Inf-SSM constrains the infinite-horizon evolution of SSMs via Grassmannian geometry and an efficient O(n^2) Sylvester solver to enable exemplar-free continual learning with reduced forgetting.
MambaVision: A hybrid mamba- transformer vision backbone
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FractalMamba++ scales Vision Mamba across resolutions by using Hilbert fractal serialization, hierarchy-based skip connections, and fractal-aware 2D rotary position encoding.
Controlled tests on LoveDA and ISPRS Potsdam show visual SSM encoders deliver favorable speed-accuracy trade-offs but suffer most from boundary errors under domain shift, indicating that robustness and boundary-aware decoding will matter more than intra-family encoder scaling.
HAMSA achieves 85.7% ImageNet-1K top-1 accuracy as a spectral-domain SSM with 2.2x faster inference and lower memory than transformers or scanning-based SSMs.
LiMA reformulates attribution as submodular subset selection and uses bidirectional greedy search to identify minimal important regions, reporting 36.3% better insertion and 39.6% better deletion scores than prior methods on eight foundation models.
On scarce dual-view pasture data, a simple two-layer gated depthwise convolution fusion achieves R²=0.903, beating cross-view attention transformers (0.833), bidirectional SSMs (0.819), and Mamba (0.793), while backbone pretraining scale dominates all other choices.
The paper consolidates existing research on Mamba models, their architecture variants, adaptations to different data modalities, and applications across domains.
citing papers explorer
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Exemplar-Free Continual Learning for State Space Models
Inf-SSM constrains the infinite-horizon evolution of SSMs via Grassmannian geometry and an efficient O(n^2) Sylvester solver to enable exemplar-free continual learning with reduced forgetting.
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FractalMamba++: Scaling Vision Mamba Across Resolutions via Hilbert Fractal Geometry
FractalMamba++ scales Vision Mamba across resolutions by using Hilbert fractal serialization, hierarchy-based skip connections, and fractal-aware 2D rotary position encoding.
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A Controlled Benchmark of Visual State-Space Backbones with Domain-Shift and Boundary Analysis for Remote-Sensing Segmentation
Controlled tests on LoveDA and ISPRS Potsdam show visual SSM encoders deliver favorable speed-accuracy trade-offs but suffer most from boundary errors under domain shift, indicating that robustness and boundary-aware decoding will matter more than intra-family encoder scaling.
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HAMSA: Scanning-Free Vision State Space Models via SpectralPulseNet
HAMSA achieves 85.7% ImageNet-1K top-1 accuracy as a spectral-domain SSM with 2.2x faster inference and lower memory than transformers or scanning-based SSMs.
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Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection
LiMA reformulates attribution as submodular subset selection and uses bidirectional greedy search to identify minimal important regions, reporting 36.3% better insertion and 39.6% better deletion scores than prior methods on eight foundation models.
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Fusion Complexity Inversion: Why Simpler Cross View Modules Outperform SSMs and Cross View Attention Transformers for Pasture Biomass Regression
On scarce dual-view pasture data, a simple two-layer gated depthwise convolution fusion achieves R²=0.903, beating cross-view attention transformers (0.833), bidirectional SSMs (0.819), and Mamba (0.793), while backbone pretraining scale dominates all other choices.
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A Survey of Mamba
The paper consolidates existing research on Mamba models, their architecture variants, adaptations to different data modalities, and applications across domains.