexity of their attention mechanism limits their efficiency as the sequence length increases, particularly in high-resolution 3D data such as point clouds. Recently, state space models (SSMs) like Mamba have emerged as promising alternatives, offering linear complexity, scalability, and high performance in long-sequence tasks. The key challenge in the application of SSMs in this domain lies in reconciling the non-sequential structure of point clouds with the inherently directional (or bi-directional) order-dependent processing of recurrent models like Mamba. To achieve this, previous research proposed reorganizing point clouds along multiple directions or predetermined paths in 3D space, concatenating the results to produce a single 1D sequence capturing different views. In our work, we introduce a method to convert point clouds into 1D sequences that maintain 3D spatial structure with no need for data replication, allowing Mamba sequential processing to be applied effectively in an almost permutation-invariant manner. In contrast to other works, we found that our method does not require positional embeddings and allows for shorter sequence lengths while still achieving state-of-the-art results in ModelNet40 and ScanObjectNN datasets and surpassing Transformer-based models in both accuracy and efficiency.", "authors": ["Nursena Köprücü", "Destiny Okpekpe", "Antonio Orvieto"], "categories": ["cs.CV", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-31", "url": "https://arxiv.org/abs/2411.00151", "pdf_url": "https://arxiv.org/pdf/2411.00151v1", "arxiv_id": "2411.00151", "doi": "10.48550/arXiv.2411.00151", "citation_count": 3, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "fca274ebcba001581dd8f5bef1b09cfd8d629e3cd9d60c4dfd119e86d9711e9c", "sources": ["arxiv", "semantic_scholar"], "title": "Sequential Order-Robust Mamba for Time Series Forecasting", "abstract": "Mamba has recently emerged as a promising alternative to Transformers, offering near-linear complexity in processing sequential data. However, while channels in time series (TS) data have no specific order in general, recent studies have adopted Mamba to capture channel dependencies (CD) in TS, introducing a sequential order bias. To address this issue, we propose SOR-Mamba, a TS forecasting method that 1) incorporates a regularization strategy to minimize the discrepancy between two embedding vectors generated from data with reversed channel orders, thereby enhancing robustness to channel order, and 2) eliminates the 1D-convolution originally designed to capture local information in sequential data. Furthermore, we introduce channel correlation modeling (CCM), a pretraining task aimed at preserving correlations between channels from the data space to the latent space in order to enhance the ability to capture CD. Extensive experiments demonstrate the efficacy of the proposed method across standard and transfer learning scenarios. Code is available at https://github.com/seunghan96/SOR-Mamba.", "authors": ["Seunghan Lee", "Juri Hong", "Kibok Lee", "Taeyoung Park"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2024-10-30", "url": "https://arxiv.org/abs/2410.23356", "pdf_url": "https://arxiv.org/pdf/2410.23356v1", "arxiv_id": "2410.23356", "doi": "10.48550/arXiv.2410.23356", "citation_count": 3, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/seunghan96/SOR-Mamba", "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "3c5f542080532847c5775814d89cfd37d09dc49d19c320c740deccc69f1b2283", "sources": ["arxiv", "semantic_scholar"], "title": "ECMamba: Consolidating Selective State Space Model with Retinex Guidance for Efficient Multiple Exposure Correction", "abstract": "Exposure Correction (EC) aims to recover proper exposure conditions for images captured under over-exposure or under-exposure scenarios. While existing deep learning models have shown promising results, few have fully embedded Retinex theory into their architecture, highlighting a gap in current methodologies. Additionally, the balance between high performance and efficiency remains an under-explored problem for exposure correction task. Inspired by Mamba which demonstrates powerful and highly efficient sequence modeling, we introduce a novel framework based on Mamba for Exposure Correction (ECMamba) with dual pathways, each dedicated to the restoration of reflectance and illumination map, respectively. Specifically, we firstly derive the Retinex theory and we train a Retinex estimator capable of mapping inputs into two intermediary spaces, each approximating the target reflectance and illumination map, respectively. This setup facilitates the refined restoration process of the subsequent Exposure Correction Mamba Module (ECMM). Moreover, we develop a novel 2D Selective State-space layer guided by Retinex information (Retinex-SS2D) as the core operator of ECMM. This architecture incorporates an innovative 2D scanning strategy based on deformable feature aggregation, thereby enhancing both efficiency and effectiveness. Extensive experiment results and comprehensive ablation studies demonstrate the outstanding performance and the importance of each component of our proposed ECMamba. Code is available at https://github.com/LowlevelAI/ECMamba.", "authors": ["Wei Dong", "Han Zhou", "Yulun Zhang", "Xiaohong Liu", "Jun Chen"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-28", "url": "https://arxiv.org/abs/2410.21535", "pdf_url": "https://arxiv.org/pdf/2410.21535v1", "arxiv_id": "2410.21535", "doi": "10.48550/arXiv.2410.21535", "citation_count": 23, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/LowlevelAI/ECMamba", "venue": "Neural Information Processing Systems", "quality_score": 0.3451}
{"id": "3642fd96b8ae029569c252917d163d0282b05e69983ff46b51626cc63a2a1124", "sources": ["arxiv", "semantic_scholar"], "title": "Revealing and Mitigating the Local Pattern Shortcuts of Mamba", "abstract": "Large language models (LLMs) have advanced significantly due to the attention mechanism, but their quadratic complexity and linear memory demands limit their performance on long-context tasks. Recently, researchers introduced Mamba, an advanced model built upon State Space Models(SSMs) that offers linear complexity and constant memory. Although Mamba is reported to match or surpass the performance of attention-based models, our analysis reveals a performance gap: Mamba excels in tasks that involve localized key information but faces challenges with tasks that require handling distributed key information. Our controlled experiments suggest that this inconsistency arises from Mamba's reliance on local pattern shortcuts, which enable the model to remember local key information within its limited memory but hinder its ability to retain more dispersed information. Therefore, we introduce a global selection module into the Mamba model to address this issue. Experiments on both existing and proposed synthetic tasks, as well as real-world tasks, demonstrate the effectiveness of our method. Notably, with the introduction of only 4M extra parameters, our approach enables the Mamba model(130M) to achieve a significant improvement on tasks with distributed information, increasing its performance from 0 to 80.54 points.", "authors": ["Wangjie You", "Zecheng Tang", "Juntao Li", "Lili Yao", "Min Zhang"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-21", "url": "https://arxiv.org/abs/2410.15678", "pdf_url": "https://arxiv.org/pdf/2410.15678v1", "arxiv_id": "2410.15678", "doi": "10.48550/arXiv.2410.15678", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Annual Meeting of the Association for Computational Linguistics", "quality_score": 0.0753}
{"id": "1a8113b6def333a98af2ac79ab4e1e7bae7a660ffffe893c8d9db0f471f88abf", "sources": ["arxiv", "semantic_scholar"], "title": "R2Gen-Mamba: A Selective State Space Model for Radiology Report Generation", "abstract": "Radiology report generation is crucial in medical imaging,but the manual annotation process by physicians is time-consuming and labor-intensive, necessitating the develop-ment of automatic report generation methods. Existingresearch predominantly utilizes Transformers to generateradiology reports, which can be computationally intensive,limiting their use in real applications. In this work, we presentR2Gen-Mamba, a novel automatic radiology report genera-tion method that leverages the efficient sequence processingof the Mamba with the contextual benefits of Transformerarchitectures. Due to lower computational complexity ofMamba, R2Gen-Mamba not only enhances training and in-ference efficiency but also produces high-quality reports.Experimental results on two benchmark datasets with morethan 210,000 X-ray image-report pairs demonstrate the ef-fectiveness of R2Gen-Mamba regarding report quality andcomputational efficiency compared with several state-of-the-art methods. The source code can be accessed online.", "authors": ["Yongheng Sun", "Yueh Z. Lee", "Genevieve A. Woodard", "Hongtu Zhu", "Chunfeng Lian", "Mingxia Liu"], "categories": ["cs.CL", "cs.AI", "cs.CV", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-21", "url": "https://arxiv.org/abs/2410.18135", "pdf_url": "https://arxiv.org/pdf/2410.18135v1", "arxiv_id": "2410.18135", "doi": "10.1109/ISBI60581.2025.10980814", "citation_count": 19, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "IEEE International Symposium on Biomedical Imaging", "quality_score": 0.3253}
{"id": "305a4bc32bbf7d3e3281fef68d8bf393b8fd1eeec36458f456baae82fd746297", "sources": ["arxiv", "semantic_scholar"], "title": "Spatial-Mamba: Effective Visual State Space Models via Structure-aware State Fusion", "abstract": "Selective state space models (SSMs), such as Mamba, highly excel at capturing long-range dependencies in 1D sequential data, while their applications to 2D vision tasks still face challenges. Current visual SSMs often convert images into 1D sequences and employ various scanning patterns to incorporate local spatial dependencies. However, these methods are limited in effectively capturing the complex image spatial structures and the increased computational cost caused by the lengthened scanning paths. To address these limitations, we propose Spatial-Mamba, a novel approach that establishes neighborhood connectivity directly in the state space. Instead of relying solely on sequential state transitions, we introduce a structure-aware state fusion equation, which leverages dilated convolutions to capture image spatial structural dependencies, significantly enhancing the flow of visual contextual information. Spatial-Mamba proceeds in three stages: initial state computation in a unidirectional scan, spatial context acquisition through structure-aware state fusion, and final state computation using the observation equation. Our theoretical analysis shows that Spatial-Mamba unifies the original Mamba and linear attention under the same matrix multiplication framework, providing a deeper understanding of our method. Experimental results demonstrate that Spatial-Mamba, even with a single scan, attains or surpasses the state-of-the-art SSM-based models in image classification, detection and segmentation. Source codes and trained models can be found at https://github.com/EdwardChasel/Spatial-Mamba.", "authors": ["Chaodong Xiao", "Minghan Li", "Zhengqiang Zhang", "Deyu Meng", "Lei Zhang"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-19", "url": "https://arxiv.org/abs/2410.15091", "pdf_url": "https://arxiv.org/pdf/2410.15091v2", "arxiv_id": "2410.15091", "doi": "10.48550/arXiv.2410.15091", "citation_count": 57, "influential_citation_count": 6, "has_code": true, "code_url": "https://github.com/EdwardChasel/Spatial-Mamba", "venue": "International Conference on Learning Representations", "quality_score": 0.4409}
{"id": "a63bc2d6dac9e1c748e5df4b1ecbabe0863cab6e186ede0d5bd7963d1b51977a", "sources": ["arxiv", "semantic_scholar"], "title": "Quamba: A Post-Training Quantization Recipe for Selective State Space Models", "abstract": "State Space Models (SSMs) have emerged as an appealing alternative to Transformers for large language models, achieving state-of-the-art accuracy with constant memory complexity which allows for holding longer context lengths than attention-based networks. The superior computational efficiency of SSMs in long sequence modeling positions them favorably over Transformers in many scenarios. However, improving the efficiency of SSMs on request-intensive cloud-serving and resource-limited edge applications is still a formidable task. SSM quantization is a possible solution to this problem, making SSMs more suitable for wide deployment, while still maintaining their accuracy. Quantization is a common technique to reduce the model size and to utilize the low bit-width acceleration features on modern computing units, yet existing quantization techniques are poorly suited for SSMs. Most notably, SSMs have highly sensitive feature maps within the selective scan mechanism (i.e., linear recurrence) and massive outliers in the output activations which are not present in the output of token-mixing in the self-attention modules. To address this issue, we propose a static 8-bit per-tensor SSM quantization method which suppresses the maximum values of the input activations to the selective SSM for finer quantization precision and quantizes the output activations in an outlier-free space with Hadamard transform. Our 8-bit weight-activation quantized Mamba 2.8B SSM benefits from hardware acceleration and achieves a 1.72x lower generation latency on an Nvidia Orin Nano 8G, with only a 0.9% drop in average accuracy on zero-shot tasks. The experiments demonstrate the effectiveness and practical applicability of our approach for deploying SSM-based models of all sizes on both cloud and edge platforms.", "authors": ["Hung-Yueh Chiang", "Chi-Chih Chang", "Natalia Frumkin", "Kai-Chiang Wu", "Diana Marculescu"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-17", "url": "https://arxiv.org/abs/2410.13229", "pdf_url": "https://arxiv.org/pdf/2410.13229v2", "arxiv_id": "2410.13229", "doi": "10.48550/arXiv.2410.13229", "citation_count": 22, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3404}
{"id": "5b8fbaaff66e40afcc0bcb45882e0b1eea9276eb9eca876e1cae38c9ab1f5bc7", "sources": ["arxiv", "semantic_scholar"], "title": "Provable Benefits of Complex Parameterizations for Structured State Space Models", "abstract": "Structured state space models (SSMs), the core engine behind prominent neural networks such as S4 and Mamba, are linear dynamical systems adhering to a specified structure, most notably diagonal. In contrast to typical neural network modules, whose parameterizations are real, SSMs often use complex parameterizations. Theoretically explaining the benefits of complex parameterizations for SSMs is an open problem. The current paper takes a step towards its resolution, by establishing formal gaps between real and complex diagonal SSMs. Firstly, we prove that while a moderate dimension suffices in order for a complex SSM to express all mappings of a real SSM, a much higher dimension is needed for a real SSM to express mappings of a complex SSM. Secondly, we prove that even if the dimension of a real SSM is high enough to express a given mapping, typically, doing so requires the parameters of the real SSM to hold exponentially large values, which cannot be learned in practice. In contrast, a complex SSM can express any given mapping with moderate parameter values. Experiments corroborate our theory, and suggest a potential extension of the theory that accounts for selectivity, a new architectural feature yielding state of the art performance.", "authors": ["Yuval Ran-Milo", "Eden Lumbroso", "Edo Cohen-Karlik", "Raja Giryes", "Amir Globerson", "Nadav Cohen"], "categories": ["cs.LG", "cs.AI", "cs.NE"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-17", "url": "https://arxiv.org/abs/2410.14067", "pdf_url": "https://arxiv.org/pdf/2410.14067v2", "arxiv_id": "2410.14067", "doi": "10.48550/arXiv.2410.14067", "citation_count": 11, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Neural Information Processing Systems", "quality_score": 0.2698}
{"id": "572e60c2e3b7c8e373c18a3a851edd69362f22b52c4f2faea68f8e322261f177", "sources": ["arxiv", "semantic_scholar"], "title": "Rethinking Token Reduction for State Space Models", "abstract": "Recent advancements in State Space Models (SSMs) have attracted significant interest, particularly in models optimized for parallel training and handling long-range dependencies. Architectures like Mamba have scaled to billions of parameters with selective SSM. To facilitate broader applications using Mamba, exploring its efficiency is crucial. While token reduction techniques offer a straightforward post-training strategy, we find that applying existing methods directly to SSMs leads to substantial performance drops. Through insightful analysis, we identify the reasons for this failure and the limitations of current techniques. In response, we propose a tailored, unified post-training token reduction method for SSMs. Our approach integrates token importance and similarity, thus taking advantage of both pruning and merging, to devise a fine-grained intra-layer token reduction strategy. Extensive experiments show that our method improves the average accuracy by 5.7% to 13.1% on six benchmarks with Mamba-2 compared to existing methods, while significantly reducing computational demands and memory requirements.", "authors": ["Zheng Zhan", "Yushu Wu", "Zhenglun Kong", "Changdi Yang", "Yifan Gong", "Xuan Shen", "Xue Lin", "Pu Zhao", "Yanzhi Wang"], "categories": ["cs.LG", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-16", "url": "https://arxiv.org/abs/2410.14725", "pdf_url": "https://arxiv.org/pdf/2410.14725v1", "arxiv_id": "2410.14725", "doi": "10.48550/arXiv.2410.14725", "citation_count": 20, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Conference on Empirical Methods in Natural Language Processing", "quality_score": 0.3306}
{"id": "53bf7b9c976e4206c411e8fe038b873aaa425541d39ad801ea8aa44411b95720", "sources": ["arxiv", "semantic_scholar"], "title": "Learning in the Recurrent State: Gradient Descent with Linear Recurrent Networks", "abstract": "Linear recurrent networks (LRNNs) offer linear-time sequence modeling, but standard recurrent updates do not directly expose the supervised products needed for in-context gradient descent. We propose a sufficient constructive inductive bias for LRNNs: equip a diagonal recurrent state with multiplicative readout and a short sliding-window cross-product self-attention update. The resulting architecture, Gradient-based Recurrent In-context Learner (GRIL), can implement minibatch gradient descent on a task-specific linear predictor during a single forward pass. The same design extends to multi-step updates and cross-entropy classification, with a limited MLP-based extension to non-linear regression. Empirically, trained GRILs recover the behavior and parameters predicted by the construction on synthetic ICL tasks, and the same architectural bias yields useful performance on Long Range Arena and language modelling. These results present windowed cross-product self-attention as a practical, testable inductive bias for LRNNs that learn in context through gradient-descent-like updates.", "authors": ["Yudou Tian", "Neeraj Mohan Sushma", "Harshvardhan Mestha", "Nicolo Colombo", "David Kappel", "Anand Subramoney"], "categories": ["cs.LG", "cs.AI", "cs.NE"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-15", "url": "https://arxiv.org/abs/2410.11687", "pdf_url": "https://arxiv.org/pdf/2410.11687v3", "arxiv_id": "2410.11687", "doi": null, "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1747}
{"id": "c50bc147b4ad5018a145d768021ad1203bfc4f332a5523bdd749a2813e211ef2", "sources": ["arxiv", "semantic_scholar"], "title": "UmambaTSF: A U-shaped Multi-Scale Long-Term Time Series Forecasting Method Using Mamba", "abstract": "Multivariate Time series forecasting is crucial in domains such as transportation, meteorology, and finance, especially for predicting extreme weather events. State-of-the-art methods predominantly rely on Transformer architectures, which utilize attention mechanisms to capture temporal dependencies. However, these methods are hindered by quadratic time complexity, limiting the model's scalability with respect to input sequence length. This significantly restricts their practicality in the real world. Mamba, based on state space models (SSM), provides a solution with linear time complexity, increasing the potential for efficient forecasting of sequential data. In this study, we propose UmambaTSF, a novel long-term time series forecasting framework that integrates multi-scale feature extraction capabilities of U-shaped encoder-decoder multilayer perceptrons (MLP) with Mamba's long sequence representation. To improve performance and efficiency, the Mamba blocks introduced in the framework adopt a refined residual structure and adaptable design, enabling the capture of unique temporal signals and flexible channel processing. In the experiments, UmambaTSF achieves state-of-the-art performance and excellent generality on widely used benchmark datasets while maintaining linear time complexity and low memory consumption.", "authors": ["Li Wu", "Wenbin Pei", "Jiulong Jiao", "Qiang Zhang"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-15", "url": "https://arxiv.org/abs/2410.11278", "pdf_url": "https://arxiv.org/pdf/2410.11278v1", "arxiv_id": "2410.11278", "doi": "10.48550/arXiv.2410.11278", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1945}
{"id": "f5a9a532802c9e5e0e51bfdd564e58386bc4749f93b6cf6f49ea601a2135df1e", "sources": ["arxiv", "semantic_scholar"], "title": "Hi-Mamba: Hierarchical Mamba for Efficient Image Super-Resolution", "abstract": "State Space Models (SSM), such as Mamba, have shown strong representation ability in modeling long-range dependency with linear complexity, achieving successful applications from high-level to low-level vision tasks. However, SSM's sequential nature necessitates multiple scans in different directions to compensate for the loss of spatial dependency when unfolding the image into a 1D sequence. This multi-direction scanning strategy significantly increases the computation overhead and is unbearable for high-resolution image processing. To address this problem, we propose a novel Hierarchical Mamba network, namely, Hi-Mamba, for image super-resolution (SR). Hi-Mamba consists of two key designs: (1) The Hierarchical Mamba Block (HMB) assembled by a Local SSM (L-SSM) and a Region SSM (R-SSM) both with the single-direction scanning, aggregates multi-scale representations to enhance the context modeling ability. (2) The Direction Alternation Hierarchical Mamba Group (DA-HMG) allocates the isomeric single-direction scanning into cascading HMBs to enrich the spatial relationship modeling. Extensive experiments demonstrate the superiority of Hi-Mamba across five benchmark datasets for efficient SR. For example, Hi-Mamba achieves a significant PSNR improvement of 0.29 dB on Manga109 for $\\times3$ SR, compared to the strong lightweight MambaIR.", "authors": ["Junbo Qiao", "Jincheng Liao", "Wei Li", "Yulun Zhang", "Yong Guo", "Yi Wen", "Zhangxizi Qiu", "Jiao Xie", "Jie Hu", "Shaohui Lin"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-14", "url": "https://arxiv.org/abs/2410.10140", "pdf_url": "https://arxiv.org/pdf/2410.10140v1", "arxiv_id": "2410.10140", "doi": "10.48550/arXiv.2410.10140", "citation_count": 21, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3356}
{"id": "43557ac2ae2c86db30dde1586873728a220e8fa5c1ec30d4e68ff608c80abd11", "sources": ["arxiv", "semantic_scholar"], "title": "V2M: Visual 2-Dimensional Mamba for Image Representation Learning", "abstract": "Mamba has garnered widespread attention due to its flexible design and efficient hardware performance to process 1D sequences based on the state space model (SSM). Recent studies have attempted to apply Mamba to the visual domain by flattening 2D images into patches and then regarding them as a 1D sequence. To compensate for the 2D structure information loss (e.g., local similarity) of the original image, most existing methods focus on designing different orders to sequentially process the tokens, which could only alleviate this issue to some extent. In this paper, we propose a Visual 2-Dimensional Mamba (V2M) model as a complete solution, which directly processes image tokens in the 2D space. We first generalize SSM to the 2-dimensional space which generates the next state considering two adjacent states on both dimensions (e.g., columns and rows). We then construct our V2M based on the 2-dimensional SSM formulation and incorporate Mamba to achieve hardware-efficient parallel processing. The proposed V2M effectively incorporates the 2D locality prior yet inherits the efficiency and input-dependent scalability of Mamba. Extensive experimental results on ImageNet classification and downstream visual tasks including object detection and instance segmentation on COCO and semantic segmentation on ADE20K demonstrate the effectiveness of our V2M compared with other visual backbones.", "authors": ["Chengkun Wang", "Wenzhao Zheng", "Yuanhui Huang", "Jie Zhou", "Jiwen Lu"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-14", "url": "https://arxiv.org/abs/2410.10382", "pdf_url": "https://arxiv.org/pdf/2410.10382v1", "arxiv_id": "2410.10382", "doi": "10.48550/arXiv.2410.10382", "citation_count": 8, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2386}
{"id": "46d882d52ac331aa47b5845acbfc010ca9e62dfa05c46b10cb3761e7ba0cff30", "sources": ["arxiv", "semantic_scholar"], "title": "Parameter-Efficient Fine-Tuning of State Space Models", "abstract": "Deep State Space Models (SSMs), such as Mamba (Gu & Dao, 2024), have become powerful tools for language modeling, offering high performance and linear scalability with sequence length. However, the application of parameter-efficient fine-tuning (PEFT) methods to SSM-based models remains largely underexplored. We start by investigating two fundamental questions on existing PEFT methods: (i) How do they perform on SSM-based models? (ii) Which parameters should they target for optimal results? Our analysis shows that LoRA and its variants consistently outperform all other PEFT methods. While LoRA is effective for linear projection matrices, it fails on SSM modules-yet still outperforms other methods applicable to SSMs, indicating their limitations. This underscores the need for a specialized SSM tuning approach. To address this, we propose Sparse Dimension Tuning (SDT), a PEFT method tailored for SSM modules. Combining SDT for SSMs with LoRA for linear projection matrices, we achieve state-of-the-art performance across extensive experiments.", "authors": ["Kevin Galim", "Wonjun Kang", "Yuchen Zeng", "Hyung Il Koo", "Kangwook Lee"], "categories": ["cs.LG", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-11", "url": "https://arxiv.org/abs/2410.09016", "pdf_url": "https://arxiv.org/pdf/2410.09016v3", "arxiv_id": "2410.09016", "doi": "10.48550/arXiv.2410.09016", "citation_count": 10, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/furiosa-ai/ssm-peft", "venue": "International Conference on Machine Learning", "quality_score": 0.2603}
{"id": "f6e7736a10f67cae2ad026c9205e00e101a85cc77119ffe18d419c8727d5fcb6", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba-based Segmentation Model for Speaker Diarization", "abstract": "Mamba is a newly proposed architecture which behaves like a recurrent neural network (RNN) with attention-like capabilities. These properties are promising for speaker diarization, as attention-based models have unsuitable memory requirements for long-form audio, and traditional RNN capabilities are too limited. In this paper, we propose to assess the potential of Mamba for diarization by comparing the state-of-the-art neural segmentation of the pyannote pipeline with our proposed Mamba-based variant. Mamba's stronger processing capabilities allow usage of longer local windows, which significantly improve diarization quality by making the speaker embedding extraction more reliable. We find Mamba to be a superior alternative to both traditional RNN and the tested attention-based model. Our proposed Mamba-based system achieves state-of-the-art performance on three widely used diarization datasets.", "authors": ["Alexis Plaquet", "Naohiro Tawara", "Marc Delcroix", "Shota Horiguchi", "Atsushi Ando", "Shoko Araki"], "categories": ["cs.SD", "eess.AS"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-10-09", "url": "https://arxiv.org/abs/2410.06459", "pdf_url": "https://arxiv.org/pdf/2410.06459v2", "arxiv_id": "2410.06459", "doi": "10.1109/ICASSP49660.2025.10889446", "citation_count": 14, "influential_citation_count": 3, "has_code": true, "code_url": "https://github.com/nttcslab-sp/mamba-diarization", "venue": "IEEE International Conference on Acoustics, Speech, and Signal Processing", "quality_score": 0.301}
{"id": "0bea78d60309f6bb23ce7c8d8a391cff963811e12376e763cb346d2e84533b38", "sources": ["arxiv", "semantic_scholar"], "title": "Stuffed Mamba: Oversized States Lead to the Inability to Forget", "abstract": "Recent advancements in recurrent architectures, such as Mamba and RWKV, have showcased strong language capabilities. Unlike transformer-based models, these architectures encode all contextual information into a fixed-size state, leading to great inference efficiency. However, this approach can cause information interference, where different token data conflicts, resulting in performance degradation and incoherent outputs beyond a certain context length. To prevent this, most RNNs incorporate mechanisms designed to \"forget\" earlier tokens. In this paper, we reveal that Mamba-based models struggle to effectively forget earlier tokens even with built-in forgetting mechanisms. We demonstrate that this issue stems from training on contexts that are too short for the state size, enabling the model to perform well without needing to learn how to forget. Then, we show that the minimum training length required for the model to learn forgetting scales linearly with the state size, and the maximum context length for accurate retrieval of a 5-digit passkey scales exponentially with the state size, indicating that the model retains some information beyond the point where forgetting begins. These findings highlight a critical limitation in current RNN architectures and provide valuable insights for improving long-context modeling. Our work suggests that future RNN designs must account for the interplay between state size, training length, and forgetting mechanisms to achieve robust performance in long-context tasks.", "authors": ["Yingfa Chen", "Xinrong Zhang", "Shengding Hu", "Xu Han", "Zhiyuan Liu", "Maosong Sun"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-09", "url": "https://arxiv.org/abs/2410.07145", "pdf_url": "https://arxiv.org/pdf/2410.07145v4", "arxiv_id": "2410.07145", "doi": null, "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1945}
{"id": "3386cc07fdee2dd428959f7a8eedf08f104337cad9b13480a790ae9ba30abc49", "sources": ["arxiv", "semantic_scholar"], "title": "TIMBA: Time series Imputation with Bi-directional Mamba Blocks and Diffusion models", "abstract": "The problem of imputing multivariate time series spans a wide range of fields, from clinical healthcare to multi-sensor systems. Initially, Recurrent Neural Networks (RNNs) were employed for this task; however, their error accumulation issues led to the adoption of Transformers, leveraging attention mechanisms to mitigate these problems. Concurrently, the promising results of diffusion models in capturing original distributions have positioned them at the forefront of current research, often in conjunction with Transformers. In this paper, we propose replacing time-oriented Transformers with State-Space Models (SSM), which are better suited for temporal data modeling. Specifically, we utilize the latest SSM variant, S6, which incorporates attention-like mechanisms. By embedding S6 within Mamba blocks, we develop a model that integrates SSM, Graph Neural Networks, and node-oriented Transformers to achieve enhanced spatiotemporal representations. Implementing these architectural modifications, previously unexplored in this field, we present Time series Imputation with Bi-directional mamba blocks and diffusion models (TIMBA). TIMBA achieves superior performance in almost all benchmark scenarios and performs comparably in others across a diverse range of missing value situations and three real-world datasets. We also evaluate how the performance of our model varies with different amounts of missing values and analyse its performance on downstream tasks. In addition, we provide the original code to replicate the results.", "authors": ["Javier Solís-García", "Belén Vega-Márquez", "Juan A. Nepomuceno", "Isabel A. Nepomuceno-Chamorro"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-08", "url": "https://arxiv.org/abs/2410.05916", "pdf_url": "https://arxiv.org/pdf/2410.05916v1", "arxiv_id": "2410.05916", "doi": "10.48550/arXiv.2410.05916", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "fc389c5bd724f137d670305b0b4d4b70354a2b1ac8d0991bf02ea260b310bca9", "sources": ["arxiv", "semantic_scholar"], "title": "SPikE-SSM: A Sparse, Precise, and Efficient Spiking State Space Model for Long Sequences Learning", "abstract": "Spiking neural networks (SNNs) provide an energy-efficient solution by utilizing the spike-based and sparse nature of biological systems. Since the advent of Transformers, SNNs have struggled to compete with artificial networks on long sequential tasks, until the recent emergence of state space models (SSMs), which offer superior computational efficiency and modeling capability. However, applying the highly capable SSMs to SNNs for long sequences learning poses three major challenges: (1) The membrane potential is determined by the past spiking history of the neuron, leading to reduced efficiency for sequence modeling in parallel computing scenarios. (2) Complex dynamics of biological spiking neurons are crucial for functionality but challenging to simulate and exploit effectively in large networks. (3) It is arduous to maintain high sparsity while achieving high accuracy for spiking neurons without resorting to dense computing, as utilized in artificial neuron-based SSMs. To address them, we propose a sparse, precise and efficient spiking SSM framework, termed SPikE-SSM. For (1), we propose a boundary compression strategy (PMBC) to accelerate the inference of the spiking neuron model, enabling parallel processing for long sequence learning. For (2), we propose a novel and concise neuron model incorporating reset-refractory mechanism to leverage the inherent temporal dimension for dynamic computing with biological interpretability. For (3), we hierarchically integrate the proposed neuron model to the original SSM block, and enhance the dynamics of SPikE-SSM by incorporating trainable thresholds and refractory magnitudes to balance accuracy and sparsity. Extensive experiments verify the effectiveness and robustness of SPikE-SSM on the long range arena benchmarks and large language dataset WikiText-103, showing the potential of dynamic spiking neurons in efficient long sequence learning.", "authors": ["Yan Zhong", "Ruoyu Zhao", "Chao Wang", "Qinghai Guo", "Jianguo Zhang", "Zhichao Lu", "Luziwei Leng"], "categories": ["cs.NE", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-07", "url": "https://arxiv.org/abs/2410.17268", "pdf_url": "https://arxiv.org/pdf/2410.17268v1", "arxiv_id": "2410.17268", "doi": "10.48550/arXiv.2410.17268", "citation_count": 12, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2785}
{"id": "226b2e8e0c5dc464fb4b60bbf95a5cf09117054fbfb29142a08413494e6b817c", "sources": ["arxiv", "semantic_scholar"], "title": "Falcon Mamba: The First Competitive Attention-free 7B Language Model", "abstract": "In this technical report, we present Falcon Mamba 7B, a new base large language model based on the novel Mamba architecture. Falcon Mamba 7B is trained on 5.8 trillion tokens with carefully selected data mixtures. As a pure Mamba-based model, Falcon Mamba 7B surpasses leading open-weight models based on Transformers, such as Mistral 7B, Llama3.1 8B, and Falcon2 11B. It is on par with Gemma 7B and outperforms models with different architecture designs, such as RecurrentGemma 9B and RWKV-v6 Finch 7B/14B. Currently, Falcon Mamba 7B is the best-performing Mamba model in the literature at this scale, surpassing both existing Mamba and hybrid Mamba-Transformer models, according to the Open LLM Leaderboard. Due to its architecture, Falcon Mamba 7B is significantly faster at inference and requires substantially less memory for long sequence generation. Despite recent studies suggesting that hybrid Mamba-Transformer models outperform pure architecture designs, we demonstrate that even the pure Mamba design can achieve similar, or even superior results compared to the Transformer and hybrid designs. We make the weights of our implementation of Falcon Mamba 7B publicly available on https://huggingface.co/tiiuae/falcon-mamba-7b, under a permissive license.", "authors": ["Jingwei Zuo", "Maksim Velikanov", "Dhia Eddine Rhaiem", "Ilyas Chahed", "Younes Belkada", "Guillaume Kunsch", "Hakim Hacid"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-07", "url": "https://arxiv.org/abs/2410.05355", "pdf_url": "https://arxiv.org/pdf/2410.05355v1", "arxiv_id": "2410.05355", "doi": "10.48550/arXiv.2410.05355", "citation_count": 47, "influential_citation_count": 6, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4225}
{"id": "0a74129afdb790f4a7031b2c7a435f07a1cd171b17c818976493beb49d4afb7a", "sources": ["arxiv", "semantic_scholar"], "title": "Exploring the Limitations of Mamba in COPY and CoT Reasoning", "abstract": "Transformers have become the backbone of modern Large Language Models (LLMs); however, their inference overhead grows linearly with the sequence length, posing challenges for modeling long sequences. In light of this, Mamba has attracted attention for maintaining a constant inference size, with empirical evidence demonstrating that it can match Transformer performance in sequence modeling while significantly reducing computational costs. However, an open question remains: can Mamba always bring savings while achieving performance comparable to Transformers? In this paper, we focus on analyzing the expressive ability of Mamba to perform our defined COPY operation and Chain of Thought (CoT) reasoning. First, inspired by the connection between Mamba and linear attention, we show that constant-sized Mamba may struggle to perform COPY operations while Transformers can handle them more easily. However, when the size of Mamba grows linearly with the input sequence length, it can accurately perform COPY, but in this case, Mamba no longer provides overhead savings. Based on this observation, we further analyze Mamba's ability to tackle CoT tasks, which can be described by the Dynamic Programming (DP) problems. Our findings suggest that to solve arbitrary DP problems, the total cost of Mamba is still comparable to standard Transformers. However, similar to efficient Transformers, when facing DP problems with favorable properties such as locality, Mamba can provide savings in overhead. Our experiments on the copy and CoT tasks further demonstrate Mamba's limitations compared to Transformers in learning these tasks.", "authors": ["Ruifeng Ren", "Zhicong Li", "Yong Liu"], "categories": ["cs.LG", "cs.AI", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-04", "url": "https://arxiv.org/abs/2410.03810", "pdf_url": "https://arxiv.org/pdf/2410.03810v3", "arxiv_id": "2410.03810", "doi": "10.18653/v1/2025.emnlp-main.634", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Conference on Empirical Methods in Natural Language Processing", "quality_score": 0.2258}
{"id": "0c3bc38f38ba8c4d92b151c17bdf91b339e75d260a92e363fb9673dc4272bd61", "sources": ["arxiv", "semantic_scholar"], "title": "Crafting Narrative Closures: Zero-Shot Learning with SSM Mamba for Short Story Ending Generation", "abstract": "Writing stories is an engaging yet challenging endeavor. Often, authors encounter moments of creative block, where the path forward in their narrative becomes obscured. This paper is designed to address such moments by providing an innovative solution: A tool that completes stories based on given prompts. By inputting a short story prompt, users can receive a conclusion to their story, articulated in one sentence or more, thereby enhancing the storytelling process with AI-driven creativity. This tool aims not only to assist authors in navigating writer's block but also to offer a fun and interactive way for anyone to expand on story ideas spontaneously. Through this paper, we explore the intersection of artificial intelligence and creative writing, pushing the boundaries of how stories can be crafted and concluded. To create our final text-generation models, we used a pre-trained GPT-3.5 model and a newly created finetuned SSM-Mamba model, both of which perform well on a comprehensive list of metrics including BERT score, METEOR, BLEU, ROUGE, and Perplexity. The SSM model has also been made public for the NLP community on HuggingFace models as an open source contribution, which for the timebeing is a first of its kind state-space model for story-generation task on HuggingFace.", "authors": ["Divyam Sharma", "Divya Santhanam"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-04", "url": "https://arxiv.org/abs/2410.10848", "pdf_url": "https://arxiv.org/pdf/2410.10848v1", "arxiv_id": "2410.10848", "doi": "10.48550/arXiv.2410.10848", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0}
{"id": "e8fe853bf9a8472b2756eeeab5192dd168a14b87545d4988b6b453a9cdff04da", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba in Vision: A Comprehensive Survey of Techniques and Applications", "abstract": "Mamba is emerging as a novel approach to overcome the challenges faced by Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) in computer vision. While CNNs excel at extracting local features, they often struggle to capture long-range dependencies without complex architectural modifications. In contrast, ViTs effectively model global relationships but suffer from high computational costs due to the quadratic complexity of their self-attention mechanisms. Mamba addresses these limitations by leveraging Selective Structured State Space Models to effectively capture long-range dependencies with linear computational complexity. This survey analyzes the unique contributions, computational benefits, and applications of Mamba models while also identifying challenges and potential future research directions. We provide a foundational resource for advancing the understanding and growth of Mamba models in computer vision. An overview of this work is available at https://github.com/maklachur/Mamba-in-Computer-Vision.", "authors": ["Md Maklachur Rahman", "Abdullah Aman Tutul", "Ankur Nath", "Lamyanba Laishram", "Soon Ki Jung", "Tracy Hammond"], "categories": ["cs.CV", "cs.AI", "cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-04", "url": "https://arxiv.org/abs/2410.03105", "pdf_url": "https://arxiv.org/pdf/2410.03105v1", "arxiv_id": "2410.03105", "doi": "10.48550/arXiv.2410.03105", "citation_count": 45, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/maklachur/Mamba-in-Computer-Vision", "venue": "arXiv.org", "quality_score": 0.4157}
{"id": "99e786f08a1bfe81b1ace0985bc07dbec8939417a45cec22fc6fcc1ebc4760b5", "sources": ["arxiv", "semantic_scholar"], "title": "Demystifying the Token Dynamics of Deep Selective State Space Models", "abstract": "Selective state space models (SSM), such as Mamba, have gained prominence for their effectiveness in modeling sequential data. Despite their outstanding empirical performance, a comprehensive theoretical understanding of deep selective SSM remains elusive, hindering their further development and adoption for applications that need high fidelity. In this paper, we investigate the dynamical properties of tokens in a pre-trained Mamba model. In particular, we derive the dynamical system governing the continuous-time limit of the Mamba model and characterize the asymptotic behavior of its solutions. In the one-dimensional case, we prove that only one of the following two scenarios happens: either all tokens converge to zero, or all tokens diverge to infinity. We provide criteria based on model parameters to determine when each scenario occurs. For the convergent scenario, we empirically verify that this scenario negatively impacts the model's performance. For the divergent scenario, we prove that different tokens will diverge to infinity at different rates, thereby contributing unequally to the updates during model training. Based on these investigations, we propose two refinements for the model: excluding the convergent scenario and reordering tokens based on their importance scores, both aimed at improving practical performance. Our experimental results validate these refinements, offering insights into enhancing Mamba's effectiveness in real-world applications.", "authors": ["Thieu N Vo", "Tung D. Pham", "Xin T. Tong", "Tan Minh Nguyen"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-04", "url": "https://arxiv.org/abs/2410.03292", "pdf_url": "https://arxiv.org/pdf/2410.03292v2", "arxiv_id": "2410.03292", "doi": "10.48550/arXiv.2410.03292", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Learning Representations", "quality_score": 0.0753}
{"id": "a245c895b30faed6264f50c815e23ece06aac54a9cafe317d8562774bf0be7ca", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba Neural Operator: Who Wins? Transformers vs. State-Space Models for PDEs", "abstract": "Partial differential equations (PDEs) are widely used to model complex physical systems, but solving them efficiently remains a significant challenge. Recently, Transformers have emerged as the preferred architecture for PDEs due to their ability to capture intricate dependencies. However, they struggle with representing continuous dynamics and long-range interactions. To overcome these limitations, we introduce the Mamba Neural Operator (MNO), a novel framework that enhances neural operator-based techniques for solving PDEs. MNO establishes a formal theoretical connection between structured state-space models (SSMs) and neural operators, offering a unified structure that can adapt to diverse architectures, including Transformer-based models. By leveraging the structured design of SSMs, MNO captures long-range dependencies and continuous dynamics more effectively than traditional Transformers. Through extensive analysis, we show that MNO significantly boosts the expressive power and accuracy of neural operators, making it not just a complement but a superior framework for PDE-related tasks, bridging the gap between efficient representation and accurate solution approximation. Our code is available on https://github.com/Math-ML-X/Mamba-Neural-Operator", "authors": ["Chun-Wun Cheng", "Jiahao Huang", "Yi Zhang", "Guang Yang", "Carola-Bibiane Schönlieb", "Angelica I. Aviles-Rivero"], "categories": ["cs.LG", "math.NA"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2024-10-03", "url": "https://arxiv.org/abs/2410.02113", "pdf_url": "https://arxiv.org/pdf/2410.02113v3", "arxiv_id": "2410.02113", "doi": "10.48550/arXiv.2410.02113", "citation_count": 16, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Math-ML-X/Mamba-Neural-Operator", "venue": "Journal of Computational Physics", "quality_score": 0.3076}
{"id": "6a79d1a32febd4b0039d91dc3f2fd091a611896edffcf74536aed6f36d3d5b2b", "sources": ["arxiv", "semantic_scholar"], "title": "A Comprehensive Survey of Mamba Architectures for Medical Image Analysis: Classification, Segmentation, Restoration and Beyond", "abstract": "Mamba, a special case of the State Space Model, is gaining popularity as an alternative to template-based deep learning approaches in medical image analysis. While transformers are powerful architectures, they have drawbacks, including quadratic computational complexity and an inability to address long-range dependencies efficiently. This limitation affects the analysis of large and complex datasets in medical imaging, where there are many spatial and temporal relationships. In contrast, Mamba offers benefits that make it well-suited for medical image analysis. It has linear time complexity, which is a significant improvement over transformers. Mamba processes longer sequences without attention mechanisms, enabling faster inference and requiring less memory. Mamba also demonstrates strong performance in merging multimodal data, improving diagnosis accuracy and patient outcomes. The organization of this paper allows readers to appreciate the capabilities of Mamba in medical imaging step by step. We begin by defining core concepts of SSMs and models, including S4, S5, and S6, followed by an exploration of Mamba architectures such as pure Mamba, U-Net variants, and hybrid models with convolutional neural networks, transformers, and Graph Neural Networks. We also cover Mamba optimizations, techniques and adaptations, scanning, datasets, applications, experimental results, and conclude with its challenges and future directions in medical imaging. This review aims to demonstrate the transformative potential of Mamba in overcoming existing barriers within medical imaging while paving the way for innovative advancements in the field. A comprehensive list of Mamba architectures applied in the medical field, reviewed in this work, is available at Github.", "authors": ["Shubhi Bansal", "Sreeharish A", "Madhava Prasath J", "Manikandan S", "Sreekanth Madisetty", "Mohammad Zia Ur Rehman", "Chandravardhan Singh Raghaw", "Gaurav Duggal", "Nagendra Kumar"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-03", "url": "https://arxiv.org/abs/2410.02362", "pdf_url": "https://arxiv.org/pdf/2410.02362v3", "arxiv_id": "2410.02362", "doi": "10.48550/arXiv.2410.02362", "citation_count": 30, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3728}
{"id": "f6f366010c26a24780e54ef9ca12088f2ccdacafe6485dc6e27fbae48e1bc3c3", "sources": ["arxiv", "semantic_scholar"], "title": "A SSM is Polymerized from Multivariate Time Series", "abstract": "For multivariate time series (MTS) tasks, previous state space models (SSMs) followed the modeling paradigm of Transformer-based methods. However, none of them explicitly model the complex dependencies of MTS: the Channel Dependency variations with Time (CDT). In view of this, we delve into the derivation of SSM, which involves approximating continuously updated functions by orthogonal function basis. We then develop Poly-Mamba, a novel method for MTS forecasting. Its core concept is to expand the original orthogonal function basis space into a multivariate orthogonal function space containing variable mixing terms, and make a projection on this space so as to explicitly describe the CDT by weighted coefficients. In Poly-Mamba, we propose the Multivariate Orthogonal Polynomial Approximation (MOPA) as a simplified implementation of this concept. For the simple linear relationship between channels, we propose Linear Channel Mixing (LCM) and generate CDT patterns adaptively for different channels through a proposed Order Combining method. Experiments on six real-world datasets demonstrate that Poly-Mamba outperforms the SOTA methods, especially when dealing with datasets having a large number of channels and complex correlations. The codes and log files will be released at: https://github.com/Joeland4/Poly-Mamba.", "authors": ["Haixiang Wu"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-30", "url": "https://arxiv.org/abs/2409.20310", "pdf_url": "https://arxiv.org/pdf/2409.20310v2", "arxiv_id": "2409.20310", "doi": "10.48550/arXiv.2409.20310", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Joeland4/Poly-Mamba", "venue": "arXiv.org", "quality_score": 0.0}
{"id": "655da328920363e9300936f0e98629ec795944f7b3a7ef16f0fd5cb1f050f984", "sources": ["arxiv", "semantic_scholar"], "title": "Speech-Mamba: Long-Context Speech Recognition with Selective State Spaces Models", "abstract": "Current automatic speech recognition systems struggle with modeling long speech sequences due to high quadratic complexity of Transformer-based models. Selective state space models such as Mamba has performed well on long-sequence modeling in natural language processing and computer vision tasks. However, research endeavors in speech technology tasks has been under-explored. We propose Speech-Mamba, which incorporates selective state space modeling in Transformer neural architectures. Long sequence representations with selective state space models in Speech-Mamba is complemented with lower-level representations from Transformer-based modeling. Speech-mamba achieves better capacity to model long-range dependencies, as it scales near-linearly with sequence length.", "authors": ["Xiaoxue Gao", "Nancy F. Chen"], "categories": ["eess.AS", "cs.SD"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2024-09-27", "url": "https://arxiv.org/abs/2409.18654", "pdf_url": "https://arxiv.org/pdf/2409.18654v1", "arxiv_id": "2409.18654", "doi": "10.1109/SLT61566.2024.10832137", "citation_count": 14, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Spoken Language Technology Workshop", "quality_score": 0.294}
{"id": "7ffb067db96e49d7c899100bb4e265ecd055bb673ce3fa3e29db5545aeb72bbe", "sources": ["arxiv", "semantic_scholar"], "title": "DepMamba: Progressive Fusion Mamba for Multimodal Depression Detection", "abstract": "Depression is a common mental disorder that affects millions of people worldwide. Although promising, current multimodal methods hinge on aligned or aggregated multimodal fusion, suffering two significant limitations: (i) inefficient long-range temporal modeling, and (ii) sub-optimal multimodal fusion between intermodal fusion and intramodal processing. In this paper, we propose an audio-visual progressive fusion Mamba for multimodal depression detection, termed DepMamba. DepMamba features two core designs: hierarchical contextual modeling and progressive multimodal fusion. On the one hand, hierarchical modeling introduces convolution neural networks and Mamba to extract the local-to-global features within long-range sequences. On the other hand, the progressive fusion first presents a multimodal collaborative State Space Model (SSM) extracting intermodal and intramodal information for each modality, and then utilizes a multimodal enhanced SSM for modality cohesion. Extensive experimental results on two large-scale depression datasets demonstrate the superior performance of our DepMamba over existing state-of-the-art methods. Code is available at https://github.com/Jiaxin-Ye/DepMamba.", "authors": ["Jiaxin Ye", "Junping Zhang", "Hongming Shan"], "categories": ["cs.CY", "cs.CV", "cs.HC"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-24", "url": "https://arxiv.org/abs/2409.15936", "pdf_url": "https://arxiv.org/pdf/2409.15936v1", "arxiv_id": "2409.15936", "doi": "10.1109/ICASSP49660.2025.10889975", "citation_count": 36, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/Jiaxin-Ye/DepMamba", "venue": "IEEE International Conference on Acoustics, Speech, and Signal Processing", "quality_score": 0.3921}
{"id": "1da622b2a2dde97ed8837fec5c38996e7d610a32754bc660562fb1e3b7849f0b", "sources": ["arxiv", "semantic_scholar"], "title": "DiSPo: Diffusion-SSM based Policy Learning for Coarse-to-Fine Action Discretization", "abstract": "We aim to solve the problem of generating coarse-to-fine skills learning from demonstrations (LfD). To scale precision, traditional LfD approaches often rely on extensive fine-grained demonstrations with external interpolations or dynamics models with limited generalization capabilities. For memory-efficient learning and convenient granularity change, we propose a novel diffusion-state space model (SSM) based policy (DiSPo) that learns from diverse coarse skills and produces varying control scales of actions by leveraging an SSM, Mamba. Our evaluations show the adoption of Mamba and the proposed step-scaling method enable DiSPo to outperform in three coarse-to-fine benchmark tests with maximum 81% higher success rate than baselines. In addition, DiSPo improves inference efficiency by generating coarse motions in less critical regions. We finally demonstrate the scalability of actions with simulation and real-world manipulation tasks. Code and Videos are available at https://robo-dispo.github.io.", "authors": ["Nayoung Oh", "Jaehyeong Jang", "Moonkyeong Jung", "Daehyung Park"], "categories": ["cs.RO"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-23", "url": "https://arxiv.org/abs/2409.14719", "pdf_url": "https://arxiv.org/pdf/2409.14719v4", "arxiv_id": "2409.14719", "doi": "10.48550/arXiv.2409.14719", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1747}
{"id": "32b24b10152b1ea843de26f0e4a224b4261aa3b117f42ada8836c59aa1edc90d", "sources": ["arxiv", "semantic_scholar"], "title": "Topological Deep Learning with State-Space Models: A Mamba Approach for Simplicial Complexes", "abstract": "Graph Neural Networks based on the message-passing (MP) mechanism are a dominant approach for handling graph-structured data. However, they are inherently limited to modeling only pairwise interactions, making it difficult to explicitly capture the complexity of systems with $n$-body relations. To address this, topological deep learning has emerged as a promising field for studying and modeling higher-order interactions using various topological domains, such as simplicial and cellular complexes. While these new domains provide powerful representations, they introduce new challenges, such as effectively modeling the interactions among higher-order structures through higher-order MP. Meanwhile, structured state-space sequence models have proven to be effective for sequence modeling and have recently been adapted for graph data by encoding the neighborhood of a node as a sequence, thereby avoiding the MP mechanism. In this work, we propose a novel architecture designed to operate with simplicial complexes, utilizing the Mamba state-space model as its backbone. Our approach generates sequences for the nodes based on the neighboring cells, enabling direct communication between all higher-order structures, regardless of their rank. We extensively validate our model, demonstrating that it achieves competitive performance compared to state-of-the-art models developed for simplicial complexes.", "authors": ["Marco Montagna", "Simone Scardapane", "Lev Telyatnikov"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-18", "url": "https://arxiv.org/abs/2409.12033", "pdf_url": "https://arxiv.org/pdf/2409.12033v1", "arxiv_id": "2409.12033", "doi": "10.1109/IJCNN64981.2025.11227272", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE International Joint Conference on Neural Network", "quality_score": 0.1193}
{"id": "86ed9c0e2804c95e75cca8c6a2aae4e533dcfca6dd937a94ea98f888838a0448", "sources": ["arxiv", "semantic_scholar"], "title": "Bio-Inspired Mamba: Temporal Locality and Bioplausible Learning in Selective State Space Models", "abstract": "This paper introduces Bio-Inspired Mamba (BIM), a novel online learning framework for selective state space models that integrates biological learning principles with the Mamba architecture. BIM combines Real-Time Recurrent Learning (RTRL) with Spike-Timing-Dependent Plasticity (STDP)-like local learning rules, addressing the challenges of temporal locality and biological plausibility in training spiking neural networks. Our approach leverages the inherent connection between backpropagation through time and STDP, offering a computationally efficient alternative that maintains the ability to capture long-range dependencies. We evaluate BIM on language modeling, speech recognition, and biomedical signal analysis tasks, demonstrating competitive performance against traditional methods while adhering to biological learning principles. Results show improved energy efficiency and potential for neuromorphic hardware implementation. BIM not only advances the field of biologically plausible machine learning but also provides insights into the mechanisms of temporal information processing in biological neural networks.", "authors": ["Jiahao Qin"], "categories": ["cs.NE", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-17", "url": "https://arxiv.org/abs/2409.11263", "pdf_url": "https://arxiv.org/pdf/2409.11263v1", "arxiv_id": "2409.11263", "doi": "10.48550/arXiv.2409.11263", "citation_count": 2, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "6deb9b6231ac4f31a9cb581dbcc075c276b322014db84f16b16330f000d67c86", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba-ST: State Space Model for Efficient Style Transfer", "abstract": "The goal of style transfer is, given a content image and a style source, generating a new image preserving the content but with the artistic representation of the style source. Most of the state-of-the-art architectures use transformers or diffusion-based models to perform this task, despite the heavy computational burden that they require. In particular, transformers use self- and cross-attention layers which have large memory footprint, while diffusion models require high inference time. To overcome the above, this paper explores a novel design of Mamba, an emergent State-Space Model (SSM), called Mamba-ST, to perform style transfer. To do so, we adapt Mamba linear equation to simulate the behavior of cross-attention layers, which are able to combine two separate embeddings into a single output, but drastically reducing memory usage and time complexity. We modified the Mamba's inner equations so to accept inputs from, and combine, two separate data streams. To the best of our knowledge, this is the first attempt to adapt the equations of SSMs to a vision task like style transfer without requiring any other module like cross-attention or custom normalization layers. An extensive set of experiments demonstrates the superiority and efficiency of our method in performing style transfer compared to transformers and diffusion models. Results show improved quality in terms of both ArtFID and FID metrics. Code is available at https://github.com/FilippoBotti/MambaST.", "authors": ["Filippo Botti", "Alex Ergasti", "Leonardo Rossi", "Tomaso Fontanini", "Claudio Ferrari", "Massimo Bertozzi", "Andrea Prati"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-16", "url": "https://arxiv.org/abs/2409.10385", "pdf_url": "https://arxiv.org/pdf/2409.10385v1", "arxiv_id": "2409.10385", "doi": "10.1109/WACV61041.2025.00757", "citation_count": 15, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/FilippoBotti/MambaST", "venue": "IEEE Workshop/Winter Conference on Applications of Computer Vision", "quality_score": 0.301}
{"id": "74f5d524a1422429e46920f2f0727a810a4ba39d5ac04d14bf1a0b74b2240b62", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba for Scalable and Efficient Personalized Recommendations", "abstract": "In this effort, we propose using the Mamba for handling tabular data in personalized recommendation systems. We present the \\textit{FT-Mamba} (Feature Tokenizer\\,$+$\\,Mamba), a novel hybrid model that replaces Transformer layers with Mamba layers within the FT-Transformer architecture, for handling tabular data in personalized recommendation systems. The \\textit{Mamba model} offers an efficient alternative to Transformers, reducing computational complexity from quadratic to linear by enhancing the capabilities of State Space Models (SSMs). FT-Mamba is designed to improve the scalability and efficiency of recommendation systems while maintaining performance. We evaluate FT-Mamba in comparison to a traditional Transformer-based model within a Two-Tower architecture on three datasets: Spotify music recommendation, H\\&M fashion recommendation, and vaccine messaging recommendation. Each model is trained on 160,000 user-action pairs, and performance is measured using precision (P), recall (R), Mean Reciprocal Rank (MRR), and Hit Ratio (HR) at several truncation values. Our results demonstrate that FT-Mamba outperforms the Transformer-based model in terms of computational efficiency while maintaining or exceeding performance across key recommendation metrics. By leveraging Mamba layers, FT-Mamba provides a scalable and effective solution for large-scale personalized recommendation systems, showcasing the potential of the Mamba architecture to enhance both efficiency and accuracy.", "authors": ["Andrew Starnes", "Clayton Webster"], "categories": ["cs.IR", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-11", "url": "https://arxiv.org/abs/2409.17165", "pdf_url": "https://arxiv.org/pdf/2409.17165v1", "arxiv_id": "2409.17165", "doi": "10.1109/ICDMW65004.2024.00018", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0753}
{"id": "85ce6199b28efade71df75c9daa995c6de1d29a0a017d6218ddbefa745a80ad1", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba Policy: Towards Efficient 3D Diffusion Policy with Hybrid Selective State Models", "abstract": "Diffusion models have been widely employed in the field of 3D manipulation due to their efficient capability to learn distributions, allowing for precise prediction of action trajectories. However, diffusion models typically rely on large parameter UNet backbones as policy networks, which can be challenging to deploy on resource-constrained devices. Recently, the Mamba model has emerged as a promising solution for efficient modeling, offering low computational complexity and strong performance in sequence modeling. In this work, we propose the Mamba Policy, a lighter but stronger policy that reduces the parameter count by over 80% compared to the original policy network while achieving superior performance. Specifically, we introduce the XMamba Block, which effectively integrates input information with conditional features and leverages a combination of Mamba and Attention mechanisms for deep feature extraction. Extensive experiments demonstrate that the Mamba Policy excels on the Adroit, Dexart, and MetaWorld datasets, requiring significantly fewer computational resources. Additionally, we highlight the Mamba Policy's enhanced robustness in long-horizon scenarios compared to baseline methods and explore the performance of various Mamba variants within the Mamba Policy framework. Real-world experiments are also conducted to further validate its effectiveness. Our open-source project page can be found at https://andycao1125.github.io/mamba_policy/.", "authors": ["Jiahang Cao", "Qiang Zhang", "Jingkai Sun", "Jiaxu Wang", "Hao Cheng", "Yulin Li", "Jun Ma", "Kun Wu", "Zhiyuan Xu", "Yecheng Shao", "Wen Zhao", "Gang Han", "Yijie Guo", "Renjing Xu"], "categories": ["cs.RO", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-11", "url": "https://arxiv.org/abs/2409.07163", "pdf_url": "https://arxiv.org/pdf/2409.07163v2", "arxiv_id": "2409.07163", "doi": "10.1109/IROS60139.2025.11247625", "citation_count": 27, "influential_citation_count": 2, "has_code": true, "code_url": null, "venue": "IEEE/RJS International Conference on Intelligent RObots and Systems", "quality_score": 0.3618}
{"id": "3f271b2ec373e6ebfeb8acf545a539b6e6696563e4e3249d086fcdf90089c12a", "sources": ["arxiv", "semantic_scholar"], "title": "Rethinking Mamba in Speech Processing by Self-Supervised Models", "abstract": "The Mamba-based model has demonstrated outstanding performance across tasks in computer vision, natural language processing, and speech processing. However, in the realm of speech processing, the Mamba-based model's performance varies across different tasks. For instance, in tasks such as speech enhancement and spectrum reconstruction, the Mamba model performs well when used independently. However, for tasks like speech recognition, additional modules are required to surpass the performance of attention-based models. We propose the hypothesis that the Mamba-based model excels in \"reconstruction\" tasks within speech processing. However, for \"classification tasks\" such as Speech Recognition, additional modules are necessary to accomplish the \"reconstruction\" step. To validate our hypothesis, we analyze the previous Mamba-based Speech Models from an information theory perspective. Furthermore, we leveraged the properties of HuBERT in our study. We trained a Mamba-based HuBERT model, and the mutual information patterns, along with the model's performance metrics, confirmed our assumptions.", "authors": ["Xiangyu Zhang", "Jianbo Ma", "Mostafa Shahin", "Beena Ahmed", "Julien Epps"], "categories": ["eess.AS"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-09-11", "url": "https://arxiv.org/abs/2409.07273", "pdf_url": "https://arxiv.org/pdf/2409.07273v1", "arxiv_id": "2409.07273", "doi": "10.1109/ICASSP49660.2025.10889111", "citation_count": 21, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE International Conference on Acoustics, Speech, and Signal Processing", "quality_score": 0.3356}
{"id": "2bcb9bbb4676e4729f63e30e243485b7fe29ee7706203ae17963de023b63e9d3", "sources": ["arxiv", "semantic_scholar"], "title": "PPMamba: A Pyramid Pooling Local Auxiliary SSM-Based Model for Remote Sensing Image Semantic Segmentation", "abstract": "Semantic segmentation is a vital task in the field of remote sensing (RS). However, conventional convolutional neural network (CNN) and transformer-based models face limitations in capturing long-range dependencies or are often computationally intensive. Recently, an advanced state space model (SSM), namely Mamba, was introduced, offering linear computational complexity while effectively establishing long-distance dependencies. Despite their advantages, Mamba-based methods encounter challenges in preserving local semantic information. To cope with these challenges, this paper proposes a novel network called Pyramid Pooling Mamba (PPMamba), which integrates CNN and Mamba for RS semantic segmentation tasks. The core structure of PPMamba, the Pyramid Pooling-State Space Model (PP-SSM) block, combines a local auxiliary mechanism with an omnidirectional state space model (OSS) that selectively scans feature maps from eight directions, capturing comprehensive feature information. Additionally, the auxiliary mechanism includes pyramid-shaped convolutional branches designed to extract features at multiple scales. Extensive experiments on two widely-used datasets, ISPRS Vaihingen and LoveDA Urban, demonstrate that PPMamba achieves competitive performance compared to state-of-the-art models.", "authors": ["Yin Hu", "Xianping Ma", "Jialu Sui", "Man-On Pun"], "categories": ["cs.CV", "eess.IV"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-09-10", "url": "https://arxiv.org/abs/2409.06309", "pdf_url": "https://arxiv.org/pdf/2409.06309v1", "arxiv_id": "2409.06309", "doi": "10.48550/arXiv.2409.06309", "citation_count": 15, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "APSIPA Transactions on Signal and Information Processing", "quality_score": 0.301}
{"id": "b49024daf167822ad24527ac3f0e0d001039ad4081031d2c4c89012c4d1c4881", "sources": ["arxiv", "semantic_scholar"], "title": "Serp-Mamba: Advancing High-Resolution Retinal Vessel Segmentation with Selective State-Space Model", "abstract": "Ultra-Wide-Field Scanning Laser Ophthalmoscopy (UWF-SLO) images capture high-resolution views of the retina with typically 200 spanning degrees. Accurate segmentation of vessels in UWF-SLO images is essential for detecting and diagnosing fundus disease. Recent studies have revealed that the selective State Space Model (SSM) in Mamba performs well in modeling long-range dependencies, which is crucial for capturing the continuity of elongated vessel structures. Inspired by this, we propose the first Serpentine Mamba (Serp-Mamba) network to address this challenging task. Specifically, we recognize the intricate, varied, and delicate nature of the tubular structure of vessels. Furthermore, the high-resolution of UWF-SLO images exacerbates the imbalance between the vessel and background categories. Based on the above observations, we first devise a Serpentine Interwoven Adaptive (SIA) scan mechanism, which scans UWF-SLO images along curved vessel structures in a snake-like crawling manner. This approach, consistent with vascular texture transformations, ensures the effective and continuous capture of curved vascular structure features. Second, we propose an Ambiguity-Driven Dual Recalibration (ADDR) module to address the category imbalance problem intensified by high-resolution images. Our ADDR module delineates pixels by two learnable thresholds and refines ambiguous pixels through a dual-driven strategy, thereby accurately distinguishing vessels and background regions. Experiment results on three datasets demonstrate the superior performance of our Serp-Mamba on high-resolution vessel segmentation. We also conduct a series of ablation studies to verify the impact of our designs. Our code shall be released upon publication of this work.", "authors": ["Hongqiu Wang", "Yixian Chen", "Wu Chen", "Huihui Xu", "Haoyu Zhao", "Bin Sheng", "Huazhu Fu", "Guang Yang", "Lei Zhu"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science", "Medicine"], "published_date": "2024-09-06", "url": "https://arxiv.org/abs/2409.04356", "pdf_url": "https://arxiv.org/pdf/2409.04356v2", "arxiv_id": "2409.04356", "doi": "10.1109/TMI.2025.3584468", "citation_count": 39, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Medical Imaging", "quality_score": 0.4005}
{"id": "9b4f3bbe688dbd1f566fa8e12ac5c7f127b62178f155ee201ff5752aa8e3e43e", "sources": ["arxiv", "semantic_scholar"], "title": "UV-Mamba: A DCN-Enhanced State Space Model for Urban Village Boundary Identification in High-Resolution Remote Sensing Images", "abstract": "Due to the diverse geographical environments, intricate landscapes, and high-density settlements, the automatic identification of urban village boundaries using remote sensing images remains a highly challenging task. This paper proposes a novel and efficient neural network model called UV-Mamba for accurate boundary detection in high-resolution remote sensing images. UV-Mamba mitigates the memory loss problem in lengthy sequence modeling, which arises in state space models with increasing image size, by incorporating deformable convolutions. Its architecture utilizes an encoder-decoder framework and includes an encoder with four deformable state space augmentation blocks for efficient multi-level semantic extraction and a decoder to integrate the extracted semantic information. We conducted experiments on two large datasets showing that UV-Mamba achieves state-of-the-art performance. Specifically, our model achieves 73.3% and 78.1% IoU on the Beijing and Xi'an datasets, respectively, representing improvements of 1.2% and 3.4% IoU over the previous best model while also being 6x faster in inference speed and 40x smaller in parameter count. Source code and pre-trained models are available at https://github.com/Devin-Egber/UV-Mamba.", "authors": ["Lulin Li", "Ben Chen", "Xuechao Zou", "Junliang Xing", "Pin Tao"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-05", "url": "https://arxiv.org/abs/2409.03431", "pdf_url": "https://arxiv.org/pdf/2409.03431v3", "arxiv_id": "2409.03431", "doi": "10.1109/ICASSP49660.2025.10888896", "citation_count": 10, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/Devin-Egber/UV-Mamba", "venue": "IEEE International Conference on Acoustics, Speech, and Signal Processing", "quality_score": 0.2603}
{"id": "acb8b2f2e7cd4538952389ee28530e09fd0eaf51fc77252a267063f4b0070bc8", "sources": ["arxiv", "semantic_scholar"], "title": "Why mamba is effective? Exploit Linear Transformer-Mamba Network for Multi-Modality Image Fusion", "abstract": "Multi-modality image fusion aims to integrate the merits of images from different sources and render high-quality fusion images. However, existing feature extraction and fusion methods are either constrained by inherent local reduction bias and static parameters during inference (CNN) or limited by quadratic computational complexity (Transformers), and cannot effectively extract and fuse features. To solve this problem, we propose a dual-branch image fusion network called Tmamba. It consists of linear Transformer and Mamba, which has global modeling capabilities while maintaining linear complexity. Due to the difference between the Transformer and Mamba structures, the features extracted by the two branches carry channel and position information respectively. T-M interaction structure is designed between the two branches, using global learnable parameters and convolutional layers to transfer position and channel information respectively. We further propose cross-modal interaction at the attention level to obtain cross-modal attention. Experiments show that our Tmamba achieves promising results in multiple fusion tasks, including infrared-visible image fusion and medical image fusion. Code with checkpoints will be available after the peer-review process.", "authors": ["Chenguang Zhu", "Shan Gao", "Huafeng Chen", "Guangqian Guo", "Chaowei Wang", "Yaoxing Wang", "Chen Shu Lei", "Quanjiang Fan"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-05", "url": "https://arxiv.org/abs/2409.03223", "pdf_url": "https://arxiv.org/pdf/2409.03223v1", "arxiv_id": "2409.03223", "doi": "10.48550/arXiv.2409.03223", "citation_count": 3, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "549036c00508e3797458ae77218a15a49413fd7addbc4be0ade5e2283fe695f0", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba as a motion encoder for robotic imitation learning", "abstract": "Recent advancements in imitation learning, particularly with the integration of LLM techniques, are set to significantly improve robots' dexterity and adaptability. This paper proposes using Mamba, a state-of-the-art architecture with potential applications in LLMs, for robotic imitation learning, highlighting its ability to function as an encoder that effectively captures contextual information. By reducing the dimensionality of the state space, Mamba operates similarly to an autoencoder. It effectively compresses the sequential information into state variables while preserving the essential temporal dynamics necessary for accurate motion prediction. Experimental results in tasks such as cup placing and case loading demonstrate that despite exhibiting higher estimation errors, Mamba achieves superior success rates compared to Transformers in practical task execution. This performance is attributed to Mamba's structure, which encompasses the state space model. Additionally, the study investigates Mamba's capacity to serve as a real-time motion generator with a limited amount of training data.", "authors": ["Toshiaki Tsuji"], "categories": ["cs.RO", "eess.SY"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-09-04", "url": "https://arxiv.org/abs/2409.02636", "pdf_url": "https://arxiv.org/pdf/2409.02636v2", "arxiv_id": "2409.02636", "doi": "10.1109/ACCESS.2025.3561283", "citation_count": 14, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Access", "quality_score": 0.294}
{"id": "18e4159ab30bf8b514289ba7eb3f377bf0ebad8bcf8b6a428d7e518a005a3c4f", "sources": ["arxiv", "semantic_scholar"], "title": "Shuffle Mamba: State Space Models with Random Shuffle for Multi-Modal Image Fusion", "abstract": "Multi-modal image fusion integrates complementary information from different modalities to produce enhanced and informative images. Although State-Space Models, such as Mamba, are proficient in long-range modeling with linear complexity, most Mamba-based approaches use fixed scanning strategies, which can introduce biased prior information. To mitigate this issue, we propose a novel Bayesian-inspired scanning strategy called Random Shuffle, supplemented by a theoretically feasible inverse shuffle to maintain information coordination invariance, aiming to eliminate biases associated with fixed sequence scanning. Based on this transformation pair, we customized the Shuffle Mamba Framework, penetrating modality-aware information representation and cross-modality information interaction across spatial and channel axes to ensure robust interaction and an unbiased global receptive field for multi-modal image fusion. Furthermore, we develop a testing methodology based on Monte-Carlo averaging to ensure the model's output aligns more closely with expected results. Extensive experiments across multiple multi-modal image fusion tasks demonstrate the effectiveness of our proposed method, yielding excellent fusion quality compared to state-of-the-art alternatives. The code is available at https://github.com/caoke-963/Shuffle-Mamba.", "authors": ["Ke Cao", "Xuanhua He", "Tao Hu", "Chengjun Xie", "Man Zhou", "Jie Zhang"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-03", "url": "https://arxiv.org/abs/2409.01728", "pdf_url": "https://arxiv.org/pdf/2409.01728v2", "arxiv_id": "2409.01728", "doi": "10.48550/arXiv.2409.01728", "citation_count": 11, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/caoke-963/Shuffle-Mamba", "venue": null, "quality_score": 0.2698}
{"id": "a4bf5e6b20be096be6d110f609d6c145202206e16a0c5484b52943666a76727d", "sources": ["arxiv", "semantic_scholar"], "title": "Sparse Mamba: Introducing Controllability, Observability, And Stability To Structural State Space Models", "abstract": "Structured state space models' (SSMs) development in recent studies, such as Mamba and Mamba2, outperformed and solved the computational inefficiency of transformers and large language models at small to medium scale. In this work, we introduce the concept of controllability and observability to the original Mamba SSM's architecture in our Sparse-Mamba (S-Mamba) for natural language processing (NLP) applications. Moreover, we reinforce stability on the $nxn$ $A$ matrix on Mmaba2. The Mamba SSMs architecture drops the need for attention layers or multilayer perception blocks in transformers. However, current Mamba models lack reinforcement of controllability in state-space equations for computing the $A$, $B$, $C$, and $D$ matrices at each time step, leading to increased complexity and computational costs. Furthermore, the $A$ matrix in Mamba2 is not always stable. We demonstrate a reduction of parameters compared to the first published Mamba and Mamba2. We showcase an improvement in perplexity by 5\\% and a decrease in training time by 3\\% after reinforcing controllability and observability on the original Mamba architecture in our proposed S-Mamba. We further enforce stability on the $A$ matrix in Mamba2 to improve the loss and perplexity of the model. The controllable and stable $n \\times n$ state matrix $A$ is sparse, and it has only $n$ free parameters. Our novel approach will ensure controllable/observable and stable SSMs, which will be the gate key for Mamba3.", "authors": ["Emadeldeen Hamdan", "Hongyi Pan", "Ahmet Enis Cetin"], "categories": ["cs.LG", "eess.SY"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-08-31", "url": "https://arxiv.org/abs/2409.00563", "pdf_url": "https://arxiv.org/pdf/2409.00563v3", "arxiv_id": "2409.00563", "doi": null, "citation_count": 2, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1505}
{"id": "10d743176b3caf19bc8a4ba79321dfce17f3d13f6dc3214c8844646c3230e069", "sources": ["arxiv", "semantic_scholar"], "title": "DrowzEE-G-Mamba: Leveraging EEG and State Space Models for Driver Drowsiness Detection", "abstract": "Driver drowsiness is identified as a critical factor in road accidents, necessitating robust detection systems to enhance road safety. This study proposes a driver drowsiness detection system, DrowzEE-G-Mamba, that combines Electroencephalography (EEG) with State Space Models (SSMs). EEG data, known for its sensitivity to alertness, is used to model driver state transitions between alert and drowsy. Compared to traditional methods, DrowzEE-G-Mamba achieves significantly improved detection rates and reduced false positives. Notably, it achieves a peak accuracy of 83.24% on the SEED-VIG dataset, surpassing existing techniques. The system maintains high accuracy across varying complexities, making it suitable for real-time applications with limited resources. This robustness is attributed to the combination of channel-split, channel-concatenation, and channel-shuffle operations within the architecture, optimizing information flow from EEG data. Additionally, the integration of convolutional layers and SSMs facilitates comprehensive analysis, capturing both local features and long-range dependencies in the EEG signals. These findings suggest the potential of DrowzEE-G-Mamba for enhancing road safety through accurate drowsiness detection. It also paves the way for developing powerful SSM-based AI algorithms in Brain-Computer Interface applications.", "authors": ["Gourav Siddhad", "Sayantan Dey", "Partha Pratim Roy"], "categories": ["cs.HC"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-28", "url": "https://arxiv.org/abs/2408.16145", "pdf_url": "https://arxiv.org/pdf/2408.16145v2", "arxiv_id": "2408.16145", "doi": "10.1007/978-3-031-78398-2_19", "citation_count": 14, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Pattern Recognition", "quality_score": 0.294}
{"id": "a29b63ac083c472e85fc6558020bb27a52751eb03841f0e1f70d97e041904732", "sources": ["arxiv", "semantic_scholar"], "title": "The Mamba in the Llama: Distilling and Accelerating Hybrid Models", "abstract": "Linear RNN architectures, like Mamba, can be competitive with Transformer models in language modeling while having advantageous deployment characteristics. Given the focus on training large-scale Transformer models, we consider the challenge of converting these pretrained models for deployment. We demonstrate that it is feasible to distill large Transformers into linear RNNs by reusing the linear projection weights from attention layers with academic GPU resources. The resulting hybrid model, which incorporates a quarter of the attention layers, achieves performance comparable to the original Transformer in chat benchmarks and outperforms open-source hybrid Mamba models trained from scratch with trillions of tokens in both chat benchmarks and general benchmarks. Moreover, we introduce a hardware-aware speculative decoding algorithm that accelerates the inference speed of Mamba and hybrid models. Overall we show how, with limited computation resources, we can remove many of the original attention layers and generate from the resulting model more efficiently. Our top-performing model, distilled from Llama3-8B-Instruct, achieves a 29.61 length-controlled win rate on AlpacaEval 2 against GPT-4 and 7.35 on MT-Bench, surpassing the best 8B scale instruction-tuned linear RNN model. We also find that the distilled model has natural length extrapolation, showing almost perfect accuracy in the needle-in-a-haystack test at 20x the distillation length. Code and pre-trained checkpoints are open-sourced at https://github.com/jxiw/MambaInLlama and https://github.com/itsdaniele/speculative_mamba.", "authors": ["Junxiong Wang", "Daniele Paliotta", "Avner May", "Alexander M. Rush", "Tri Dao"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-27", "url": "https://arxiv.org/abs/2408.15237", "pdf_url": "https://arxiv.org/pdf/2408.15237v4", "arxiv_id": "2408.15237", "doi": "10.48550/arXiv.2408.15237", "citation_count": 124, "influential_citation_count": 21, "has_code": true, "code_url": "https://github.com/jxiw/MambaInLlama", "venue": "Neural Information Processing Systems", "quality_score": 0.6712}
{"id": "adc820d17e0286c20712b5da85bd67091448d32a38ee6b1ba435004083afcc95", "sources": ["arxiv", "semantic_scholar"], "title": "LoG-VMamba: Local-Global Vision Mamba for Medical Image Segmentation", "abstract": "Mamba, a State Space Model (SSM), has recently shown competitive performance to Convolutional Neural Networks (CNNs) and Transformers in Natural Language Processing and general sequence modeling. Various attempts have been made to adapt Mamba to Computer Vision tasks, including medical image segmentation (MIS). Vision Mamba (VM)-based networks are particularly attractive due to their ability to achieve global receptive fields, similar to Vision Transformers, while also maintaining linear complexity in the number of tokens. However, the existing VM models still struggle to maintain both spatially local and global dependencies of tokens in high dimensional arrays due to their sequential nature. Employing multiple and/or complicated scanning strategies is computationally costly, which hinders applications of SSMs to high-dimensional 2D and 3D images that are common in MIS problems. In this work, we propose Local-Global Vision Mamba, LoG-VMamba, that explicitly enforces spatially adjacent tokens to remain nearby on the channel axis, and retains the global context in a compressed form. Our method allows the SSMs to access the local and global contexts even before reaching the last token while requiring only a simple scanning strategy. Our segmentation models are computationally efficient and substantially outperform both CNN and Transformers-based baselines on a diverse set of 2D and 3D MIS tasks. The implementation of LoG-VMamba is available at \\url{https://github.com/Oulu-IMEDS/LoG-VMamba}.", "authors": ["Trung Dinh Quoc Dang", "Huy Hoang Nguyen", "Aleksei Tiulpin"], "categories": ["cs.CV", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-26", "url": "https://arxiv.org/abs/2408.14415", "pdf_url": "https://arxiv.org/pdf/2408.14415v1", "arxiv_id": "2408.14415", "doi": "10.48550/arXiv.2408.14415", "citation_count": 33, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/Oulu-IMEDS/LoG-VMamba}", "venue": "arXiv.org", "quality_score": 0.3829}
{"id": "602e769094ded2ff52d3b280b2fa6260469d798d5d317ef5081d6da23d07e020", "sources": ["arxiv", "semantic_scholar"], "title": "MSVM-UNet: Multi-Scale Vision Mamba UNet for Medical Image Segmentation", "abstract": "State Space Models (SSMs), especially Mamba, have shown great promise in medical image segmentation due to their ability to model long-range dependencies with linear computational complexity. However, accurate medical image segmentation requires the effective learning of both multi-scale detailed feature representations and global contextual dependencies. Although existing works have attempted to address this issue by integrating CNNs and SSMs to leverage their respective strengths, they have not designed specialized modules to effectively capture multi-scale feature representations, nor have they adequately addressed the directional sensitivity problem when applying Mamba to 2D image data. To overcome these limitations, we propose a Multi-Scale Vision Mamba UNet model for medical image segmentation, termed MSVM-UNet. Specifically, by introducing multi-scale convolutions in the VSS blocks, we can more effectively capture and aggregate multi-scale feature representations from the hierarchical features of the VMamba encoder and better handle 2D visual data. Additionally, the large kernel patch expanding (LKPE) layers achieve more efficient upsampling of feature maps by simultaneously integrating spatial and channel information. Extensive experiments on the Synapse and ACDC datasets demonstrate that our approach is more effective than some state-of-the-art methods in capturing and aggregating multi-scale feature representations and modeling long-range dependencies between pixels.", "authors": ["Chaowei Chen", "Li Yu", "Shiquan Min", "Shunfang Wang"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-25", "url": "https://arxiv.org/abs/2408.13735", "pdf_url": "https://arxiv.org/pdf/2408.13735v1", "arxiv_id": "2408.13735", "doi": "10.1109/BIBM62325.2024.10821761", "citation_count": 24, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "IEEE International Conference on Bioinformatics and Biomedicine", "quality_score": 0.3495}
{"id": "0f366417dbf3e5a86a3df3bac529462e05cc1317282e4a45800acd77fc151500", "sources": ["arxiv", "semantic_scholar"], "title": "O-Mamba: O-shape State-Space Model for Underwater Image Enhancement", "abstract": "Underwater image enhancement (UIE) face significant challenges due to complex underwater lighting conditions. Recently, mamba-based methods have achieved promising results in image enhancement tasks. However, these methods commonly rely on Vmamba, which focuses only on spatial information modeling and struggles to deal with the cross-color channel dependency problem in underwater images caused by the differential attenuation of light wavelengths, limiting the effective use of deep networks. In this paper, we propose a novel UIE framework called O-mamba. O-mamba employs an O-shaped dual-branch network to separately model spatial and cross-channel information, utilizing the efficient global receptive field of state-space models optimized for underwater images. To enhance information interaction between the two branches and effectively utilize multi-scale information, we design a Multi-scale Bi-mutual Promotion Module. This branch includes MS-MoE for fusing multi-scale information within branches, Mutual Promotion module for interaction between spatial and channel information across branches, and Cyclic Multi-scale optimization strategy to maximize the use of multi-scale information. Extensive experiments demonstrate that our method achieves state-of-the-art (SOTA) results.The code is available at https://github.com/chenydong/O-Mamba.", "authors": ["Chenyu Dong", "Chen Zhao", "Weiling Cai", "Bo Yang"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-23", "url": "https://arxiv.org/abs/2408.12816", "pdf_url": "https://arxiv.org/pdf/2408.12816v1", "arxiv_id": "2408.12816", "doi": "10.48550/arXiv.2408.12816", "citation_count": 14, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/chenydong/O-Mamba", "venue": "arXiv.org", "quality_score": 0.294}
{"id": "c425a384a056eb2f91ae813d9b7e534dba5951c3b2d5999ad703361ef85ea290", "sources": ["arxiv", "semantic_scholar"], "title": "Scalable Autoregressive Image Generation with Mamba", "abstract": "We introduce AiM, an autoregressive (AR) image generative model based on Mamba architecture. AiM employs Mamba, a novel state-space model characterized by its exceptional performance for long-sequence modeling with linear time complexity, to supplant the commonly utilized Transformers in AR image generation models, aiming to achieve both superior generation quality and enhanced inference speed. Unlike existing methods that adapt Mamba to handle two-dimensional signals via multi-directional scan, AiM directly utilizes the next-token prediction paradigm for autoregressive image generation. This approach circumvents the need for extensive modifications to enable Mamba to learn 2D spatial representations. By implementing straightforward yet strategically targeted modifications for visual generative tasks, we preserve Mamba's core structure, fully exploiting its efficient long-sequence modeling capabilities and scalability. We provide AiM models in various scales, with parameter counts ranging from 148M to 1.3B. On the ImageNet1K 256*256 benchmark, our best AiM model achieves a FID of 2.21, surpassing all existing AR models of comparable parameter counts and demonstrating significant competitiveness against diffusion models, with 2 to 10 times faster inference speed. Code is available at https://github.com/hp-l33/AiM", "authors": ["Haopeng Li", "Jinyue Yang", "Kexin Wang", "Xuerui Qiu", "Yuhong Chou", "Xin Li", "Guoqi Li"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-22", "url": "https://arxiv.org/abs/2408.12245", "pdf_url": "https://arxiv.org/pdf/2408.12245v5", "arxiv_id": "2408.12245", "doi": "10.48550/arXiv.2408.12245", "citation_count": 27, "influential_citation_count": 2, "has_code": true, "code_url": "https://github.com/hp-l33/AiM", "venue": "arXiv.org", "quality_score": 0.3618}
{"id": "62ade412ec8f34b88de91430272052390b17064e06a9967400b3b7ef26b6a394", "sources": ["arxiv", "semantic_scholar"], "title": "HMT-UNet: A hybird Mamba-Transformer Vision UNet for Medical Image Segmentation", "abstract": "In the field of medical image segmentation, models based on both CNN and Transformer have been thoroughly investigated. However, CNNs have limited modeling capabilities for long-range dependencies, making it challenging to exploit the semantic information within images fully. On the other hand, the quadratic computational complexity poses a challenge for Transformers. State Space Models (SSMs), such as Mamba, have been recognized as a promising method. They not only demonstrate superior performance in modeling long-range interactions, but also preserve a linear computational complexity. The hybrid mechanism of SSM (State Space Model) and Transformer, after meticulous design, can enhance its capability for efficient modeling of visual features. Extensive experiments have demonstrated that integrating the self-attention mechanism into the hybrid part behind the layers of Mamba's architecture can greatly improve the modeling capacity to capture long-range spatial dependencies. In this paper, leveraging the hybrid mechanism of SSM, we propose a U-shape architecture model for medical image segmentation, named Hybird Transformer vision Mamba UNet (HTM-UNet). We conduct comprehensive experiments on the ISIC17, ISIC18, CVC-300, CVC-ClinicDB, Kvasir, CVC-ColonDB, ETIS-Larib PolypDB public datasets and ZD-LCI-GIM private dataset. The results indicate that HTM-UNet exhibits competitive performance in medical image segmentation tasks. Our code is available at https://github.com/simzhangbest/HMT-Unet.", "authors": ["Mingya Zhang", "Zhihao Chen", "Yiyuan Ge", "Xianping Tao"], "categories": ["eess.IV", "cs.CV"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2024-08-21", "url": "https://arxiv.org/abs/2408.11289", "pdf_url": "https://arxiv.org/pdf/2408.11289v2", "arxiv_id": "2408.11289", "doi": "10.48550/arXiv.2408.11289", "citation_count": 15, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/simzhangbest/HMT-Unet", "venue": "arXiv.org", "quality_score": 0.301}
{"id": "75369da5543a52c5653255e6411e10663099fd56c21336a8bb37174a2f0d33aa", "sources": ["arxiv", "semantic_scholar"], "title": "Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models", "abstract": "Transformer architectures have become a dominant paradigm for domains like language modeling but suffer in many inference settings due to their quadratic-time self-attention. Recently proposed subquadratic architectures, such as Mamba, have shown promise, but have been pretrained with substantially less computational resources than the strongest Transformer models. In this work, we present a method that is able to distill a pretrained Transformer architecture into alternative architectures such as state space models (SSMs). The key idea to our approach is that we can view both Transformers and SSMs as applying different forms of mixing matrices over the token sequences. We can thus progressively distill the Transformer architecture by matching different degrees of granularity in the SSM: first matching the mixing matrices themselves, then the hidden units at each block, and finally the end-to-end predictions. Our method, called MOHAWK, is able to distill a Mamba-2 variant based on the Phi-1.5 architecture (Phi-Mamba) using only 3B tokens and a hybrid version (Hybrid Phi-Mamba) using 5B tokens. Despite using less than 1% of the training data typically used to train models from scratch, Phi-Mamba boasts substantially stronger performance compared to all past open-source non-Transformer models. MOHAWK allows models like SSMs to leverage computational resources invested in training Transformer-based architectures, highlighting a new avenue for building such models.", "authors": ["Aviv Bick", "Kevin Y. Li", "Eric P. Xing", "J. Zico Kolter", "Albert Gu"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-19", "url": "https://arxiv.org/abs/2408.10189", "pdf_url": "https://arxiv.org/pdf/2408.10189v2", "arxiv_id": "2408.10189", "doi": "10.48550/arXiv.2408.10189", "citation_count": 65, "influential_citation_count": 10, "has_code": true, "code_url": null, "venue": "Neural Information Processing Systems", "quality_score": 0.5207}
{"id": "f03a3edea781063bc5db3c3639d58518bd5a583364910fc68298674f485032de", "sources": ["arxiv", "semantic_scholar"], "title": "MambaMIM: Pre-training Mamba with State Space Token Interpolation and its Application to Medical Image Segmentation", "abstract": "Recently, the state space model Mamba has demonstrated efficient long-sequence modeling capabilities, particularly for addressing long-sequence visual tasks in 3D medical imaging. However, existing generative self-supervised learning methods have not yet fully unleashed Mamba's potential for handling long-range dependencies because they overlook the inherent causal properties of state space sequences in masked modeling. To address this challenge, we propose a general-purpose pre-training framework called MambaMIM, a masked image modeling method based on a novel TOKen-Interpolation strategy (TOKI) for the selective structure state space sequence, which learns causal relationships of state space within the masked sequence. Further, MambaMIM introduces a bottom-up 3D hybrid masking strategy to maintain a masking consistency across different architectures and can be used on any single or hybrid Mamba architecture to enhance its multi-scale and long-range representation capability. We pre-train MambaMIM on a large-scale dataset of 6.8K CT scans and evaluate its performance across eight public medical segmentation benchmarks. Extensive downstream experiments reveal the feasibility and advancement of using Mamba for medical image pre-training. In particular, when we apply the MambaMIM to a customized architecture that hybridizes MedNeXt and Vision Mamba, we consistently obtain the state-of-the-art segmentation performance. The code is available at: https://github.com/FengheTan9/MambaMIM.", "authors": ["Fenghe Tang", "Bingkun Nian", "Yingtai Li", "Zihang Jiang", "Jie Yang", "Wei Liu", "S. Kevin Zhou"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science", "Medicine"], "published_date": "2024-08-15", "url": "https://arxiv.org/abs/2408.08070", "pdf_url": "https://arxiv.org/pdf/2408.08070v2", "arxiv_id": "2408.08070", "doi": "10.1016/j.media.2025.103606", "citation_count": 14, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/FengheTan9/MambaMIM", "venue": "Medical Image Analysis, Volume 103, 2025, Article 103606", "quality_score": 0.294}
{"id": "b1e3bffdabb306204280e5039082cd28d3f21b5f6c2a517576d26eaabb0f2c91", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba Retriever: Utilizing Mamba for Effective and Efficient Dense Retrieval", "abstract": "In the information retrieval (IR) area, dense retrieval (DR) models use deep learning techniques to encode queries and passages into embedding space to compute their semantic relations. It is important for DR models to balance both efficiency and effectiveness. Pre-trained language models (PLMs), especially Transformer-based PLMs, have been proven to be effective encoders of DR models. However, the self-attention component in Transformer-based PLM results in a computational complexity that grows quadratically with sequence length, and thus exhibits a slow inference speed for long-text retrieval. Some recently proposed non-Transformer PLMs, especially the Mamba architecture PLMs, have demonstrated not only comparable effectiveness to Transformer-based PLMs on generative language tasks but also better efficiency due to linear time scaling in sequence length. This paper implements the Mamba Retriever to explore whether Mamba can serve as an effective and efficient encoder of DR model for IR tasks. We fine-tune the Mamba Retriever on the classic short-text MS MARCO passage ranking dataset and the long-text LoCoV0 dataset. Experimental results show that (1) on the MS MARCO passage ranking dataset and BEIR, the Mamba Retriever achieves comparable or better effectiveness compared to Transformer-based retrieval models, and the effectiveness grows with the size of the Mamba model; (2) on the long-text LoCoV0 dataset, the Mamba Retriever can extend to longer text length than its pre-trained length after fine-tuning on retrieval task, and it has comparable or better effectiveness compared to other long-text retrieval models; (3) the Mamba Retriever has superior inference speed for long-text retrieval. In conclusion, Mamba Retriever is both effective and efficient, making it a practical model, especially for long-text retrieval.", "authors": ["Hanqi Zhang", "Chong Chen", "Lang Mei", "Qi Liu", "Jiaxin Mao"], "categories": ["cs.IR"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-15", "url": "https://arxiv.org/abs/2408.08066", "pdf_url": "https://arxiv.org/pdf/2408.08066v2", "arxiv_id": "2408.08066", "doi": "10.1145/3627673.3679959", "citation_count": 11, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Information and Knowledge Management", "quality_score": 0.2698}
{"id": "1b7f5dfe7b8064d871d8720e7064a11411b4216653109d19fc7843f39365b818", "sources": ["arxiv", "semantic_scholar"], "title": "DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs", "abstract": "Dynamic graph modeling aims to uncover evolutionary patterns in real-world systems, enabling accurate social recommendation and early detection of cancer cells. Inspired by the success of recent state space models in efficiently capturing long-term dependencies, we propose DyG-Mamba by translating dynamic graph modeling into a long-term sequence modeling problem. Specifically, inspired by Ebbinghaus' forgetting curve, we treat the irregular timespans between events as control signals, allowing DyG-Mamba to dynamically adjust the forgetting of historical information. This mechanism ensures effective usage of irregular timespans, thereby improving both model effectiveness and inductive capability. In addition, inspired by Ebbinghaus' review cycle, we redefine core parameters to ensure that DyG-Mamba selectively reviews historical information and filters out noisy inputs, further enhancing the model's robustness. Through exhaustive experiments on 12 datasets covering dynamic link prediction and node classification tasks, we show that DyG-Mamba achieves state-of-the-art performance on most datasets, while demonstrating significantly improved computational and memory efficiency. Code is available at https://github.com/Clearloveyuan/DyG-Mamba.", "authors": ["Dongyuan Li", "Shiyin Tan", "Ying Zhang", "Ming Jin", "Shirui Pan", "Manabu Okumura", "Renhe Jiang"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-13", "url": "https://arxiv.org/abs/2408.06966", "pdf_url": "https://arxiv.org/pdf/2408.06966v2", "arxiv_id": "2408.06966", "doi": "10.48550/arXiv.2408.06966", "citation_count": 25, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Clearloveyuan/DyG-Mamba", "venue": "arXiv.org", "quality_score": 0.3537}
{"id": "ff93bfd81f0e05559b0b8ad246a52003ebe2ae170bdbf715e96c8336c72d3f93", "sources": ["arxiv", "semantic_scholar"], "title": "SELD-Mamba: Selective State-Space Model for Sound Event Localization and Detection with Source Distance Estimation", "abstract": "In the Sound Event Localization and Detection (SELD) task, Transformer-based models have demonstrated impressive capabilities. However, the quadratic complexity of the Transformer's self-attention mechanism results in computational inefficiencies. In this paper, we propose a network architecture for SELD called SELD-Mamba, which utilizes Mamba, a selective state-space model. We adopt the Event-Independent Network V2 (EINV2) as the foundational framework and replace its Conformer blocks with bidirectional Mamba blocks to capture a broader range of contextual information while maintaining computational efficiency. Additionally, we implement a two-stage training method, with the first stage focusing on Sound Event Detection (SED) and Direction of Arrival (DoA) estimation losses, and the second stage reintroducing the Source Distance Estimation (SDE) loss. Our experimental results on the 2024 DCASE Challenge Task3 dataset demonstrate the effectiveness of the selective state-space model in SELD and highlight the benefits of the two-stage training approach in enhancing SELD performance.", "authors": ["Da Mu", "Zhicheng Zhang", "Haobo Yue", "Zehao Wang", "Jin Tang", "Jianqin Yin"], "categories": ["cs.SD", "cs.AI", "eess.AS"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-08-09", "url": "https://arxiv.org/abs/2408.05057", "pdf_url": "https://arxiv.org/pdf/2408.05057v1", "arxiv_id": "2408.05057", "doi": "10.48550/arXiv.2408.05057", "citation_count": 14, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.294}
{"id": "79a6dac361df2b802ca677d8eda7bdfa71da183ee3464cf5b63da91bc82f1683", "sources": ["arxiv", "semantic_scholar"], "title": "PoseMamba: Monocular 3D Human Pose Estimation with Bidirectional Global-Local Spatio-Temporal State Space Model", "abstract": "Transformers have significantly advanced the field of 3D human pose estimation (HPE). However, existing transformer-based methods primarily use self-attention mechanisms for spatio-temporal modeling, leading to a quadratic complexity, unidirectional modeling of spatio-temporal relationships, and insufficient learning of spatial-temporal correlations. Recently, the Mamba architecture, utilizing the state space model (SSM), has exhibited superior long-range modeling capabilities in a variety of vision tasks with linear complexity. In this paper, we propose PoseMamba, a novel purely SSM-based approach with linear complexity for 3D human pose estimation in monocular video. Specifically, we propose a bidirectional global-local spatio-temporal SSM block that comprehensively models human joint relations within individual frames as well as temporal correlations across frames. Within this bidirectional global-local spatio-temporal SSM block, we introduce a reordering strategy to enhance the local modeling capability of the SSM. This strategy provides a more logical geometric scanning order and integrates it with the global SSM, resulting in a combined global-local spatial scan. We have quantitatively and qualitatively evaluated our approach using two benchmark datasets: Human3.6M and MPI-INF-3DHP. Extensive experiments demonstrate that PoseMamba achieves state-of-the-art performance on both datasets while maintaining a smaller model size and reducing computational costs. The code and models will be released.", "authors": ["Yunlong Huang", "Junshuo Liu", "Ke Xian", "Robert Caiming Qiu"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-07", "url": "https://arxiv.org/abs/2408.03540", "pdf_url": "https://arxiv.org/pdf/2408.03540v2", "arxiv_id": "2408.03540", "doi": "10.48550/arXiv.2408.03540", "citation_count": 27, "influential_citation_count": 5, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3891}
{"id": "203cb6f2cf76d7696c6adc5d0b7cc3e815c3f918ec8c3993ca3e6ef7ad2bc95b", "sources": ["arxiv", "semantic_scholar"], "title": "PackMamba: Efficient Processing of Variable-Length Sequences in Mamba training", "abstract": "With the evolution of large language models, traditional Transformer models become computationally demanding for lengthy sequences due to the quadratic growth in computation with respect to the sequence length. Mamba, emerging as a groundbreaking architecture in the field of generative AI, demonstrates remarkable proficiency in handling elongated sequences with reduced computational and memory complexity. Nevertheless, the existing training framework of Mamba presents inefficiency with variable-length sequence inputs. Either single-sequence training results in low GPU utilization, or batched processing of variable-length sequences to a maximum length incurs considerable memory and computational overhead. To address this problem, we analyze the performance of bottleneck operators in Mamba under diverse tensor shapes and proposed PackMamba, a high-throughput Mamba that efficiently handles variable-length sequences. Diving deep into state-space models (SSMs), we modify the parallel operators to avoid passing information between individual sequences while maintaining high performance. Experimental results on an NVIDIA A100 GPU demonstrate throughput exceeding the baseline single-sequence processing scheme: 3.06x speedup on the 1.4B model and 2.62x on the 2.8B model.", "authors": ["Haoran Xu", "Ziqian Liu", "Rong Fu", "Zhongling Su", "Zerui Wang", "Zheng Cai", "Zhilin Pei", "Xingcheng Zhang"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-07", "url": "https://arxiv.org/abs/2408.03865", "pdf_url": "https://arxiv.org/pdf/2408.03865v2", "arxiv_id": "2408.03865", "doi": "10.48550/arXiv.2408.03865", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1193}
{"id": "6ae585e7734790016a0493e26c934189746a29af04320494f6c2595f7c05dc0b", "sources": ["arxiv", "semantic_scholar"], "title": "LaMamba-Diff: Linear-Time High-Fidelity Diffusion Models Based on Local Attention and Mamba", "abstract": "Recent Transformer-based diffusion models have shown remarkable performance, largely attributed to the ability of the self-attention mechanism to accurately capture both global and local contexts by computing all-pair interactions among input tokens. However, their quadratic complexity poses significant computational challenges for long-sequence inputs. Conversely, a recent state space model called Mamba offers linear complexity by compressing a filtered global context into a hidden state. Despite its efficiency, compression inevitably leads to information loss of fine-grained local dependencies among tokens, which are crucial for effective visual generative modeling. Motivated by these observations, we introduce Local Attentional Mamba (LaMamba) blocks that combine the strengths of self-attention and Mamba, capturing both global contexts and local details with linear complexity. Leveraging the efficient U-Net architecture, our model exhibits exceptional scalability and surpasses the performance of DiT across various model scales on ImageNet at 256x256 resolution, all while utilizing substantially fewer GFLOPs and a comparable number of parameters. Compared to state-of-the-art diffusion models on ImageNet 256x256 and 512x512, our largest model presents notable advantages, such as a reduction of up to 62% GFLOPs compared to DiT-XL/2, while achieving superior performance with comparable or fewer parameters. Our code is available at https://github.com/yunxiangfu2001/LaMamba-Diff.", "authors": ["Yunxiang Fu", "Chaoqi Chen", "Yizhou Yu"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-05", "url": "https://arxiv.org/abs/2408.02615", "pdf_url": "https://arxiv.org/pdf/2408.02615v3", "arxiv_id": "2408.02615", "doi": "10.48550/arXiv.2408.02615", "citation_count": 6, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/yunxiangfu2001/LaMamba-Diff", "venue": "arXiv.org", "quality_score": 0.2113}
{"id": "736be9490b20227695dbb4d72561838b5a20a780be0c5d8b34f7768b6e2e6b58", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba-Spike: Enhancing the Mamba Architecture with a Spiking Front-End for Efficient Temporal Data Processing", "abstract": "The field of neuromorphic computing has gained significant attention in recent years, aiming to bridge the gap between the efficiency of biological neural networks and the performance of artificial intelligence systems. This paper introduces Mamba-Spike, a novel neuromorphic architecture that integrates a spiking front-end with the Mamba backbone to achieve efficient and robust temporal data processing. The proposed approach leverages the event-driven nature of spiking neural networks (SNNs) to capture and process asynchronous, time-varying inputs, while harnessing the power of the Mamba backbone's selective state spaces and linear-time sequence modeling capabilities to model complex temporal dependencies effectively. The spiking front-end of Mamba-Spike employs biologically inspired neuron models, along with adaptive threshold and synaptic dynamics. These components enable efficient spatiotemporal feature extraction and encoding of the input data. The Mamba backbone, on the other hand, utilizes a hierarchical structure with gated recurrent units and attention mechanisms to capture long-term dependencies and selectively process relevant information. To evaluate the efficacy of the proposed architecture, a comprehensive empirical study is conducted on both neuromorphic datasets, including DVS Gesture and TIDIGITS, and standard datasets, such as Sequential MNIST and CIFAR10-DVS. The results demonstrate that Mamba-Spike consistently outperforms state-of-the-art baselines, achieving higher accuracy, lower latency, and improved energy efficiency. Moreover, the model exhibits robustness to various input perturbations and noise levels, highlighting its potential for real-world applications. The code will be available at https://github.com/ECNU-Cross-Innovation-Lab/Mamba-Spike.", "authors": ["Jiahao Qin", "Feng Liu"], "categories": ["cs.NE", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-04", "url": "https://arxiv.org/abs/2408.11823", "pdf_url": "https://arxiv.org/pdf/2408.11823v1", "arxiv_id": "2408.11823", "doi": "10.48550/arXiv.2408.11823", "citation_count": 16, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/ECNU-Cross-Innovation-Lab/Mamba-Spike", "venue": "Computer Graphics International Conference", "quality_score": 0.3076}
{"id": "d0b8753b371dd2a53b2b85300b243fd8d20f6c299421178bcced2955eb9ee7a0", "sources": ["arxiv", "semantic_scholar"], "title": "JambaTalk: Speech-Driven 3D Talking Head Generation Based on Hybrid Transformer-Mamba Model", "abstract": "In recent years, the talking head generation has become a focal point for researchers. Considerable effort is being made to refine lip-sync motion, capture expressive facial expressions, generate natural head poses, and achieve high-quality video. However, no single model has yet achieved equivalence across all quantitative and qualitative metrics. We introduce Jamba, a hybrid Transformer-Mamba model, to animate a 3D face. Mamba, a pioneering Structured State Space Model (SSM) architecture, was developed to overcome the limitations of conventional Transformer architectures, particularly in handling long sequences. This challenge has constrained traditional models. Jamba combines the advantages of both the Transformer and Mamba approaches, offering a comprehensive solution. Based on the foundational Jamba block, we present JambaTalk to enhance motion variety and lip sync through multimodal integration. Extensive experiments reveal that our method achieves performance comparable or superior to state-of-the-art models.", "authors": ["Farzaneh Jafari", "Stefano Berretti", "Anup Basu"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-03", "url": "https://arxiv.org/abs/2408.01627", "pdf_url": "https://arxiv.org/pdf/2408.01627v3", "arxiv_id": "2408.01627", "doi": "10.1145/3793196", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)", "quality_score": 0.1945}
{"id": "2d304f1a313da1604999471aa52da2e9ec8a579c92406f719faeb29eb7ecdc20", "sources": ["arxiv", "semantic_scholar"], "title": "A Survey of Mamba", "abstract": "As one of the most representative DL techniques, Transformer architecture has empowered numerous advanced models, especially the large language models (LLMs) that comprise billions of parameters, becoming a cornerstone in deep learning. Despite the impressive achievements, Transformers still face inherent limitations, particularly the time-consuming inference resulting from the quadratic computation complexity of attention calculation. Recently, a novel architecture named Mamba, drawing inspiration from classical state space models (SSMs), has emerged as a promising alternative for building foundation models, delivering comparable modeling abilities to Transformers while preserving near-linear scalability concerning sequence length. This has sparked an increasing number of studies actively exploring Mamba's potential to achieve impressive performance across diverse domains. Given such rapid evolution, there is a critical need for a systematic review that consolidates existing Mamba-empowered models, offering a comprehensive understanding of this emerging model architecture. In this survey, we therefore conduct an in-depth investigation of recent Mamba-associated studies, covering three main aspects: the advancements of Mamba-based models, the techniques of adapting Mamba to diverse data, and the applications where Mamba can excel. Specifically, we first review the foundational knowledge of various representative deep learning models and the details of Mamba-1&2 as preliminaries. Then, to showcase the significance of Mamba for AI, we comprehensively review the related studies focusing on Mamba models' architecture design, data adaptability, and applications. Finally, we present a discussion of current limitations and explore various promising research directions to provide deeper insights for future investigations.", "authors": ["Haohao Qu", "Liangbo Ning", "Rui An", "Wenqi Fan", "Tyler Derr", "Hui Liu", "Xin Xu", "Qing Li"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-02", "url": "https://arxiv.org/abs/2408.01129", "pdf_url": "https://arxiv.org/pdf/2408.01129v8", "arxiv_id": "2408.01129", "doi": "10.48550/arXiv.2408.01129", "citation_count": 95, "influential_citation_count": 6, "has_code": false, "code_url": null, "venue": "ACM Transactions on Intelligent Systems and Technology", "quality_score": 0.4956}
{"id": "e80bb3d396d591efdd5f9e918e59aca12f31ac284ffb92e81d0244ea75cf2db7", "sources": ["arxiv", "semantic_scholar"], "title": "Wave-Mamba: Wavelet State Space Model for Ultra-High-Definition Low-Light Image Enhancement", "abstract": "Ultra-high-definition (UHD) technology has attracted widespread attention due to its exceptional visual quality, but it also poses new challenges for low-light image enhancement (LLIE) techniques. UHD images inherently possess high computational complexity, leading existing UHD LLIE methods to employ high-magnification downsampling to reduce computational costs, which in turn results in information loss. The wavelet transform not only allows downsampling without loss of information, but also separates the image content from the noise. It enables state space models (SSMs) to avoid being affected by noise when modeling long sequences, thus making full use of the long-sequence modeling capability of SSMs. On this basis, we propose Wave-Mamba, a novel approach based on two pivotal insights derived from the wavelet domain: 1) most of the content information of an image exists in the low-frequency component, less in the high-frequency component. 2) The high-frequency component exerts a minimal influence on the outcomes of low-light enhancement. Specifically, to efficiently model global content information on UHD images, we proposed a low-frequency state space block (LFSSBlock) by improving SSMs to focus on restoring the information of low-frequency sub-bands. Moreover, we propose a high-frequency enhance block (HFEBlock) for high-frequency sub-band information, which uses the enhanced low-frequency information to correct the high-frequency information and effectively restore the correct high-frequency details. Through comprehensive evaluation, our method has demonstrated superior performance, significantly outshining current leading techniques while maintaining a more streamlined architecture. The code is available at https://github.com/AlexZou14/Wave-Mamba.", "authors": ["Wenbin Zou", "Hongxia Gao", "Weipeng Yang", "Tongtong Liu"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-02", "url": "https://arxiv.org/abs/2408.01276", "pdf_url": "https://arxiv.org/pdf/2408.01276v1", "arxiv_id": "2408.01276", "doi": "10.1145/3664647.3681580", "citation_count": 114, "influential_citation_count": 11, "has_code": true, "code_url": "https://github.com/AlexZou14/Wave-Mamba", "venue": "ACM Multimedia", "quality_score": 0.5396}
{"id": "7564674a3ecf6f978595083ccb072c225008bca7aefdcdb36cbddc3ff6cf648a", "sources": ["arxiv", "semantic_scholar"], "title": "Multi-head Spatial-Spectral Mamba for Hyperspectral Image Classification", "abstract": "Spatial-Spectral Mamba (SSM) improves computational efficiency and captures long-range dependencies, addressing Transformer limitations. However, traditional Mamba models overlook rich spectral information in HSIs and struggle with high dimensionality and sequential data. To address these issues, we propose the SSM with multi-head self-attention and token enhancement (MHSSMamba). This model integrates spectral and spatial information by enhancing spectral tokens and using multi-head attention to capture complex relationships between spectral bands and spatial locations. It also manages long-range dependencies and the sequential nature of HSI data, preserving contextual information across spectral bands. MHSSMamba achieved remarkable classification accuracies of 97.62\\% on Pavia University, 96.92\\% on the University of Houston, 96.85\\% on Salinas, and 99.49\\% on Wuhan-longKou datasets. The source code is available at \\href{https://github.com/MHassaanButt/MHA\\_SS\\_Mamba}{GitHub}.", "authors": ["Muhammad Ahmad", "Muhammad Hassaan Farooq Butt", "Muhammad Usama", "Hamad Ahmed Altuwaijri", "Manuel Mazzara", "Salvatore Distefano"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-02", "url": "https://arxiv.org/abs/2408.01224", "pdf_url": "https://arxiv.org/pdf/2408.01224v3", "arxiv_id": "2408.01224", "doi": "10.1080/2150704X.2025.2461330", "citation_count": 22, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/MHassaanButt/MHA\\_SS\\_Mamba}{GitHub}", "venue": "Remote Sensing Letters", "quality_score": 0.3404}
{"id": "676e3d46981811b3809b85b07aed9edf3a778c911c975054253cb1f158f067a0", "sources": ["arxiv", "semantic_scholar"], "title": "Enhanced Structured State Space Models via Grouped FIR Filtering and Attention Sink Mechanisms", "abstract": "Structured State Space Models (SSMs) have emerged as compelling alternatives to Transformer architectures, offering linear-time complexity and superior performance in various sequence modeling tasks. Despite their advantages, SSMs like the original Mamba-2 face training difficulties due to the sensitivities introduced by the extended series of recurrent matrix multiplications. In this paper, we propose an advanced architecture that mitigates these challenges by decomposing A-multiplications into multiple groups and optimizing positional encoding through Grouped Finite Impulse Response (FIR) filtering. This new structure, denoted as Grouped FIR-enhanced SSM (GFSSM), employs semiseparable matrices for efficient computation. Furthermore, inspired by the \"attention sink\" phenomenon identified in streaming language models, we incorporate a similar mechanism to enhance the stability and performance of our model over extended sequences. Our approach further bridges the gap between SSMs and Transformer architectures, offering a viable path forward for scalable and high-performing sequence modeling.", "authors": ["Tian Meng", "Yang Tao", "Wuliang Yin"], "categories": ["cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-01", "url": "https://arxiv.org/abs/2408.00244", "pdf_url": "https://arxiv.org/pdf/2408.00244v1", "arxiv_id": "2408.00244", "doi": "10.48550/arXiv.2408.00244", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0753}
{"id": "c234bcf4778d9229da5be5d067e3aaf1cc9786b702a6e1df0ee3ac6b77e76914", "sources": ["arxiv", "semantic_scholar"], "title": "ML-Mamba: Efficient Multi-Modal Large Language Model Utilizing Mamba-2", "abstract": "Multimodal Large Language Models (MLLMs) have attracted much attention for their multifunctionality. However, traditional Transformer architectures incur significant overhead due to their secondary computational complexity. To address this issue, we introduce ML-Mamba, a multimodal language model, which utilizes the latest and efficient Mamba-2 model for inference. Mamba-2 is known for its linear scalability and fast processing of long sequences. We replace the Transformer-based backbone with a pre-trained Mamba-2 model and explore methods for integrating 2D visual selective scanning mechanisms into multimodal learning while also trying various visual encoders and Mamba-2 model variants. Our extensive experiments in various multimodal benchmark tests demonstrate the competitive performance of ML-Mamba and highlight the potential of state space models in multimodal tasks. The experimental results show that: (1) we empirically explore how to effectively apply the 2D vision selective scan mechanism for multimodal learning. We propose a novel multimodal connector called the Mamba-2 Scan Connector (MSC), which enhances representational capabilities. (2) ML-Mamba achieves performance comparable to state-of-the-art methods such as TinyLaVA and MobileVLM v2 through its linear sequential modeling while faster inference speed; (3) Compared to multimodal models utilizing Mamba-1, the Mamba-2-based ML-Mamba exhibits superior inference performance and effectiveness.", "authors": ["Wenjun Huang", "Jiakai Pan", "Jiahao Tang", "Yanyu Ding", "Yifei Xing", "Yuhe Wang", "Zhengzhuo Wang", "Jianguo Hu"], "categories": ["cs.CV", "cs.AI", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-29", "url": "https://arxiv.org/abs/2407.19832", "pdf_url": "https://arxiv.org/pdf/2407.19832v3", "arxiv_id": "2407.19832", "doi": "10.48550/arXiv.2407.19832", "citation_count": 16, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3076}
{"id": "aa675b10e831810363e511c7e5e3f5f245505ba30d0657266b7a53802394b6c1", "sources": ["arxiv", "semantic_scholar"], "title": "MaTrRec: Uniting Mamba and Transformer for Sequential Recommendation", "abstract": "Sequential recommendation systems aim to provide personalized recommendations by analyzing dynamic preferences and dependencies within user behavior sequences. Recently, Transformer models can effectively capture user preferences. However, their quadratic computational complexity limits recommendation performance on long interaction sequence data. Inspired by the State Space Model (SSM)representative model, Mamba, which efficiently captures user preferences in long interaction sequences with linear complexity, we find that Mamba's recommendation effectiveness is limited in short interaction sequences, with failing to recall items of actual interest to users and exacerbating the data sparsity cold start problem. To address this issue, we innovatively propose a new model, MaTrRec, which combines the strengths of Mamba and Transformer. This model fully leverages Mamba's advantages in handling long-term dependencies and Transformer's global attention advantages in short-term dependencies, thereby enhances predictive capabilities on both long and short interaction sequence datasets while balancing model efficiency. Notably, our model significantly improves the data sparsity cold start problem, with an improvement of up to 33% on the highly sparse Amazon Musical Instruments dataset. We conducted extensive experimental evaluations on five widely used public datasets. The experimental results show that our model outperforms the current state-of-the-art sequential recommendation models on all five datasets. The code is available at https://github.com/Unintelligentmumu/MaTrRec.", "authors": ["Shun Zhang", "Runsen Zhang", "Zhirong Yang"], "categories": ["cs.IR"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-27", "url": "https://arxiv.org/abs/2407.19239", "pdf_url": "https://arxiv.org/pdf/2407.19239v1", "arxiv_id": "2407.19239", "doi": "10.48550/arXiv.2407.19239", "citation_count": 10, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/Unintelligentmumu/MaTrRec", "venue": "arXiv.org", "quality_score": 0.2603}
{"id": "031b863b5c4dc011f8e5f927472a897158425d938a6063c6a601434a67e2f8e8", "sources": ["arxiv", "semantic_scholar"], "title": "EEG-SSM: Leveraging State-Space Model for Dementia Detection", "abstract": "State-space models (SSMs) have garnered attention for effectively processing long data sequences, reducing the need to segment time series into shorter intervals for model training and inference. Traditionally, SSMs capture only the temporal dynamics of time series data, omitting the equally critical spectral features. This study introduces EEG-SSM, a novel state-space model-based approach for dementia classification using EEG data. Our model features two primary innovations: EEG-SSM temporal and EEG-SSM spectral components. The temporal component is designed to efficiently process EEG sequences of varying lengths, while the spectral component enhances the model by integrating frequency-domain information from EEG signals. The synergy of these components allows EEG-SSM to adeptly manage the complexities of multivariate EEG data, significantly improving accuracy and stability across different temporal resolutions. Demonstrating a remarkable 91.0 percent accuracy in classifying Healthy Control (HC), Frontotemporal Dementia (FTD), and Alzheimer's Disease (AD) groups, EEG-SSM outperforms existing models on the same dataset. The development of EEG-SSM represents an improvement in the use of state-space models for screening dementia, offering more precise and cost-effective tools for clinical neuroscience.", "authors": ["Xuan-The Tran", "Linh Le", "Quoc Toan Nguyen", "Thomas Do", "Chin-Teng Lin"], "categories": ["cs.LG", "cs.AI", "cs.HC"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-25", "url": "https://arxiv.org/abs/2407.17801", "pdf_url": "https://arxiv.org/pdf/2407.17801v1", "arxiv_id": "2407.17801", "doi": "10.48550/arXiv.2407.17801", "citation_count": 15, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.301}
{"id": "2dda4c21f8d27b74a8cccb4fdd908f0d8058ca9b4a3bd3c152aff6022083a677", "sources": ["arxiv", "semantic_scholar"], "title": "Extended invariant cones as Nonlinear Normal Modes of inhomogeneous piecewise linear systems", "abstract": "The aim of this paper is to explore the relationship between invariant cones and nonlinear normal modes in piecewise linear mechanical systems. As a key result, we extend the invariant cone concept, originally established for homogeneous piecewise linear systems, to a class of inhomogeneous continuous piecewise linear systems. The inhomogeneous terms can be constant and/or time-dependent, modeling nonsmooth mechanical systems with a clearance gap and external harmonic forcing, respectively. Using an augmented state vector, a modified invariant cone problem is formulated and solved to compute the nonlinear normal modes, understood as periodic solutions of the underlying conservative dynamics. An important contribution is that invariant cones of the underlying homogeneous system can be regarded as a singularity in the theory of nonlinear normal modes of continuous piecewise linear systems. In addition, we use a similar methodology to take external harmonic forcing into account. We illustrate our approach using numerical examples of mechanical oscillators with a unilateral elastic contact. The resulting backbone curves and frequency response diagrams are compared to the results obtained using the shooting method and brute force time integration.", "authors": ["A. Yassine Karoui", "Remco I. Leine"], "categories": ["math.DS"], "fields_of_study": ["Mathematics"], "published_date": "2024-07-22", "url": "https://arxiv.org/abs/2407.16096", "pdf_url": "https://arxiv.org/pdf/2407.16096v2", "arxiv_id": "2407.16096", "doi": "10.1016/j.ijnonlinmec.2025.105072", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Journal of Non-Linear Mechanics", "quality_score": 0.1505}
{"id": "25ae4c419347bd2dd418c2c811459f89fafc5c82eb7c88a134494d972c7623e0", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba meets crack segmentation", "abstract": "Cracks pose safety risks to infrastructure and cannot be overlooked. The prevailing structures in existing crack segmentation networks predominantly consist of CNNs or Transformers. However, CNNs exhibit a deficiency in global modeling capability, hindering the representation to entire crack features. Transformers can capture long-range dependencies but suffer from high and quadratic complexity. Recently, Mamba has garnered extensive attention due to its linear spatial and computational complexity and its powerful global perception. This study explores the representation capabilities of Mamba to crack features. Specifically, this paper uncovers the connection between Mamba and the attention mechanism, providing a profound insight, an attention perspective, into interpreting Mamba and devising a novel Mamba module following the principles of attention blocks, namely CrackMamba. We compare CrackMamba with the most prominent visual Mamba modules, Vim and Vmamba, on two datasets comprising asphalt pavement and concrete pavement cracks, and steel cracks, respectively. The quantitative results show that CrackMamba stands out as the sole Mamba block consistently enhancing the baseline model's performance across all evaluation measures, while reducing its parameters and computational costs. Moreover, this paper substantiates that Mamba can achieve global receptive fields through both theoretical analysis and visual interpretability. The discoveries of this study offer a dual contribution. First, as a plug-and-play and simple yet effective Mamba module, CrackMamba exhibits immense potential for integration into various crack segmentation models. Second, the proposed innovative Mamba design concept, integrating Mamba with the attention mechanism, holds significant reference value for all Mamba-based computer vision models, not limited to crack segmentation networks, as investigated in this study.", "authors": ["Zhili He", "Yu-Hsing Wang"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-22", "url": "https://arxiv.org/abs/2407.15714", "pdf_url": "https://arxiv.org/pdf/2407.15714v1", "arxiv_id": "2407.15714", "doi": "10.48550/arXiv.2407.15714", "citation_count": 6, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2113}
{"id": "590f5d0464b49d74299d45cbb7f8fdd1d1efe0df0abb5ca803fff98cfe314c84", "sources": ["arxiv", "semantic_scholar"], "title": "FMamba: Mamba based on Fast-attention for Multivariate Time-series Forecasting", "abstract": "In multivariate time-series forecasting (MTSF), extracting the temporal correlations of the input sequences is crucial. While popular Transformer-based predictive models can perform well, their quadratic computational complexity results in inefficiency and high overhead. The recently emerged Mamba, a selective state space model, has shown promising results in many fields due to its strong temporal feature extraction capabilities and linear computational complexity. However, due to the unilateral nature of Mamba, channel-independent predictive models based on Mamba cannot attend to the relationships among all variables in the manner of Transformer-based models. To address this issue, we combine fast-attention with Mamba to introduce a novel framework named FMamba for MTSF. Technically, we first extract the temporal features of the input variables through an embedding layer, then compute the dependencies among input variables via the fast-attention module. Subsequently, we use Mamba to selectively deal with the input features and further extract the temporal dependencies of the variables through the multi-layer perceptron block (MLP-block). Finally, FMamba obtains the predictive results through the projector, a linear layer. Experimental results on eight public datasets demonstrate that FMamba can achieve state-of-the-art performance while maintaining low computational overhead.", "authors": ["Shusen Ma", "Yu Kang", "Peng Bai", "Yun-Bo Zhao"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-20", "url": "https://arxiv.org/abs/2407.14814", "pdf_url": "https://arxiv.org/pdf/2407.14814v1", "arxiv_id": "2407.14814", "doi": "10.48550/arXiv.2407.14814", "citation_count": 11, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2698}
{"id": "0d9bf0f063c75b8012bae8df02c2667ed40b4e7f3a957846b04ab218acbe0e8c", "sources": ["arxiv", "semantic_scholar"], "title": "Longhorn: State Space Models are Amortized Online Learners", "abstract": "Modern large language models are built on sequence modeling via next-token prediction. While the Transformer remains the dominant architecture for sequence modeling, its quadratic decoding complexity in sequence length poses a major limitation. State-space models (SSMs) present a competitive alternative, offering linear decoding efficiency while maintaining parallelism during training. However, most existing SSMs rely on linear recurrence designs that appear somewhat ad hoc. In this work, we explore SSM design through the lens of online learning, conceptualizing SSMs as meta-modules for specific online learning problems. This approach links SSM design to formulating precise online learning objectives, with state transition rules derived from solving these objectives. Based on this insight, we introduce a novel deep SSM architecture, Longhorn, whose update resembles the closed-form solution for solving the online associative recall problem. Our experimental results show that Longhorn outperforms state-of-the-art SSMs, including the Mamba model, on standard sequence modeling benchmarks, language modeling, and vision tasks. Specifically, Longhorn achieves a 1.8x improvement in sample efficiency compared to Mamba, and can extrapolate over contexts that are up to 16x longer during inference.", "authors": ["Bo Liu", "Rui Wang", "Lemeng Wu", "Yihao Feng", "Peter Stone", "Qiang Liu"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-19", "url": "https://arxiv.org/abs/2407.14207", "pdf_url": "https://arxiv.org/pdf/2407.14207v5", "arxiv_id": "2407.14207", "doi": "10.48550/arXiv.2407.14207", "citation_count": 47, "influential_citation_count": 8, "has_code": false, "code_url": null, "venue": "International Conference on Learning Representations", "quality_score": 0.4771}
{"id": "8340e233dacd0cad9179b29eec157240edb00cf1977df1c5a0b5c6d7ac997e38", "sources": ["arxiv", "semantic_scholar"], "title": "Investigating the Indirect Object Identification circuit in Mamba", "abstract": "How well will current interpretability techniques generalize to future models? A relevant case study is Mamba, a recent recurrent architecture with scaling comparable to Transformers. We adapt pre-Mamba techniques to Mamba and partially reverse-engineer the circuit responsible for the Indirect Object Identification (IOI) task. Our techniques provide evidence that 1) Layer 39 is a key bottleneck, 2) Convolutions in layer 39 shift names one position forward, and 3) The name entities are stored linearly in Layer 39's SSM. Finally, we adapt an automatic circuit discovery tool, positional Edge Attribution Patching, to identify a Mamba IOI circuit. Our contributions provide initial evidence that circuit-based mechanistic interpretability tools work well for the Mamba architecture.", "authors": ["Danielle Ensign", "Adrià Garriga-Alonso"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-19", "url": "https://arxiv.org/abs/2407.14008", "pdf_url": "https://arxiv.org/pdf/2407.14008v2", "arxiv_id": "2407.14008", "doi": "10.48550/arXiv.2407.14008", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "ba2c7979838621cd0a994959a0c0a6b91f64838646337ee0c1109ab0878d1df7", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba-PTQ: Outlier Channels in Recurrent Large Language Models", "abstract": "Modern recurrent layers are emerging as a promising path toward edge deployment of foundation models, especially in the context of large language models (LLMs). Compressing the whole input sequence in a finite-dimensional representation enables recurrent layers to model long-range dependencies while maintaining a constant inference cost for each token and a fixed memory requirement. However, the practical deployment of LLMs in resource-limited environments often requires further model compression, such as quantization and pruning. While these techniques are well-established for attention-based models, their effects on recurrent layers remain underexplored. In this preliminary work, we focus on post-training quantization for recurrent LLMs and show that Mamba models exhibit the same pattern of outlier channels observed in attention-based LLMs. We show that the reason for the difficulty of quantizing SSMs is caused by activation outliers, similar to those observed in transformer-based LLMs. We report baseline results for post-training quantization of Mamba that do not take into account the activation outliers and suggest first steps for outlier-aware quantization.", "authors": ["Alessandro Pierro", "Steven Abreu"], "categories": ["cs.LG", "cs.AI", "cs.NE"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-17", "url": "https://arxiv.org/abs/2407.12397", "pdf_url": "https://arxiv.org/pdf/2407.12397v1", "arxiv_id": "2407.12397", "doi": "10.48550/arXiv.2407.12397", "citation_count": 15, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.301}
{"id": "3248709460d565dd83408190610f16210a696076703e1b6a980e65fbe7d64050", "sources": ["arxiv", "semantic_scholar"], "title": "Serialized Point Mamba: A Serialized Point Cloud Mamba Segmentation Model", "abstract": "Point cloud segmentation is crucial for robotic visual perception and environmental understanding, enabling applications such as robotic navigation and 3D reconstruction. However, handling the sparse and unordered nature of point cloud data presents challenges for efficient and accurate segmentation. Inspired by the Mamba model's success in natural language processing, we propose the Serialized Point Cloud Mamba Segmentation Model (Serialized Point Mamba), which leverages a state-space model to dynamically compress sequences, reduce memory usage, and enhance computational efficiency. Serialized Point Mamba integrates local-global modeling capabilities with linear complexity, achieving state-of-the-art performance on both indoor and outdoor datasets. This approach includes novel techniques such as staged point cloud sequence learning, grid pooling, and Conditional Positional Encoding, facilitating effective segmentation across diverse point cloud tasks. Our method achieved 76.8 mIoU on Scannet and 70.3 mIoU on S3DIS. In Scannetv2 instance segmentation, it recorded 40.0 mAP. It also had the lowest latency and reasonable memory use, making it the SOTA among point semantic segmentation models based on mamba.", "authors": ["Tao Wang", "Wei Wen", "Jingzhi Zhai", "Kang Xu", "Haoming Luo"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-17", "url": "https://arxiv.org/abs/2407.12319", "pdf_url": "https://arxiv.org/pdf/2407.12319v1", "arxiv_id": "2407.12319", "doi": "10.48550/arXiv.2407.12319", "citation_count": 11, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2698}
{"id": "2a086a78a6dd4877f78c132a2ec4d47d113d8629ea017e3eb629dd254a143dd8", "sources": ["arxiv", "semantic_scholar"], "title": "SR-Mamba: Effective Surgical Phase Recognition with State Space Model", "abstract": "Surgical phase recognition is crucial for enhancing the efficiency and safety of computer-assisted interventions. One of the fundamental challenges involves modeling the long-distance temporal relationships present in surgical videos. Inspired by the recent success of Mamba, a state space model with linear scalability in sequence length, this paper presents SR-Mamba, a novel attention-free model specifically tailored to meet the challenges of surgical phase recognition. In SR-Mamba, we leverage a bidirectional Mamba decoder to effectively model the temporal context in overlong sequences. Moreover, the efficient optimization of the proposed Mamba decoder facilitates single-step neural network training, eliminating the need for separate training steps as in previous works. This single-step training approach not only simplifies the training process but also ensures higher accuracy, even with a lighter spatial feature extractor. Our SR-Mamba establishes a new benchmark in surgical video analysis by demonstrating state-of-the-art performance on the Cholec80 and CATARACTS Challenge datasets. The code is accessible at https://github.com/rcao-hk/SR-Mamba.", "authors": ["Rui Cao", "Jiangliu Wang", "Yun-Hui Liu"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-11", "url": "https://arxiv.org/abs/2407.08333", "pdf_url": "https://arxiv.org/pdf/2407.08333v1", "arxiv_id": "2407.08333", "doi": "10.48550/arXiv.2407.08333", "citation_count": 5, "influential_citation_count": 2, "has_code": true, "code_url": "https://github.com/rcao-hk/SR-Mamba", "venue": "arXiv.org", "quality_score": 0.2386}
{"id": "32230a00ce8145a9333b78b84431b039b698d0d8e7b2ffe7efa1cb9af0a0b831", "sources": ["arxiv", "semantic_scholar"], "title": "Parallelizing Autoregressive Generation with Variational State Space Models", "abstract": "Attention-based models such as Transformers and recurrent models like state space models (SSMs) have emerged as successful methods for autoregressive sequence modeling. Although both enable parallel training, none enable parallel generation due to their autoregressiveness. We propose the variational SSM (VSSM), a variational autoencoder (VAE) where both the encoder and decoder are SSMs. Since sampling the latent variables and decoding them with the SSM can be parallelized, both training and generation can be conducted in parallel. Moreover, the decoder recurrence allows generation to be resumed without reprocessing the whole sequence. Finally, we propose the autoregressive VSSM that can be conditioned on a partial realization of the sequence, as is common in language generation tasks. Interestingly, the autoregressive VSSM still enables parallel generation. We highlight on toy problems (MNIST, CIFAR) the empirical gains in speed-up and show that it competes with traditional models in terms of generation quality (Transformer, Mamba SSM).", "authors": ["Gaspard Lambrechts", "Yann Claes", "Pierre Geurts", "Damien Ernst"], "categories": ["cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2024-07-11", "url": "https://arxiv.org/abs/2407.08415", "pdf_url": "https://arxiv.org/pdf/2407.08415v1", "arxiv_id": "2407.08415", "doi": "10.48550/arXiv.2407.08415", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "195a77399d00621b887fa108ee2e2fefadd831043f4f0d8440e692446bf56d6e", "sources": ["arxiv", "semantic_scholar"], "title": "HTD-Mamba: Efficient Hyperspectral Target Detection with Pyramid State Space Model", "abstract": "Hyperspectral target detection (HTD) identifies objects of interest from complex backgrounds at the pixel level, playing a vital role in Earth observation. However, HTD faces challenges due to limited prior knowledge and spectral variation, leading to underfitting models and unreliable performance. To address these challenges, this paper proposes an efficient self-supervised HTD method with a pyramid state space model (SSM), named HTD-Mamba, which employs spectrally contrastive learning to distinguish between target and background based on the similarity measurement of intrinsic features. Specifically, to obtain sufficient training samples and leverage spatial contextual information, we propose a spatial-encoded spectral augmentation technique that encodes all surrounding pixels within a patch into a transformed view of the center pixel. Additionally, to explore global band correlations, we divide pixels into continuous group-wise spectral embeddings and introduce Mamba to HTD for the first time to model long-range dependencies of the spectral sequence with linear complexity. Furthermore, to alleviate spectral variation and enhance robust representation, we propose a pyramid SSM as a backbone to capture and fuse multiresolution spectral-wise intrinsic features. Extensive experiments conducted on four public datasets demonstrate that the proposed method outperforms state-of-the-art methods in both quantitative and qualitative evaluations. Code is available at \\url{https://github.com/shendb2022/HTD-Mamba}.", "authors": ["Dunbin Shen", "Xuanbing Zhu", "Jiacheng Tian", "Jianjun Liu", "Zhenrong Du", "Hongyu Wang", "Xiaorui Ma"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-09", "url": "https://arxiv.org/abs/2407.06841", "pdf_url": "https://arxiv.org/pdf/2407.06841v2", "arxiv_id": "2407.06841", "doi": "10.1109/TGRS.2025.3547019", "citation_count": 10, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/shendb2022/HTD-Mamba}", "venue": "IEEE Transactions on Geoscience and Remote Sensing", "quality_score": 0.2603}
{"id": "1b49533495e4387a8ee171a6eba1ea84643ffa527d4de5c9dd0c22bbc23bb186", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba-FSCIL: Dynamic Adaptation with Selective State Space Model for Few-Shot Class-Incremental Learning", "abstract": "Few-shot class-incremental learning (FSCIL) aims to incrementally learn novel classes from limited examples while preserving knowledge of previously learned classes. Existing methods face a critical dilemma: static architectures rely on a fixed parameter space to learn from data that arrive sequentially, prone to overfitting to the current session, while dynamic architectures require the expansion of the parameter space continually, leading to increased complexity. In this study, we explore the potential of Selective State Space Models (SSMs) for FSCIL. Mamba leverages its input-dependent parameters to dynamically adjust its processing patterns and generate content-aware scan patterns within a fixed architecture. This enables it to configure distinct processing for base and novel classes, effectively preserving existing knowledge while adapting to new ones. To leverage Mamba's potential for FSCIL, we design two key modules: First, we propose a dual selective SSM projector that dynamically adjusts the projection parameters based on the intermediate features for dynamic adaptation. The dual-design structurally decouples base and novel class processing with a frozen base branch, employing a frozen base branch to maintain robust base-class features and a dynamic incremental branch that adaptively learns distinctive feature shifts for novel classes. Second, we develop a class-sensitive selective scan mechanism to guide dynamic adaptation of the incremental branch. It minimizes the disruption to base-class representations caused by training on novel data, and meanwhile, forces the selective scan to perform in distinct patterns between base and novel classes. Extensive experiments on miniImageNet, CUB-200, and CIFAR-100 demonstrate that Mamba-FSCIL achieves state-of-the-art performance. The code is available at https://github.com/xiaojieli0903/Mamba-FSCIL.", "authors": ["Xiaojie Li", "Yibo Yang", "Jianlong Wu", "Yue Yu", "Ming-Hsuan Yang", "Liqiang Nie", "Min Zhang"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-08", "url": "https://arxiv.org/abs/2407.06136", "pdf_url": "https://arxiv.org/pdf/2407.06136v3", "arxiv_id": "2407.06136", "doi": "10.48550/arXiv.2407.06136", "citation_count": 14, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/xiaojieli0903/Mamba-FSCIL", "venue": "arXiv.org", "quality_score": 0.294}
{"id": "e0c6578263f994690642e807b46a4f37d99677d6efb92674f7c2377294f387c1", "sources": ["arxiv", "semantic_scholar"], "title": "Self-Prior Guided Mamba-UNet Networks for Medical Image Super-Resolution", "abstract": "In this paper, we propose a self-prior guided Mamba-UNet network (SMamba-UNet) for medical image super-resolution. Existing methods are primarily based on convolutional neural networks (CNNs) or Transformers. CNNs-based methods fail to capture long-range dependencies, while Transformer-based approaches face heavy calculation challenges due to their quadratic computational complexity. Recently, State Space Models (SSMs) especially Mamba have emerged, capable of modeling long-range dependencies with linear computational complexity. Inspired by Mamba, our approach aims to learn the self-prior multi-scale contextual features under Mamba-UNet networks, which may help to super-resolve low-resolution medical images in an efficient way. Specifically, we obtain self-priors by perturbing the brightness inpainting of the input image during network training, which can learn detailed texture and brightness information that is beneficial for super-resolution. Furthermore, we combine Mamba with Unet network to mine global features at different levels. We also design an improved 2D-Selective-Scan (ISS2D) module to divide image features into different directional sequences to learn long-range dependencies in multiple directions, and adaptively fuse sequence information to enhance super-resolved feature representation. Both qualitative and quantitative experimental results demonstrate that our approach outperforms current state-of-the-art methods on two public medical datasets: the IXI and fastMRI.", "authors": ["Zexin Ji", "Beiji Zou", "Xiaoyan Kui", "Pierre Vera", "Su Ruan"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-08", "url": "https://arxiv.org/abs/2407.05993", "pdf_url": "https://arxiv.org/pdf/2407.05993v1", "arxiv_id": "2407.05993", "doi": "10.48550/arXiv.2407.05993", "citation_count": 9, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Pattern Recognition", "quality_score": 0.25}
{"id": "4aa3a417d2cb6a5956842d25699c50413bc2732c502887097cff7c13e4470423", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba Hawkes Process", "abstract": "Irregular and asynchronous event sequences are prevalent in many domains, such as social media, finance, and healthcare. Traditional temporal point processes (TPPs), like Hawkes processes, often struggle to model mutual inhibition and nonlinearity effectively. While recent neural network models, including RNNs and Transformers, address some of these issues, they still face challenges with long-term dependencies and computational efficiency. In this paper, we introduce the Mamba Hawkes Process (MHP), which leverages the Mamba state space architecture to capture long-range dependencies and dynamic event interactions. Our results show that MHP outperforms existing models across various datasets. Additionally, we propose the Mamba Hawkes Process Extension (MHP-E), which combines Mamba and Transformer models to enhance predictive capabilities. We present the novel application of the Mamba architecture to Hawkes processes, a flexible and extensible model structure, and a theoretical analysis of the synergy between state space models and Hawkes processes. Experimental results demonstrate the superior performance of both MHP and MHP-E, advancing the field of temporal point process modeling.", "authors": ["Anningzhe Gao", "Shan Dai", "Yan Hu"], "categories": ["cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2024-07-07", "url": "https://arxiv.org/abs/2407.05302", "pdf_url": "https://arxiv.org/pdf/2407.05302v1", "arxiv_id": "2407.05302", "doi": "10.48550/arXiv.2407.05302", "citation_count": 5, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2386}
{"id": "189063f658cc4b3c63839293a02fa77fc7ecec347d63ff04a0faca66e5024d22", "sources": ["arxiv", "semantic_scholar"], "title": "How Effective are State Space Models for Machine Translation?", "abstract": "Transformers are the current architecture of choice for NLP, but their attention layers do not scale well to long contexts. Recent works propose to replace attention with linear recurrent layers -- this is the case for state space models, which enjoy efficient training and inference. However, it remains unclear whether these models are competitive with transformers in machine translation (MT). In this paper, we provide a rigorous and comprehensive experimental comparison between transformers and linear recurrent models for MT. Concretely, we experiment with RetNet, Mamba, and hybrid versions of Mamba which incorporate attention mechanisms. Our findings demonstrate that Mamba is highly competitive with transformers on sentence and paragraph-level datasets, where in the latter both models benefit from shifting the training distribution towards longer sequences. Further analysis show that integrating attention into Mamba improves translation quality, robustness to sequence length extrapolation, and the ability to recall named entities.", "authors": ["Hugo Pitorro", "Pavlo Vasylenko", "Marcos Treviso", "André F. T. Martins"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-07", "url": "https://arxiv.org/abs/2407.05489", "pdf_url": "https://arxiv.org/pdf/2407.05489v1", "arxiv_id": "2407.05489", "doi": "10.48550/arXiv.2407.05489", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Conference on Machine Translation", "quality_score": 0.1747}
{"id": "5a17fb88411c6ded11287df9823907f6d32fa2d04145f703e47787b58a8c8541", "sources": ["arxiv", "semantic_scholar"], "title": "Exploring the Capability of Mamba in Speech Applications", "abstract": "This paper explores the capability of Mamba, a recently proposed architecture based on state space models (SSMs), as a competitive alternative to Transformer-based models. In the speech domain, well-designed Transformer-based models, such as the Conformer and E-Branchformer, have become the de facto standards. Extensive evaluations have demonstrated the effectiveness of these Transformer-based models across a wide range of speech tasks. In contrast, the evaluation of SSMs has been limited to a few tasks, such as automatic speech recognition (ASR) and speech synthesis. In this paper, we compared Mamba with state-of-the-art Transformer variants for various speech applications, including ASR, text-to-speech, spoken language understanding, and speech summarization. Experimental evaluations revealed that Mamba achieves comparable or better performance than Transformer-based models, and demonstrated its efficiency in long-form speech processing.", "authors": ["Koichi Miyazaki", "Yoshiki Masuyama", "Masato Murata"], "categories": ["cs.SD", "eess.AS"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-06-24", "url": "https://arxiv.org/abs/2406.16808", "pdf_url": "https://arxiv.org/pdf/2406.16808v1", "arxiv_id": "2406.16808", "doi": "10.48550/arXiv.2406.16808", "citation_count": 38, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "Interspeech", "quality_score": 0.3978}
{"id": "e384a2555515ff5d1cbe09a5e34bf24817a4db45d633b4c76b75e3723ce5475a", "sources": ["arxiv", "semantic_scholar"], "title": "Venturing into Uncharted Waters: The Navigation Compass from Transformer to Mamba", "abstract": "Transformer, a deep neural network architecture, has long dominated the field of natural language processing and beyond. Nevertheless, the recent introduction of Mamba challenges its supremacy, sparks considerable interest among researchers, and gives rise to a series of Mamba-based models that have exhibited notable potential. This survey paper orchestrates a comprehensive discussion, diving into essential research dimensions, covering: (i) the functioning of the Mamba mechanism and its foundation on the principles of structured state space models; (ii) the proposed improvements and the integration of Mamba with various networks, exploring its potential as a substitute for Transformers; (iii) the combination of Transformers and Mamba to compensate for each other's shortcomings. We have also made efforts to interpret Mamba and Transformer in the framework of kernel functions, allowing for a comparison of their mathematical nature within a unified context. Our paper encompasses the vast majority of improvements related to Mamba to date.", "authors": ["Yuchen Zou", "Yineng Chen", "Zuchao Li", "Lefei Zhang", "Hai Zhao"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-06-24", "url": "https://arxiv.org/abs/2406.16722", "pdf_url": "https://arxiv.org/pdf/2406.16722v1", "arxiv_id": "2406.16722", "doi": "10.48550/arXiv.2406.16722", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "eb94bae605f917523852beb94d1da6989e7de7aa1830b470377a4cf689e533ed", "sources": ["arxiv", "semantic_scholar"], "title": "Soft Masked Mamba Diffusion Model for CT to MRI Conversion", "abstract": "Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the predominant modalities utilized in the field of medical imaging. Although MRI capture the complexity of anatomical structures with greater detail than CT, it entails a higher financial costs and requires longer image acquisition times. In this study, we aim to train latent diffusion model for CT to MRI conversion, replacing the commonly-used U-Net or Transformer backbone with a State-Space Model (SSM) called Mamba that operates on latent patches. First, we noted critical oversights in the scan scheme of most Mamba-based vision methods, including inadequate attention to the spatial continuity of patch tokens and the lack of consideration for their varying importance to the target task. Secondly, extending from this insight, we introduce Diffusion Mamba (DiffMa), employing soft masked to integrate Cross-Sequence Attention into Mamba and conducting selective scan in a spiral manner. Lastly, extensive experiments demonstrate impressive performance by DiffMa in medical image generation tasks, with notable advantages in input scaling efficiency over existing benchmark models. The code and models are available at https://github.com/wongzbb/DiffMa-Diffusion-Mamba", "authors": ["Zhenbin Wang", "Lei Zhang", "Lituan Wang", "Zhenwei Zhang"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-06-22", "url": "https://arxiv.org/abs/2406.15910", "pdf_url": "https://arxiv.org/pdf/2406.15910v1", "arxiv_id": "2406.15910", "doi": "10.48550/arXiv.2406.15910", "citation_count": 14, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/wongzbb/DiffMa-Diffusion-Mamba", "venue": "arXiv.org", "quality_score": 0.294}
{"id": "5e341c6043f103360bfca024255a19860b05c35415bda9ea2efcb5a02b3af914", "sources": ["arxiv", "semantic_scholar"], "title": "KalMamba: Towards Efficient Probabilistic State Space Models for RL under Uncertainty", "abstract": "Probabilistic State Space Models (SSMs) are essential for Reinforcement Learning (RL) from high-dimensional, partial information as they provide concise representations for control. Yet, they lack the computational efficiency of their recent deterministic counterparts such as S4 or Mamba. We propose KalMamba, an efficient architecture to learn representations for RL that combines the strengths of probabilistic SSMs with the scalability of deterministic SSMs. KalMamba leverages Mamba to learn the dynamics parameters of a linear Gaussian SSM in a latent space. Inference in this latent space amounts to standard Kalman filtering and smoothing. We realize these operations using parallel associative scanning, similar to Mamba, to obtain a principled, highly efficient, and scalable probabilistic SSM. Our experiments show that KalMamba competes with state-of-the-art SSM approaches in RL while significantly improving computational efficiency, especially on longer interaction sequences.", "authors": ["Philipp Becker", "Niklas Freymuth", "Gerhard Neumann"], "categories": ["cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2024-06-21", "url": "https://arxiv.org/abs/2406.15131", "pdf_url": "https://arxiv.org/pdf/2406.15131v1", "arxiv_id": "2406.15131", "doi": "10.48550/arXiv.2406.15131", "citation_count": 8, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2386}
{"id": "2673cf7f23ca211551b582542febe709cdace153d802e0bf6a9f1d79d31ecccb", "sources": ["arxiv", "semantic_scholar"], "title": "Slot State Space Models", "abstract": "Recent State Space Models (SSMs) such as S4, S5, and Mamba have shown remarkable computational benefits in long-range temporal dependency modeling. However, in many sequence modeling problems, the underlying process is inherently modular and it is of interest to have inductive biases that mimic this modular structure. In this paper, we introduce SlotSSMs, a novel framework for incorporating independent mechanisms into SSMs to preserve or encourage separation of information. Unlike conventional SSMs that maintain a monolithic state vector, SlotSSMs maintains the state as a collection of multiple vectors called slots. Crucially, the state transitions are performed independently per slot with sparse interactions across slots implemented via the bottleneck of self-attention. In experiments, we evaluate our model in object-centric learning, 3D visual reasoning, and long-context video understanding tasks, which involve modeling multiple objects and their long-range temporal dependencies. We find that our proposed design offers substantial performance gains over existing sequence modeling methods. Project page is available at https://slotssms.github.io/", "authors": ["Jindong Jiang", "Fei Deng", "Gautam Singh", "Minseung Lee", "Sungjin Ahn"], "categories": ["cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-06-18", "url": "https://arxiv.org/abs/2406.12272", "pdf_url": "https://arxiv.org/pdf/2406.12272v6", "arxiv_id": "2406.12272", "doi": "10.48550/arXiv.2406.12272", "citation_count": 16, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/JindongJiang/SlotSSMs", "venue": "Neural Information Processing Systems", "quality_score": 0.3076}
{"id": "e1fb91734556843d34f0cedaf352ae2916925a863dad352e16695ae236a62b6f", "sources": ["arxiv", "semantic_scholar"], "title": "SpoT-Mamba: Learning Long-Range Dependency on Spatio-Temporal Graphs with Selective State Spaces", "abstract": "Spatio-temporal graph (STG) forecasting is a critical task with extensive applications in the real world, including traffic and weather forecasting. Although several recent methods have been proposed to model complex dynamics in STGs, addressing long-range spatio-temporal dependencies remains a significant challenge, leading to limited performance gains. Inspired by a recently proposed state space model named Mamba, which has shown remarkable capability of capturing long-range dependency, we propose a new STG forecasting framework named SpoT-Mamba. SpoT-Mamba generates node embeddings by scanning various node-specific walk sequences. Based on the node embeddings, it conducts temporal scans to capture long-range spatio-temporal dependencies. Experimental results on the real-world traffic forecasting dataset demonstrate the effectiveness of SpoT-Mamba.", "authors": ["Jinhyeok Choi", "Heehyeon Kim", "Minhyeong An", "Joyce Jiyoung Whang"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-06-17", "url": "https://arxiv.org/abs/2406.11244", "pdf_url": "https://arxiv.org/pdf/2406.11244v1", "arxiv_id": "2406.11244", "doi": "10.48550/arXiv.2406.11244", "citation_count": 9, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.25}
{"id": "babade6d8442bf33ae3e1f6ded9020dabe611edf32dc99968e06b6ca0c9f3cbb", "sources": ["arxiv", "semantic_scholar"], "title": "Voxel Mamba: Group-Free State Space Models for Point Cloud based 3D Object Detection", "abstract": "Serialization-based methods, which serialize the 3D voxels and group them into multiple sequences before inputting to Transformers, have demonstrated their effectiveness in 3D object detection. However, serializing 3D voxels into 1D sequences will inevitably sacrifice the voxel spatial proximity. Such an issue is hard to be addressed by enlarging the group size with existing serialization-based methods due to the quadratic complexity of Transformers with feature sizes. Inspired by the recent advances of state space models (SSMs), we present a Voxel SSM, termed as Voxel Mamba, which employs a group-free strategy to serialize the whole space of voxels into a single sequence. The linear complexity of SSMs encourages our group-free design, alleviating the loss of spatial proximity of voxels. To further enhance the spatial proximity, we propose a Dual-scale SSM Block to establish a hierarchical structure, enabling a larger receptive field in the 1D serialization curve, as well as more complete local regions in 3D space. Moreover, we implicitly apply window partition under the group-free framework by positional encoding, which further enhances spatial proximity by encoding voxel positional information. Our experiments on Waymo Open Dataset and nuScenes dataset show that Voxel Mamba not only achieves higher accuracy than state-of-the-art methods, but also demonstrates significant advantages in computational efficiency.", "authors": ["Guowen Zhang", "Lue Fan", "Chenhang He", "Zhen Lei", "Zhaoxiang Zhang", "Lei Zhang"], "categories": ["cs.CV", "cs.RO"], "fields_of_study": ["Computer Science"], "published_date": "2024-06-15", "url": "https://arxiv.org/abs/2406.10700", "pdf_url": "https://arxiv.org/pdf/2406.10700v2", "arxiv_id": "2406.10700", "doi": "10.48550/arXiv.2406.10700", "citation_count": 113, "influential_citation_count": 7, "has_code": false, "code_url": null, "venue": "Neural Information Processing Systems", "quality_score": 0.5142}
{"id": "8fc5f290d0c4ffa21176be9f40a55e976e6ad55476c28b94aacd1bdfdb9945b8", "sources": ["arxiv", "semantic_scholar"], "title": "An Empirical Study of Mamba-based Language Models", "abstract": "Selective state-space models (SSMs) like Mamba overcome some of the shortcomings of Transformers, such as quadratic computational complexity with sequence length and large inference-time memory requirements from the key-value cache. Moreover, recent studies have shown that SSMs can match or exceed the language modeling capabilities of Transformers, making them an attractive alternative. In a controlled setting (e.g., same data), however, studies so far have only presented small scale experiments comparing SSMs to Transformers. To understand the strengths and weaknesses of these architectures at larger scales, we present a direct comparison between 8B-parameter Mamba, Mamba-2, and Transformer models trained on the same datasets of up to 3.5T tokens. We also compare these models to a hybrid architecture consisting of 43% Mamba-2, 7% attention, and 50% MLP layers (Mamba-2-Hybrid). Using a diverse set of tasks, we answer the question of whether Mamba models can match Transformers at larger training budgets. Our results show that while pure SSMs match or exceed Transformers on many tasks, they lag behind Transformers on tasks which require strong copying or in-context learning abilities (e.g., 5-shot MMLU, Phonebook) or long-context reasoning. In contrast, we find that the 8B Mamba-2-Hybrid exceeds the 8B Transformer on all 12 standard tasks we evaluated (+2.65 points on average) and is predicted to be up to 8x faster when generating tokens at inference time. To validate long-context capabilities, we provide additional experiments evaluating variants of the Mamba-2-Hybrid and Transformer extended to support 16K, 32K, and 128K sequences. On an additional 23 long-context tasks, the hybrid model continues to closely match or exceed the Transformer on average. To enable further study, we release the checkpoints as well as the code used to train our models as part of NVIDIA's Megatron-LM project.", "authors": ["Roger Waleffe", "Wonmin Byeon", "Duncan Riach", "Brandon Norick", "Vijay Korthikanti", "Tri Dao", "Albert Gu", "Ali Hatamizadeh", "Sudhakar Singh", "Deepak Narayanan", "Garvit Kulshreshtha", "Vartika Singh", "Jared Casper", "Jan Kautz", "Mohammad Shoeybi", "Bryan Catanzaro"], "categories": ["cs.LG", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-06-12", "url": "https://arxiv.org/abs/2406.07887", "pdf_url": "https://arxiv.org/pdf/2406.07887v1", "arxiv_id": "2406.07887", "doi": null, "citation_count": 183, "influential_citation_count": 15, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.6021}
{"id": "acb78bf6c7854627555bd9d0803ba8b7b7db74d49f1806a282edd35097aeb981", "sources": ["arxiv", "semantic_scholar"], "title": "Autoregressive Pretraining with Mamba in Vision", "abstract": "The vision community has started to build with the recently developed state space model, Mamba, as the new backbone for a range of tasks. This paper shows that Mamba's visual capability can be significantly enhanced through autoregressive pretraining, a direction not previously explored. Efficiency-wise, the autoregressive nature can well capitalize on the Mamba's unidirectional recurrent structure, enabling faster overall training speed compared to other training strategies like mask modeling. Performance-wise, autoregressive pretraining equips the Mamba architecture with markedly higher accuracy over its supervised-trained counterparts and, more importantly, successfully unlocks its scaling potential to large and even huge model sizes. For example, with autoregressive pretraining, a base-size Mamba attains 83.2\\% ImageNet accuracy, outperforming its supervised counterpart by 2.0\\%; our huge-size Mamba, the largest Vision Mamba to date, attains 85.0\\% ImageNet accuracy (85.5\\% when finetuned with $384\\times384$ inputs), notably surpassing all other Mamba variants in vision. The code is available at \\url{https://github.com/OliverRensu/ARM}.", "authors": ["Sucheng Ren", "Xianhang Li", "Haoqin Tu", "Feng Wang", "Fangxun Shu", "Lei Zhang", "Jieru Mei", "Linjie Yang", "Peng Wang", "Heng Wang", "Alan Yuille", "Cihang Xie"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-06-11", "url": "https://arxiv.org/abs/2406.07537", "pdf_url": "https://arxiv.org/pdf/2406.07537v1", "arxiv_id": "2406.07537", "doi": "10.48550/arXiv.2406.07537", "citation_count": 26, "influential_citation_count": 7, "has_code": true, "code_url": "https://github.com/OliverRensu/ARM}", "venue": "International Conference on Learning Representations", "quality_score": 0.4515}
{"id": "3fb4565f0e53c6a424a820e89fb5b26b31628d179242cb1307fe6796e7f9bc65", "sources": ["arxiv", "semantic_scholar"], "title": "PointABM:Integrating Bidirectional State Space Model with Multi-Head Self-Attention for Point Cloud Analysis", "abstract": "Mamba, based on state space model (SSM) with its linear complexity and great success in classification provide its superiority in 3D point cloud analysis. Prior to that, Transformer has emerged as one of the most prominent and successful architectures for point cloud analysis. We present PointABM, a hybrid model that integrates the Mamba and Transformer architectures for enhancing local feature to improve performance of 3D point cloud analysis. In order to enhance the extraction of global features, we introduce a bidirectional SSM (bi-SSM) framework, which comprises both a traditional token forward SSM and an innovative backward SSM. To enhance the bi-SSM's capability of capturing more comprehensive features without disrupting the sequence relationships required by the bidirectional Mamba, we introduce Transformer, utilizing its self-attention mechanism to process point clouds. Extensive experimental results demonstrate that integrating Mamba with Transformer significantly enhance the model's capability to analysis 3D point cloud.", "authors": ["Jia-wei Chen", "Yu-jie Xiong", "Yong-bin Gao"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-06-10", "url": "https://arxiv.org/abs/2406.06069", "pdf_url": "https://arxiv.org/pdf/2406.06069v1", "arxiv_id": "2406.06069", "doi": "10.48550/arXiv.2406.06069", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "08e95c50cd90e447cd6ad935c80498cf34c0984b139a9ac0a835712d69626aa6", "sources": ["arxiv", "semantic_scholar"], "title": "MHS-VM: Multi-Head Scanning in Parallel Subspaces for Vision Mamba", "abstract": "Recently, State Space Models (SSMs), with Mamba as a prime example, have shown great promise for long-range dependency modeling with linear complexity. Then, Vision Mamba and the subsequent architectures are presented successively, and they perform well on visual tasks. The crucial step of applying Mamba to visual tasks is to construct 2D visual features in sequential manners. To effectively organize and construct visual features within the 2D image space through 1D selective scan, we propose a novel Multi-Head Scan (MHS) module. The embeddings extracted from the preceding layer are projected into multiple lower-dimensional subspaces. Subsequently, within each subspace, the selective scan is performed along distinct scan routes. The resulting sub-embeddings, obtained from the multi-head scan process, are then integrated and ultimately projected back into the high-dimensional space. Moreover, we incorporate a Scan Route Attention (SRA) mechanism to enhance the module's capability to discern complex structures. To validate the efficacy of our module, we exclusively substitute the 2D-Selective-Scan (SS2D) block in VM-UNet with our proposed module, and we train our models from scratch without using any pre-trained weights. The results indicate a significant improvement in performance while reducing the parameters of the original VM-UNet. The code for this study is publicly available at https://github.com/PixDeep/MHS-VM.", "authors": ["Zhongping Ji"], "categories": ["eess.IV", "cs.CV"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-06-10", "url": "https://arxiv.org/abs/2406.05992", "pdf_url": "https://arxiv.org/pdf/2406.05992v1", "arxiv_id": "2406.05992", "doi": "10.48550/arXiv.2406.05992", "citation_count": 2, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/PixDeep/MHS-VM", "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "7e119508aca3a27762f0b890eea2922e54f7030ca35ee6cbf54076b6f21916ae", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba YOLO: A Simple Baseline for Object Detection with State Space Model", "abstract": "Driven by the rapid development of deep learning technology, the YOLO series has set a new benchmark for real-time object detectors. Additionally, transformer-based structures have emerged as the most powerful solution in the field, greatly extending the model's receptive field and achieving significant performance improvements. However, this improvement comes at a cost as the quadratic complexity of the self-attentive mechanism increases the computational burden of the model. To address this problem, we introduce a simple yet effective baseline approach called Mamba YOLO. Our contributions are as follows: 1) We propose that the ODMamba backbone introduce a \\textbf{S}tate \\textbf{S}pace \\textbf{M}odel (\\textbf{SSM}) with linear complexity to address the quadratic complexity of self-attention. Unlike the other Transformer-base and SSM-base method, ODMamba is simple to train without pretraining. 2) For real-time requirement, we designed the macro structure of ODMamba, determined the optimal stage ratio and scaling size. 3) We design the RG Block that employs a multi-branch structure to model the channel dimensions, which addresses the possible limitations of SSM in sequence modeling, such as insufficient receptive fields and weak image localization. This design captures localized image dependencies more accurately and significantly. Extensive experiments on the publicly available COCO benchmark dataset show that Mamba YOLO achieves state-of-the-art performance compared to previous methods. Specifically, a tiny version of Mamba YOLO achieves a \\textbf{7.5}\\% improvement in mAP on a single 4090 GPU with an inference time of \\textbf{1.5} ms. The pytorch code is available at: \\url{https://github.com/HZAI-ZJNU/Mamba-YOLO}", "authors": ["Zeyu Wang", "Chen Li", "Huiying Xu", "Xinzhong Zhu", "Hongbo Li"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-06-09", "url": "https://arxiv.org/abs/2406.05835", "pdf_url": "https://arxiv.org/pdf/2406.05835v2", "arxiv_id": "2406.05835", "doi": "10.1609/aaai.v39i8.32885", "citation_count": 161, "influential_citation_count": 6, "has_code": true, "code_url": "https://github.com/HZAI-ZJNU/Mamba-YOLO}", "venue": "AAAI Conference on Artificial Intelligence", "quality_score": 0.5524}
{"id": "2620824759050dda1119ed3f58caa46fea84561b0a1bd63ce835914803e22c87", "sources": ["arxiv", "semantic_scholar"], "title": "Decision Mamba: A Multi-Grained State Space Model with Self-Evolution Regularization for Offline RL", "abstract": "While the conditional sequence modeling with the transformer architecture has demonstrated its effectiveness in dealing with offline reinforcement learning (RL) tasks, it is struggle to handle out-of-distribution states and actions. Existing work attempts to address this issue by data augmentation with the learned policy or adding extra constraints with the value-based RL algorithm. However, these studies still fail to overcome the following challenges: (1) insufficiently utilizing the historical temporal information among inter-steps, (2) overlooking the local intrastep relationships among return-to-gos (RTGs), states, and actions, (3) overfitting suboptimal trajectories with noisy labels. To address these challenges, we propose Decision Mamba (DM), a novel multi-grained state space model (SSM) with a self-evolving policy learning strategy. DM explicitly models the historical hidden state to extract the temporal information by using the mamba architecture. To capture the relationship among RTG-state-action triplets, a fine-grained SSM module is designed and integrated into the original coarse-grained SSM in mamba, resulting in a novel mamba architecture tailored for offline RL. Finally, to mitigate the overfitting issue on noisy trajectories, a self-evolving policy is proposed by using progressive regularization. The policy evolves by using its own past knowledge to refine the suboptimal actions, thus enhancing its robustness on noisy demonstrations. Extensive experiments on various tasks show that DM outperforms other baselines substantially.", "authors": ["Qi Lv", "Xiang Deng", "Gongwei Chen", "Michael Yu Wang", "Liqiang Nie"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-06-08", "url": "https://arxiv.org/abs/2406.05427", "pdf_url": "https://arxiv.org/pdf/2406.05427v3", "arxiv_id": "2406.05427", "doi": "10.48550/arXiv.2406.05427", "citation_count": 22, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/aopolin-lv/DecisionMamba", "venue": "Neural Information Processing Systems", "quality_score": 0.3404}
{"id": "9c3de1db38c56ec795ab031d0c0b21ef055486f9fc50d13d759e0ad4aa4274db", "sources": ["arxiv", "semantic_scholar"], "title": "Efficient 3D Shape Generation via Diffusion Mamba with Bidirectional SSMs", "abstract": "Recent advancements in sequence modeling have led to the development of the Mamba architecture, noted for its selective state space approach, offering a promising avenue for efficient long sequence handling. However, its application in 3D shape generation, particularly at high resolutions, remains underexplored. Traditional diffusion transformers (DiT) with self-attention mechanisms, despite their potential, face scalability challenges due to the cubic complexity of attention operations as input length increases. This complexity becomes a significant hurdle when dealing with high-resolution voxel sizes. To address this challenge, we introduce a novel diffusion architecture tailored for 3D point clouds generation-Diffusion Mamba (DiM-3D). This architecture forgoes traditional attention mechanisms, instead utilizing the inherent efficiency of the Mamba architecture to maintain linear complexity with respect to sequence length. DiM-3D is characterized by fast inference times and substantially lower computational demands, quantified in reduced Gflops, thereby addressing the key scalability issues of prior models. Our empirical results on the ShapeNet benchmark demonstrate that DiM-3D achieves state-of-the-art performance in generating high-fidelity and diverse 3D shapes. Additionally, DiM-3D shows superior capabilities in tasks like 3D point cloud completion. This not only proves the model's scalability but also underscores its efficiency in generating detailed, high-resolution voxels necessary for advanced 3D shape modeling, particularly excelling in environments requiring high-resolution voxel sizes. Through these findings, we illustrate the exceptional scalability and efficiency of the Diffusion Mamba framework in 3D shape generation, setting a new standard for the field and paving the way for future explorations in high-resolution 3D modeling technologies.", "authors": ["Shentong Mo"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-06-07", "url": "https://arxiv.org/abs/2406.05038", "pdf_url": "https://arxiv.org/pdf/2406.05038v1", "arxiv_id": "2406.05038", "doi": "10.48550/arXiv.2406.05038", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2386}
{"id": "6e92a9c27c2525a450fd38c0ebcd1ed48c0a5cf572355be97f4ab54d17d5b209", "sources": ["arxiv", "semantic_scholar"], "title": "Chimera: Effectively Modeling Multivariate Time Series with 2-Dimensional State Space Models", "abstract": "Modeling multivariate time series is a well-established problem with a wide range of applications from healthcare to financial markets. Traditional State Space Models (SSMs) are classical approaches for univariate time series modeling due to their simplicity and expressive power to represent linear dependencies. They, however, have fundamentally limited expressive power to capture non-linear dependencies, are slow in practice, and fail to model the inter-variate information flow. Despite recent attempts to improve the expressive power of SSMs by using deep structured SSMs, the existing methods are either limited to univariate time series, fail to model complex patterns (e.g., seasonal patterns), fail to dynamically model the dependencies of variate and time dimensions, and/or are input-independent. We present Chimera that uses two input-dependent 2-D SSM heads with different discretization processes to learn long-term progression and seasonal patterns. To improve the efficiency of complex 2D recurrence, we present a fast training using a new 2-dimensional parallel selective scan. We further present and discuss 2-dimensional Mamba and Mamba-2 as the spacial cases of our 2D SSM. Our experimental evaluation shows the superior performance of Chimera on extensive and diverse benchmarks, including ECG and speech time series classification, long-term and short-term time series forecasting, and time series anomaly detection.", "authors": ["Ali Behrouz", "Michele Santacatterina", "Ramin Zabih"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-06-06", "url": "https://arxiv.org/abs/2406.04320", "pdf_url": "https://arxiv.org/pdf/2406.04320v1", "arxiv_id": "2406.04320", "doi": "10.48550/arXiv.2406.04320", "citation_count": 19, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "Neural Information Processing Systems", "quality_score": 0.3253}
{"id": "8247f00955fb13e71eaa531f9cfcd8d633ae70f3dad5ff68068b74ac3078efda", "sources": ["arxiv", "semantic_scholar"], "title": "Audio Mamba: Bidirectional State Space Model for Audio Representation Learning", "abstract": "Transformers have rapidly become the preferred choice for audio classification, surpassing methods based on CNNs. However, Audio Spectrogram Transformers (ASTs) exhibit quadratic scaling due to self-attention. The removal of this quadratic self-attention cost presents an appealing direction. Recently, state space models (SSMs), such as Mamba, have demonstrated potential in language and vision tasks in this regard. In this study, we explore whether reliance on self-attention is necessary for audio classification tasks. By introducing Audio Mamba (AuM), the first self-attention-free, purely SSM-based model for audio classification, we aim to address this question. We evaluate AuM on various audio datasets - comprising six different benchmarks - where it achieves comparable or better performance compared to well-established AST model.", "authors": ["Mehmet Hamza Erol", "Arda Senocak", "Jiu Feng", "Joon Son Chung"], "categories": ["cs.SD", "cs.AI", "eess.AS"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-06-05", "url": "https://arxiv.org/abs/2406.03344", "pdf_url": "https://arxiv.org/pdf/2406.03344v1", "arxiv_id": "2406.03344", "doi": "10.1109/LSP.2024.3483009", "citation_count": 59, "influential_citation_count": 3, "has_code": true, "code_url": "https://github.com/mhamzaerol/Audio-Mamba-AuM", "venue": "IEEE Signal Processing Letters", "quality_score": 0.4445}
{"id": "c900db4dd079b55dbb87f8bc77baef361d19b12907b72d0614c10052c6da325d", "sources": ["arxiv", "semantic_scholar"], "title": "Computation-Efficient Era: A Comprehensive Survey of State Space Models in Medical Image Analysis", "abstract": "Sequence modeling plays a vital role across various domains, with recurrent neural networks being historically the predominant method of performing these tasks. However, the emergence of transformers has altered this paradigm due to their superior performance. Built upon these advances, transformers have conjoined CNNs as two leading foundational models for learning visual representations. However, transformers are hindered by the $\\mathcal{O}(N^2)$ complexity of their attention mechanisms, while CNNs lack global receptive fields and dynamic weight allocation. State Space Models (SSMs), specifically the \\textit{\\textbf{Mamba}} model with selection mechanisms and hardware-aware architecture, have garnered immense interest lately in sequential modeling and visual representation learning, challenging the dominance of transformers by providing infinite context lengths and offering substantial efficiency maintaining linear complexity in the input sequence. Capitalizing on the advances in computer vision, medical imaging has heralded a new epoch with Mamba models. Intending to help researchers navigate the surge, this survey seeks to offer an encyclopedic review of Mamba models in medical imaging. Specifically, we start with a comprehensive theoretical review forming the basis of SSMs, including Mamba architecture and its alternatives for sequence modeling paradigms in this context. Next, we offer a structured classification of Mamba models in the medical field and introduce a diverse categorization scheme based on their application, imaging modalities, and targeted organs. Finally, we summarize key challenges, discuss different future research directions of the SSMs in the medical domain, and propose several directions to fulfill the demands of this field. In addition, we have compiled the studies discussed in this paper along with their open-source implementations on our GitHub repository.", "authors": ["Moein Heidari", "Sina Ghorbani Kolahi", "Sanaz Karimijafarbigloo", "Bobby Azad", "Afshin Bozorgpour", "Soheila Hatami", "Reza Azad", "Ali Diba", "Ulas Bagci", "Dorit Merhof", "Ilker Hacihaliloglu"], "categories": ["eess.IV", "cs.CV"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2024-06-05", "url": "https://arxiv.org/abs/2406.03430", "pdf_url": "https://arxiv.org/pdf/2406.03430v1", "arxiv_id": "2406.03430", "doi": "10.48550/arXiv.2406.03430", "citation_count": 21, "influential_citation_count": 2, "has_code": true, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3356}
{"id": "6da3df42cfe11d568ef4bac9a67dff1c1904097f37750ac81f58e89f1d84383f", "sources": ["arxiv", "semantic_scholar"], "title": "Audio Mamba: Selective State Spaces for Self-Supervised Audio Representations", "abstract": "Despite its widespread adoption as the prominent neural architecture, the Transformer has spurred several independent lines of work to address its limitations. One such approach is selective state space models, which have demonstrated promising results for language modelling. However, their feasibility for learning self-supervised, general-purpose audio representations is yet to be investigated. This work proposes Audio Mamba, a selective state space model for learning general-purpose audio representations from randomly masked spectrogram patches through self-supervision. Empirical results on ten diverse audio recognition downstream tasks show that the proposed models, pretrained on the AudioSet dataset, consistently outperform comparable self-supervised audio spectrogram transformer (SSAST) baselines by a considerable margin and demonstrate better performance in dataset size, sequence length and model size comparisons.", "authors": ["Sarthak Yadav", "Zheng-Hua Tan"], "categories": ["cs.SD", "cs.AI", "eess.AS"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-06-04", "url": "https://arxiv.org/abs/2406.02178", "pdf_url": "https://arxiv.org/pdf/2406.02178v2", "arxiv_id": "2406.02178", "doi": "10.48550/arXiv.2406.02178", "citation_count": 33, "influential_citation_count": 4, "has_code": false, "code_url": null, "venue": "Interspeech", "quality_score": 0.3829}
{"id": "69f20958d3fe8eff3edfd851032b5d2308232bf7f360427df1abc4ffb08ebc4b", "sources": ["arxiv", "semantic_scholar"], "title": "Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality", "abstract": "While Transformers have been the main architecture behind deep learning's success in language modeling, state-space models (SSMs) such as Mamba have recently been shown to match or outperform Transformers at small to medium scale. We show that these families of models are actually quite closely related, and develop a rich framework of theoretical connections between SSMs and variants of attention, connected through various decompositions of a well-studied class of structured semiseparable matrices. Our state space duality (SSD) framework allows us to design a new architecture (Mamba-2) whose core layer is an a refinement of Mamba's selective SSM that is 2-8X faster, while continuing to be competitive with Transformers on language modeling.", "authors": ["Tri Dao", "Albert Gu"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-31", "url": "https://arxiv.org/abs/2405.21060", "pdf_url": "https://arxiv.org/pdf/2405.21060v1", "arxiv_id": "2405.21060", "doi": null, "citation_count": 1592, "influential_citation_count": 274, "has_code": false, "code_url": null, "venue": "International Conference on Machine Learning", "quality_score": 1.0}
{"id": "9a32bbd1d37d6c41b66ab90503745137dd0dd7412cf9e80c8629d793f8347c62", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba State-Space Models Are Lyapunov-Stable Learners", "abstract": "Mamba state-space models (SSMs) have recently outperformed state-of-the-art (SOTA) Transformer large language models (LLMs) in various tasks and been widely adapted. However, a major concern for stable learning in recurrent-based deep models (such as SSMs) is the sensitivity of their recurrent dynamics. Despite widespread adaptation, the sensitivity of Mamba's recurrent dynamics under common fine-tuning methods-e.g., mixed-precision fine-tuning (MPFT) and parameter-efficient fine-tuning (PEFT)-remains unexplored. Empirically, we show that Mamba LLMs are extremely stable to changes introduced by combinations of MPFT and PEFT, in stark contrast to Transformer LLMs, which we demonstrate may drastically diverge from their respective full-precision counterparts under different combinations of MPFT and PEFT (despite the near-ubiquitous adaptation of these fine-tuning frameworks for attention-based models). The demonstrated robustness of Mamba LLMs are due to their recurrent dynamics, which we prove are guaranteed to be stable using dynamical systems theory (in particular, Lyapunov stability). We conclude by using MPFT and PEFT to novelly study Mamba LLMs' in-context learning (ICL) abilities on natural language tasks, thus supplementing other recent work.", "authors": ["John T. Halloran", "Manbir Gulati", "Paul F. Roysdon"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-31", "url": "https://arxiv.org/abs/2406.00209", "pdf_url": "https://arxiv.org/pdf/2406.00209v3", "arxiv_id": "2406.00209", "doi": null, "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1747}
{"id": "0377aa1544e88e2c9c44c5521c88c354750f11e25786fdc61adb0941bfe1a53f", "sources": ["arxiv", "semantic_scholar"], "title": "Determining state space anomalies in mean field games", "abstract": "In this paper, we are concerned with the inverse problem of determining anomalies in the state space associated with the stationary mean field game (MFG) system. We establish novel unique identifiability results for the intrinsic structure of these anomalies in mean field games systems, including their topological structure and parameter configurations, in several general scenarios of practical interest, including traffic flow, market economics and epidemics. To the best of our knowledge, this is the first work that considers anomalies in the state space for the nonlinear coupled MFG system.", "authors": ["Hongyu Liu", "Catharine W. K. Lo"], "categories": ["math.AP", "math.OC"], "fields_of_study": ["Mathematics", "Physics"], "published_date": "2024-05-29", "url": "https://arxiv.org/abs/2405.18954", "pdf_url": "https://arxiv.org/pdf/2405.18954v1", "arxiv_id": "2405.18954", "doi": "10.1088/1361-6544/ada67d", "citation_count": 12, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Nonlinearity", "quality_score": 0.2785}
{"id": "e529ba58bfa15061342ca8d552474e46b39318ce8d7bb59143a0c8e0511b9083", "sources": ["arxiv", "semantic_scholar"], "title": "Coupled Mamba: Enhanced Multi-modal Fusion with Coupled State Space Model", "abstract": "The essence of multi-modal fusion lies in exploiting the complementary information inherent in diverse modalities. However, prevalent fusion methods rely on traditional neural architectures and are inadequately equipped to capture the dynamics of interactions across modalities, particularly in presence of complex intra- and inter-modality correlations. Recent advancements in State Space Models (SSMs), notably exemplified by the Mamba model, have emerged as promising contenders. Particularly, its state evolving process implies stronger modality fusion paradigm, making multi-modal fusion on SSMs an appealing direction. However, fusing multiple modalities is challenging for SSMs due to its hardware-aware parallelism designs. To this end, this paper proposes the Coupled SSM model, for coupling state chains of multiple modalities while maintaining independence of intra-modality state processes. Specifically, in our coupled scheme, we devise an inter-modal hidden states transition scheme, in which the current state is dependent on the states of its own chain and that of the neighbouring chains at the previous time-step. To fully comply with the hardware-aware parallelism, we devise an expedite coupled state transition scheme and derive its corresponding global convolution kernel for parallelism. Extensive experiments on CMU-MOSEI, CH-SIMS, CH-SIMSV2 through multi-domain input verify the effectiveness of our model compared to current state-of-the-art methods, improved F1-Score by 0.4\\%, 0.9\\%, and 2.3\\% on the three datasets respectively, 49\\% faster inference and 83.7\\% GPU memory save. The results demonstrate that Coupled Mamba model is capable of enhanced multi-modal fusion.", "authors": ["Wenbing Li", "Hang Zhou", "Junqing Yu", "Zikai Song", "Wei Yang"], "categories": ["cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-28", "url": "https://arxiv.org/abs/2405.18014", "pdf_url": "https://arxiv.org/pdf/2405.18014v2", "arxiv_id": "2405.18014", "doi": "10.48550/arXiv.2405.18014", "citation_count": 55, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Neural Information Processing Systems", "quality_score": 0.437}
{"id": "110e037ae90b3d706ba82d7f0f7600e2ea4ff25f13f0a8832d76541ee7c955bc", "sources": ["arxiv", "semantic_scholar"], "title": "Modeling Long Sequences in Bladder Cancer Recurrence: A Comparative Evaluation of LSTM,Transformer,and Mamba", "abstract": "Traditional survival analysis methods often struggle with complex time-dependent data,failing to capture and interpret dynamic characteristics adequately.This study aims to evaluate the performance of three long-sequence models,LSTM,Transformer,and Mamba,in analyzing recurrence event data and integrating them with the Cox proportional hazards model.This study integrates the advantages of deep learning models for handling long-sequence data with the Cox proportional hazards model to enhance the performance in analyzing recurrent events with dynamic time information.Additionally,this study compares the ability of different models to extract and utilize features from time-dependent clinical recurrence data.The LSTM-Cox model outperformed both the Transformer-Cox and Mamba-Cox models in prediction accuracy and model fit,achieving a Concordance index of up to 0.90 on the test set.Significant predictors of bladder cancer recurrence,such as treatment stop time,maximum tumor size at recurrence and recurrence frequency,were identified.The LSTM-Cox model aligned well with clinical outcomes,effectively distinguishing between high-risk and low-risk patient groups.This study demonstrates that the LSTM-Cox model is a robust and efficient method for recurrent data analysis and feature extraction,surpassing newer models like Transformer and Mamba.It offers a practical approach for integrating deep learning technologies into clinical risk prediction systems,thereby improving patient management and treatment outcomes.", "authors": ["Runquan Zhang", "Jiawen Jiang", "Xiaoping Shi"], "categories": ["cs.LG", "stat.ME", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2024-05-28", "url": "https://arxiv.org/abs/2405.18518", "pdf_url": "https://arxiv.org/pdf/2405.18518v2", "arxiv_id": "2405.18518", "doi": null, "citation_count": 3, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1505}
{"id": "3fd3885eac6b511a78be7a7392095321230ca0e156353b23a488484a2db77d22", "sources": ["arxiv", "semantic_scholar"], "title": "The Expressive Capacity of State Space Models: A Formal Language Perspective", "abstract": "Recently, recurrent models based on linear state space models (SSMs) have shown promising performance in language modeling (LM), competititve with transformers. However, there is little understanding of the in-principle abilities of such models, which could provide useful guidance to the search for better LM architectures. We present a comprehensive theoretical study of the capacity of such SSMs as it compares to that of transformers and traditional RNNs. We find that SSMs and transformers have overlapping but distinct strengths. In star-free state tracking, SSMs implement straightforward and exact solutions to problems that transformers struggle to represent exactly. They can also model bounded hierarchical structure with optimal memory even without simulating a stack. On the other hand, we identify a design choice in current SSMs that limits their expressive power. We discuss implications for SSM and LM research, and verify results empirically on a recent SSM, Mamba.", "authors": ["Yash Sarrof", "Yana Veitsman", "Michael Hahn"], "categories": ["cs.CL", "cs.FL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-27", "url": "https://arxiv.org/abs/2405.17394", "pdf_url": "https://arxiv.org/pdf/2405.17394v3", "arxiv_id": "2405.17394", "doi": "10.52202/079017-1304", "citation_count": 42, "influential_citation_count": 14, "has_code": false, "code_url": null, "venue": "Neural Information Processing Systems", "quality_score": 0.588}
{"id": "409b88ee308b9867526b3b2d76d512286295e928247976f87559ab951925e324", "sources": ["arxiv", "semantic_scholar"], "title": "Demystify Mamba in Vision: A Linear Attention Perspective", "abstract": "Mamba is an effective state space model with linear computation complexity. It has recently shown impressive efficiency in dealing with high-resolution inputs across various vision tasks. In this paper, we reveal that the powerful Mamba model shares surprising similarities with linear attention Transformer, which typically underperform conventional Transformer in practice. By exploring the similarities and disparities between the effective Mamba and subpar linear attention Transformer, we provide comprehensive analyses to demystify the key factors behind Mamba's success. Specifically, we reformulate the selective state space model and linear attention within a unified formulation, rephrasing Mamba as a variant of linear attention Transformer with six major distinctions: input gate, forget gate, shortcut, no attention normalization, single-head, and modified block design. For each design, we meticulously analyze its pros and cons, and empirically evaluate its impact on model performance in vision tasks. Interestingly, the results highlight the forget gate and block design as the core contributors to Mamba's success, while the other four designs are less crucial. Based on these findings, we propose a Mamba-Inspired Linear Attention (MILA) model by incorporating the merits of these two key designs into linear attention. The resulting model outperforms various vision Mamba models in both image classification and high-resolution dense prediction tasks, while enjoying parallelizable computation and fast inference speed. Code is available at https://github.com/LeapLabTHU/MLLA.", "authors": ["Dongchen Han", "Ziyi Wang", "Zhuofan Xia", "Yizeng Han", "Yifan Pu", "Chunjiang Ge", "Jun Song", "Shiji Song", "Bo Zheng", "Gao Huang"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-26", "url": "https://arxiv.org/abs/2405.16605", "pdf_url": "https://arxiv.org/pdf/2405.16605v2", "arxiv_id": "2405.16605", "doi": "10.48550/arXiv.2405.16605", "citation_count": 236, "influential_citation_count": 20, "has_code": true, "code_url": "https://github.com/LeapLabTHU/MLLA", "venue": "Neural Information Processing Systems", "quality_score": 0.6611}
{"id": "234e0c31b53ed20c436acca82409d41a92ae9c087a2a83a980e26069fa8c177a", "sources": ["arxiv", "semantic_scholar"], "title": "Zamba: A Compact 7B SSM Hybrid Model", "abstract": "In this technical report, we present Zamba, a novel 7B SSM-transformer hybrid model which achieves competitive performance against leading open-weight models at a comparable scale. Zamba is trained on 1T tokens from openly available datasets and is the best non-transformer model at this scale. Zamba pioneers a unique architecture combining a Mamba backbone with a single shared attention module, thus obtaining the benefits of attention at minimal parameter cost. Due to its architecture, Zamba is significantly faster at inference than comparable transformer models and requires substantially less memory for generation of long sequences. Zamba is pretrained in two phases: the first phase is based on existing web datasets, while the second one consists of annealing the model over high-quality instruct and synthetic datasets, and is characterized by a rapid learning rate decay. We open-source the weights and all checkpoints for Zamba, through both phase 1 and annealing phases.", "authors": ["Paolo Glorioso", "Quentin Anthony", "Yury Tokpanov", "James Whittington", "Jonathan Pilault", "Adam Ibrahim", "Beren Millidge"], "categories": ["cs.LG", "cs.AI", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-26", "url": "https://arxiv.org/abs/2405.16712", "pdf_url": "https://arxiv.org/pdf/2405.16712v1", "arxiv_id": "2405.16712", "doi": "10.48550/arXiv.2405.16712", "citation_count": 117, "influential_citation_count": 13, "has_code": true, "code_url": null, "venue": "arXiv.org", "quality_score": 0.5731}
{"id": "4de0b5dc61f0f9d052a38d49cef5a4ad583161bb3fc6c849fe3f95a64a618de8", "sources": ["arxiv", "semantic_scholar"], "title": "Time-SSM: Simplifying and Unifying State Space Models for Time Series Forecasting", "abstract": "State Space Models (SSMs) have emerged as a potent tool in sequence modeling tasks in recent years. These models approximate continuous systems using a set of basis functions and discretize them to handle input data, making them well-suited for modeling time series data collected at specific frequencies from continuous systems. Despite its potential, the application of SSMs in time series forecasting remains underexplored, with most existing models treating SSMs as a black box for capturing temporal or channel dependencies. To address this gap, this paper proposes a novel theoretical framework termed Dynamic Spectral Operator, offering more intuitive and general guidance on applying SSMs to time series data. Building upon our theory, we introduce Time-SSM, a novel SSM-based foundation model with only one-seventh of the parameters compared to Mamba. Various experiments validate both our theoretical framework and the superior performance of Time-SSM.", "authors": ["Jiaxi Hu", "Disen Lan", "Ziyu Zhou", "Qingsong Wen", "Yuxuan Liang"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-25", "url": "https://arxiv.org/abs/2405.16312", "pdf_url": "https://arxiv.org/pdf/2405.16312v2", "arxiv_id": "2405.16312", "doi": "10.48550/arXiv.2405.16312", "citation_count": 16, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3076}
{"id": "0ae441cc225aeeee162a15191bf0b294a4892f9fc57f51e8a53441bb0c7b4f17", "sources": ["arxiv", "semantic_scholar"], "title": "Scaling Diffusion Mamba with Bidirectional SSMs for Efficient Image and Video Generation", "abstract": "In recent developments, the Mamba architecture, known for its selective state space approach, has shown potential in the efficient modeling of long sequences. However, its application in image generation remains underexplored. Traditional diffusion transformers (DiT), which utilize self-attention blocks, are effective but their computational complexity scales quadratically with the input length, limiting their use for high-resolution images. To address this challenge, we introduce a novel diffusion architecture, Diffusion Mamba (DiM), which foregoes traditional attention mechanisms in favor of a scalable alternative. By harnessing the inherent efficiency of the Mamba architecture, DiM achieves rapid inference times and reduced computational load, maintaining linear complexity with respect to sequence length. Our architecture not only scales effectively but also outperforms existing diffusion transformers in both image and video generation tasks. The results affirm the scalability and efficiency of DiM, establishing a new benchmark for image and video generation techniques. This work advances the field of generative models and paves the way for further applications of scalable architectures.", "authors": ["Shentong Mo", "Yapeng Tian"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-24", "url": "https://arxiv.org/abs/2405.15881", "pdf_url": "https://arxiv.org/pdf/2405.15881v1", "arxiv_id": "2405.15881", "doi": "10.48550/arXiv.2405.15881", "citation_count": 27, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3618}
{"id": "d6b6159ee93025c1bb77371b2538f8c8a24fbe023e2ae2f359b429eba8a2016d", "sources": ["arxiv", "semantic_scholar"], "title": "Understanding the differences in Foundation Models: Attention, State Space Models, and Recurrent Neural Networks", "abstract": "Softmax attention is the principle backbone of foundation models for various artificial intelligence applications, yet its quadratic complexity in sequence length can limit its inference throughput in long-context settings. To address this challenge, alternative architectures such as linear attention, State Space Models (SSMs), and Recurrent Neural Networks (RNNs) have been considered as more efficient alternatives. While connections between these approaches exist, such models are commonly developed in isolation and there is a lack of theoretical understanding of the shared principles underpinning these architectures and their subtle differences, greatly influencing performance and scalability. In this paper, we introduce the Dynamical Systems Framework (DSF), which allows a principled investigation of all these architectures in a common representation. Our framework facilitates rigorous comparisons, providing new insights on the distinctive characteristics of each model class. For instance, we compare linear attention and selective SSMs, detailing their differences and conditions under which both are equivalent. We also provide principled comparisons between softmax attention and other model classes, discussing the theoretical conditions under which softmax attention can be approximated. Additionally, we substantiate these new insights with empirical validations and mathematical arguments. This shows the DSF's potential to guide the systematic development of future more efficient and scalable foundation models.", "authors": ["Jerome Sieber", "Carmen Amo Alonso", "Alexandre Didier", "Melanie N. Zeilinger", "Antonio Orvieto"], "categories": ["cs.LG", "cs.AI", "eess.SY"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-05-24", "url": "https://arxiv.org/abs/2405.15731", "pdf_url": "https://arxiv.org/pdf/2405.15731v3", "arxiv_id": "2405.15731", "doi": "10.48550/arXiv.2405.15731", "citation_count": 35, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Neural Information Processing Systems", "quality_score": 0.3891}
{"id": "fce629f82cec56cf3da172fd24be70816cafead1f4e3f825c5227b4ffaf37adf", "sources": ["arxiv", "semantic_scholar"], "title": "I2I-Mamba: Multi-modal medical image synthesis via selective state space modeling", "abstract": "Multi-modal medical image synthesis involves nonlinear transformation of tissue signals between source and target modalities, where tissues exhibit contextual interactions across diverse spatial distances. As such, the utility of a network architecture in synthesis depends on its ability to express the broad set of contextual features in medical images. Convolutional neural networks (CNNs) offer high local precision at the expense of poor sensitivity to long-range context. While transformers promise to alleviate this issue, they suffer from an unfavorable trade-off between sensitivity to long- versus short-range context due to the intrinsic complexity of attention filters. To effectively capture contextual features while avoiding the complexitydriven trade-offs, here we introduce a novel multi-modal synthesis method, I2I-Mamba, based on the state space modeling (SSM) framework. Focusing on high-level representations across a hybrid residual architecture, I2I-Mamba leverages novel dual-domain Mamba (ddMamba) blocks for complementary contextual modeling in image and Fourier domains, while maintaining spatial precision with convolutional layers. Diverting from conventional raster-scan trajectories, ddMamba leverages novel SSM operators based on a spiral-scan trajectory to learn context with enhanced angular isotropy and radial coverage, and a channel-mixing layer to aggregate context across the channel dimension. Comprehensive demonstrations on multi-contrast MRI and MRI-CT protocols indicate that I2I-Mamba outperforms state-of-the-art CNNs, transformers and SSMs.", "authors": ["Omer F. Atli", "Bilal Kabas", "Fuat Arslan", "Arda C. Demirtas", "Mahmut Yurt", "Onat Dalmaz", "Tolga Çukur"], "categories": ["eess.IV", "cs.CV"], "fields_of_study": ["Medicine", "Computer Science", "Engineering"], "published_date": "2024-05-22", "url": "https://arxiv.org/abs/2405.14022", "pdf_url": "https://arxiv.org/pdf/2405.14022v6", "arxiv_id": "2405.14022", "doi": "10.48550/arXiv.2405.14022", "citation_count": 63, "influential_citation_count": 5, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.4515}
{"id": "f2d9bab338b1660f118c2910b15ec9efdfbe11fa574aac33b8362621b171af7f", "sources": ["arxiv", "semantic_scholar"], "title": "Audio Mamba: Pretrained Audio State Space Model For Audio Tagging", "abstract": "Audio tagging is an important task of mapping audio samples to their corresponding categories. Recently endeavours that exploit transformer models in this field have achieved great success. However, the quadratic self-attention cost limits the scaling of audio transformer models and further constrains the development of more universal audio models. In this paper, we attempt to solve this problem by proposing Audio Mamba, a self-attention-free approach that captures long audio spectrogram dependency with state space models. Our experimental results on two audio-tagging datasets demonstrate the parameter efficiency of Audio Mamba, it achieves comparable results to SOTA audio spectrogram transformers with one third parameters.", "authors": ["Jiaju Lin", "Haoxuan Hu"], "categories": ["cs.SD", "cs.AI", "eess.AS"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-05-22", "url": "https://arxiv.org/abs/2405.13636", "pdf_url": "https://arxiv.org/pdf/2405.13636v1", "arxiv_id": "2405.13636", "doi": "10.48550/arXiv.2405.13636", "citation_count": 15, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.301}
{"id": "67c4af5d04aab930727c9b697bd8f87302ea8f6e7d29252a3bd8e1b081c59f5e", "sources": ["arxiv", "semantic_scholar"], "title": "HeteGraph-Mamba: Heterogeneous Graph Learning via Selective State Space Model", "abstract": "We propose a heterogeneous graph mamba network (HGMN) as the first exploration in leveraging the selective state space models (SSSMs) for heterogeneous graph learning. Compared with the literature, our HGMN overcomes two major challenges: (i) capturing long-range dependencies among heterogeneous nodes and (ii) adapting SSSMs to heterogeneous graph data. Our key contribution is a general graph architecture that can solve heterogeneous nodes in real-world scenarios, followed an efficient flow. Methodologically, we introduce a two-level efficient tokenization approach that first captures long-range dependencies within identical node types, and subsequently across all node types. Empirically, we conduct comparisons between our framework and 19 state-of-the-art methods on the heterogeneous benchmarks. The extensive comparisons demonstrate that our framework outperforms other methods in both the accuracy and efficiency dimensions.", "authors": ["Zhenyu Pan", "Yoonsung Jeong", "Xiaoda Liu", "Han Liu"], "categories": ["cs.LG", "cs.SI"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-22", "url": "https://arxiv.org/abs/2405.13915", "pdf_url": "https://arxiv.org/pdf/2405.13915v1", "arxiv_id": "2405.13915", "doi": "10.48550/arXiv.2405.13915", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1747}
{"id": "b9fe006a21cd64c00a5e58c74c412311db608754c0730905b1ed62b1d5e8b9a6", "sources": ["arxiv", "semantic_scholar"], "title": "3DSS-Mamba: 3D-Spectral-Spatial Mamba for Hyperspectral Image Classification", "abstract": "Hyperspectral image (HSI) classification constitutes the fundamental research in remote sensing fields. Convolutional Neural Networks (CNNs) and Transformers have demonstrated impressive capability in capturing spectral-spatial contextual dependencies. However, these architectures suffer from limited receptive fields and quadratic computational complexity, respectively. Fortunately, recent Mamba architectures built upon the State Space Model integrate the advantages of long-range sequence modeling and linear computational efficiency, exhibiting substantial potential in low-dimensional scenarios. Motivated by this, we propose a novel 3D-Spectral-Spatial Mamba (3DSS-Mamba) framework for HSI classification, allowing for global spectral-spatial relationship modeling with greater computational efficiency. Technically, a spectral-spatial token generation (SSTG) module is designed to convert the HSI cube into a set of 3D spectral-spatial tokens. To overcome the limitations of traditional Mamba, which is confined to modeling causal sequences and inadaptable to high-dimensional scenarios, a 3D-Spectral-Spatial Selective Scanning (3DSS) mechanism is introduced, which performs pixel-wise selective scanning on 3D hyperspectral tokens along the spectral and spatial dimensions. Five scanning routes are constructed to investigate the impact of dimension prioritization. The 3DSS scanning mechanism combined with conventional mapping operations forms the 3D-spectral-spatial mamba block (3DMB), enabling the extraction of global spectral-spatial semantic representations. Experimental results and analysis demonstrate that the proposed method outperforms the state-of-the-art methods on HSI classification benchmarks.", "authors": ["Yan He", "Bing Tu", "Bo Liu", "Jun Li", "Antonio Plaza"], "categories": ["cs.CV", "eess.IV"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-05-21", "url": "https://arxiv.org/abs/2405.12487", "pdf_url": "https://arxiv.org/pdf/2405.12487v2", "arxiv_id": "2405.12487", "doi": "10.1109/TGRS.2024.3472091", "citation_count": 130, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Geoscience and Remote Sensing", "quality_score": 0.5293}
{"id": "a4f727289b004f9952eb2003f232e40e9db46ec9d3ada5d325e03bd470f1c896", "sources": ["arxiv", "semantic_scholar"], "title": "SSAMBA: Self-Supervised Audio Representation Learning with Mamba State Space Model", "abstract": "Transformers have revolutionized deep learning across various tasks, including audio representation learning, due to their powerful modeling capabilities. However, they often suffer from quadratic complexity in both GPU memory usage and computational inference time, affecting their efficiency. Recently, state space models (SSMs) like Mamba have emerged as a promising alternative, offering a more efficient approach by avoiding these complexities. Given these advantages, we explore the potential of SSM-based models in audio tasks. In this paper, we introduce Self-Supervised Audio Mamba (SSAMBA), the first self-supervised, attention-free, and SSM-based model for audio representation learning. SSAMBA leverages the bidirectional Mamba to capture complex audio patterns effectively. We incorporate a self-supervised pretraining framework that optimizes both discriminative and generative objectives, enabling the model to learn robust audio representations from large-scale, unlabeled datasets. We evaluated SSAMBA on various tasks such as audio classification, keyword spotting, and speaker identification. Our results demonstrate that SSAMBA outperforms the Self-Supervised Audio Spectrogram Transformer (SSAST) in most tasks. Notably, SSAMBA is approximately 92.7% faster in batch inference speed and 95.4% more memory-efficient than SSAST for the tiny model size with an input token size of 22k. These efficiency gains, combined with superior performance, underscore the effectiveness of SSAMBA's architectural innovation, making it a compelling choice for a wide range of audio processing applications.", "authors": ["Siavash Shams", "Sukru Samet Dindar", "Xilin Jiang", "Nima Mesgarani"], "categories": ["eess.AS", "cs.LG"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2024-05-20", "url": "https://arxiv.org/abs/2405.11831", "pdf_url": "https://arxiv.org/pdf/2405.11831v2", "arxiv_id": "2405.11831", "doi": "10.1109/SLT61566.2024.10832304", "citation_count": 48, "influential_citation_count": 5, "has_code": true, "code_url": "https://github.com/SiavashShams/ssamba", "venue": "Spoken Language Technology Workshop", "quality_score": 0.4225}
{"id": "641b27151cffb3fd4b1b04dea7b6f32aca97efbb0562a09a557569819815a38f", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba-in-Mamba: Centralized Mamba-Cross-Scan in Tokenized Mamba Model for Hyperspectral Image Classification", "abstract": "Hyperspectral image (HSI) classification is pivotal in the remote sensing (RS) field, particularly with the advancement of deep learning techniques. Sequential models, adapted from the natural language processing (NLP) field such as Recurrent Neural Networks (RNNs) and Transformers, have been tailored to this task, offering a unique viewpoint. However, several challenges persist 1) RNNs struggle with centric feature aggregation and are sensitive to interfering pixels, 2) Transformers require significant computational resources and often underperform with limited HSI training samples, and 3) Current scanning methods for converting images into sequence-data are simplistic and inefficient. In response, this study introduces the innovative Mamba-in-Mamba (MiM) architecture for HSI classification, the first attempt of deploying State Space Model (SSM) in this task. The MiM model includes 1) A novel centralized Mamba-Cross-Scan (MCS) mechanism for transforming images into sequence-data, 2) A Tokenized Mamba (T-Mamba) encoder that incorporates a Gaussian Decay Mask (GDM), a Semantic Token Learner (STL), and a Semantic Token Fuser (STF) for enhanced feature generation and concentration, and 3) A Weighted MCS Fusion (WMF) module coupled with a Multi-Scale Loss Design to improve decoding efficiency. Experimental results from three public HSI datasets with fixed and disjoint training-testing samples demonstrate that our method outperforms existing baselines and state-of-the-art approaches, highlighting its efficacy and potential in HSI applications.", "authors": ["Weilian Zhou", "Sei-Ichiro Kamata", "Haipeng Wang", "Man-Sing Wong", " Huiying", " Hou"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-20", "url": "https://arxiv.org/abs/2405.12003", "pdf_url": "https://arxiv.org/pdf/2405.12003v4", "arxiv_id": "2405.12003", "doi": "10.48550/arXiv.2405.12003", "citation_count": 120, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "Neurocomputing", "quality_score": 0.5207}
{"id": "85db926939cdeb9b6f0ad0ae75dae6225c692741b77d967c13d079514efc4c15", "sources": ["arxiv", "semantic_scholar"], "title": "A New Monte-Carlo Model for the Space Environment", "abstract": "This paper introduces a novel Monte Carlo (MC) method to simulate the evolution of the low-earth orbit environment, enhancing the MIT Orbital Capacity Analysis Tool (MOCAT). In recent decades, numerous space environment models have been developed by government agencies and research groups to understand and predict the dynamics of space debris. Our MC approach advances this by simulating the trajectories of space objects and modeling their interactions, such as collisions and explosions. This aids in analyzing the trends of space-object and debris populations. A key innovation of our method is the computational efficiency in orbit propagation, which is crucial for handling potentially large numbers of objects over centuries. We present validation results against the IADC (Inter-Agency Space Debris Coordination Committee) study and explore various scenarios, including ones without future launches and those involving the launch of proposed megaconstellations with over 80,000 active payloads. With the improvement in computational efficiencies provided by this work, we can run these new scenarios that predict millions of trackable objects over a 200-year period. The previous state-of-the-art was 400,000 objects over the same period of time. Notably, while fewer megaconstellations are planned for altitudes above 800 km, even minimal failures in post-mission disposal or collision avoidance maneuvers can significantly impact orbital debris accumulation.", "authors": ["Daniel Jang", "Davide Gusmini", "Peng Mun Siew", "Andrea D'Ambrosio", "Simone Servadio", "Pablo Machuca", "Richard Linares"], "categories": ["astro-ph.EP", "astro-ph.IM", "physics.space-ph"], "fields_of_study": ["Physics"], "published_date": "2024-05-16", "url": "https://arxiv.org/abs/2405.10430", "pdf_url": "https://arxiv.org/pdf/2405.10430v3", "arxiv_id": "2405.10430", "doi": null, "citation_count": 11, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2698}
{"id": "e1a510224974e901488c2eefd00b81b365c1ac7681fc61cda91ad12414a4ebd1", "sources": ["arxiv", "semantic_scholar"], "title": "MambaOut: Do We Really Need Mamba for Vision?", "abstract": "Mamba, an architecture with RNN-like token mixer of state space model (SSM), was recently introduced to address the quadratic complexity of the attention mechanism and subsequently applied to vision tasks. Nevertheless, the performance of Mamba for vision is often underwhelming when compared with convolutional and attention-based models. In this paper, we delve into the essence of Mamba, and conceptually conclude that Mamba is ideally suited for tasks with long-sequence and autoregressive characteristics. For vision tasks, as image classification does not align with either characteristic, we hypothesize that Mamba is not necessary for this task; Detection and segmentation tasks are also not autoregressive, yet they adhere to the long-sequence characteristic, so we believe it is still worthwhile to explore Mamba's potential for these tasks. To empirically verify our hypotheses, we construct a series of models named MambaOut through stacking Mamba blocks while removing their core token mixer, SSM. Experimental results strongly support our hypotheses. Specifically, our MambaOut model surpasses all visual Mamba models on ImageNet image classification, indicating that Mamba is indeed unnecessary for this task. As for detection and segmentation, MambaOut cannot match the performance of state-of-the-art visual Mamba models, demonstrating the potential of Mamba for long-sequence visual tasks. The code is available at https://github.com/yuweihao/MambaOut", "authors": ["Weihao Yu", "Xinchao Wang"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-13", "url": "https://arxiv.org/abs/2405.07992", "pdf_url": "https://arxiv.org/pdf/2405.07992v3", "arxiv_id": "2405.07992", "doi": "10.1109/CVPR52734.2025.00423", "citation_count": 284, "influential_citation_count": 16, "has_code": true, "code_url": "https://github.com/yuweihao/MambaOut", "venue": "Computer Vision and Pattern Recognition", "quality_score": 0.6152}
{"id": "5cf71be12508c96ef6efd99f1c5d7035d5a9874083c9370c6e84cfb667e7b96a", "sources": ["arxiv", "semantic_scholar"], "title": "VM-DDPM: Vision Mamba Diffusion for Medical Image Synthesis", "abstract": "In the realm of smart healthcare, researchers enhance the scale and diversity of medical datasets through medical image synthesis. However, existing methods are limited by CNN local perception and Transformer quadratic complexity, making it difficult to balance structural texture consistency. To this end, we propose the Vision Mamba DDPM (VM-DDPM) based on State Space Model (SSM), fully combining CNN local perception and SSM global modeling capabilities, while maintaining linear computational complexity. Specifically, we designed a multi-level feature extraction module called Multi-level State Space Block (MSSBlock), and a basic unit of encoder-decoder structure called State Space Layer (SSLayer) for medical pathological images. Besides, we designed a simple, Plug-and-Play, zero-parameter Sequence Regeneration strategy for the Cross-Scan Module (CSM), which enabled the S6 module to fully perceive the spatial features of the 2D image and stimulate the generalization potential of the model. To our best knowledge, this is the first medical image synthesis model based on the SSM-CNN hybrid architecture. Our experimental evaluation on three datasets of different scales, i.e., ACDC, BraTS2018, and ChestXRay, as well as qualitative evaluation by radiologists, demonstrate that VM-DDPM achieves state-of-the-art performance.", "authors": ["Zhihan Ju", "Wanting Zhou"], "categories": ["eess.IV", "cs.CV"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2024-05-09", "url": "https://arxiv.org/abs/2405.05667", "pdf_url": "https://arxiv.org/pdf/2405.05667v1", "arxiv_id": "2405.05667", "doi": "10.48550/arXiv.2405.05667", "citation_count": 12, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2785}
{"id": "fd4fbe415b2efcd80a15f270f3d6c59516294ae7742fb5bb89d153abc641d657", "sources": ["arxiv", "semantic_scholar"], "title": "Vision Mamba: A Comprehensive Survey and Taxonomy", "abstract": "State Space Model (SSM) is a mathematical model used to describe and analyze the behavior of dynamic systems. This model has witnessed numerous applications in several fields, including control theory, signal processing, economics and machine learning. In the field of deep learning, state space models are used to process sequence data, such as time series analysis, natural language processing (NLP) and video understanding. By mapping sequence data to state space, long-term dependencies in the data can be better captured. In particular, modern SSMs have shown strong representational capabilities in NLP, especially in long sequence modeling, while maintaining linear time complexity. Notably, based on the latest state-space models, Mamba merges time-varying parameters into SSMs and formulates a hardware-aware algorithm for efficient training and inference. Given its impressive efficiency and strong long-range dependency modeling capability, Mamba is expected to become a new AI architecture that may outperform Transformer. Recently, a number of works have attempted to study the potential of Mamba in various fields, such as general vision, multi-modal, medical image analysis and remote sensing image analysis, by extending Mamba from natural language domain to visual domain. To fully understand Mamba in the visual domain, we conduct a comprehensive survey and present a taxonomy study. This survey focuses on Mamba's application to a variety of visual tasks and data types, and discusses its predecessors, recent advances and far-reaching impact on a wide range of domains. Since Mamba is now on an upward trend, please actively notice us if you have new findings, and new progress on Mamba will be included in this survey in a timely manner and updated on the Mamba project at https://github.com/lx6c78/Vision-Mamba-A-Comprehensive-Survey-and-Taxonomy.", "authors": ["Xiao Liu", "Chenxu Zhang", "Lei Zhang"], "categories": ["cs.CV", "cs.AI", "cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-07", "url": "https://arxiv.org/abs/2405.04404", "pdf_url": "https://arxiv.org/pdf/2405.04404v1", "arxiv_id": "2405.04404", "doi": "10.48550/arXiv.2405.04404", "citation_count": 142, "influential_citation_count": 7, "has_code": true, "code_url": "https://github.com/lx6c78/Vision-Mamba-A-Comprehensive-Survey-and-Taxonomy", "venue": "arXiv.org", "quality_score": 0.5388}
{"id": "2867dc5d058a3878ec686c94b2bee6104320c87a9792234244acb2cd9ceea6b1", "sources": ["arxiv", "semantic_scholar"], "title": "FER-YOLO-Mamba: Facial Expression Detection and Classification Based on Selective State Space", "abstract": "Facial Expression Recognition (FER) plays a pivotal role in understanding human emotional cues. However, traditional FER methods based on visual information have some limitations, such as preprocessing, feature extraction, and multi-stage classification procedures. These not only increase computational complexity but also require a significant amount of computing resources. Considering Convolutional Neural Network (CNN)-based FER schemes frequently prove inadequate in identifying the deep, long-distance dependencies embedded within facial expression images, and the Transformer's inherent quadratic computational complexity, this paper presents the FER-YOLO-Mamba model, which integrates the principles of Mamba and YOLO technologies to facilitate efficient coordination in facial expression image recognition and localization. Within the FER-YOLO-Mamba model, we further devise a FER-YOLO-VSS dual-branch module, which combines the inherent strengths of convolutional layers in local feature extraction with the exceptional capability of State Space Models (SSMs) in revealing long-distance dependencies. To the best of our knowledge, this is the first Vision Mamba model designed for facial expression detection and classification. To evaluate the performance of the proposed FER-YOLO-Mamba model, we conducted experiments on two benchmark datasets, RAF-DB and SFEW. The experimental results indicate that the FER-YOLO-Mamba model achieved better results compared to other models. The code is available from https://github.com/SwjtuMa/FER-YOLO-Mamba.", "authors": ["Hui Ma", "Sen Lei", "Turgay Celik", "Heng-Chao Li"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-03", "url": "https://arxiv.org/abs/2405.01828", "pdf_url": "https://arxiv.org/pdf/2405.01828v3", "arxiv_id": "2405.01828", "doi": "10.48550/arXiv.2405.01828", "citation_count": 42, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/SwjtuMa/FER-YOLO-Mamba", "venue": "arXiv.org", "quality_score": 0.4084}
{"id": "70d7b72ffd817101c038ec600be999e43782a6cb7c9d2e483297f366d5d21080", "sources": ["arxiv", "semantic_scholar"], "title": "CLIP-Mamba: CLIP Pretrained Mamba Models with OOD and Hessian Evaluation", "abstract": "State space models and Mamba-based models have been increasingly applied across various domains, achieving state-of-the-art performance. This technical report introduces the first attempt to train a transferable Mamba model utilizing contrastive language-image pretraining (CLIP). We have trained Mamba models of varying sizes and undertaken comprehensive evaluations of these models on 26 zero-shot classification datasets and 16 out-of-distribution (OOD) datasets. Our findings reveal that a Mamba model with 67 million parameters is on par with a 307 million-parameter Vision Transformer (ViT) model in zero-shot classification tasks, highlighting the parameter efficiency of Mamba models. In tests of OOD generalization, Mamba-based models exhibit exceptional performance in conditions of OOD image contrast or when subjected to high-pass filtering. However, a Hessian analysis indicates that Mamba models feature a sharper and more non-convex landscape compared to ViT-based models, making them more challenging to train. The source code is available at https://github.com/raytrun/mamba-clip.", "authors": ["Weiquan Huang", "Yifei Shen", "Yifan Yang"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-30", "url": "https://arxiv.org/abs/2404.19394", "pdf_url": "https://arxiv.org/pdf/2404.19394v1", "arxiv_id": "2404.19394", "doi": "10.48550/arXiv.2404.19394", "citation_count": 11, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/raytrun/mamba-clip", "venue": "arXiv.org", "quality_score": 0.2698}
{"id": "ec6fb10b2a537615523f7b07a975cb1e6e59990889c9885676699e6d3f840989", "sources": ["arxiv", "semantic_scholar"], "title": "Spectral-Spatial Mamba for Hyperspectral Image Classification", "abstract": "Recently, deep learning models have achieved excellent performance in hyperspectral image (HSI) classification. Among the many deep models, Transformer has gradually attracted interest for its excellence in modeling the long-range dependencies of spatial-spectral features in HSI. However, Transformer has the problem of quadratic computational complexity due to the self-attention mechanism, which is heavier than other models and thus has limited adoption in HSI processing. Fortunately, the recently emerging state space model-based Mamba shows great computational efficiency while achieving the modeling power of Transformers. Therefore, in this paper, we make a preliminary attempt to apply the Mamba to HSI classification, leading to the proposed spectral-spatial Mamba (SS-Mamba). Specifically, the proposed SS-Mamba mainly consists of spectral-spatial token generation module and several stacked spectral-spatial Mamba blocks. Firstly, the token generation module converts any given HSI cube to spatial and spectral tokens as sequences. And then these tokens are sent to stacked spectral-spatial mamba blocks (SS-MB). Each SS-MB block consists of two basic mamba blocks and a spectral-spatial feature enhancement module. The spatial and spectral tokens are processed separately by the two basic mamba blocks, respectively. Besides, the feature enhancement module modulates spatial and spectral tokens using HSI sample's center region information. In this way, the spectral and spatial tokens cooperate with each other and achieve information fusion within each block. The experimental results conducted on widely used HSI datasets reveal that the proposed model achieves competitive results compared with the state-of-the-art methods. The Mamba-based method opens a new window for HSI classification.", "authors": ["Lingbo Huang", "Yushi Chen", "Xin He"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-29", "url": "https://arxiv.org/abs/2404.18401", "pdf_url": "https://arxiv.org/pdf/2404.18401v3", "arxiv_id": "2404.18401", "doi": "10.3390/rs16132449", "citation_count": 111, "influential_citation_count": 5, "has_code": false, "code_url": null, "venue": "Remote Sensing", "quality_score": 0.5123}
{"id": "ea62e21611f510a59189c802c7e09387fabe43caa8b731a5dcf35829c765c6f0", "sources": ["arxiv", "semantic_scholar"], "title": "RSCaMa: Remote Sensing Image Change Captioning with State Space Model", "abstract": "Remote Sensing Image Change Captioning (RSICC) aims to describe surface changes between multi-temporal remote sensing images in language, including the changed object categories, locations, and dynamics of changing objects (e.g., added or disappeared). This poses challenges to spatial and temporal modeling of bi-temporal features. Despite previous methods progressing in the spatial change perception, there are still weaknesses in joint spatial-temporal modeling. To address this, in this paper, we propose a novel RSCaMa model, which achieves efficient joint spatial-temporal modeling through multiple CaMa layers, enabling iterative refinement of bi-temporal features. To achieve efficient spatial modeling, we introduce the recently popular Mamba (a state space model) with a global receptive field and linear complexity into the RSICC task and propose the Spatial Difference-aware SSM (SD-SSM), overcoming limitations of previous CNN- and Transformer-based methods in the receptive field and computational complexity. SD-SSM enhances the model's ability to capture spatial changes sharply. In terms of efficient temporal modeling, considering the potential correlation between the temporal scanning characteristics of Mamba and the temporality of the RSICC, we propose the Temporal-Traversing SSM (TT-SSM), which scans bi-temporal features in a temporal cross-wise manner, enhancing the model's temporal understanding and information interaction. Experiments validate the effectiveness of the efficient joint spatial-temporal modeling and demonstrate the outstanding performance of RSCaMa and the potential of the Mamba in the RSICC task. Additionally, we systematically compare three different language decoders, including Mamba, GPT-style decoder, and Transformer decoder, providing valuable insights for future RSICC research. The code will be available at \\emph{\\url{https://github.com/Chen-Yang-Liu/RSCaMa}}", "authors": ["Chenyang Liu", "Keyan Chen", "Bowen Chen", "Haotian Zhang", "Zhengxia Zou", "Zhenwei Shi"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-29", "url": "https://arxiv.org/abs/2404.18895", "pdf_url": "https://arxiv.org/pdf/2404.18895v3", "arxiv_id": "2404.18895", "doi": "10.1109/LGRS.2024.3404604", "citation_count": 106, "influential_citation_count": 4, "has_code": true, "code_url": "https://github.com/Chen-Yang-Liu/RSCaMa}}", "venue": "IEEE Geoscience and Remote Sensing Letters", "quality_score": 0.5073}
{"id": "1aa572773f2b47293de86136c932713882ca09f6277d1a9204f9e510aac22443", "sources": ["arxiv", "semantic_scholar"], "title": "Visual Mamba: A Survey and New Outlooks", "abstract": "Mamba, a recent selective structured state space model, excels in long sequence modeling, which is vital in the large model era. Long sequence modeling poses significant challenges, including capturing long-range dependencies within the data and handling the computational demands caused by their extensive length. Mamba addresses these challenges by overcoming the local perception limitations of convolutional neural networks and the quadratic computational complexity of Transformers. Given its advantages over these mainstream foundation architectures, Mamba exhibits great potential to be a visual foundation architecture. Since January 2024, Mamba has been actively applied to diverse computer vision tasks, yielding numerous contributions. To help keep pace with the rapid advancements, this paper reviews visual Mamba approaches, analyzing over 200 papers. This paper begins by delineating the formulation of the original Mamba model. Subsequently, it delves into representative backbone networks, and applications categorized using different modalities, including image, video, point cloud, and multi-modal data. Particularly, we identify scanning techniques as critical for adapting Mamba to vision tasks, and decouple these scanning techniques to clarify their functionality and enhance their flexibility across various applications. Finally, we discuss the challenges and future directions, providing insights into new outlooks in this fast evolving area. A comprehensive list of visual Mamba models reviewed in this work is available at https://github.com/Ruixxxx/Awesome-Vision-Mamba-Models.", "authors": ["Rui Xu", "Shu Yang", "Yihui Wang", "Yu Cai", "Bo Du", "Hao Chen"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-29", "url": "https://arxiv.org/abs/2404.18861", "pdf_url": "https://arxiv.org/pdf/2404.18861v3", "arxiv_id": "2404.18861", "doi": null, "citation_count": 75, "influential_citation_count": 3, "has_code": true, "code_url": "https://github.com/Ruixxxx/Awesome-Vision-Mamba-Models", "venue": null, "quality_score": 0.4702}
{"id": "3229f7349c2524aa82c8e8e287463c048e72a9272ba860eb245513b6582c3756", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba-FETrack: Frame-Event Tracking via State Space Model", "abstract": "RGB-Event based tracking is an emerging research topic, focusing on how to effectively integrate heterogeneous multi-modal data (synchronized exposure video frames and asynchronous pulse Event stream). Existing works typically employ Transformer based networks to handle these modalities and achieve decent accuracy through input-level or feature-level fusion on multiple datasets. However, these trackers require significant memory consumption and computational complexity due to the use of self-attention mechanism. This paper proposes a novel RGB-Event tracking framework, Mamba-FETrack, based on the State Space Model (SSM) to achieve high-performance tracking while effectively reducing computational costs and realizing more efficient tracking. Specifically, we adopt two modality-specific Mamba backbone networks to extract the features of RGB frames and Event streams. Then, we also propose to boost the interactive learning between the RGB and Event features using the Mamba network. The fused features will be fed into the tracking head for target object localization. Extensive experiments on FELT and FE108 datasets fully validated the efficiency and effectiveness of our proposed tracker. Specifically, our Mamba-based tracker achieves 43.5/55.6 on the SR/PR metric, while the ViT-S based tracker (OSTrack) obtains 40.0/50.9. The GPU memory cost of ours and ViT-S based tracker is 13.98GB and 15.44GB, which decreased about $9.5\\%$. The FLOPs and parameters of ours/ViT-S based OSTrack are 59GB/1076GB and 7MB/60MB, which decreased about $94.5\\%$ and $88.3\\%$, respectively. We hope this work can bring some new insights to the tracking field and greatly promote the application of the Mamba architecture in tracking. The source code of this work will be released on \\url{https://github.com/Event-AHU/Mamba_FETrack}.", "authors": ["Ju Huang", "Shiao Wang", "Shuai Wang", "Zhe Wu", "Xiao Wang", "Bo Jiang"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-28", "url": "https://arxiv.org/abs/2404.18174", "pdf_url": "https://arxiv.org/pdf/2404.18174v1", "arxiv_id": "2404.18174", "doi": "10.48550/arXiv.2404.18174", "citation_count": 40, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Event-AHU/Mamba_FETrack}", "venue": "Chinese Conference on Pattern Recognition and Computer Vision", "quality_score": 0.4032}
{"id": "c2177c0a63beab5c6e45e0c45f20fd5e6cc57405d7a734e80de3266976dacbdb", "sources": ["arxiv", "semantic_scholar"], "title": "S$^2$Mamba: A Spatial-spectral State Space Model for Hyperspectral Image Classification", "abstract": "Land cover analysis using hyperspectral images (HSI) remains an open problem due to their low spatial resolution and complex spectral information. Recent studies are primarily dedicated to designing Transformer-based architectures for spatial-spectral long-range dependencies modeling, which is computationally expensive with quadratic complexity. Selective structured state space model (Mamba), which is efficient for modeling long-range dependencies with linear complexity, has recently shown promising progress. However, its potential in hyperspectral image processing that requires handling numerous spectral bands has not yet been explored. In this paper, we innovatively propose S$^2$Mamba, a spatial-spectral state space model for hyperspectral image classification, to excavate spatial-spectral contextual features, resulting in more efficient and accurate land cover analysis. In S$^2$Mamba, two selective structured state space models through different dimensions are designed for feature extraction, one for spatial, and the other for spectral, along with a spatial-spectral mixture gate for optimal fusion. More specifically, S$^2$Mamba first captures spatial contextual relations by interacting each pixel with its adjacent through a Patch Cross Scanning module and then explores semantic information from continuous spectral bands through a Bi-directional Spectral Scanning module. Considering the distinct expertise of the two attributes in homogenous and complicated texture scenes, we realize the Spatial-spectral Mixture Gate by a group of learnable matrices, allowing for the adaptive incorporation of representations learned across different dimensions. Extensive experiments conducted on HSI classification benchmarks demonstrate the superiority and prospect of S$^2$Mamba. The code will be made available at: https://github.com/PURE-melo/S2Mamba.", "authors": ["Guanchun Wang", "Xiangrong Zhang", "Zelin Peng", "Tianyang Zhang", "Licheng Jiao"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-28", "url": "https://arxiv.org/abs/2404.18213", "pdf_url": "https://arxiv.org/pdf/2404.18213v2", "arxiv_id": "2404.18213", "doi": "10.1109/TGRS.2025.3530993", "citation_count": 44, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/PURE-melo/S2Mamba", "venue": "IEEE Transactions on Geoscience and Remote Sensing", "quality_score": 0.4133}
{"id": "9216f520c52c7fcecbe1460109dadd270449fcaba5ecfeb87f26f35affdbceeb", "sources": ["arxiv", "semantic_scholar"], "title": "Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting", "abstract": "Long-term time series forecasting (LTSF) provides longer insights into future trends and patterns. Over the past few years, deep learning models especially Transformers have achieved advanced performance in LTSF tasks. However, LTSF faces inherent challenges such as long-term dependencies capturing and sparse semantic characteristics. Recently, a new state space model (SSM) named Mamba is proposed. With the selective capability on input data and the hardware-aware parallel computing algorithm, Mamba has shown great potential in balancing predicting performance and computational efficiency compared to Transformers. To enhance Mamba's ability to preserve historical information in a longer range, we design a novel Mamba+ block by adding a forget gate inside Mamba to selectively combine the new features with the historical features in a complementary manner. Furthermore, we apply Mamba+ both forward and backward and propose Bi-Mamba+, aiming to promote the model's ability to capture interactions among time series elements. Additionally, multivariate time series data in different scenarios may exhibit varying emphasis on intra- or inter-series dependencies. Therefore, we propose a series-relation-aware decider that controls the utilization of channel-independent or channel-mixing tokenization strategy for specific datasets. Extensive experiments on 8 real-world datasets show that our model achieves more accurate predictions compared with state-of-the-art methods.", "authors": ["Aobo Liang", "Xingguo Jiang", "Yan Sun", "Xiaohou Shi", "Ke Li"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-24", "url": "https://arxiv.org/abs/2404.15772", "pdf_url": "https://arxiv.org/pdf/2404.15772v3", "arxiv_id": "2404.15772", "doi": null, "citation_count": 29, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3693}
{"id": "3d8ca34d04d588e93cd3793a3f74245937fd9f8eef17d8c7a8c28e180e127106", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges", "abstract": "Sequence modeling is a crucial area across various domains, including Natural Language Processing (NLP), speech recognition, time series forecasting, music generation, and bioinformatics. Recurrent Neural Networks (RNNs) and Long Short Term Memory Networks (LSTMs) have historically dominated sequence modeling tasks like Machine Translation, Named Entity Recognition (NER), etc. However, the advancement of transformers has led to a shift in this paradigm, given their superior performance. Yet, transformers suffer from $O(N^2)$ attention complexity and challenges in handling inductive bias. Several variations have been proposed to address these issues which use spectral networks or convolutions and have performed well on a range of tasks. However, they still have difficulty in dealing with long sequences. State Space Models(SSMs) have emerged as promising alternatives for sequence modeling paradigms in this context, especially with the advent of S4 and its variants, such as S4nd, Hippo, Hyena, Diagnol State Spaces (DSS), Gated State Spaces (GSS), Linear Recurrent Unit (LRU), Liquid-S4, Mamba, etc. In this survey, we categorize the foundational SSMs based on three paradigms namely, Gating architectures, Structural architectures, and Recurrent architectures. This survey also highlights diverse applications of SSMs across domains such as vision, video, audio, speech, language (especially long sequence modeling), medical (including genomics), chemical (like drug design), recommendation systems, and time series analysis, including tabular data. Moreover, we consolidate the performance of SSMs on benchmark datasets like Long Range Arena (LRA), WikiText, Glue, Pile, ImageNet, Kinetics-400, sstv2, as well as video datasets such as Breakfast, COIN, LVU, and various time series datasets. The project page for Mamba-360 work is available on this webpage.\\url{https://github.com/badripatro/mamba360}.", "authors": ["Badri Narayana Patro", "Vijay Srinivas Agneeswaran"], "categories": ["cs.LG", "cs.AI", "cs.CV", "cs.MM", "eess.IV"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-04-24", "url": "https://arxiv.org/abs/2404.16112", "pdf_url": "https://arxiv.org/pdf/2404.16112v1", "arxiv_id": "2404.16112", "doi": "10.48550/arXiv.2404.16112", "citation_count": 89, "influential_citation_count": 4, "has_code": true, "code_url": "https://github.com/badripatro/mamba360}", "venue": "arXiv.org", "quality_score": 0.4886}
{"id": "1c4efd00bd527c9aba928f2558f4cc3cbc5c9174abd8fa632d85fb5f09580970", "sources": ["arxiv", "semantic_scholar"], "title": "A Survey on Visual Mamba", "abstract": "State space models (SSMs) with selection mechanisms and hardware-aware architectures, namely Mamba, have recently demonstrated significant promise in long-sequence modeling. Since the self-attention mechanism in transformers has quadratic complexity with image size and increasing computational demands, the researchers are now exploring how to adapt Mamba for computer vision tasks. This paper is the first comprehensive survey aiming to provide an in-depth analysis of Mamba models in the field of computer vision. It begins by exploring the foundational concepts contributing to Mamba's success, including the state space model framework, selection mechanisms, and hardware-aware design. Next, we review these vision mamba models by categorizing them into foundational ones and enhancing them with techniques such as convolution, recurrence, and attention to improve their sophistication. We further delve into the widespread applications of Mamba in vision tasks, which include their use as a backbone in various levels of vision processing. This encompasses general visual tasks, Medical visual tasks (e.g., 2D / 3D segmentation, classification, and image registration, etc.), and Remote Sensing visual tasks. We specially introduce general visual tasks from two levels: High/Mid-level vision (e.g., Object detection, Segmentation, Video classification, etc.) and Low-level vision (e.g., Image super-resolution, Image restoration, Visual generation, etc.). We hope this endeavor will spark additional interest within the community to address current challenges and further apply Mamba models in computer vision.", "authors": ["Hanwei Zhang", "Ying Zhu", "Dan Wang", "Lijun Zhang", "Tianxiang Chen", "Zi Ye"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-24", "url": "https://arxiv.org/abs/2404.15956", "pdf_url": "https://arxiv.org/pdf/2404.15956v2", "arxiv_id": "2404.15956", "doi": "10.48550/arXiv.2404.15956", "citation_count": 197, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Applied Sciences", "quality_score": 0.5742}
{"id": "d436f61df850c5fd192b04fe4bcd6ed2b3ec824eb5f92aae104681c4edbcd696", "sources": ["arxiv", "semantic_scholar"], "title": "SST: Multi-Scale Hybrid Mamba-Transformer Experts for Time Series Forecasting", "abstract": "Time series forecasting has made significant advances, including with Transformer-based models. The attention mechanism in Transformer effectively captures temporal dependencies by attending to all past inputs simultaneously. However, its quadratic complexity with respect to sequence length limits the scalability for long-range modeling. Recent state space models (SSMs) such as Mamba offer a promising alternative by achieving linear complexity without attention. Yet, Mamba compresses historical information into a fixed-size latent state, potentially causing information loss and limiting representational effectiveness. This raises a key research question: Can we design a hybrid Mamba-Transformer architecture that is both effective and efficient for time series forecasting? To address it, we adapt a hybrid Mamba-Transformer architecture Mambaformer, originally proposed for language modeling, to the time series domain. Preliminary experiments reveal that naively stacking Mamba and Transformer layers in Mambaformer is suboptimal for time series forecasting, due to an information interference problem. To mitigate this issue, we introduce a new time series decomposition strategy that separates time series into long-range patterns and short-range variations. Then we show that Mamba excels at capturing long-term structures, while Transformer is more effective at modeling short-term dynamics. Building on this insight, we propose State Space Transformer (SST), a multi-scale hybrid model with expert modules: a Mamba expert for long-range patterns and a Transformer expert for short-term variations. SST also employs a multi-scale patching mechanism to adaptively adjust time series resolution: low resolution for long-term patterns and high resolution for short-term variations. Experiments show that SST obtains SOTA performance with linear scalability. The code is at https://github.com/XiongxiaoXu/SST.", "authors": ["Xiongxiao Xu", "Canyu Chen", "Yueqing Liang", "Baixiang Huang", "Guangji Bai", "Liang Zhao", "Kai Shu"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-23", "url": "https://arxiv.org/abs/2404.14757", "pdf_url": "https://arxiv.org/pdf/2404.14757v3", "arxiv_id": "2404.14757", "doi": "10.1145/3746252.3761394", "citation_count": 33, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/XiongxiaoXu/SST", "venue": "International Conference on Information and Knowledge Management", "quality_score": 0.3829}
{"id": "32c357179e2e57b18c63f77f3c7212b0eca3eecf82d60ea003e4acb99883f9fa", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Model", "abstract": "Existing Transformer-based models for point cloud analysis suffer from quadratic complexity, leading to compromised point cloud resolution and information loss. In contrast, the newly proposed Mamba model, based on state space models (SSM), outperforms Transformer in multiple areas with only linear complexity. However, the straightforward adoption of Mamba does not achieve satisfactory performance on point cloud tasks. In this work, we present Mamba3D, a state space model tailored for point cloud learning to enhance local feature extraction, achieving superior performance, high efficiency, and scalability potential. Specifically, we propose a simple yet effective Local Norm Pooling (LNP) block to extract local geometric features. Additionally, to obtain better global features, we introduce a bidirectional SSM (bi-SSM) with both a token forward SSM and a novel backward SSM that operates on the feature channel. Extensive experimental results show that Mamba3D surpasses Transformer-based counterparts and concurrent works in multiple tasks, with or without pre-training. Notably, Mamba3D achieves multiple SoTA, including an overall accuracy of 92.6% (train from scratch) on the ScanObjectNN and 95.1% (with single-modal pre-training) on the ModelNet40 classification task, with only linear complexity. Our code and weights are available at https://github.com/xhanxu/Mamba3D.", "authors": ["Xu Han", "Yuan Tang", "Zhaoxuan Wang", "Xianzhi Li"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-23", "url": "https://arxiv.org/abs/2404.14966", "pdf_url": "https://arxiv.org/pdf/2404.14966v2", "arxiv_id": "2404.14966", "doi": "10.1145/3664647.3681173", "citation_count": 110, "influential_citation_count": 12, "has_code": true, "code_url": "https://github.com/xhanxu/Mamba3D", "venue": "ACM Multimedia", "quality_score": 0.557}
{"id": "bd3ad8dc10ac75056af2988f1bb9e379adcfe1aa76af4badda0c6fd14f2d1501", "sources": ["arxiv", "semantic_scholar"], "title": "Comparison of Empirical Models of Ionospheric Heating to Global Simulations", "abstract": "Intense currents produced during geomagnetic storms dissipate energy in the ionosphere through Joule heating. This dissipation has significant space weather effects, and thus it is important to determine the ability of physics-based simulations to replicate real events quantitatively. Several empirical models estimate Joule heating based on ionospheric currents using the AE index. In this study, we select 11 magnetic storm simulations from the CCMC database and compare the integrated Joule heating in the simulations with the results of empirical models. We also use the SWMF global magnetohydrodynamic simulations for 12 storms to reproduce the correlation between the simulated AE index and simulated Joule heating. We find that the scale factors in the empirical models are half what is predicted by the SWMF simulations.", "authors": ["Fatemeh Bagheri", "Ramon E. Lopez"], "categories": ["astro-ph.EP", "physics.space-ph"], "fields_of_study": ["Physics"], "published_date": "2024-04-22", "url": "https://arxiv.org/abs/2404.14330", "pdf_url": "https://arxiv.org/pdf/2404.14330v1", "arxiv_id": "2404.14330", "doi": "10.3389/fspas.2023.1170390", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Frontiers in Astronomy and Space Sciences", "quality_score": 0.1193}
{"id": "14588a57b770bfbe3e7c20909f1ff31f2abeaf4785f6ee4547fd70e0a2586ab3", "sources": ["arxiv", "semantic_scholar"], "title": "ST-Mamba: Spatial-Temporal Selective State Space Model for Traffic Flow Prediction", "abstract": "Traffic flow prediction, a critical aspect of intelligent transportation systems, has been increasingly popular in the field of artificial intelligence, driven by the availability of extensive traffic data. The current challenges of traffic flow prediction lie in integrating diverse factors while balancing the trade-off between computational complexity and the precision necessary for effective long-range and large-scale predictions. To address these challenges, we introduce a Spatial-Temporal Selective State Space (ST-Mamba) model, which is the first to leverage the power of spatial-temporal learning in traffic flow prediction without using graph modeling. The ST-Mamba model can effectively capture the long-range dependency for traffic flow data, thereby avoiding the issue of over-smoothing. The proposed ST-Mamba model incorporates an effective Spatial-Temporal Mixer (ST-Mixer) to seamlessly integrate spatial and temporal data processing into a unified framework and employs a Spatial-Temporal Selective State Space (ST-SSM) block to improve computational efficiency. The proposed ST-Mamba model, specifically designed for spatial-temporal data, simplifies processing procedure and enhances generalization capabilities, thereby significantly improving the accuracy of long-range traffic flow prediction. Compared to the previous state-of-the-art (SOTA) model, the proposed ST-Mamba model achieves a 61.11\\% improvement in computational speed and increases prediction accuracy by 0.67\\%. Extensive experiments with real-world traffic datasets demonstrate that the \\textsf{ST-Mamba} model sets a new benchmark in traffic flow prediction, achieving SOTA performance in computational efficiency for both long- and short-range predictions and significantly improving the overall efficiency and effectiveness of traffic management.", "authors": ["Zhiqi Shao", "Michael G. H. Bell", "Ze Wang", "D. Glenn Geers", "Haoning Xi", "Junbin Gao"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-20", "url": "https://arxiv.org/abs/2404.13257", "pdf_url": "https://arxiv.org/pdf/2404.13257v2", "arxiv_id": "2404.13257", "doi": null, "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1945}
{"id": "4c0280bdd03d7b0b6b5048b04ec57278abd9257f34a214509434d4ba9c3e5d64", "sources": ["arxiv", "semantic_scholar"], "title": "CU-Mamba: Selective State Space Models with Channel Learning for Image Restoration", "abstract": "Reconstructing degraded images is a critical task in image processing. Although CNN and Transformer-based models are prevalent in this field, they exhibit inherent limitations, such as inadequate long-range dependency modeling and high computational costs. To overcome these issues, we introduce the Channel-Aware U-Shaped Mamba (CU-Mamba) model, which incorporates a dual State Space Model (SSM) framework into the U-Net architecture. CU-Mamba employs a Spatial SSM module for global context encoding and a Channel SSM component to preserve channel correlation features, both in linear computational complexity relative to the feature map size. Extensive experimental results validate CU-Mamba's superiority over existing state-of-the-art methods, underscoring the importance of integrating both spatial and channel contexts in image restoration.", "authors": ["Rui Deng", "Tianpei Gu"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-17", "url": "https://arxiv.org/abs/2404.11778", "pdf_url": "https://arxiv.org/pdf/2404.11778v1", "arxiv_id": "2404.11778", "doi": "10.1109/MIPR62202.2024.00059", "citation_count": 42, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Conference on Multimedia Information Processing and Retrieval", "quality_score": 0.4084}
{"id": "21a5a6243403097b9baef48bd5b69c681be71c293f9de47eae1d288da33f9c0b", "sources": ["arxiv", "semantic_scholar"], "title": "State Space Model for New-Generation Network Alternative to Transformers: A Survey", "abstract": "In the post-deep learning era, the Transformer architecture has demonstrated its powerful performance across pre-trained big models and various downstream tasks. However, the enormous computational demands of this architecture have deterred many researchers. To further reduce the complexity of attention models, numerous efforts have been made to design more efficient methods. Among them, the State Space Model (SSM), as a possible replacement for the self-attention based Transformer model, has drawn more and more attention in recent years. In this paper, we give the first comprehensive review of these works and also provide experimental comparisons and analysis to better demonstrate the features and advantages of SSM. Specifically, we first give a detailed description of principles to help the readers quickly capture the key ideas of SSM. After that, we dive into the reviews of existing SSMs and their various applications, including natural language processing, computer vision, graph, multi-modal and multi-media, point cloud/event stream, time series data, and other domains. In addition, we give statistical comparisons and analysis of these models and hope it helps the readers to understand the effectiveness of different structures on various tasks. Then, we propose possible research points in this direction to better promote the development of the theoretical model and application of SSM. More related works will be continuously updated on the following GitHub: https://github.com/Event-AHU/Mamba_State_Space_Model_Paper_List.", "authors": ["Xiao Wang", "Shiao Wang", "Yuhe Ding", "Yuehang Li", "Wentao Wu", "Yao Rong", "Weizhe Kong", "Ju Huang", "Shihao Li", "Haoxiang Yang", "Ziwen Wang", "Bo Jiang", "Chenglong Li", "Yaowei Wang", "Yonghong Tian", "Jin Tang"], "categories": ["cs.LG", "cs.AI", "cs.CL", "cs.CV", "cs.MM"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-15", "url": "https://arxiv.org/abs/2404.09516", "pdf_url": "https://arxiv.org/pdf/2404.09516v1", "arxiv_id": "2404.09516", "doi": "10.48550/arXiv.2404.09516", "citation_count": 98, "influential_citation_count": 2, "has_code": true, "code_url": "https://github.com/Event-AHU/Mamba_State_Space_Model_Paper_List", "venue": "arXiv.org", "quality_score": 0.4989}
{"id": "0c094b1a447df05755b56aff35984df0df6be18d1616881b4ac13b0fadf75242", "sources": ["arxiv", "semantic_scholar"], "title": "The Illusion of State in State-Space Models", "abstract": "State-space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking (Merrill & Sabharwal, 2023), which SSMs are explicitly designed to address via their close architectural similarity to recurrent neural networks (RNNs). But do SSMs truly have an advantage (over transformers) in expressive power for state tracking? Surprisingly, the answer is no. Our analysis reveals that the expressive power of SSMs is limited very similarly to transformers: SSMs cannot express computation outside the complexity class $\\mathsf{TC}^0$. In particular, this means they cannot solve simple state-tracking problems like permutation composition. It follows that SSMs are provably unable to accurately track chess moves with certain notation, evaluate code, or track entities in a long narrative. To supplement our formal analysis, we report experiments showing that Mamba-style SSMs indeed struggle with state tracking. Thus, despite its recurrent formulation, the \"state\" in an SSM is an illusion: SSMs have similar expressiveness limitations to non-recurrent models like transformers, which may fundamentally limit their ability to solve real-world state-tracking problems.", "authors": ["William Merrill", "Jackson Petty", "Ashish Sabharwal"], "categories": ["cs.LG", "cs.CC", "cs.CL", "cs.FL"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-12", "url": "https://arxiv.org/abs/2404.08819", "pdf_url": "https://arxiv.org/pdf/2404.08819v3", "arxiv_id": "2404.08819", "doi": "10.48550/arXiv.2404.08819", "citation_count": 173, "influential_citation_count": 33, "has_code": false, "code_url": null, "venue": "International Conference on Machine Learning", "quality_score": 0.7657}
{"id": "507eb6d4cc35ecb627f82bf4e90d58d1a9e099f4e8c77e1c1f9184d20eb85102", "sources": ["arxiv", "semantic_scholar"], "title": "Deep Mamba Multi-modal Learning", "abstract": "Inspired by the excellent performance of Mamba networks, we propose a novel Deep Mamba Multi-modal Learning (DMML). It can be used to achieve the fusion of multi-modal features. We apply DMML to the field of multimedia retrieval and propose an innovative Deep Mamba Multi-modal Hashing (DMMH) method. It combines the advantages of algorithm accuracy and inference speed. We validated the effectiveness of DMMH on three public datasets and achieved state-of-the-art results.", "authors": ["Jian Zhu", "Xin Zou", "Yu Cui", "Zhangmin Huang", "Chenshu Hu", "Bo Lyu"], "categories": ["cs.MM"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-09", "url": "https://arxiv.org/abs/2406.18007", "pdf_url": "https://arxiv.org/pdf/2406.18007v1", "arxiv_id": "2406.18007", "doi": "10.48550/arXiv.2406.18007", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0}
{"id": "adc9e2982e75357d70fa0ac895871acb02229c0685d12c9006e6f0639b207032", "sources": ["arxiv", "semantic_scholar"], "title": "MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection", "abstract": "Recent advancements in anomaly detection have seen the efficacy of CNN- and transformer-based approaches. However, CNNs struggle with long-range dependencies, while transformers are burdened by quadratic computational complexity. Mamba-based models, with their superior long-range modeling and linear efficiency, have garnered substantial attention. This study pioneers the application of Mamba to multi-class unsupervised anomaly detection, presenting MambaAD, which consists of a pre-trained encoder and a Mamba decoder featuring (Locality-Enhanced State Space) LSS modules at multi-scales. The proposed LSS module, integrating parallel cascaded (Hybrid State Space) HSS blocks and multi-kernel convolutions operations, effectively captures both long-range and local information. The HSS block, utilizing (Hybrid Scanning) HS encoders, encodes feature maps into five scanning methods and eight directions, thereby strengthening global connections through the (State Space Model) SSM. The use of Hilbert scanning and eight directions significantly improves feature sequence modeling. Comprehensive experiments on six diverse anomaly detection datasets and seven metrics demonstrate state-of-the-art performance, substantiating the method's effectiveness. The code and models are available at https://lewandofskee.github.io/projects/MambaAD.", "authors": ["Haoyang He", "Yuhu Bai", "Jiangning Zhang", "Qingdong He", "Hongxu Chen", "Zhenye Gan", "Chengjie Wang", "Xiangtai Li", "Guanzhong Tian", "Lei Xie"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-09", "url": "https://arxiv.org/abs/2404.06564", "pdf_url": "https://arxiv.org/pdf/2404.06564v4", "arxiv_id": "2404.06564", "doi": "10.48550/arXiv.2404.06564", "citation_count": 180, "influential_citation_count": 20, "has_code": false, "code_url": null, "venue": "Neural Information Processing Systems", "quality_score": 0.6611}
{"id": "ed3b3384f745deed8bb849a3ea4d4aece2593a7dcfc82784f9c4030dd775686c", "sources": ["arxiv", "semantic_scholar"], "title": "Locating and Editing Factual Associations in Mamba", "abstract": "We investigate the mechanisms of factual recall in the Mamba state space model. Our work is inspired by previous findings in autoregressive transformer language models suggesting that their knowledge recall is localized to particular modules at specific token locations; we therefore ask whether factual recall in Mamba can be similarly localized. To investigate this, we conduct four lines of experiments on Mamba. First, we apply causal tracing or interchange interventions to localize key components inside Mamba that are responsible for recalling facts, revealing that specific components within middle layers show strong causal effects at the last token of the subject, while the causal effect of intervening on later layers is most pronounced at the last token of the prompt, matching previous findings on autoregressive transformers. Second, we show that rank-one model editing methods can successfully insert facts at specific locations, again resembling findings on transformer LMs. Third, we examine the linearity of Mamba's representations of factual relations. Finally we adapt attention-knockout techniques to Mamba in order to dissect information flow during factual recall. We compare Mamba directly to a similar-sized autoregressive transformer LM and conclude that despite significant differences in architectural approach, when it comes to factual recall, the two architectures share many similarities.", "authors": ["Arnab Sen Sharma", "David Atkinson", "David Bau"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-04", "url": "https://arxiv.org/abs/2404.03646", "pdf_url": "https://arxiv.org/pdf/2404.03646v2", "arxiv_id": "2404.03646", "doi": "10.48550/arXiv.2404.03646", "citation_count": 43, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4109}
{"id": "bee37675654f121ee8e45c89d0e4d8d73929073bb2d78fec410a7bab3a6e6664", "sources": ["arxiv", "semantic_scholar"], "title": "Decision Mamba: Reinforcement Learning via Sequence Modeling with Selective State Spaces", "abstract": "Decision Transformer, a promising approach that applies Transformer architectures to reinforcement learning, relies on causal self-attention to model sequences of states, actions, and rewards. While this method has shown competitive results, this paper investigates the integration of the Mamba framework, known for its advanced capabilities in efficient and effective sequence modeling, into the Decision Transformer architecture, focusing on the potential performance enhancements in sequential decision-making tasks. Our study systematically evaluates this integration by conducting a series of experiments across various decision-making environments, comparing the modified Decision Transformer, Decision Mamba, with its traditional counterpart. This work contributes to the advancement of sequential decision-making models, suggesting that the architecture and training methodology of neural networks can significantly impact their performance in complex tasks, and highlighting the potential of Mamba as a valuable tool for improving the efficacy of Transformer-based models in reinforcement learning scenarios.", "authors": ["Toshihiro Ota"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-29", "url": "https://arxiv.org/abs/2403.19925", "pdf_url": "https://arxiv.org/pdf/2403.19925v1", "arxiv_id": "2403.19925", "doi": "10.48550/arXiv.2403.19925", "citation_count": 35, "influential_citation_count": 6, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4225}
{"id": "9bc44e863992969ae86777d79105b73407ac4da8dd02382b6bf49ebb576aa822", "sources": ["arxiv", "semantic_scholar"], "title": "Dual-path Mamba: Short and Long-term Bidirectional Selective Structured State Space Models for Speech Separation", "abstract": "Transformers have been the most successful architecture for various speech modeling tasks, including speech separation. However, the self-attention mechanism in transformers with quadratic complexity is inefficient in computation and memory. Recent models incorporate new layers and modules along with transformers for better performance but also introduce extra model complexity. In this work, we replace transformers with Mamba, a selective state space model, for speech separation. We propose dual-path Mamba, which models short-term and long-term forward and backward dependency of speech signals using selective state spaces. Our experimental results on the WSJ0-2mix data show that our dual-path Mamba models of comparably smaller sizes outperform state-of-the-art RNN model DPRNN, CNN model WaveSplit, and transformer model Sepformer. Code: https://github.com/xi-j/Mamba-TasNet", "authors": ["Xilin Jiang", "Cong Han", "Nima Mesgarani"], "categories": ["eess.AS", "cs.SD"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-03-27", "url": "https://arxiv.org/abs/2403.18257", "pdf_url": "https://arxiv.org/pdf/2403.18257v2", "arxiv_id": "2403.18257", "doi": "10.1109/ICASSP49660.2025.10888514", "citation_count": 83, "influential_citation_count": 3, "has_code": true, "code_url": "https://github.com/xi-j/Mamba-TasNet", "venue": "IEEE International Conference on Acoustics, Speech, and Signal Processing", "quality_score": 0.4811}
{"id": "fde50f0353dc19372c5f196d2f79b541a77469c836342e4eb68763f0276890bb", "sources": ["arxiv", "semantic_scholar"], "title": "Rotate to Scan: UNet-like Mamba with Triplet SSM Module for Medical Image Segmentation", "abstract": "Image segmentation holds a vital position in the realms of diagnosis and treatment within the medical domain. Traditional convolutional neural networks (CNNs) and Transformer models have made significant advancements in this realm, but they still encounter challenges because of limited receptive field or high computing complexity. Recently, State Space Models (SSMs), particularly Mamba and its variants, have demonstrated notable performance in the field of vision. However, their feature extraction methods may not be sufficiently effective and retain some redundant structures, leaving room for parameter reduction. Motivated by previous spatial and channel attention methods, we propose Triplet Mamba-UNet. The method leverages residual VSS Blocks to extract intensive contextual features, while Triplet SSM is employed to fuse features across spatial and channel dimensions. We conducted experiments on ISIC17, ISIC18, CVC-300, CVC-ClinicDB, Kvasir-SEG, CVC-ColonDB, and Kvasir-Instrument datasets, demonstrating the superior segmentation performance of our proposed TM-UNet. Additionally, compared to the previous VM-UNet, our model achieves a one-third reduction in parameters.", "authors": ["Hao Tang", "Lianglun Cheng", "Guoheng Huang", "Zhengguang Tan", "Junhao Lu", "Kaihong Wu"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2024-03-26", "url": "https://arxiv.org/abs/2403.17701", "pdf_url": "https://arxiv.org/pdf/2403.17701v4", "arxiv_id": "2403.17701", "doi": "10.48550/arXiv.2403.17701", "citation_count": 17, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3138}
{"id": "f90e41b7a3444e5d9c7f26217f3b16582d9fec49ca2efa7ca0f7a64673c39085", "sources": ["arxiv", "semantic_scholar"], "title": "Heracles: A Hybrid SSM-Transformer Model for High-Resolution Image and Time-Series Analysis", "abstract": "Transformers have revolutionized image modeling tasks with adaptations like DeIT, Swin, SVT, Biformer, STVit, and FDVIT. However, these models often face challenges with inductive bias and high quadratic complexity, making them less efficient for high-resolution images. State space models (SSMs) such as Mamba, V-Mamba, ViM, and SiMBA offer an alternative to handle high resolution images in computer vision tasks. These SSMs encounter two major issues. First, they become unstable when scaled to large network sizes. Second, although they efficiently capture global information in images, they inherently struggle with handling local information. To address these challenges, we introduce Heracles, a novel SSM that integrates a local SSM, a global SSM, and an attention-based token interaction module. Heracles leverages a Hartely kernel-based state space model for global image information, a localized convolutional network for local details, and attention mechanisms in deeper layers for token interactions. Our extensive experiments demonstrate that Heracles-C-small achieves state-of-the-art performance on the ImageNet dataset with 84.5\\% top-1 accuracy. Heracles-C-Large and Heracles-C-Huge further improve accuracy to 85.9\\% and 86.4\\%, respectively. Additionally, Heracles excels in transfer learning tasks on datasets such as CIFAR-10, CIFAR-100, Oxford Flowers, and Stanford Cars, and in instance segmentation on the MSCOCO dataset. Heracles also proves its versatility by achieving state-of-the-art results on seven time-series datasets, showcasing its ability to generalize across domains with spectral data, capturing both local and global information. The project page is available at this link.\\url{https://github.com/badripatro/heracles}", "authors": ["Badri N. Patro", "Suhas Ranganath", "Vinay P. Namboodiri", "Vijay S. Agneeswaran"], "categories": ["cs.CV", "cs.AI", "cs.CL", "cs.LG", "cs.MM"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-26", "url": "https://arxiv.org/abs/2403.18063", "pdf_url": "https://arxiv.org/pdf/2403.18063v2", "arxiv_id": "2403.18063", "doi": null, "citation_count": 5, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/badripatro/heracles}", "venue": null, "quality_score": 0.1945}
{"id": "06ffc240bca9218cbdffa659f87ad48101fa21ee2959bebc35c748e55c35fa71", "sources": ["arxiv", "semantic_scholar"], "title": "Integrating Mamba Sequence Model and Hierarchical Upsampling Network for Accurate Semantic Segmentation of Multiple Sclerosis Legion", "abstract": "Integrating components from convolutional neural networks and state space models in medical image segmentation presents a compelling approach to enhance accuracy and efficiency. We introduce Mamba HUNet, a novel architecture tailored for robust and efficient segmentation tasks. Leveraging strengths from Mamba UNet and the lighter version of Hierarchical Upsampling Network (HUNet), Mamba HUNet combines convolutional neural networks local feature extraction power with state space models long range dependency modeling capabilities. We first converted HUNet into a lighter version, maintaining performance parity and then integrated this lighter HUNet into Mamba HUNet, further enhancing its efficiency. The architecture partitions input grayscale images into patches, transforming them into 1D sequences for processing efficiency akin to Vision Transformers and Mamba models. Through Visual State Space blocks and patch merging layers, hierarchical features are extracted while preserving spatial information. Experimental results on publicly available Magnetic Resonance Imaging scans, notably in Multiple Sclerosis lesion segmentation, demonstrate Mamba HUNet's effectiveness across diverse segmentation tasks. The model's robustness and flexibility underscore its potential in handling complex anatomical structures. These findings establish Mamba HUNet as a promising solution in advancing medical image segmentation, with implications for improving clinical decision making processes.", "authors": ["Kazi Shahriar Sanjid", "Md. Tanzim Hossain", "Md. Shakib Shahariar Junayed", "Mohammad Monir Uddin"], "categories": ["eess.IV", "cs.CV"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-03-26", "url": "https://arxiv.org/abs/2403.17432", "pdf_url": "https://arxiv.org/pdf/2403.17432v1", "arxiv_id": "2403.17432", "doi": "10.48550/arXiv.2403.17432", "citation_count": 10, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2603}
{"id": "6e66fd098013c581ffebf94d8e808fe751eabc26b32dd57369106c24ecc7d52c", "sources": ["arxiv", "semantic_scholar"], "title": "State Space Models as Foundation Models: A Control Theoretic Overview", "abstract": "In recent years, there has been a growing interest in integrating linear state-space models (SSM) in deep neural network architectures of foundation models. This is exemplified by the recent success of Mamba, showing better performance than the state-of-the-art Transformer architectures in language tasks. Foundation models, like e.g. GPT-4, aim to encode sequential data into a latent space in order to learn a compressed representation of the data. The same goal has been pursued by control theorists using SSMs to efficiently model dynamical systems. Therefore, SSMs can be naturally connected to deep sequence modeling, offering the opportunity to create synergies between the corresponding research areas. This paper is intended as a gentle introduction to SSM-based architectures for control theorists and summarizes the latest research developments. It provides a systematic review of the most successful SSM proposals and highlights their main features from a control theoretic perspective. Additionally, we present a comparative analysis of these models, evaluating their performance on a standardized benchmark designed for assessing a model's efficiency at learning long sequences.", "authors": ["Carmen Amo Alonso", "Jerome Sieber", "Melanie N. Zeilinger"], "categories": ["eess.SY", "cs.CL", "cs.LG"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2024-03-25", "url": "https://arxiv.org/abs/2403.16899", "pdf_url": "https://arxiv.org/pdf/2403.16899v1", "arxiv_id": "2403.16899", "doi": "10.23919/ACC63710.2025.11107969", "citation_count": 35, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "American Control Conference", "quality_score": 0.3891}
{"id": "32bc5ba07e8b9cec3b5e89a70670fdb9f8532c59642c76decada698e089f2e35", "sources": ["arxiv", "semantic_scholar"], "title": "SiMBA: Simplified Mamba-Based Architecture for Vision and Multivariate Time series", "abstract": "Transformers have widely adopted attention networks for sequence mixing and MLPs for channel mixing, playing a pivotal role in achieving breakthroughs across domains. However, recent literature highlights issues with attention networks, including low inductive bias and quadratic complexity concerning input sequence length. State Space Models (SSMs) like S4 and others (Hippo, Global Convolutions, liquid S4, LRU, Mega, and Mamba), have emerged to address the above issues to help handle longer sequence lengths. Mamba, while being the state-of-the-art SSM, has a stability issue when scaled to large networks for computer vision datasets. We propose SiMBA, a new architecture that introduces Einstein FFT (EinFFT) for channel modeling by specific eigenvalue computations and uses the Mamba block for sequence modeling. Extensive performance studies across image and time-series benchmarks demonstrate that SiMBA outperforms existing SSMs, bridging the performance gap with state-of-the-art transformers. Notably, SiMBA establishes itself as the new state-of-the-art SSM on ImageNet and transfer learning benchmarks such as Stanford Car and Flower as well as task learning benchmarks as well as seven time series benchmark datasets. The project page is available on this website ~\\url{https://github.com/badripatro/Simba}.", "authors": ["Badri N. Patro", "Vijay S. Agneeswaran"], "categories": ["cs.CV", "cs.LG", "eess.IV", "eess.SY"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-03-22", "url": "https://arxiv.org/abs/2403.15360", "pdf_url": "https://arxiv.org/pdf/2403.15360v2", "arxiv_id": "2403.15360", "doi": "10.48550/arXiv.2403.15360", "citation_count": 93, "influential_citation_count": 8, "has_code": true, "code_url": "https://github.com/badripatro/Simba}", "venue": "arXiv.org", "quality_score": 0.4933}
{"id": "8ce4709cd3a05b17c5592390b4c8debe858f7cf78ada60923bcd3b832cefd186", "sources": ["arxiv", "semantic_scholar"], "title": "VL-Mamba: Exploring State Space Models for Multimodal Learning", "abstract": "Multimodal large language models (MLLMs) have attracted widespread interest and have rich applications. However, the inherent attention mechanism in its Transformer structure requires quadratic complexity and results in expensive computational overhead. Therefore, in this work, we propose VL-Mamba, a multimodal large language model based on state space models, which have been shown to have great potential for long-sequence modeling with fast inference and linear scaling in sequence length. Specifically, we first replace the transformer-based backbone language model such as LLama or Vicuna with the pre-trained Mamba language model. Then, we empirically explore how to effectively apply the 2D vision selective scan mechanism for multimodal learning and the combinations of different vision encoders and variants of pretrained Mamba language models. The extensive experiments on diverse multimodal benchmarks with competitive performance show the effectiveness of our proposed VL-Mamba and demonstrate the great potential of applying state space models for multimodal learning tasks.", "authors": ["Yanyuan Qiao", "Zheng Yu", "Longteng Guo", "Sihan Chen", "Zijia Zhao", "Mingzhen Sun", "Qi Wu", "Jing Liu"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-20", "url": "https://arxiv.org/abs/2403.13600", "pdf_url": "https://arxiv.org/pdf/2403.13600v1", "arxiv_id": "2403.13600", "doi": "10.48550/arXiv.2403.13600", "citation_count": 125, "influential_citation_count": 6, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.5251}
{"id": "cf6a41d8070cdc63d88c7e1798b5fd71a90c8c70f6285a9441986501f6b3cf5e", "sources": ["arxiv", "semantic_scholar"], "title": "ZigMa: A DiT-style Zigzag Mamba Diffusion Model", "abstract": "The diffusion model has long been plagued by scalability and quadratic complexity issues, especially within transformer-based structures. In this study, we aim to leverage the long sequence modeling capability of a State-Space Model called Mamba to extend its applicability to visual data generation. Firstly, we identify a critical oversight in most current Mamba-based vision methods, namely the lack of consideration for spatial continuity in the scan scheme of Mamba. Secondly, building upon this insight, we introduce a simple, plug-and-play, zero-parameter method named Zigzag Mamba, which outperforms Mamba-based baselines and demonstrates improved speed and memory utilization compared to transformer-based baselines. Lastly, we integrate Zigzag Mamba with the Stochastic Interpolant framework to investigate the scalability of the model on large-resolution visual datasets, such as FacesHQ $1024\\times 1024$ and UCF101, MultiModal-CelebA-HQ, and MS COCO $256\\times 256$ . Code will be released at https://taohu.me/zigma/", "authors": ["Vincent Tao Hu", "Stefan Andreas Baumann", "Ming Gui", "Olga Grebenkova", "Pingchuan Ma", "Johannes Schusterbauer", "Björn Ommer"], "categories": ["cs.CV", "cs.AI", "cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-20", "url": "https://arxiv.org/abs/2403.13802", "pdf_url": "https://arxiv.org/pdf/2403.13802v3", "arxiv_id": "2403.13802", "doi": "10.48550/arXiv.2403.13802", "citation_count": 146, "influential_citation_count": 8, "has_code": false, "code_url": null, "venue": "European Conference on Computer Vision", "quality_score": 0.5418}
{"id": "94ee83462079c8c329f09b23a127f9c1bb8dbe3a6a14d41abd5ac6f001e16f19", "sources": ["arxiv", "semantic_scholar"], "title": "STG-Mamba: Spatial-Temporal Graph Learning via Selective State Space Model", "abstract": "Spatial-Temporal Graph (STG) data is characterized as dynamic, heterogenous, and non-stationary, leading to the continuous challenge of spatial-temporal graph learning. In the past few years, various GNN-based methods have been proposed to solely focus on mimicking the relationships among node individuals of the STG network, ignoring the significance of modeling the intrinsic features that exist in STG system over time. In contrast, modern Selective State Space Models (SSSMs) present a new approach which treat STG Network as a system, and meticulously explore the STG system's dynamic state evolution across temporal dimension. In this work, we introduce Spatial-Temporal Graph Mamba (STG-Mamba) as the first exploration of leveraging the powerful selective state space models for STG learning by treating STG Network as a system, and employing the Spatial-Temporal Selective State Space Module (ST-S3M) to precisely focus on the selected STG latent features. Furthermore, to strengthen GNN's ability of modeling STG data under the setting of selective state space models, we propose Kalman Filtering Graph Neural Networks (KFGN) for dynamically integrate and upgrade the STG embeddings from different temporal granularities through a learnable Kalman Filtering statistical theory-based approach. Extensive empirical studies are conducted on three benchmark STG forecasting datasets, demonstrating the performance superiority and computational efficiency of STG-Mamba. It not only surpasses existing state-of-the-art methods in terms of STG forecasting performance, but also effectively alleviate the computational bottleneck of large-scale graph networks in reducing the computational cost of FLOPs and test inference time. The implementation code is available at: \\url{https://github.com/LincanLi98/STG-Mamba}.", "authors": ["Lincan Li", "Hanchen Wang", "Wenjie Zhang", "Adelle Coster"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-19", "url": "https://arxiv.org/abs/2403.12418", "pdf_url": "https://arxiv.org/pdf/2403.12418v4", "arxiv_id": "2403.12418", "doi": "10.48550/arXiv.2403.12418", "citation_count": 42, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/LincanLi98/STG-Mamba}", "venue": "arXiv.org", "quality_score": 0.4084}
{"id": "18fa7ace9b9717ec2d2ad8d921aedb842e78f5b8f53f276d22cbaa3c44323001", "sources": ["arxiv", "semantic_scholar"], "title": "Is Mamba Effective for Time Series Forecasting?", "abstract": "In the realm of time series forecasting (TSF), it is imperative for models to adeptly discern and distill hidden patterns within historical time series data to forecast future states. Transformer-based models exhibit formidable efficacy in TSF, primarily attributed to their advantage in apprehending these patterns. However, the quadratic complexity of the Transformer leads to low computational efficiency and high costs, which somewhat hinders the deployment of the TSF model in real-world scenarios. Recently, Mamba, a selective state space model, has gained traction due to its ability to process dependencies in sequences while maintaining near-linear complexity. For TSF tasks, these characteristics enable Mamba to comprehend hidden patterns as the Transformer and reduce computational overhead compared to the Transformer. Therefore, we propose a Mamba-based model named Simple-Mamba (S-Mamba) for TSF. Specifically, we tokenize the time points of each variate autonomously via a linear layer. A bidirectional Mamba layer is utilized to extract inter-variate correlations and a Feed-Forward Network is set to learn temporal dependencies. Finally, the generation of forecast outcomes through a linear mapping layer. Experiments on thirteen public datasets prove that S-Mamba maintains low computational overhead and achieves leading performance. Furthermore, we conduct extensive experiments to explore Mamba's potential in TSF tasks. Our code is available at https://github.com/wzhwzhwzh0921/S-D-Mamba.", "authors": ["Zihan Wang", "Fanheng Kong", "Shi Feng", "Ming Wang", "Xiaocui Yang", "Han Zhao", "Daling Wang", "Yifei Zhang"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-17", "url": "https://arxiv.org/abs/2403.11144", "pdf_url": "https://arxiv.org/pdf/2403.11144v3", "arxiv_id": "2403.11144", "doi": "10.48550/arXiv.2403.11144", "citation_count": 299, "influential_citation_count": 27, "has_code": true, "code_url": "https://github.com/wzhwzhwzh0921/S-D-Mamba", "venue": "Neurocomputing", "quality_score": 0.7236}
{"id": "5a98f51e223f34d50555a6d73ec56bfd2e315ab382db50ee8f8b253cb0aab1df", "sources": ["arxiv", "semantic_scholar"], "title": "Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding", "abstract": "Understanding videos is one of the fundamental directions in computer vision research, with extensive efforts dedicated to exploring various architectures such as RNN, 3D CNN, and Transformers. The newly proposed architecture of state space model, e.g., Mamba, shows promising traits to extend its success in long sequence modeling to video modeling. To assess whether Mamba can be a viable alternative to Transformers in the video understanding domain, in this work, we conduct a comprehensive set of studies, probing different roles Mamba can play in modeling videos, while investigating diverse tasks where Mamba could exhibit superiority. We categorize Mamba into four roles for modeling videos, deriving a Video Mamba Suite composed of 14 models/modules, and evaluating them on 12 video understanding tasks. Our extensive experiments reveal the strong potential of Mamba on both video-only and video-language tasks while showing promising efficiency-performance trade-offs. We hope this work could provide valuable data points and insights for future research on video understanding. Code is public: https://github.com/OpenGVLab/video-mamba-suite.", "authors": ["Guo Chen", "Yifei Huang", "Jilan Xu", "Baoqi Pei", "Zhe Chen", "Zhiqi Li", "Jiahao Wang", "Kunchang Li", "Tong Lu", "Limin Wang"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-14", "url": "https://arxiv.org/abs/2403.09626", "pdf_url": "https://arxiv.org/pdf/2403.09626v1", "arxiv_id": "2403.09626", "doi": "10.1007/s11263-025-02597-y", "citation_count": 158, "influential_citation_count": 25, "has_code": true, "code_url": "https://github.com/OpenGVLab/video-mamba-suite", "venue": "International Journal of Computer Vision", "quality_score": 0.7075}
{"id": "cfd85e2089eeda06a2da9fba993efafa35ce0bc63b331eb4ad4ee0e3a62b28ef", "sources": ["arxiv", "semantic_scholar"], "title": "VM-UNET-V2 Rethinking Vision Mamba UNet for Medical Image Segmentation", "abstract": "In the field of medical image segmentation, models based on both CNN and Transformer have been thoroughly investigated. However, CNNs have limited modeling capabilities for long-range dependencies, making it challenging to exploit the semantic information within images fully. On the other hand, the quadratic computational complexity poses a challenge for Transformers. Recently, State Space Models (SSMs), such as Mamba, have been recognized as a promising method. They not only demonstrate superior performance in modeling long-range interactions, but also preserve a linear computational complexity. Inspired by the Mamba architecture, We proposed Vison Mamba-UNetV2, the Visual State Space (VSS) Block is introduced to capture extensive contextual information, the Semantics and Detail Infusion (SDI) is introduced to augment the infusion of low-level and high-level features. We conduct comprehensive experiments on the ISIC17, ISIC18, CVC-300, CVC-ClinicDB, Kvasir, CVC-ColonDB and ETIS-LaribPolypDB public datasets. The results indicate that VM-UNetV2 exhibits competitive performance in medical image segmentation tasks. Our code is available at https://github.com/nobodyplayer1/VM-UNetV2.", "authors": ["Mingya Zhang", "Yue Yu", "Limei Gu", "Tingsheng Lin", "Xianping Tao"], "categories": ["eess.IV", "cs.CV"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2024-03-14", "url": "https://arxiv.org/abs/2403.09157", "pdf_url": "https://arxiv.org/pdf/2403.09157v1", "arxiv_id": "2403.09157", "doi": "10.48550/arXiv.2403.09157", "citation_count": 148, "influential_citation_count": 11, "has_code": true, "code_url": "https://github.com/nobodyplayer1/VM-UNetV2", "venue": "arXiv.org", "quality_score": 0.5433}
{"id": "f86ffcd95db3a0b3704c52a9887b1e1f8a9fcec557102b01d5b97b033acc514b", "sources": ["arxiv", "semantic_scholar"], "title": "SSM Meets Video Diffusion Models: Efficient Long-Term Video Generation with Structured State Spaces", "abstract": "Given the remarkable achievements in image generation through diffusion models, the research community has shown increasing interest in extending these models to video generation. Recent diffusion models for video generation have predominantly utilized attention layers to extract temporal features. However, attention layers are limited by their computational costs, which increase quadratically with the sequence length. This limitation presents significant challenges when generating longer video sequences using diffusion models. To overcome this challenge, we propose leveraging state-space models (SSMs) as temporal feature extractors. SSMs (e.g., Mamba) have recently gained attention as promising alternatives due to their linear-time memory consumption relative to sequence length. In line with previous research suggesting that using bidirectional SSMs is effective for understanding spatial features in image generation, we found that bidirectionality is also beneficial for capturing temporal features in video data, rather than relying on traditional unidirectional SSMs. We conducted comprehensive evaluations on multiple long-term video datasets, such as MineRL Navigate, across various model sizes. For sequences up to 256 frames, SSM-based models require less memory to achieve the same FVD as attention-based models. Moreover, SSM-based models often deliver better performance with comparable GPU memory usage. Our codes are available at https://github.com/shim0114/SSM-Meets-Video-Diffusion-Models.", "authors": ["Yuta Oshima", "Shohei Taniguchi", "Masahiro Suzuki", "Yutaka Matsuo"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-12", "url": "https://arxiv.org/abs/2403.07711", "pdf_url": "https://arxiv.org/pdf/2403.07711v4", "arxiv_id": "2403.07711", "doi": null, "citation_count": 12, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/shim0114/SSM-Meets-Video-Diffusion-Models", "venue": null, "quality_score": 0.2785}
{"id": "323d14eb3997426a88d32440b13fb7311496819172d069630437f5cc3bb3ebf1", "sources": ["arxiv", "semantic_scholar"], "title": "Motion Mamba: Efficient and Long Sequence Motion Generation", "abstract": "Human motion generation stands as a significant pursuit in generative computer vision, while achieving long-sequence and efficient motion generation remains challenging. Recent advancements in state space models (SSMs), notably Mamba, have showcased considerable promise in long sequence modeling with an efficient hardware-aware design, which appears to be a promising direction to build motion generation model upon it. Nevertheless, adapting SSMs to motion generation faces hurdles since the lack of a specialized design architecture to model motion sequence. To address these challenges, we propose Motion Mamba, a simple and efficient approach that presents the pioneering motion generation model utilized SSMs. Specifically, we design a Hierarchical Temporal Mamba (HTM) block to process temporal data by ensemble varying numbers of isolated SSM modules across a symmetric U-Net architecture aimed at preserving motion consistency between frames. We also design a Bidirectional Spatial Mamba (BSM) block to bidirectionally process latent poses, to enhance accurate motion generation within a temporal frame. Our proposed method achieves up to 50% FID improvement and up to 4 times faster on the HumanML3D and KIT-ML datasets compared to the previous best diffusion-based method, which demonstrates strong capabilities of high-quality long sequence motion modeling and real-time human motion generation. See project website https://steve-zeyu-zhang.github.io/MotionMamba/", "authors": ["Zeyu Zhang", "Akide Liu", "Ian Reid", "Richard Hartley", "Bohan Zhuang", "Hao Tang"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-12", "url": "https://arxiv.org/abs/2403.07487", "pdf_url": "https://arxiv.org/pdf/2403.07487v4", "arxiv_id": "2403.07487", "doi": "10.48550/arXiv.2403.07487", "citation_count": 144, "influential_citation_count": 9, "has_code": false, "code_url": null, "venue": "European Conference on Computer Vision", "quality_score": 0.5403}
{"id": "acb724e5ef808e4f2368f3afad27edcf01aaaed7f19ad6406c0dbeb063223595", "sources": ["arxiv", "semantic_scholar"], "title": "LKM-UNet: Large Kernel Vision Mamba UNet for Medical Image Segmentation", "abstract": "In clinical practice, medical image segmentation provides useful information on the contours and dimensions of target organs or tissues, facilitating improved diagnosis, analysis, and treatment. In the past few years, convolutional neural networks (CNNs) and Transformers have dominated this area, but they still suffer from either limited receptive fields or costly long-range modeling. Mamba, a State Space Sequence Model (SSM), recently emerged as a promising paradigm for long-range dependency modeling with linear complexity. In this paper, we introduce a Large Kernel Vision Mamba U-shape Network, or LKM-UNet, for medical image segmentation. A distinguishing feature of our LKM-UNet is its utilization of large Mamba kernels, excelling in locally spatial modeling compared to small kernel-based CNNs and Transformers, while maintaining superior efficiency in global modeling compared to self-attention with quadratic complexity. Additionally, we design a novel hierarchical and bidirectional Mamba block to further enhance Mamba's global and neighborhood spatial modeling capability for vision inputs. Comprehensive experiments demonstrate the feasibility and the effectiveness of using large-size Mamba kernels to achieve large receptive fields. Codes are available at https://github.com/wjh892521292/LKM-UNet.", "authors": ["Jinhong Wang", "Jintai Chen", "Danny Chen", "Jian Wu"], "categories": ["cs.CV", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-12", "url": "https://arxiv.org/abs/2403.07332", "pdf_url": "https://arxiv.org/pdf/2403.07332v2", "arxiv_id": "2403.07332", "doi": null, "citation_count": 80, "influential_citation_count": 7, "has_code": true, "code_url": "https://github.com/wjh892521292/LKM-UNet", "venue": null, "quality_score": 0.4771}
{"id": "090847f4360af24ecd4540f116db98f29a5b0c5029c4bc2badfd6014d31abafe", "sources": ["arxiv", "semantic_scholar"], "title": "Point Mamba: A Novel Point Cloud Backbone Based on State Space Model with Octree-Based Ordering Strategy", "abstract": "Recently, state space model (SSM) has gained great attention due to its promising performance, linear complexity, and long sequence modeling ability in both language and image domains. However, it is non-trivial to extend SSM to the point cloud field, because of the causality requirement of SSM and the disorder and irregularity nature of point clouds. In this paper, we propose a novel SSM-based point cloud processing backbone, named Point Mamba, with a causality-aware ordering mechanism. To construct the causal dependency relationship, we design an octree-based ordering strategy on raw irregular points, globally sorting points in a z-order sequence and also retaining their spatial proximity. Our method achieves state-of-the-art performance compared with transformer-based counterparts, with 93.4% accuracy and 75.7 mIOU respectively on the ModelNet40 classification dataset and ScanNet semantic segmentation dataset. Furthermore, our Point Mamba has linear complexity, which is more efficient than transformer-based methods. Our method demonstrates the great potential that SSM can serve as a generic backbone in point cloud understanding. Codes are released at https://github.com/IRMVLab/Point-Mamba.", "authors": ["Jiuming Liu", "Ruiji Yu", "Yian Wang", "Yu Zheng", "Tianchen Deng", "Weicai Ye", "Hesheng Wang"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-11", "url": "https://arxiv.org/abs/2403.06467", "pdf_url": "https://arxiv.org/pdf/2403.06467v2", "arxiv_id": "2403.06467", "doi": "10.48550/arXiv.2403.06467", "citation_count": 90, "influential_citation_count": 11, "has_code": true, "code_url": "https://github.com/IRMVLab/Point-Mamba", "venue": "arXiv.org", "quality_score": 0.5396}
{"id": "4d833948aecbc39f82f57f342e19cfd2c4f0e55d2bc596c7ea997bc8df4aee7e", "sources": ["arxiv", "semantic_scholar"], "title": "LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation", "abstract": "UNet and its variants have been widely used in medical image segmentation. However, these models, especially those based on Transformer architectures, pose challenges due to their large number of parameters and computational loads, making them unsuitable for mobile health applications. Recently, State Space Models (SSMs), exemplified by Mamba, have emerged as competitive alternatives to CNN and Transformer architectures. Building upon this, we employ Mamba as a lightweight substitute for CNN and Transformer within UNet, aiming at tackling challenges stemming from computational resource limitations in real medical settings. To this end, we introduce the Lightweight Mamba UNet (LightM-UNet) that integrates Mamba and UNet in a lightweight framework. Specifically, LightM-UNet leverages the Residual Vision Mamba Layer in a pure Mamba fashion to extract deep semantic features and model long-range spatial dependencies, with linear computational complexity. Extensive experiments conducted on two real-world 2D/3D datasets demonstrate that LightM-UNet surpasses existing state-of-the-art literature. Notably, when compared to the renowned nnU-Net, LightM-UNet achieves superior segmentation performance while drastically reducing parameter and computation costs by 116x and 21x, respectively. This highlights the potential of Mamba in facilitating model lightweighting. Our code implementation is publicly available at https://github.com/MrBlankness/LightM-UNet.", "authors": ["Weibin Liao", "Yinghao Zhu", "Xinyuan Wang", "Chengwei Pan", "Yasha Wang", "Liantao Ma"], "categories": ["eess.IV", "cs.CV"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2024-03-08", "url": "https://arxiv.org/abs/2403.05246", "pdf_url": "https://arxiv.org/pdf/2403.05246v2", "arxiv_id": "2403.05246", "doi": "10.48550/arXiv.2403.05246", "citation_count": 165, "influential_citation_count": 13, "has_code": true, "code_url": "https://github.com/MrBlankness/LightM-UNet", "venue": "arXiv.org", "quality_score": 0.5731}
{"id": "ce24f974452ac083a7b37c5cc1781fd37f79628d8bf0cca172e535efc7b6f4d7", "sources": ["arxiv", "semantic_scholar"], "title": "Exploring Spatial Generalized Functional Linear Models: A Comparative Simulation Study and Analysis of COVID-19", "abstract": "Implementation of spatial generalized linear models with a functional covariate can be accomplished through the use of a truncated basis expansion of the covariate process. In practice, one must select a truncation level for use. We compare five criteria for the selection of an appropriate truncation level, including AIC and BIC based on a log composite likelihood, a fraction of variance explained criterion, a fitted mean squared error, and a prediction error with one standard error rule. Based on the use of extensive simulation studies, we propose that BIC constitutes a reasonable default criterion for the selection of the truncation level for use in a spatial functional generalized linear model. In addition, we demonstrate that the spatial model with a functional covariate outperforms other models when the data contain spatial structure and response variables are in fact influenced by a functional covariate process. We apply the spatial functional generalized linear model to a problem in which the objective is to relate COVID-19 vaccination rates in counties of states in the Midwestern United States to the number of new cases from previous weeks in those same geographic regions.", "authors": ["Sooran Kim", "Mark S. Kaiser", "Xiongtao Dai"], "categories": ["stat.ME"], "fields_of_study": ["Mathematics"], "published_date": "2024-03-06", "url": "https://arxiv.org/abs/2403.03389", "pdf_url": "https://arxiv.org/pdf/2403.03389v3", "arxiv_id": "2403.03389", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0}
{"id": "80c487218d6bb3d1d9922c321e708a171e9e2a38772f9f48878112013c89f525", "sources": ["arxiv", "semantic_scholar"], "title": "MedMamba: Vision Mamba for Medical Image Classification", "abstract": "Since the era of deep learning, convolutional neural networks (CNNs) and vision transformers (ViTs) have been extensively studied and widely used in medical image classification tasks. Unfortunately, CNN's limitations in modeling long-range dependencies result in poor classification performances. In contrast, ViTs are hampered by the quadratic computational complexity of their self-attention mechanism, making them difficult to deploy in real-world settings with limited computational resources. Recent studies have shown that state space models (SSMs) represented by Mamba can effectively model long-range dependencies while maintaining linear computational complexity. Inspired by it, we proposed MedMamba, the first Vision Mamba for generalized medical image classification. Concretely, we introduced a novel hybrid basic block named SS-Conv-SSM, which purely integrates the convolutional layers for extracting local features with the abilities of SSM to capture long-range dependencies, aiming to model medical images from different image modalities efficiently. By employing the grouped convolution strategy and channel-shuffle operation, MedMamba successfully provides fewer model parameters and a lower computational burden for efficient applications without sacrificing accuracy. We thoroughly evaluated MedMamba using 16 datasets containing ten imaging modalities and 411,007 images. Experimental results show that MedMamba demonstrates competitive performance on most tasks compared with the state-of-the-art methods. This work aims to explore the potential of Vision Mamba and establish a new baseline for medical image classification, thereby providing valuable insights for developing more powerful Mamba-based artificial intelligence algorithms and applications in medicine. The source codes and all pre-trained weights of MedMamba are available at https://github.com/YubiaoYue/MedMamba.", "authors": ["Yubiao Yue", "Zhenzhang Li"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-03-06", "url": "https://arxiv.org/abs/2403.03849", "pdf_url": "https://arxiv.org/pdf/2403.03849v5", "arxiv_id": "2403.03849", "doi": "10.48550/arXiv.2403.03849", "citation_count": 216, "influential_citation_count": 15, "has_code": true, "code_url": "https://github.com/YubiaoYue/MedMamba", "venue": "arXiv.org", "quality_score": 0.6021}
{"id": "49ca86faa7c25fe19d8111b043cb2fffb202236f97538e65c50532e7d6c22399", "sources": ["arxiv", "semantic_scholar"], "title": "MiM-ISTD: Mamba-in-Mamba for Efficient Infrared Small Target Detection", "abstract": "Recently, infrared small target detection (ISTD) has made significant progress, thanks to the development of basic models. Specifically, the models combining CNNs with transformers can successfully extract both local and global features. However, the disadvantage of the transformer is also inherited, i.e., the quadratic computational complexity to sequence length. Inspired by the recent basic model with linear complexity for long-distance modeling, Mamba, we explore the potential of this state space model for ISTD task in terms of effectiveness and efficiency in the paper. However, directly applying Mamba achieves suboptimal performances due to the insufficient harnessing of local features, which are imperative for detecting small targets. Instead, we tailor a nested structure, Mamba-in-Mamba (MiM-ISTD), for efficient ISTD. It consists of Outer and Inner Mamba blocks to adeptly capture both global and local features. Specifically, we treat the local patches as \"visual sentences\" and use the Outer Mamba to explore the global information. We then decompose each visual sentence into sub-patches as \"visual words\" and use the Inner Mamba to further explore the local information among words in the visual sentence with negligible computational costs. By aggregating the visual word and visual sentence features, our MiM-ISTD can effectively explore both global and local information. Experiments on NUAA-SIRST and IRSTD-1k show the superior accuracy and efficiency of our method. Specifically, MiM-ISTD is $8 \\times$ faster than the SOTA method and reduces GPU memory usage by 62.2$\\%$ when testing on $2048 \\times 2048$ images, overcoming the computation and memory constraints on high-resolution infrared images.", "authors": ["Tianxiang Chen", "Zi Ye", "Zhentao Tan", "Tao Gong", "Yue Wu", "Qi Chu", "Bin Liu", "Nenghai Yu", "Jieping Ye"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-04", "url": "https://arxiv.org/abs/2403.02148", "pdf_url": "https://arxiv.org/pdf/2403.02148v4", "arxiv_id": "2403.02148", "doi": "10.1109/TGRS.2024.3485721", "citation_count": 182, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Geoscience and Remote Sensing", "quality_score": 0.5656}
{"id": "03bc65d10f40fdb0de717236242c2dff5141165191c7061a4b293e0682f9fa64", "sources": ["arxiv", "semantic_scholar"], "title": "The Hidden Attention of Mamba Models", "abstract": "The Mamba layer offers an efficient selective state space model (SSM) that is highly effective in modeling multiple domains, including NLP, long-range sequence processing, and computer vision. Selective SSMs are viewed as dual models, in which one trains in parallel on the entire sequence via an IO-aware parallel scan, and deploys in an autoregressive manner. We add a third view and show that such models can be viewed as attention-driven models. This new perspective enables us to empirically and theoretically compare the underlying mechanisms to that of the self-attention layers in transformers and allows us to peer inside the inner workings of the Mamba model with explainability methods. Our code is publicly available.", "authors": ["Ameen Ali", "Itamar Zimerman", "Lior Wolf"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-03", "url": "https://arxiv.org/abs/2403.01590", "pdf_url": "https://arxiv.org/pdf/2403.01590v2", "arxiv_id": "2403.01590", "doi": "10.48550/arXiv.2403.01590", "citation_count": 113, "influential_citation_count": 9, "has_code": false, "code_url": null, "venue": "Annual Meeting of the Association for Computational Linguistics", "quality_score": 0.5142}
{"id": "70a440013b926a5f20d1621a47db3f69dba4497bf2d0c2fd9e9362b32b283427", "sources": ["arxiv", "semantic_scholar"], "title": "Point Cloud Mamba: Point Cloud Learning via State Space Model", "abstract": "Recently, state space models have exhibited strong global modeling capabilities and linear computational complexity in contrast to transformers. This research focuses on applying such architecture to more efficiently and effectively model point cloud data globally with linear computational complexity. In particular, for the first time, we demonstrate that Mamba-based point cloud methods can outperform previous methods based on transformer or multi-layer perceptrons (MLPs). To enable Mamba to process 3-D point cloud data more effectively, we propose a novel Consistent Traverse Serialization method to convert point clouds into 1-D point sequences while ensuring that neighboring points in the sequence are also spatially adjacent. Consistent Traverse Serialization yields six variants by permuting the order of \\textit{x}, \\textit{y}, and \\textit{z} coordinates, and the synergistic use of these variants aids Mamba in comprehensively observing point cloud data. Furthermore, to assist Mamba in handling point sequences with different orders more effectively, we introduce point prompts to inform Mamba of the sequence's arrangement rules. Finally, we propose positional encoding based on spatial coordinate mapping to inject positional information into point cloud sequences more effectively. Point Cloud Mamba surpasses the state-of-the-art (SOTA) point-based method PointNeXt and achieves new SOTA performance on the ScanObjectNN, ModelNet40, ShapeNetPart, and S3DIS datasets. It is worth mentioning that when using a more powerful local feature extraction module, our PCM achieves 79.6 mIoU on S3DIS, significantly surpassing the previous SOTA models, DeLA and PTv3, by 5.5 mIoU and 4.9 mIoU, respectively.", "authors": ["Tao Zhang", "Haobo Yuan", "Lu Qi", "Jiangning Zhang", "Qianyu Zhou", "Shunping Ji", "Shuicheng Yan", "Xiangtai Li"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-01", "url": "https://arxiv.org/abs/2403.00762", "pdf_url": "https://arxiv.org/pdf/2403.00762v4", "arxiv_id": "2403.00762", "doi": "10.48550/arXiv.2403.00762", "citation_count": 125, "influential_citation_count": 10, "has_code": false, "code_url": null, "venue": "AAAI Conference on Artificial Intelligence", "quality_score": 0.5251}
{"id": "ab2511cfac0c79a90f60da8470767781284e47a8df5d98f42248b861eeba09d8", "sources": ["arxiv", "semantic_scholar"], "title": "Theoretical Foundations of Deep Selective State-Space Models", "abstract": "Structured state-space models (SSMs) such as S4, stemming from the seminal work of Gu et al., are gaining popularity as effective approaches for modeling sequential data. Deep SSMs demonstrate outstanding performance across a diverse set of domains, at a reduced training and inference cost compared to attention-based transformers. Recent developments show that if the linear recurrence powering SSMs allows for multiplicative interactions between inputs and hidden states (e.g. GateLoop, Mamba, GLA), then the resulting architecture can surpass in both in accuracy and efficiency attention-powered foundation models trained on text, at scales of billion parameters. In this paper, we give theoretical grounding to this recent finding using tools from Rough Path Theory: we show that when random linear recurrences are equipped with simple input-controlled transitions (selectivity mechanism), then the hidden state is provably a low-dimensional projection of a powerful mathematical object called the signature of the input -- capturing non-linear interactions between tokens at distinct timescales. Our theory not only motivates the success of modern selective state-space models such as Mamba but also provides a solid framework to understand the expressive power of future SSM variants.", "authors": ["Nicola Muca Cirone", "Antonio Orvieto", "Benjamin Walker", "Cristopher Salvi", "Terry Lyons"], "categories": ["cs.LG", "math.DS"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2024-02-29", "url": "https://arxiv.org/abs/2402.19047", "pdf_url": "https://arxiv.org/pdf/2402.19047v4", "arxiv_id": "2402.19047", "doi": "10.48550/arXiv.2402.19047", "citation_count": 83, "influential_citation_count": 6, "has_code": false, "code_url": null, "venue": "Neural Information Processing Systems", "quality_score": 0.4811}
{"id": "134dca3faf75305c35e6de377ce35e656f4f91edbb8ac0255d354751be818bf0", "sources": ["arxiv", "semantic_scholar"], "title": "Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models", "abstract": "Recurrent neural networks (RNNs) have fast inference and scale efficiently on long sequences, but they are difficult to train and hard to scale. We propose Hawk, an RNN with gated linear recurrences, and Griffin, a hybrid model that mixes gated linear recurrences with local attention. Hawk exceeds the reported performance of Mamba on downstream tasks, while Griffin matches the performance of Llama-2 despite being trained on over 6 times fewer tokens. We also show that Griffin can extrapolate on sequences significantly longer than those seen during training. Our models match the hardware efficiency of Transformers during training, and during inference they have lower latency and significantly higher throughput. We scale Griffin up to 14B parameters, and explain how to shard our models for efficient distributed training.", "authors": ["Soham De", "Samuel L. Smith", "Anushan Fernando", "Aleksandar Botev", "George Cristian-Muraru", "Albert Gu", "Ruba Haroun", "Leonard Berrada", "Yutian Chen", "Srivatsan Srinivasan", "Guillaume Desjardins", "Arnaud Doucet", "David Budden", "Yee Whye Teh", "Razvan Pascanu", "Nando De Freitas", "Caglar Gulcehre"], "categories": ["cs.LG", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-29", "url": "https://arxiv.org/abs/2402.19427", "pdf_url": "https://arxiv.org/pdf/2402.19427v1", "arxiv_id": "2402.19427", "doi": null, "citation_count": 246, "influential_citation_count": 23, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.6901}
{"id": "8daa2983ff2d42a30ff4bf7448856911db611f581e280d2c78f80462083c0a9f", "sources": ["arxiv", "semantic_scholar"], "title": "Efficient State Space Model via Fast Tensor Convolution and Block Diagonalization", "abstract": "Existing models encounter bottlenecks in balancing performance and computational efficiency when modeling long sequences. Although the state space model (SSM) has achieved remarkable success in handling long sequence tasks, it still faces the problem of large number of parameters. In order to further improve the efficiency of SSM, we propose a new state space layer based on multiple-input multiple-output SSM, called efficient SSM (eSSM). Our eSSM is built on the convolutional representation of multi-input and multi-input (MIMO) SSM. We propose a variety of effective strategies to improve the computational efficiency. The diagonalization of the system matrix first decouples the original system. Then a fast tensor convolution is proposed based on the fast Fourier transform. In addition, the block diagonalization of the SSM further reduces the model parameters and improves the model flexibility. Extensive experimental results show that the performance of the proposed model on multiple databases matches the performance of state-of-the-art models, such as S4, and is significantly better than Transformers and LSTM. In the model efficiency benchmark, the parameters of eSSM are only 12.89\\% of LSTM and 13.24\\% of Mamba. The training speed of eSSM is 3.94 times faster than LSTM and 1.35 times faster than Mamba. Code is available at: \\href{https://github.com/leonty1/essm}{https://github.com/leonty1/essm}.", "authors": ["Tongyi Liang", "Han-Xiong Li"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-23", "url": "https://arxiv.org/abs/2402.15290", "pdf_url": "https://arxiv.org/pdf/2402.15290v4", "arxiv_id": "2402.15290", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/leonty1/essm}{https://github.com/leonty1/essm}", "venue": null, "quality_score": 0.0}
{"id": "ade1fc107d469ed4fbae7e154897ad5e3deb001a6323fc0e31c49293f2645434", "sources": ["arxiv", "semantic_scholar"], "title": "Pan-Mamba: Effective pan-sharpening with State Space Model", "abstract": "Pan-sharpening involves integrating information from low-resolution multi-spectral and high-resolution panchromatic images to generate high-resolution multi-spectral counterparts. While recent advancements in the state space model, particularly the efficient long-range dependency modeling achieved by Mamba, have revolutionized computer vision community, its untapped potential in pan-sharpening motivates our exploration. Our contribution, Pan-Mamba, represents a novel pan-sharpening network that leverages the efficiency of the Mamba model in global information modeling. In Pan-Mamba, we customize two core components: channel swapping Mamba and cross-modal Mamba, strategically designed for efficient cross-modal information exchange and fusion. The former initiates a lightweight cross-modal interaction through the exchange of partial panchromatic and multi-spectral channels, while the latter facilities the information representation capability by exploiting inherent cross-modal relationships. Through extensive experiments across diverse datasets, our proposed approach surpasses state-of-the-art methods, showcasing superior fusion results in pan-sharpening. To the best of our knowledge, this work is the first attempt in exploring the potential of the Mamba model and establishes a new frontier in the pan-sharpening techniques. The source code is available at \\url{https://github.com/alexhe101/Pan-Mamba}.", "authors": ["Xuanhua He", "Ke Cao", "Keyu Yan", "Rui Li", "Chengjun Xie", "Jie Zhang", "Man Zhou"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-19", "url": "https://arxiv.org/abs/2402.12192", "pdf_url": "https://arxiv.org/pdf/2402.12192v2", "arxiv_id": "2402.12192", "doi": "10.48550/arXiv.2402.12192", "citation_count": 204, "influential_citation_count": 7, "has_code": true, "code_url": "https://github.com/alexhe101/Pan-Mamba}", "venue": "Information Fusion", "quality_score": 0.5779}
{"id": "0a365cb158e0df5fc0f67ff572f2cb675dea9bddb659274175bc75c98a10d6d6", "sources": ["arxiv", "semantic_scholar"], "title": "PointMamba: A Simple State Space Model for Point Cloud Analysis", "abstract": "Transformers have become one of the foundational architectures in point cloud analysis tasks due to their excellent global modeling ability. However, the attention mechanism has quadratic complexity, making the design of a linear complexity method with global modeling appealing. In this paper, we propose PointMamba, transferring the success of Mamba, a recent representative state space model (SSM), from NLP to point cloud analysis tasks. Unlike traditional Transformers, PointMamba employs a linear complexity algorithm, presenting global modeling capacity while significantly reducing computational costs. Specifically, our method leverages space-filling curves for effective point tokenization and adopts an extremely simple, non-hierarchical Mamba encoder as the backbone. Comprehensive evaluations demonstrate that PointMamba achieves superior performance across multiple datasets while significantly reducing GPU memory usage and FLOPs. This work underscores the potential of SSMs in 3D vision-related tasks and presents a simple yet effective Mamba-based baseline for future research. The code will be made available at \\url{https://github.com/LMD0311/PointMamba}.", "authors": ["Dingkang Liang", "Xin Zhou", "Wei Xu", "Xingkui Zhu", "Zhikang Zou", "Xiaoqing Ye", "Xiao Tan", "Xiang Bai"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-16", "url": "https://arxiv.org/abs/2402.10739", "pdf_url": "https://arxiv.org/pdf/2402.10739v5", "arxiv_id": "2402.10739", "doi": "10.48550/arXiv.2402.10739", "citation_count": 300, "influential_citation_count": 33, "has_code": true, "code_url": "https://github.com/LMD0311/PointMamba", "venue": "Neural Information Processing Systems", "quality_score": 0.7657}
{"id": "e832232cf54ab674667a1aed8a2837d811b768d8cbb5f3210b0197d3e402e15f", "sources": ["arxiv", "semantic_scholar"], "title": "Graph Mamba: Towards Learning on Graphs with State Space Models", "abstract": "Graph Neural Networks (GNNs) have shown promising potential in graph representation learning. The majority of GNNs define a local message-passing mechanism, propagating information over the graph by stacking multiple layers. These methods, however, are known to suffer from two major limitations: over-squashing and poor capturing of long-range dependencies. Recently, Graph Transformers (GTs) emerged as a powerful alternative to Message-Passing Neural Networks (MPNNs). GTs, however, have quadratic computational cost, lack inductive biases on graph structures, and rely on complex Positional/Structural Encodings (SE/PE). In this paper, we show that while Transformers, complex message-passing, and SE/PE are sufficient for good performance in practice, neither is necessary. Motivated by the recent success of State Space Models (SSMs), such as Mamba, we present Graph Mamba Networks (GMNs), a general framework for a new class of GNNs based on selective SSMs. We discuss and categorize the new challenges when adapting SSMs to graph-structured data, and present four required and one optional steps to design GMNs, where we choose (1) Neighborhood Tokenization, (2) Token Ordering, (3) Architecture of Bidirectional Selective SSM Encoder, (4) Local Encoding, and dispensable (5) PE and SE. We further provide theoretical justification for the power of GMNs. Experiments demonstrate that despite much less computational cost, GMNs attain an outstanding performance in long-range, small-scale, large-scale, and heterophilic benchmark datasets.", "authors": ["Ali Behrouz", "Farnoosh Hashemi"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-13", "url": "https://arxiv.org/abs/2402.08678", "pdf_url": "https://arxiv.org/pdf/2402.08678v2", "arxiv_id": "2402.08678", "doi": "10.1145/3637528.3672044", "citation_count": 149, "influential_citation_count": 7, "has_code": false, "code_url": null, "venue": "Knowledge Discovery and Data Mining", "quality_score": 0.544}
{"id": "0467281acc9c1b5f8d6e989cdfc90776966dbf59b08132109f03fe8534d3aac3", "sources": ["arxiv", "semantic_scholar"], "title": "FD-Vision Mamba for Endoscopic Exposure Correction", "abstract": "In endoscopic imaging, the recorded images are prone to exposure abnormalities, so maintaining high-quality images is important to assist healthcare professionals in performing decision-making. To overcome this issue, We design a frequency-domain based network, called FD-Vision Mamba (FDVM-Net), which achieves high-quality image exposure correction by reconstructing the frequency domain of endoscopic images. Specifically, inspired by the State Space Sequence Models (SSMs), we develop a C-SSM block that integrates the local feature extraction ability of the convolutional layer with the ability of the SSM to capture long-range dependencies. A two-path network is built using C-SSM as the basic function cell, and these two paths deal with the phase and amplitude information of the image, respectively. Finally, a degraded endoscopic image is reconstructed by FDVM-Net to obtain a high-quality clear image. Extensive experimental results demonstrate that our method achieves state-of-the-art results in terms of speed and accuracy, and it is noteworthy that our method can enhance endoscopic images of arbitrary resolution. The URL of the code is \\url{https://github.com/zzr-idam/FDVM-Net}.", "authors": ["Zhuoran Zheng", "Jun Zhang"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-09", "url": "https://arxiv.org/abs/2402.06378", "pdf_url": "https://arxiv.org/pdf/2402.06378v2", "arxiv_id": "2402.06378", "doi": "10.48550/arXiv.2402.06378", "citation_count": 17, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/zzr-idam/FDVM-Net}", "venue": "arXiv.org", "quality_score": 0.3138}
{"id": "11cf59d7ee42d9685602b2c4a0ae4bfc8a9e20da3a266d279cf6d4e9c32a7e88", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba-ND: Selective State Space Modeling for Multi-Dimensional Data", "abstract": "In recent years, Transformers have become the de-facto architecture for sequence modeling on text and a variety of multi-dimensional data, such as images and video. However, the use of self-attention layers in a Transformer incurs prohibitive compute and memory complexity that scales quadratically w.r.t. the sequence length. A recent architecture, Mamba, based on state space models has been shown to achieve comparable performance for modeling text sequences, while scaling linearly with the sequence length. In this work, we present Mamba-ND, a generalized design extending the Mamba architecture to arbitrary multi-dimensional data. Our design alternatively unravels the input data across different dimensions following row-major orderings. We provide a systematic comparison of Mamba-ND with several other alternatives, based on prior multi-dimensional extensions such as Bi-directional LSTMs and S4ND. Empirically, we show that Mamba-ND demonstrates performance competitive with the state-of-the-art on a variety of multi-dimensional benchmarks, including ImageNet-1K classification, HMDB-51 action recognition, and ERA5 weather forecasting.", "authors": ["Shufan Li", "Harkanwar Singh", "Aditya Grover"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-08", "url": "https://arxiv.org/abs/2402.05892", "pdf_url": "https://arxiv.org/pdf/2402.05892v5", "arxiv_id": "2402.05892", "doi": "10.48550/arXiv.2402.05892", "citation_count": 127, "influential_citation_count": 10, "has_code": false, "code_url": null, "venue": "European Conference on Computer Vision", "quality_score": 0.5268}
{"id": "0e1eac98cc96fb01a7eac59b7e429e3a4eb50d2590c881ce6341c331fec21526", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation", "abstract": "In recent advancements in medical image analysis, Convolutional Neural Networks (CNN) and Vision Transformers (ViT) have set significant benchmarks. While the former excels in capturing local features through its convolution operations, the latter achieves remarkable global context understanding by leveraging self-attention mechanisms. However, both architectures exhibit limitations in efficiently modeling long-range dependencies within medical images, which is a critical aspect for precise segmentation. Inspired by the Mamba architecture, known for its proficiency in handling long sequences and global contextual information with enhanced computational efficiency as a State Space Model (SSM), we propose Mamba-UNet, a novel architecture that synergizes the U-Net in medical image segmentation with Mamba's capability. Mamba-UNet adopts a pure Visual Mamba (VMamba)-based encoder-decoder structure, infused with skip connections to preserve spatial information across different scales of the network. This design facilitates a comprehensive feature learning process, capturing intricate details and broader semantic contexts within medical images. We introduce a novel integration mechanism within the VMamba blocks to ensure seamless connectivity and information flow between the encoder and decoder paths, enhancing the segmentation performance. We conducted experiments on publicly available ACDC MRI Cardiac segmentation dataset, and Synapse CT Abdomen segmentation dataset. The results show that Mamba-UNet outperforms several types of UNet in medical image segmentation under the same hyper-parameter setting. The source code and baseline implementations are available.", "authors": ["Ziyang Wang", "Jian-Qing Zheng", "Yichi Zhang", "Ge Cui", "Lei Li"], "categories": ["eess.IV", "cs.CV"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-02-07", "url": "https://arxiv.org/abs/2402.05079", "pdf_url": "https://arxiv.org/pdf/2402.05079v2", "arxiv_id": "2402.05079", "doi": "10.48550/arXiv.2402.05079", "citation_count": 324, "influential_citation_count": 12, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.628}
{"id": "da46eca2bce18acc0c586066b03a41ad7f5c15e641a410ea5e9c589519a4abb4", "sources": ["arxiv", "semantic_scholar"], "title": "Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks", "abstract": "State-space models (SSMs), such as Mamba (Gu & Dao, 2023), have been proposed as alternatives to Transformer networks in language modeling, by incorporating gating, convolutions, and input-dependent token selection to mitigate the quadratic cost of multi-head attention. Although SSMs exhibit competitive performance, their in-context learning (ICL) capabilities, a remarkable emergent property of modern language models that enables task execution without parameter optimization, remain underexplored compared to Transformers. In this study, we evaluate the ICL performance of SSMs, focusing on Mamba, against Transformer models across various tasks. Our results show that SSMs perform comparably to Transformers in standard regression ICL tasks, while outperforming them in tasks like sparse parity learning. However, SSMs fall short in tasks involving non-standard retrieval functionality. To address these limitations, we introduce a hybrid model, MambaFormer, that combines Mamba with attention blocks, surpassing individual models in tasks where they struggle independently. Our findings suggest that hybrid architectures offer promising avenues for enhancing ICL in language models.", "authors": ["Jongho Park", "Jaeseung Park", "Zheyang Xiong", "Nayoung Lee", "Jaewoong Cho", "Samet Oymak", "Kangwook Lee", "Dimitris Papailiopoulos"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-06", "url": "https://arxiv.org/abs/2402.04248", "pdf_url": "https://arxiv.org/pdf/2402.04248v2", "arxiv_id": "2402.04248", "doi": "10.48550/arXiv.2402.04248", "citation_count": 124, "influential_citation_count": 13, "has_code": false, "code_url": null, "venue": "International Conference on Machine Learning", "quality_score": 0.5731}
{"id": "441fa6865111ed6a9b36bad70a65ba426f34cb14a07ab8100c91e2091c27dc16", "sources": ["arxiv", "semantic_scholar"], "title": "Is Mamba Capable of In-Context Learning?", "abstract": "State of the art foundation models such as GPT-4 perform surprisingly well at in-context learning (ICL), a variant of meta-learning concerning the learned ability to solve tasks during a neural network forward pass, exploiting contextual information provided as input to the model. This useful ability emerges as a side product of the foundation model's massive pretraining. While transformer models are currently the state of the art in ICL, this work provides empirical evidence that Mamba, a newly proposed state space model which scales better than transformers w.r.t. the input sequence length, has similar ICL capabilities. We evaluated Mamba on tasks involving simple function approximation as well as more complex natural language processing problems. Our results demonstrate that, across both categories of tasks, Mamba closely matches the performance of transformer models for ICL. Further analysis reveals that, like transformers, Mamba appears to solve ICL problems by incrementally optimizing its internal representations. Overall, our work suggests that Mamba can be an efficient alternative to transformers for ICL tasks involving long input sequences. This is an exciting finding in meta-learning and may enable generalizations of in-context learned AutoML algorithms (like TabPFN or Optformer) to long input sequences.", "authors": ["Riccardo Grazzi", "Julien Siems", "Simon Schrodi", "Thomas Brox", "Frank Hutter"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-05", "url": "https://arxiv.org/abs/2402.03170", "pdf_url": "https://arxiv.org/pdf/2402.03170v2", "arxiv_id": "2402.03170", "doi": "10.48550/arXiv.2402.03170", "citation_count": 65, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.4549}
{"id": "e015997dda1869ea8a837969eb297205e17492e5fbdbfeca6de2476914933193", "sources": ["arxiv", "semantic_scholar"], "title": "VM-UNet: Vision Mamba UNet for Medical Image Segmentation", "abstract": "In the realm of medical image segmentation, both CNN-based and Transformer-based models have been extensively explored. However, CNNs exhibit limitations in long-range modeling capabilities, whereas Transformers are hampered by their quadratic computational complexity. Recently, State Space Models (SSMs), exemplified by Mamba, have emerged as a promising approach. They not only excel in modeling long-range interactions but also maintain a linear computational complexity. In this paper, leveraging state space models, we propose a U-shape architecture model for medical image segmentation, named Vision Mamba UNet (VM-UNet). Specifically, the Visual State Space (VSS) block is introduced as the foundation block to capture extensive contextual information, and an asymmetrical encoder-decoder structure is constructed with fewer convolution layers to save calculation cost. We conduct comprehensive experiments on the ISIC17, ISIC18, and Synapse datasets, and the results indicate that VM-UNet performs competitively in medical image segmentation tasks. To our best knowledge, this is the first medical image segmentation model constructed based on the pure SSM-based model. We aim to establish a baseline and provide valuable insights for the future development of more efficient and effective SSM-based segmentation systems. Our code is available at https://github.com/JCruan519/VM-UNet.", "authors": ["Jiacheng Ruan", "Jincheng Li", "Suncheng Xiang"], "categories": ["eess.IV", "cs.CV"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2024-02-04", "url": "https://arxiv.org/abs/2402.02491", "pdf_url": "https://arxiv.org/pdf/2402.02491v2", "arxiv_id": "2402.02491", "doi": "10.1145/3767748", "citation_count": 902, "influential_citation_count": 53, "has_code": true, "code_url": "https://github.com/JCruan519/VM-UNet", "venue": "ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)", "quality_score": 0.8662}
{"id": "08f6f20590ff08c4f10b32b94041151cd7765e24ad7877a2d2e5559c84e1b741", "sources": ["arxiv", "semantic_scholar"], "title": "Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces", "abstract": "Attention mechanisms have been widely used to capture long-range dependencies among nodes in Graph Transformers. Bottlenecked by the quadratic computational cost, attention mechanisms fail to scale in large graphs. Recent improvements in computational efficiency are mainly achieved by attention sparsification with random or heuristic-based graph subsampling, which falls short in data-dependent context reasoning. State space models (SSMs), such as Mamba, have gained prominence for their effectiveness and efficiency in modeling long-range dependencies in sequential data. However, adapting SSMs to non-sequential graph data presents a notable challenge. In this work, we introduce Graph-Mamba, the first attempt to enhance long-range context modeling in graph networks by integrating a Mamba block with the input-dependent node selection mechanism. Specifically, we formulate graph-centric node prioritization and permutation strategies to enhance context-aware reasoning, leading to a substantial improvement in predictive performance. Extensive experiments on ten benchmark datasets demonstrate that Graph-Mamba outperforms state-of-the-art methods in long-range graph prediction tasks, with a fraction of the computational cost in both FLOPs and GPU memory consumption. The code and models are publicly available at https://github.com/bowang-lab/Graph-Mamba.", "authors": ["Chloe Wang", "Oleksii Tsepa", "Jun Ma", "Bo Wang"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-01", "url": "https://arxiv.org/abs/2402.00789", "pdf_url": "https://arxiv.org/pdf/2402.00789v1", "arxiv_id": "2402.00789", "doi": "10.48550/arXiv.2402.00789", "citation_count": 177, "influential_citation_count": 8, "has_code": true, "code_url": "https://github.com/bowang-lab/Graph-Mamba", "venue": "arXiv.org", "quality_score": 0.5626}
{"id": "a48d264e80415c0ba43066fcbf161cc5094af0953b56918009c90072cbe67774", "sources": ["arxiv", "semantic_scholar"], "title": "BlackMamba: Mixture of Experts for State-Space Models", "abstract": "State-space models (SSMs) have recently demonstrated competitive performance to transformers at large-scale language modeling benchmarks while achieving linear time and memory complexity as a function of sequence length. Mamba, a recently released SSM model, shows impressive performance in both language modeling and long sequence processing tasks. Simultaneously, mixture-of-expert (MoE) models have shown remarkable performance while significantly reducing the compute and latency costs of inference at the expense of a larger memory footprint. In this paper, we present BlackMamba, a novel architecture that combines the Mamba SSM with MoE to obtain the benefits of both. We demonstrate that BlackMamba performs competitively against both Mamba and transformer baselines, and outperforms in inference and training FLOPs. We fully train and open-source 340M/1.5B and 630M/2.8B BlackMamba models on 300B tokens of a custom dataset. We show that BlackMamba inherits and combines both of the benefits of SSM and MoE architectures, combining linear-complexity generation from SSM with cheap and fast inference from MoE. We release all weights, checkpoints, and inference code open-source. Inference code at: https://github.com/Zyphra/BlackMamba", "authors": ["Quentin Anthony", "Yury Tokpanov", "Paolo Glorioso", "Beren Millidge"], "categories": ["cs.CL", "cs.AI", "cs.DC", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-01", "url": "https://arxiv.org/abs/2402.01771", "pdf_url": "https://arxiv.org/pdf/2402.01771v1", "arxiv_id": "2402.01771", "doi": "10.48550/arXiv.2402.01771", "citation_count": 40, "influential_citation_count": 2, "has_code": true, "code_url": "https://github.com/Zyphra/BlackMamba", "venue": "arXiv.org", "quality_score": 0.4032}
{"id": "6b6e36d9245bd5e4494c17d7e8c1e5a1945f9a79b29320ab13faaab008daffa7", "sources": ["arxiv", "semantic_scholar"], "title": "MambaByte: Token-free Selective State Space Model", "abstract": "Token-free language models learn directly from raw bytes and remove the inductive bias of subword tokenization. Operating on bytes, however, results in significantly longer sequences. In this setting, standard autoregressive Transformers scale poorly as the effective memory required grows with sequence length. The recent development of the Mamba state space model (SSM) offers an appealing alternative approach with a fixed-sized memory state and efficient decoding. We propose MambaByte, a token-free adaptation of the Mamba SSM trained autoregressively on byte sequences. In terms of modeling, we show MambaByte to be competitive with, and even to outperform, state-of-the-art subword Transformers on language modeling tasks while maintaining the benefits of token-free language models, such as robustness to noise. In terms of efficiency, we develop an adaptation of speculative decoding with tokenized drafting and byte-level verification. This results in a $2.6\\times$ inference speedup to the standard MambaByte implementation, showing similar decoding efficiency as the subword Mamba. These findings establish the viability of SSMs in enabling token-free language modeling.", "authors": ["Junxiong Wang", "Tushaar Gangavarapu", "Jing Nathan Yan", "Alexander M. Rush"], "categories": ["cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-01-24", "url": "https://arxiv.org/abs/2401.13660", "pdf_url": "https://arxiv.org/pdf/2401.13660v3", "arxiv_id": "2401.13660", "doi": "10.48550/arXiv.2401.13660", "citation_count": 69, "influential_citation_count": 5, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4613}
{"id": "8cf2824f4492b8380547440906c05e8561844c67924e5be8e7fe0fcd6b79f439", "sources": ["arxiv", "semantic_scholar"], "title": "SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation", "abstract": "The Transformer architecture has shown a remarkable ability in modeling global relationships. However, it poses a significant computational challenge when processing high-dimensional medical images. This hinders its development and widespread adoption in this task. Mamba, as a State Space Model (SSM), recently emerged as a notable manner for long-range dependencies in sequential modeling, excelling in natural language processing filed with its remarkable memory efficiency and computational speed. Inspired by its success, we introduce SegMamba, a novel 3D medical image \\textbf{Seg}mentation \\textbf{Mamba} model, designed to effectively capture long-range dependencies within whole volume features at every scale. Our SegMamba, in contrast to Transformer-based methods, excels in whole volume feature modeling from a state space model standpoint, maintaining superior processing speed, even with volume features at a resolution of {$64\\times 64\\times 64$}. Comprehensive experiments on the BraTS2023 dataset demonstrate the effectiveness and efficiency of our SegMamba. The code for SegMamba is available at: https://github.com/ge-xing/SegMamba", "authors": ["Zhaohu Xing", "Tian Ye", "Yijun Yang", "Guang Liu", "Lei Zhu"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-01-24", "url": "https://arxiv.org/abs/2401.13560", "pdf_url": "https://arxiv.org/pdf/2401.13560v4", "arxiv_id": "2401.13560", "doi": "10.48550/arXiv.2401.13560", "citation_count": 605, "influential_citation_count": 32, "has_code": true, "code_url": "https://github.com/ge-xing/SegMamba", "venue": "International Conference on Medical Image Computing and Computer-Assisted Intervention", "quality_score": 0.7593}
{"id": "4ecf6c33e475bb09acdc573e614d3fa6d5e376f013084e57016ba7e0e5975336", "sources": ["arxiv", "semantic_scholar"], "title": "Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model", "abstract": "Recently the state space models (SSMs) with efficient hardware-aware designs, i.e., the Mamba deep learning model, have shown great potential for long sequence modeling. Meanwhile building efficient and generic vision backbones purely upon SSMs is an appealing direction. However, representing visual data is challenging for SSMs due to the position-sensitivity of visual data and the requirement of global context for visual understanding. In this paper, we show that the reliance on self-attention for visual representation learning is not necessary and propose a new generic vision backbone with bidirectional Mamba blocks (Vim), which marks the image sequences with position embeddings and compresses the visual representation with bidirectional state space models. On ImageNet classification, COCO object detection, and ADE20k semantic segmentation tasks, Vim achieves higher performance compared to well-established vision transformers like DeiT, while also demonstrating significantly improved computation & memory efficiency. For example, Vim is 2.8$\\times$ faster than DeiT and saves 86.8% GPU memory when performing batch inference to extract features on images with a resolution of 1248$\\times$1248. The results demonstrate that Vim is capable of overcoming the computation & memory constraints on performing Transformer-style understanding for high-resolution images and it has great potential to be the next-generation backbone for vision foundation models. Code is available at https://github.com/hustvl/Vim.", "authors": ["Lianghui Zhu", "Bencheng Liao", "Qian Zhang", "Xinlong Wang", "Wenyu Liu", "Xinggang Wang"], "categories": ["cs.CV", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-01-17", "url": "https://arxiv.org/abs/2401.09417", "pdf_url": "https://arxiv.org/pdf/2401.09417v3", "arxiv_id": "2401.09417", "doi": "10.48550/arXiv.2401.09417", "citation_count": 1853, "influential_citation_count": 193, "has_code": true, "code_url": "https://github.com/hustvl/Vim", "venue": "International Conference on Machine Learning", "quality_score": 1.0}
{"id": "c2c0d1df54e0b0772e647add9193480725cf949564f02e158cbc2684379055ba", "sources": ["arxiv", "semantic_scholar"], "title": "Cross Domain Early Crop Mapping using CropSTGAN", "abstract": "Driven by abundant satellite imagery, machine learning-based approaches have recently been promoted to generate high-resolution crop cultivation maps to support many agricultural applications. One of the major challenges faced by these approaches is the limited availability of ground truth labels. In the absence of ground truth, existing work usually adopts the \"direct transfer strategy\" that trains a classifier using historical labels collected from other regions and then applies the trained model to the target region. Unfortunately, the spectral features of crops exhibit inter-region and inter-annual variability due to changes in soil composition, climate conditions, and crop progress, the resultant models perform poorly on new and unseen regions or years. Despite recent efforts, such as the application of the deep adaptation neural network (DANN) model structure in the deep adaptation crop classification network (DACCN), to tackle the above cross-domain challenges, their effectiveness diminishes significantly when there is a large dissimilarity between the source and target regions. This paper introduces the Crop Mapping Spectral-temporal Generative Adversarial Neural Network (CropSTGAN), a novel solution for cross-domain challenges, that doesn't require target domain labels. CropSTGAN learns to transform the target domain's spectral features to those of the source domain, effectively bridging large dissimilarities. Additionally, it employs an identity loss to maintain the intrinsic local structure of the data. Comprehensive experiments across various regions and years demonstrate the benefits and effectiveness of the proposed approach. In experiments, CropSTGAN is benchmarked against various state-of-the-art (SOTA) methods. Notably, CropSTGAN significantly outperforms these methods in scenarios with large data distribution dissimilarities between the target and source domains.", "authors": ["Yiqun Wang", "Hui Huang", "Radu State"], "categories": ["cs.CV", "cs.LG", "eess.IV"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-01-15", "url": "https://arxiv.org/abs/2401.07398", "pdf_url": "https://arxiv.org/pdf/2401.07398v2", "arxiv_id": "2401.07398", "doi": "10.1109/ACCESS.2024.3436620", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Access", "quality_score": 0.2258}
{"id": "1f701b3e1bae9ceb6e22d474cecdc14f9db106aeef4a020a446439ab73050647", "sources": ["arxiv", "semantic_scholar"], "title": "MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts", "abstract": "State Space Models (SSMs) have become serious contenders in the field of sequential modeling, challenging the dominance of Transformers. At the same time, Mixture of Experts (MoE) has significantly improved Transformer-based Large Language Models, including recent state-of-the-art open models. We propose that to unlock the potential of SSMs for scaling, they should be combined with MoE. We showcase this on Mamba, a recent SSM-based model that achieves remarkable performance. Our model, MoE-Mamba, outperforms both Mamba and baseline Transformer-MoE. In particular, MoE-Mamba reaches the same performance as Mamba in $2.35\\times$ fewer training steps while preserving the inference performance gains of Mamba against Transformer.", "authors": ["Maciej Pióro", "Kamil Ciebiera", "Krystian Król", "Jan Ludziejewski", "Michał Krutul", "Jakub Krajewski", "Szymon Antoniak", "Piotr Miłoś", "Marek Cygan", "Sebastian Jaszczur"], "categories": ["cs.LG", "cs.AI", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-01-08", "url": "https://arxiv.org/abs/2401.04081", "pdf_url": "https://arxiv.org/pdf/2401.04081v2", "arxiv_id": "2401.04081", "doi": "10.48550/arXiv.2401.04081", "citation_count": 97, "influential_citation_count": 5, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4978}
{"id": "e7bd36712b191fbcdf35092fcc6beaefeffac9ebd6edc1c1f05d099fadab70e6", "sources": ["arxiv", "semantic_scholar"], "title": "Self-supervised Machine Learning Based Approach to Orbit Modelling Applied to Space Traffic Management", "abstract": "This paper presents a novel methodology for improving the performance of machine learning based space traffic management tasks through the use of a pre-trained orbit model. Taking inspiration from BERT-like self-supervised language models in the field of natural language processing, we introduce ORBERT, and demonstrate the ability of such a model to leverage large quantities of readily available orbit data to learn meaningful representations that can be used to aid in downstream tasks. As a proof of concept of this approach we consider the task of all vs. all conjunction screening, phrased here as a machine learning time series classification task. We show that leveraging unlabelled orbit data leads to improved performance, and that the proposed approach can be particularly beneficial for tasks where the availability of labelled data is limited.", "authors": ["Emma Stevenson", "Victor Rodriguez-Fernandez", "Hodei Urrutxua", "Vincent Morand", "David Camacho"], "categories": ["physics.space-ph", "cs.LG"], "fields_of_study": ["Physics", "Computer Science"], "published_date": "2023-12-11", "url": "https://arxiv.org/abs/2312.06854", "pdf_url": "https://arxiv.org/pdf/2312.06854v1", "arxiv_id": "2312.06854", "doi": "10.48550/arXiv.2312.06854", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1747}
{"id": "7264502b4db06e88186f18dd76311211020afb4fde34a93922b2c7363c3bc73a", "sources": ["arxiv", "semantic_scholar"], "title": "Mamba: Linear-Time Sequence Modeling with Selective State Spaces", "abstract": "Foundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer architecture and its core attention module. Many subquadratic-time architectures such as linear attention, gated convolution and recurrent models, and structured state space models (SSMs) have been developed to address Transformers' computational inefficiency on long sequences, but they have not performed as well as attention on important modalities such as language. We identify that a key weakness of such models is their inability to perform content-based reasoning, and make several improvements. First, simply letting the SSM parameters be functions of the input addresses their weakness with discrete modalities, allowing the model to selectively propagate or forget information along the sequence length dimension depending on the current token. Second, even though this change prevents the use of efficient convolutions, we design a hardware-aware parallel algorithm in recurrent mode. We integrate these selective SSMs into a simplified end-to-end neural network architecture without attention or even MLP blocks (Mamba). Mamba enjoys fast inference (5$\\times$ higher throughput than Transformers) and linear scaling in sequence length, and its performance improves on real data up to million-length sequences. As a general sequence model backbone, Mamba achieves state-of-the-art performance across several modalities such as language, audio, and genomics. On language modeling, our Mamba-3B model outperforms Transformers of the same size and matches Transformers twice its size, both in pretraining and downstream evaluation.", "authors": ["Albert Gu", "Tri Dao"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-12-01", "url": "https://arxiv.org/abs/2312.00752", "pdf_url": "https://arxiv.org/pdf/2312.00752v2", "arxiv_id": "2312.00752", "doi": null, "citation_count": 7571, "influential_citation_count": 1158, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 1.0}
{"id": "61531476351970f4442ecec65e4ed564059b6ca6488b9437c61a059c4ef7ea2b", "sources": ["arxiv", "semantic_scholar"], "title": "GateLoop: Fully Data-Controlled Linear Recurrence for Sequence Modeling", "abstract": "Linear Recurrence has proven to be a powerful tool for modeling long sequences efficiently. In this work, we show that existing models fail to take full advantage of its potential. Motivated by this finding, we develop GateLoop, a foundational sequence model that generalizes linear recurrent models such as S4, S5, LRU and RetNet, by employing data-controlled state transitions. Utilizing this theoretical advance, GateLoop empirically outperforms existing models for auto-regressive language modeling. Our method comes with a low-cost $O(l)$ recurrent mode and an efficient $O(l \\log_{2} l)$ parallel mode making use of highly optimized associative scan implementations. Furthermore, we derive an $O(l^2)$ surrogate attention mode, revealing remarkable implications for Transformer and recently proposed architectures. Specifically, we prove that our approach can be interpreted as providing data-controlled relative-positional information to Attention. While many existing models solely rely on data-controlled cumulative sums for context aggregation, our findings suggest that incorporating data-controlled complex cumulative products may be a crucial step towards more powerful sequence models.", "authors": ["Tobias Katsch"], "categories": ["cs.LG", "cs.AI", "cs.CL", "cs.DS"], "fields_of_study": ["Computer Science"], "published_date": "2023-11-03", "url": "https://arxiv.org/abs/2311.01927", "pdf_url": "https://arxiv.org/pdf/2311.01927v2", "arxiv_id": "2311.01927", "doi": "10.48550/arXiv.2311.01927", "citation_count": 43, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4109}
{"id": "2d5b8651d228261315193fcd0377c6fedc4b396bdaf888ca4b95522a98617db1", "sources": ["arxiv", "semantic_scholar"], "title": "Generalizations of R0 and SSM properties; Extended Horizontal Linear Complementarity Problem", "abstract": "In this paper, we first introduce R0-W and SSM-W properties for the set of matrices which is a generalization of R0 and the strictly semimonotone matrix. We then prove some existence results for the extended horizontal linear complementarity problem when the involved matrices have these properties. With an additional condition on the set of matrices, we prove that the SSM-W property is equivalent to the unique solution for the corresponding extended horizontal linear complementarity problems. Finally, we give a necessary and sufficient condition for the connectedness of the solution set of the extended horizontal linear complementarity problems.", "authors": ["Punit Kumar Yadav", "K. Palpandi"], "categories": ["math.OC"], "fields_of_study": ["Mathematics"], "published_date": "2023-01-04", "url": "https://arxiv.org/abs/2301.01479", "pdf_url": "https://arxiv.org/pdf/2301.01479v1", "arxiv_id": "2301.01479", "doi": null, "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0753}
{"id": "4bfeb368be658c83c26b7fde681239979d5ff4da07a531ce8a4ada2e0122d5e9", "sources": ["arxiv", "semantic_scholar"], "title": "Space Weather: From Solar Origins to Risks and Hazards Evolving in Time", "abstract": "Space Weather is the portion of space physics that has a direct effect on humankind. Space Weather is an old branch of space physics that originates back to 1808 with the publication of a paper by the great naturalist Alexander von Humboldt (von Humboldt, 1808). Space Weather is currently experiencing explosive growth, because its effects on human technologies have become more and more diverse. Space Weather is due to the variability of solar processes that cause interplanetary, magnetospheric, ionospheric, atmospheric and ground level effects. Space Weather can at times have strong impacts on technological systems and human health. The threats and risks are not hypothetical, and in the event of extreme Space Weather events the consequences could be quite severe for humankind. The purpose of the review is to give a brief overall view of the full chain of physical processes responsible for Space Weather risks and hazards, tracing them from solar origins to effects and impacts in interplanetary space, in the Earth's magnetosphere and ionosphere and at the ground. The paper shows that the risks associated with Space Weather have not been constant over time; they have evolved as our society becomes more and more technologically advanced. The paper begins with a brief introduction to the Carrington event. Next, the descriptions of the strongest known Space Weather processes are reviewed. The concepts of geomagnetic storms and substorms are briefly introduced. The main effects/impacts of Space Weather are also considered, including geomagnetically induced currents (GICs) which are thought to cause power outages. The effects of radiation on avionics and human health, ionospheric effects and impacts, and thermosphere effects and satellite drag will also be discussed. Finally, we will discuss the current challenges of Space Weather forecasting and examine some of the worst-case scenarios.", "authors": ["Natalia Buzulukova", "Bruce Tsurutani"], "categories": ["physics.space-ph", "astro-ph.SR", "physics.geo-ph"], "fields_of_study": ["Physics"], "published_date": "2022-12-22", "url": "https://arxiv.org/abs/2212.11504", "pdf_url": "https://arxiv.org/pdf/2212.11504v1", "arxiv_id": "2212.11504", "doi": "10.3389/fspas.2022.1017103", "citation_count": 61, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Frontiers in Astronomy and Space Sciences", "quality_score": 0.4481}
{"id": "44d3a3525b759077c2b4cd5b8f7ad4d694900063c3d861fd14720c73702358eb", "sources": ["arxiv", "semantic_scholar"], "title": "Simplifying and Understanding State Space Models with Diagonal Linear RNNs", "abstract": "Sequence models based on linear state spaces (SSMs) have recently emerged as a promising choice of architecture for modeling long range dependencies across various modalities. However, they invariably rely on discretization of a continuous state space, which complicates their presentation and understanding. In this work, we dispose of the discretization step, and propose a model based on vanilla Diagonal Linear RNNs ($\\mathrm{DLR}$). We empirically show that, despite being conceptually much simpler, $\\mathrm{DLR}$ is as performant as previously-proposed SSMs on a variety of tasks and benchmarks including Long Range Arena and raw speech classification. Moreover, we characterize the expressivity of SSMs (including $\\mathrm{DLR}$) and attention-based models via a suite of $13$ synthetic sequence-to-sequence tasks involving interactions over tens of thousands of tokens, ranging from simple operations, such as shifting an input sequence, to detecting co-dependent visual features over long spatial ranges in flattened images. We find that while SSMs report near-perfect performance on tasks that can be modeled via $\\textit{few}$ convolutional kernels, they struggle on tasks requiring $\\textit{many}$ such kernels and especially when the desired sequence manipulation is $\\textit{context-dependent}$. Despite these limitations, $\\mathrm{DLR}$ reaches high performance on two higher-order reasoning tasks $\\mathrm{ListOpsSubTrees}$ and $\\mathrm{PathfinderSegmentation}\\text{-}\\mathrm{256}$ with input lengths $8K$ and $65K$ respectively, and gives encouraging performance on $\\mathrm{PathfinderSegmentation}\\text{-}\\mathrm{512}$ with input length $262K$ for which attention is not a viable choice.", "authors": ["Ankit Gupta", "Harsh Mehta", "Jonathan Berant"], "categories": ["cs.LG", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2022-12-01", "url": "https://arxiv.org/abs/2212.00768", "pdf_url": "https://arxiv.org/pdf/2212.00768v3", "arxiv_id": "2212.00768", "doi": "10.48550/arXiv.2212.00768", "citation_count": 24, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3495}
{"id": "e122223f75bfb00c1a7b6b09b5a655311de004dbac87a924bf2c49357f05c6c0", "sources": ["arxiv", "semantic_scholar"], "title": "Space Weather Observations, Modeling, and Alerts in Support of Human Exploration of Mars", "abstract": "Space weather observations and modeling at Mars have begun but they must be significantly increased to support the future of Human Exploration on the Red Planet. A comprehensive space weather understanding of a planet without a global magnetosphere and a thin atmosphere is very different from our situation at Earth so there is substantial fundamental research remaining. It is expected that the development of suitable models will lead to a comprehensive operational Mars space weather alert (MSWA) system that would provide rapid dissemination of information to Earth controllers, astronauts in transit, and those in the exploration zone (EZ) on the surface by producing alerts that are delivered rapidly and are actionable. To illustrate the importance of such a system, we use a magnetohydrodynamic code to model an extreme Carrington-type coronal mass ejection (CME) event at Mars. The results show a significant induced surface field of nearly 3000 nT on the dayside that could radically affect unprotected electrical systems that would dramatically impact human survival on Mars. Other associated problems include coronal mass ejection (CME) shock-driven acceleration of solar energetic particles producing large doses of ionizing radiation at the Martian surface. In summary, along with working more closely with international partners, the next Heliophysics Decadal Survey must include a new initiative to meet expected demands for space weather forecasting in support of humans living and working on the surface of Mars. It will require significant effort to coordinate NASA and the international community contributions.", "authors": ["James L. Green", "Chuanfei Dong", "Michael Hesse", "C. Alex Young", "Vladimir Airapetian"], "categories": ["physics.space-ph", "astro-ph.EP"], "fields_of_study": ["Physics"], "published_date": "2022-11-08", "url": "https://arxiv.org/abs/2211.04021", "pdf_url": "https://arxiv.org/pdf/2211.04021v1", "arxiv_id": "2211.04021", "doi": "10.3389/fspas.2022.1023305", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Frontiers in Astronomy and Space Sciences", "quality_score": 0.2113}
{"id": "aca490f0840786c642d33655c4986dcab80f3f9c424e494d43bcf0368cff493e", "sources": ["arxiv", "semantic_scholar"], "title": "How Do Shock Waves Define the Space-Time Structure of Gradual Solar Energetic Particle Events?", "abstract": "We revisit the full variety of observed temporal and spatial distributions of energetic solar protons in \"gradual\" solar energetic-particle (SEP) events resulting from the spatial variations in the shock waves that accelerate them. Differences in the shock strength at the solar longitude of a spacecraft and at the footpoint of its connecting magnetic field line, nominally 55 degrees to the west, drive much of that variation. The shock wave itself, together with energetic particles trapped near it by self-amplified Alfven waves, forms an underlying autonomous structure that can drive across magnetic field lines intact, spreading proton intensities in a widening SEP longitude distribution. During the formation of this fundamental structure, historically called an \"energetic storm particle\" (ESP) event, many SEPs leak away early, amplifying waves as they flow along well-connected field lines and broaden the distribution outward; behind this structure between the shock and the Sun a \"reservoir\" of quasi-trapped SEPs forms. Very large SEP events are complicated by additional extensive wave growth that can spread an extended ESP-like trapping region. The multiplicity of shock-related processes contributing to the observed SEP profiles causes correlations of the events to be poorly represented by the peak intensities commonly used. In fact, the extensive spatial distributions of SEPs are sometimes interwoven with the structures of the shocks that have accelerated them and sometimes free. We should consider new questions: Which extremes of the shock contribute most to the SEPs profile of an event, (1) the shock at the longitude of a spacecraft, (2) the shock ~55 degrees to the west at the footpoint of the field, or (3) SEPs that have collected in the reservoir? How does the space-time distribution of SEPs correspond with the underlying space-time distribution of shock strength?", "authors": ["Donald V. Reames"], "categories": ["astro-ph.SR", "physics.plasm-ph", "physics.space-ph"], "fields_of_study": ["Physics"], "published_date": "2022-10-29", "url": "https://arxiv.org/abs/2210.16693", "pdf_url": "https://arxiv.org/pdf/2210.16693v2", "arxiv_id": "2210.16693", "doi": "10.1007/s11214-023-00959-x", "citation_count": 20, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Space Science Reviews", "quality_score": 0.3306}
{"id": "8fc453eaf71ca0d5d0d4c4acf5b6800fe1a7355f160aee9bd198d574b34c1ad0", "sources": ["arxiv", "semantic_scholar"], "title": "Simplified State Space Layers for Sequence Modeling", "abstract": "Models using structured state space sequence (S4) layers have achieved state-of-the-art performance on long-range sequence modeling tasks. An S4 layer combines linear state space models (SSMs), the HiPPO framework, and deep learning to achieve high performance. We build on the design of the S4 layer and introduce a new state space layer, the S5 layer. Whereas an S4 layer uses many independent single-input, single-output SSMs, the S5 layer uses one multi-input, multi-output SSM. We establish a connection between S5 and S4, and use this to develop the initialization and parameterization used by the S5 model. The result is a state space layer that can leverage efficient and widely implemented parallel scans, allowing S5 to match the computational efficiency of S4, while also achieving state-of-the-art performance on several long-range sequence modeling tasks. S5 averages 87.4% on the long range arena benchmark, and 98.5% on the most difficult Path-X task.", "authors": ["Jimmy T. H. Smith", "Andrew Warrington", "Scott W. Linderman"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-08-09", "url": "https://arxiv.org/abs/2208.04933", "pdf_url": "https://arxiv.org/pdf/2208.04933v3", "arxiv_id": "2208.04933", "doi": "10.48550/arXiv.2208.04933", "citation_count": 1035, "influential_citation_count": 76, "has_code": false, "code_url": null, "venue": "International Conference on Learning Representations", "quality_score": 0.9432}
{"id": "aaa3b950538fc8d917ef4f3b96c4b95ffbf06bf02163e04804db0d87fa871b67", "sources": ["arxiv", "semantic_scholar"], "title": "Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios", "abstract": "Recurrent State-space models (RSSMs) are highly expressive models for learning patterns in time series data and system identification. However, these models assume that the dynamics are fixed and unchanging, which is rarely the case in real-world scenarios. Many control applications often exhibit tasks with similar but not identical dynamics which can be modeled as a latent variable. We introduce the Hidden Parameter Recurrent State Space Models (HiP-RSSMs), a framework that parametrizes a family of related dynamical systems with a low-dimensional set of latent factors. We present a simple and effective way of learning and performing inference over this Gaussian graphical model that avoids approximations like variational inference. We show that HiP-RSSMs outperforms RSSMs and competing multi-task models on several challenging robotic benchmarks both on real-world systems and simulations.", "authors": ["Vaisakh Shaj", "Dieter Buchler", "Rohit Sonker", "Philipp Becker", "Gerhard Neumann"], "categories": ["cs.LG", "eess.SY"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2022-06-29", "url": "https://arxiv.org/abs/2206.14697", "pdf_url": "https://arxiv.org/pdf/2206.14697v3", "arxiv_id": "2206.14697", "doi": "10.48550/arXiv.2206.14697", "citation_count": 9, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Learning Representations", "quality_score": 0.25}
{"id": "479eb58a10f80cbc1a51147cbb3337720d67210eb27260f3bd20e649ce093a0d", "sources": ["arxiv", "semantic_scholar"], "title": "Achievements and Lessons Learned from Successful Small Satellite Missions for Space Weather-Oriented Research", "abstract": "When the first CubeSats were launched nearly two decades ago, few people believed that the miniature satellites would likely prove to be a useful scientific tool. Skeptics abounded. However, the last decade has seen the highly successful implementation of space missions that make creative and innovative use of fast-advancing CubeSat and small satellite technology to carry out important science experiments and missions. Several projects now have used CubeSats to obtain first-of-their-kind observations and findings that have formed the basis for high-profile engineering and science publications, thereby establishing without doubt the scientific value and broad utility of CubeSats. In this paper, we describe recent achievements and lessons learned from a representative selection of successful CubeSat missions with a space weather focus. We conclude that these missions were successful in part because their limited resources promoted not only mission focus but also appropriate risk-taking for comparatively high science return. Quantitative analysis of refereed publications from these CubeSat missions and several larger missions reveals that mission outcome metrics compare favorably when publication number is normalized by mission cost or if expressed as a weighted net scientific impact of all mission publications.", "authors": ["Harlan E. Spence", "Amir Caspi", "Hasan Bahcivan", "Jesus Nieves-Chinchilla", "Geoff Crowley", "James Cutler", "Chad Fish", "David Jackson", "Therese Moretto Jørgensen", "David Klumpar", "Xinlin Li", "James P. Mason", "Nick Paschalidis", "John Sample", "Sonya Smith", "Charles M. Swenson", "Thomas N. Woods"], "categories": ["astro-ph.IM", "astro-ph.EP", "astro-ph.SR", "physics.space-ph"], "fields_of_study": ["Physics"], "published_date": "2022-06-07", "url": "https://arxiv.org/abs/2206.02968", "pdf_url": "https://arxiv.org/pdf/2206.02968v1", "arxiv_id": "2206.02968", "doi": "10.1029/2021SW003031", "citation_count": 17, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Space Weather, Vol. 20, Issue 7, e2021SW003031 (21pp); 2022 June 28", "quality_score": 0.3138}
{"id": "f677a616063d8c3165c2dce62ef0e23e499418fe9ecb49e3f99418ae1932b028", "sources": ["arxiv", "semantic_scholar"], "title": "Optical Atomic Clock aboard an Earth-orbiting Space Station (OACESS): Enhancing searches for physics beyond the standard model in space", "abstract": "We present a concept for a high-precision optical atomic clock (OAC) operating on an Earth-orbiting space station. This pathfinder science mission will compare the space-based OAC with one or more ultra-stable terrestrial OACs to search for space-time-dependent signatures of dark scalar fields that manifest as anomalies in the relative frequencies of station-based and ground-based clocks. This opens the possibility of probing models of new physics that are inaccessible to purely ground-based OAC experiments where a dark scalar field may potentially be strongly screened near Earth's surface. This unique enhancement of sensitivity to potential dark matter candidates harnesses the potential of space-based OACs.", "authors": ["Vladimir Schkolnik", "Dmitry Budker", "Oliver Fartmann", "Victor Flambaum", "Leo Hollberg", "Tigran Kalaydzhyan", "Shimon Kolkowitz", "Markus Krutzik", "Andrew Ludlow", "Nathan Newbury", "Christoph Pyrlik", "Laura Sinclair", "Yevgeny Stadnik", "Ingmari Tietje", "Jun Ye", "Jason Williams"], "categories": ["physics.atom-ph", "gr-qc", "hep-ph", "physics.space-ph"], "fields_of_study": ["Physics"], "published_date": "2022-04-20", "url": "https://arxiv.org/abs/2204.09611", "pdf_url": "https://arxiv.org/pdf/2204.09611v3", "arxiv_id": "2204.09611", "doi": "10.1088/2058-9565/ac9f2b", "citation_count": 34, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Quantum Science and Technology", "quality_score": 0.386}
{"id": "17e195a6c0453173900a2967551d89862c6400323e7ceeafa78a969967b8454d", "sources": ["arxiv", "semantic_scholar"], "title": "The Geography of Facebook Groups in the United States", "abstract": "We use exploratory factor analysis to investigate the online persistence of known community-level patterns of social capital variance in the U.S. context. Our analysis focuses on Facebook groups, specifically those that tend to connect users in the same local area. We investigate the relationship between established, localized measures of social capital at the county level and patterns of participation in Facebook groups in the same areas. We identify four main factors that distinguish Facebook group engagement by county. The first captures small, private groups, dense with friendship connections. The second captures very local and small groups. The third captures non-local, large, public groups, with more age mixing. The fourth captures partially local groups of medium to large size. The first and third factor correlate with community level social capital measures, while the second and fourth do not. Together and individually, the factors are predictive of offline social capital measures, even controlling for various demographic attributes of the counties. Our analysis reveals striking patterns of correlation between established measures of social capital and patterns of online interaction in local Facebook groups. To our knowledge this is the first systematic test of the association between offline regional social capital and patterns of online community engagement in the same regions.", "authors": ["Amaç Herdağdelen", "Lada Adamic", "Bogdan State"], "categories": ["cs.SI"], "fields_of_study": ["Computer Science"], "published_date": "2022-01-29", "url": "https://arxiv.org/abs/2201.12513", "pdf_url": "https://arxiv.org/pdf/2201.12513v2", "arxiv_id": "2201.12513", "doi": "10.1609/icwsm.v17i1.22151", "citation_count": 7, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "International Conference on Web and Social Media", "quality_score": 0.2258}
{"id": "5022889bcb98f4a7ea384183e8daa25dc00fa4a4dde141d98e72b960a0a17537", "sources": ["arxiv", "semantic_scholar"], "title": "Small Satellite Mission Concepts for Space Weather Research and as Pathfinders for Operations", "abstract": "Recent advances in miniaturization and commercial availability of critical satellite subsystems and detector technology have made small satellites (SmallSats, including CubeSats) an attractive, low-cost potential solution for space weather research and operational needs. Motivated by the 1st International Workshop on SmallSats for Space Weather Research and Forecasting, held in Washington, DC on 1-4 August 2017, we discuss the need for advanced space weather measurement capabilities, driven by analyses from the World Meteorological Organization (WMO), and how SmallSats can efficiently fill these measurement gaps. We present some current, recent missions and proposed/upcoming mission concepts using SmallSats that enhance space weather research and provide prototyping pathways for future operational applications; how they relate to the WMO requirements; and what challenges remain to be overcome to meet the WMO goals and operational needs in the future. With additional investment from cognizant funding agencies worldwide, SmallSats -- including standalone missions and constellations -- could significantly enhance space weather research and, eventually, operations, by reducing costs and enabling new measurements not feasible from traditional, large, monolithic missions.", "authors": ["Amir Caspi", "M. Barthelemy", "C. D. Bussy-Virat", "I. J. Cohen", "C. E. DeForest", "D. R. Jackson", "A. Vourlidas", "T. Nieves-Chinchilla"], "categories": ["astro-ph.IM", "astro-ph.EP", "astro-ph.SR", "physics.space-ph"], "fields_of_study": ["Physics"], "published_date": "2022-01-19", "url": "https://arxiv.org/abs/2201.07426", "pdf_url": "https://arxiv.org/pdf/2201.07426v1", "arxiv_id": "2201.07426", "doi": "10.1029/2020SW002554", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Space Weather, Vol. 20, Issue 2, e2020SW002554 (17pp); 2022 January 31", "quality_score": 0.2386}
{"id": "6534a6f46d18f1616c0c14e356523b3cbbdb51cb63e05eb3e8857a64f6e4c178", "sources": ["arxiv", "semantic_scholar"], "title": "Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers", "abstract": "Recurrent neural networks (RNNs), temporal convolutions, and neural differential equations (NDEs) are popular families of deep learning models for time-series data, each with unique strengths and tradeoffs in modeling power and computational efficiency. We introduce a simple sequence model inspired by control systems that generalizes these approaches while addressing their shortcomings. The Linear State-Space Layer (LSSL) maps a sequence $u \\mapsto y$ by simply simulating a linear continuous-time state-space representation $\\dot{x} = Ax + Bu, y = Cx + Du$. Theoretically, we show that LSSL models are closely related to the three aforementioned families of models and inherit their strengths. For example, they generalize convolutions to continuous-time, explain common RNN heuristics, and share features of NDEs such as time-scale adaptation. We then incorporate and generalize recent theory on continuous-time memorization to introduce a trainable subset of structured matrices $A$ that endow LSSLs with long-range memory. Empirically, stacking LSSL layers into a simple deep neural network obtains state-of-the-art results across time series benchmarks for long dependencies in sequential image classification, real-world healthcare regression tasks, and speech. On a difficult speech classification task with length-16000 sequences, LSSL outperforms prior approaches by 24 accuracy points, and even outperforms baselines that use hand-crafted features on 100x shorter sequences.", "authors": ["Albert Gu", "Isys Johnson", "Karan Goel", "Khaled Saab", "Tri Dao", "Atri Rudra", "Christopher Ré"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2021-10-26", "url": "https://arxiv.org/abs/2110.13985", "pdf_url": "https://arxiv.org/pdf/2110.13985v1", "arxiv_id": "2110.13985", "doi": null, "citation_count": 1160, "influential_citation_count": 48, "has_code": false, "code_url": null, "venue": "Neural Information Processing Systems", "quality_score": 0.8451}
{"id": "fae88f6afd256cb6bb6b52434978305796f756417eabf401d3028b4af2a30f4f", "sources": ["arxiv", "semantic_scholar"], "title": "RTSNet: Learning to Smooth in Partially Known State-Space Models (Preprint)", "abstract": "The smoothing task is core to many signal processing applications. A widely popular smoother is the Rauch-Tung-Striebel (RTS) algorithm, which achieves minimal mean-squared error recovery with low complexity for linear Gaussian state space (SS) models, yet is limited in systems that are only partially known, as well as non-linear and non-Gaussian. In this work we propose RTSNet, a highly efficient model-based and data-driven smoothing algorithm suitable for partially known SS models. RTSNet integrates dedicated trainable models into the flow of the classical RTS smoother, while iteratively refining its sequence estimate via deep unfolding methodology. As a result, RTSNet learns from data to reliably smooth when operating under model mismatch and non-linearities while retaining the efficiency and interpretability of the traditional RTS smoothing algorithm. Our empirical study demonstrates that RTSNet overcomes non-linearities and model mismatch, outperforming classic smoothers operating with both mismatched and accurate domain knowledge. Moreover, while RTSNet is based on compact neural networks, which leads to faster training and inference times, it demonstrates improved performance over previously proposed deep smoothers in non-linear settings.", "authors": ["Guy Revach", "Xiaoyong Ni", "Nir Shlezinger", "Ruud J. G. van Sloun", "Yonina C. Eldar"], "categories": ["eess.SP"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2021-10-10", "url": "https://arxiv.org/abs/2110.04717", "pdf_url": "https://arxiv.org/pdf/2110.04717v5", "arxiv_id": "2110.04717", "doi": "10.1109/TSP.2023.3329964", "citation_count": 22, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Signal Processing", "quality_score": 0.3404}
{"id": "70e59cc5c1933c63a144aada156fff0ae13928cedb6d67c6186fd98772156a41", "sources": ["arxiv", "semantic_scholar"], "title": "OSPREI: A Coupled Approach to Modeling CME-Driven Space Weather with Automatically-Generated, User-Friendly Outputs", "abstract": "Coronal Mass Ejections (CMEs) drive space weather activity at Earth and throughout the solar system. Current CME-related space weather predictions rely on information reconstructed from coronagraphs, sometimes from only a single viewpoint, to drive a simple interplanetary propagation model, which only gives the arrival time or limited additional information. We present the coupling of three established models into OSPREI (Open Solar Physics Rapid Ensemble Information), a new tool that describes Sun-to-Earth CME behavior, including the location, orientation, size, shape, speed, arrival time, and internal thermal and magnetic properties, on the timescale needed for forecasts. First, ForeCAT describes the trajectory that a CME takes through the solar corona. Second, ANTEATR simulates the propagation, including expansion and deformation, of a CME in interplanetary space and determines the evolution of internal properties via conservation laws. Finally, FIDO produces in situ profiles for a CME's interaction with a synthetic spacecraft. OSPREI includes ensemble modeling by varying each input parameter to probe any uncertainty in their values, yielding probabilities for all outputs. Standardized visualizations are automatically generated, providing easily-accessible, essential information for space weather forecasting. We show OSPREI results for CMEs observed in the corona on 2021 April 22 and 2021 May 09. We approach these CME as a forecasting proof-of-concept, using information analogous to what would be available in real time rather than fine-tuning input parameters to achieve a best fit for a detailed scientific study. The OSPREI prediction shows good agreement with the arrival time and in situ properties.", "authors": ["C. Kay", "M. L. Mays", "Y. M. Collado-Vega"], "categories": ["astro-ph.SR", "physics.space-ph"], "fields_of_study": ["Physics"], "published_date": "2021-09-14", "url": "https://arxiv.org/abs/2109.06960", "pdf_url": "https://arxiv.org/pdf/2109.06960v3", "arxiv_id": "2109.06960", "doi": "10.1029/2021SW002914", "citation_count": 15, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.301}
{"id": "b7f18e6ca09c8af664e84643272599bb7184f0094f80768520229a58991f851b", "sources": ["arxiv", "semantic_scholar"], "title": "Quantum Metric and Correlated States in Two-dimensional Systems", "abstract": "The recent realization of twisted, two-dimensional, bilayers exhibiting strongly correlated states has created a platform in which the relation between the properties of the electronic bands and the nature of the correlated states can be studied in unprecedented ways. The reason is that these systems allow extraordinary control of the electronic bands' properties, for example by varying the relative twist angle between the layers forming the system. In particular, in twisted bilayers the low energy bands can be tuned to be very flat and with a nontrivial quantum metric. This allows the quantitative and experimental exploration of the relation between the metric of Bloch quantum states and the properties of correlated states. In this work we first review the general connection between quantum metric and the properties of correlated states that break a continuous symmetry. We then discuss the specific case when the correlated state is a superfluid and show how the quantum metric is related to the superfluid stiffness. To exemplify such relation we show results for the case of superconductivity in magic angle twisted bilayer graphene. We conclude by discussing possible research directions to further elucidate the connection between quantum metric and correlated states' properties.", "authors": ["Enrico Rossi"], "categories": ["cond-mat.supr-con", "cond-mat.mes-hall"], "fields_of_study": ["Physics"], "published_date": "2021-08-25", "url": "https://arxiv.org/abs/2108.11478", "pdf_url": "https://arxiv.org/pdf/2108.11478v2", "arxiv_id": "2108.11478", "doi": "10.1016/j.cossms.2021.100952", "citation_count": 83, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Current opinion in solid state & materials science", "quality_score": 0.4811}
{"id": "48e574191e5c116285104ae2169dbf937e0b01e454355d72b2ae18193860147e", "sources": ["arxiv", "semantic_scholar"], "title": "Elysium: Context-Aware Bytecode-Level Patching to Automatically Heal Vulnerable Smart Contracts", "abstract": "Fixing bugs is easiest by patching source code. However, source code is not always available: only 0.3% of the ~49M smart contracts that are currently deployed on Ethereum have their source code publicly available. Moreover, since contracts may call functions from other contracts, security flaws in closed-source contracts may affect open-source contracts as well. However, current state-of-the-art approaches that operate on closed-source contracts (i.e., EVM bytecode), such as EVMPatch and SmartShield, make use of purely hard-coded templates that leverage fix patching patterns. As a result, they cannot dynamically adapt to the bytecode that is being patched, which severely limits their flexibility and scalability. For instance, when patching integer overflows using hard-coded templates, a particular patch template needs to be employed as the bounds to be checked are different for each integer size. In this paper, we propose Elysium, a scalable approach towards automatic smart contract repair at the bytecode level. Elysium combines template-based and semantic-based patching by inferring context information from bytecode. Elysium is currently able to patch 7 different types of vulnerabilities in smart contracts automatically and can easily be extended with new templates and new bug-finding tools. We evaluate its effectiveness and correctness using 3 different datasets by replaying more than 500K transactions on patched contracts. We find that Elysium outperforms existing tools by patching at least 30% more contracts correctly. Finally, we also compare the overhead of Elysium in terms of deployment and transaction cost. In comparison to other tools, we find that generally Elysium minimizes the runtime cost (i.e., transaction cost) up to a factor of 1.7, for only a marginally higher deployment cost, where deployment cost is a one-time cost as compared to the runtime cost.", "authors": ["Christof Ferreira Torres", "Hugo Jonker", "Radu State"], "categories": ["cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2021-08-23", "url": "https://arxiv.org/abs/2108.10071", "pdf_url": "https://arxiv.org/pdf/2108.10071v3", "arxiv_id": "2108.10071", "doi": "10.1145/3545948.3545975", "citation_count": 39, "influential_citation_count": 3, "has_code": true, "code_url": null, "venue": "International Symposium on Recent Advances in Intrusion Detection", "quality_score": 0.4005}
{"id": "2126b96b00fe3f327a69362559fc3390a130ac2473271fbbc299d4e756282925", "sources": ["arxiv", "semantic_scholar"], "title": "What Sustained Multi-Disciplinary Research Can Achieve: The Space Weather Modeling Framework", "abstract": "MHD-based global space weather models have mostly been developed and maintained at academic institutions. While the \"free spirit\" approach of academia enables the rapid emergence and testing of new ideas and methods, the lack of long-term stability and support makes this arrangement very challenging. This paper describes a successful example of a university-based group, the Center of Space Environment Modeling (CSEM) at the University of Michigan, that developed and maintained the Space Weather Modeling Framework (SWMF) and its core element, the BATS-R-US extended MHD code. It took a quarter of a century to develop this capability and reach its present level of maturity that makes it suitable for research use by the space physics community through the Community Coordinated Modeling Center (CCMC) as well as operational use by the NOAA Space Weather Prediction Center (SWPC).", "authors": ["Tamas I. Gombosi", "Yuxi Chen", "Alex Glocer", "Zhenguang Huang", "Xianzhe Jia", "Michael W. Liemohn", "Ward B. Manchester", "Tuija Pulkkinen", "Nishtha Sachdeva", "Qusai Al Shidi", "Igor V. Sokolov", "Judit Szente", "Valeriy Tenishev", "Gabor Toth", "Bart van der Holst", "Daniel T. Welling", "Lulu Zhao", "Shasha Zou"], "categories": ["physics.space-ph", "astro-ph.EP", "physics.comp-ph", "physics.plasm-ph"], "fields_of_study": ["Physics"], "published_date": "2021-05-27", "url": "https://arxiv.org/abs/2105.13227", "pdf_url": "https://arxiv.org/pdf/2105.13227v1", "arxiv_id": "2105.13227", "doi": "10.1051/swsc/2021020", "citation_count": 111, "influential_citation_count": 5, "has_code": false, "code_url": null, "venue": "Journal of Space Weather and Space Climate", "quality_score": 0.5123}
{"id": "065f6e82bc6c9f7f2269ef4250b794cd9667bd26408d154d94f907683f01e284", "sources": ["arxiv", "semantic_scholar"], "title": "New cosmic ray observations at Syowa Station in the Antarctic for space weather study", "abstract": "Muon detectors and neutron monitors were recently installed at Syowa Station, in the Antarctic, to observe different types of secondary particles resulting from cosmic ray interactions simultaneously from the same location. Continuing observations will give new insight into the response of muon detectors to atmospheric and geomagnetic effects. Operation began in February, 2018 and the system has been stable with a duty-cycle exceeding 94%. Muon data shows a clear seasonal variation, which is expected from the atmospheric temperature effect. We verified successful operation by showing that the muon and neutron data are consistent with those from other locations by comparing intensity variations during a space weather event. We have established a web page to make real time data available with interactive graphics (http://polaris.nipr.ac.jp/~cosmicrays/).", "authors": ["C. Kato", "W. Kihara", "Y. Ko", "A. Kadokura", "R. Kataoka", "P. Evenson", "S. Uchida", "S. Kaimi", "Y. Nakamura", "H. A. Uchida", "K. Murase", "K. Munakata"], "categories": ["physics.space-ph", "astro-ph.EP", "astro-ph.IM"], "fields_of_study": ["Physics", "Environmental Science"], "published_date": "2021-01-25", "url": "https://arxiv.org/abs/2101.09887", "pdf_url": "https://arxiv.org/pdf/2101.09887v1", "arxiv_id": "2101.09887", "doi": "10.1051/SWSC/2021005", "citation_count": 11, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Journal of Space Weather and Space Climate", "quality_score": 0.2698}
{"id": "7a9b06cb48aad5b5724218cae0776558ffc6e423bab06f20ecf4ea59fd33b42d", "sources": ["arxiv", "semantic_scholar"], "title": "On the Scaling and Spacing of Extra-Solar Multi-Planet Systems", "abstract": "We investigate whether certain extra-solar multi-planet systems simultaneously follow the scaling and spacing rules of the angular-momentum-deficit model. The masses and semi-major axes of exoplanets in ten multi-planet systems are considered. It is found that GJ 667C, HD 215152, HD 40307, and Kepler-79 systems are currently close to configurations of the angular-momentum-deficit model. In a gas-poor scenario, GJ 3293, HD 141399, and HD 34445 systems are those which had a configuration of the angular-momentum-deficit model in the past and get scattered away due to post gaseous effects. In addition, no matter in gas-free or gas-poor scenario, 55 Cnc, GJ 876, and WASP-47 systems do not follow the angular-momentum-deficit model. Therefore, our results reveal important formation histories of these multi-planet systems.", "authors": ["Li-Chin Yeh", "Ing-Guey Jiang", "Sridhar Gajendran"], "categories": ["astro-ph.EP"], "fields_of_study": ["Physics"], "published_date": "2020-12-17", "url": "https://arxiv.org/abs/2012.09431", "pdf_url": "https://arxiv.org/pdf/2012.09431v1", "arxiv_id": "2012.09431", "doi": "10.1007/s10509-020-03899-y", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Astrophysics and Space Science", "quality_score": 0.0753}
{"id": "34166612a155a40a0a19ab7c23ae8142e4bc7689ff54811f53b2474815284698", "sources": ["arxiv", "semantic_scholar"], "title": "Addressing Gaps in Space Weather Operations and Understanding with Small Satellites", "abstract": "Gaps in space weather observations that can be addressed with small satellites are identified. Potential improvements in solar inputs to space weather models, space radiation control, estimations of energy budget of the upper Earth's atmosphere, and satellite drag modeling are briefly discussed. Key observables, instruments and observation strategies by small satellites are recommended. Tracking optimization for small satellites is proposed.", "authors": ["Olga Verkhoglyadova", "Charles Bussy-Virat", "Amir Caspi", "David Jackson", "Vladimir Kalegaev", "Jeffrey Klenzing", "Jesus Nieves-Chinchilla", "Angelos Vourlidas"], "categories": ["astro-ph.IM", "astro-ph.EP", "astro-ph.SR", "physics.space-ph"], "fields_of_study": ["Environmental Science", "Computer Science", "Physics"], "published_date": "2020-12-06", "url": "https://arxiv.org/abs/2012.03343", "pdf_url": "https://arxiv.org/pdf/2012.03343v1", "arxiv_id": "2012.03343", "doi": "10.1029/2020SW002566", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Space Weather, Vol. 19, Issue 3, e2020SW002566 (7pp); 2021 March 23", "quality_score": 0.2258}
{"id": "bec9d34bac00d00d140675382c820ba8363988702caf0ff22d39213e6c5b32d9", "sources": ["arxiv", "semantic_scholar"], "title": "Toward a Next Generation Particle Precipitation Model: Mesoscale Prediction Through Machine Learning (a Case Study and Framework for Progress)", "abstract": "We advance the modeling capability of electron particle precipitation from the magnetosphere to the ionosphere through a new database and use of machine learning (ML) tools to gain utility from those data. We have compiled, curated, analyzed, and made available a new and more capable database of particle precipitation data that includes 51 satellite years of Defense Meteorological Satellite Program (DMSP) observations temporally aligned with solar wind and geomagnetic activity data. The new total electron energy flux particle precipitation nowcast model, a neural network called PrecipNet, takes advantage of increased expressive power afforded by ML approaches to appropriately utilize diverse information from the solar wind and geomagnetic activity and, importantly, their time histories. With a more capable representation of the organizing parameters and the target electron energy flux observations, PrecipNet achieves a >50% reduction in errors from a current state-of-the-art model oval variation, assessment, tracking, intensity, and online nowcasting (OVATION Prime), better captures the dynamic changes of the auroral flux, and provides evidence that it can capably reconstruct mesoscale phenomena. We create and apply a new framework for space weather model evaluation that culminates previous guidance from across the solar-terrestrial research community. The research approach and results are representative of the \"new frontier\" of space weather research at the intersection of traditional and data science-driven discovery and provides a foundation for future efforts.", "authors": ["Ryan M. McGranaghan", "Jack Ziegler", "Téo Bloch", "Spencer Hatch", "Enrico Camporeale", "Kristina Lynch", "Mathew Owens", "Jesper Gjerloev", "Binzheng Zhang", "Susan Skone"], "categories": ["physics.space-ph", "stat.ML"], "fields_of_study": ["Physics", "Mathematics", "Environmental Science"], "published_date": "2020-11-19", "url": "https://arxiv.org/abs/2011.10117", "pdf_url": "https://arxiv.org/pdf/2011.10117v2", "arxiv_id": "2011.10117", "doi": "10.1029/2020SW002684", "citation_count": 21, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Space Weather, 19, e2020SW002684 (2021)", "quality_score": 0.3356}
{"id": "071cc2c96209490c8753388651bd05c2eca5f695ce119c2965bf7429072b0da0", "sources": ["arxiv", "semantic_scholar"], "title": "Bayesian recurrent state space model for rs-fMRI", "abstract": "We propose a hierarchical Bayesian recurrent state space model for modeling switching network connectivity in resting state fMRI data. Our model allows us to uncover shared network patterns across disease conditions. We evaluate our method on the ADNI2 dataset by inferring latent state patterns corresponding to altered neural circuits in individuals with Mild Cognitive Impairment (MCI). In addition to states shared across healthy and individuals with MCI, we discover latent states that are predominantly observed in individuals with MCI. Our model outperforms current state of the art deep learning method on ADNI2 dataset.", "authors": ["Arunesh Mittal", "Scott Linderman", "John Paisley", "Paul Sajda"], "categories": ["stat.ML", "cs.LG", "q-bio.NC"], "fields_of_study": ["Mathematics", "Computer Science", "Biology"], "published_date": "2020-11-14", "url": "https://arxiv.org/abs/2011.07365", "pdf_url": "https://arxiv.org/pdf/2011.07365v1", "arxiv_id": "2011.07365", "doi": null, "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0753}
{"id": "20b68915336d9f28e8ce3d6222c71980f08e21f03a8430454a2347cea9e87992", "sources": ["arxiv", "semantic_scholar"], "title": "International Coordination and Support for SmallSat-enabled Space Weather Activities", "abstract": "Advances in space weather science and small satellite (SmallSat) technology have proceeded in parallel over the past two decades, but better communication and coordination is needed among the respective worldwide communities contributing to this rapid progress. We identify six areas where improved international coordination is especially desirable, including: (1) orbital debris mitigation; (2) spectrum management; (3) export control regulations; (4) access to timely and low-cost launch opportunities; (5) inclusive data policies; and (6) education. We argue the need for internationally coordinated policies and programs to promote the use of SmallSats for space weather research and forecasting while realizing maximum scientific and technical advances through the integration of these two increasingly important endeavors.", "authors": ["Teresa Nieves-Chinchilla", "Bhavya Lal", "Robert Robinson", "Amir Caspi", "David R. Jackson", "Therese Moretto Jørgensen", "James Spann"], "categories": ["astro-ph.IM", "astro-ph.EP", "astro-ph.SR", "physics.space-ph"], "fields_of_study": ["Physics", "Computer Science", "Business"], "published_date": "2020-11-09", "url": "https://arxiv.org/abs/2011.04759", "pdf_url": "https://arxiv.org/pdf/2011.04759v1", "arxiv_id": "2011.04759", "doi": "10.1029/2020SW002568", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Space Weather, Vol. 18, Issue 12, e2020SW002568 (6pp); 2020 November 25", "quality_score": 0.1945}
{"id": "2a002aa56639ca8b5487ecd6b40646846dff2a5599f8dda4d95ddd0860e42d66", "sources": ["arxiv", "semantic_scholar"], "title": "Domain of Influence analysis: implications for Data Assimilation in space weather forecasting", "abstract": "Solar activity, ranging from the background solar wind to energetic coronal mass ejections (CMEs), is the main driver of the conditions in the interplanetary space and in the terrestrial space environment, known as space weather. A better understanding of the Sun-Earth connection carries enormous potential to mitigate negative space weather effects with economic and social benefits. Effective space weather forecasting relies on data and models. In this paper, we discuss some of the most used space weather models, and propose suitable locations for data gathering with space weather purposes. We report on the application of \\textit{Representer analysis (RA)} and \\textit{Domain of Influence (DOI) analysis} to three models simulating different stages of the Sun-Earth connection: the OpenGGCM and Tsyganenko models, focusing on solar wind - magnetosphere interaction, and the PLUTO model, used to simulate CME propagation in interplanetary space. Our analysis is promising for space weather purposes for several reasons. First, we obtain quantitative information about the most useful locations of observation points, such as solar wind monitors. For example, we find that the absolute values of the DOI are extremely low in the magnetospheric plasma sheet. Since knowledge of that particular sub-system is crucial for space weather, enhanced monitoring of the region would be most beneficial. Second, we are able to better characterize the models. Although the current analysis focuses on spatial rather than temporal correlations, we find that time-independent models are less useful for Data Assimilation activities than time-dependent models. Third, we take the first steps towards the ambitious goal of identifying the most relevant heliospheric parameters for modelling CME propagation in the heliosphere, their arrival time, and their geoeffectiveness at Earth.", "authors": ["Dimitrios Millas", "Maria Elena Innocenti", "Brecht Laperre", "Joachim Raeder", "Stefaan Poedts", "Giovanni Lapenta"], "categories": ["physics.space-ph", "astro-ph.SR"], "fields_of_study": ["Physics", "Computer Science"], "published_date": "2020-09-09", "url": "https://arxiv.org/abs/2009.04211", "pdf_url": "https://arxiv.org/pdf/2009.04211v1", "arxiv_id": "2009.04211", "doi": "10.3389/fspas.2020.571286", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Frontiers in Astronomy and Space Sciences", "quality_score": 0.1747}
{"id": "cc66ec95ac7b1f421f5630f6bc4fc8916f11dfe88231fdd87d396cfb6c44c4c8", "sources": ["arxiv", "semantic_scholar"], "title": "Archaeology in a Vacuum: Obstacles to and Solutions for Developing a Real Space Archaeology", "abstract": "This paper outlines some of the difficulties faced by archaeologists studying human activity in outer space. The International Space Station Archaeological Project has identified solutions to these problems, including the use of historic photographic archives and documentation of discard practices such as processes associated with the return of space-flown items to Earth.", "authors": ["International Space Station Archaeological Project", " :", "Alice C. Gorman", "Justin St. P. Walsh"], "categories": ["physics.pop-ph"], "fields_of_study": ["Engineering", "Physics"], "published_date": "2020-09-05", "url": "https://arxiv.org/abs/2009.02471", "pdf_url": "https://arxiv.org/pdf/2009.02471v1", "arxiv_id": "2009.02471", "doi": null, "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1193}
{"id": "70e9bbcefe6b8a1578cbc2f93a4eea7d1c5db2e95a92c5c99a3de4828062812d", "sources": ["arxiv", "semantic_scholar"], "title": "Searching for Supersymmetry: The $μν$SSM", "abstract": "We review the role played by the '$μ$ from $ν$' supersymmetric standard model ($μν$SSM) in the search for supersymmetry. First, we discuss its theoretical motivation, that is the simultaneous solution of $μ$- and $ν$-problems through the introduction of right-handed neutrinos. The latter produces $R$-parity violation (RPV), giving rise to interesting signals of new physics. As by-products, in the $μν$SSM there are dark matter candidates, and electroweak baryogenesis can be realized. Then, we survey signals by which the model could be tested at the large hadron collider (LHC). In addition to the enlarged Higgs sector with sneutrinos, we put special emphasis in analyzing the intimate connection between the lightest supersymmetric particle (LSP) lifetime and the size of neutrino Yukawa couplings. Displaced vertices and/or multileptons are some of the interesting signatures that can be probed. Finally, we discuss possible extensions of the $μν$SSM such as the inclusion in the superpotential of the conventional trilinear lepton-number violating couplings, the addition of an extra $U(1)'$ gauge group to the symmetry of the standard model, or the reinterpretation of the Higgs doublets as a fourth family of leptons superfields motivating the existence of a fourth family of vector-like quark doublet superfields.", "authors": ["Daniel E. Lopez-Fogliani", "Carlos Munoz"], "categories": ["hep-ph", "astro-ph.HE", "hep-ex", "hep-th"], "fields_of_study": ["Physics"], "published_date": "2020-09-02", "url": "https://arxiv.org/abs/2009.01380", "pdf_url": "https://arxiv.org/pdf/2009.01380v1", "arxiv_id": "2009.01380", "doi": "10.1140/epjst/e2020-000114-9", "citation_count": 21, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "The European Physical Journal Special Topics", "quality_score": 0.3356}