RopeDreamer uses quaternionic kinematic chains in a recurrent state space model with a dual decoder to cut open-loop prediction error by 40.52% over 50 steps on simulated DLO trajectories while preserving physical constraints.
Mamba-360: Survey of state space models as transformer alternative for long sequence modelling: Methods, applications, and challenges
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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.
ABMamba uses Mamba-based linear-complexity processing plus a novel Aligned Hierarchical Bidirectional Scan to deliver competitive video captioning on VATEX and MSR-VTT at roughly 3x higher throughput than typical Transformer MLLMs.
The paper consolidates existing research on Mamba models, their architecture variants, adaptations to different data modalities, and applications across domains.
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RopeDreamer: A Kinematic Recurrent State Space Model for Dynamics of Flexible Deformable Linear Objects
RopeDreamer uses quaternionic kinematic chains in a recurrent state space model with a dual decoder to cut open-loop prediction error by 40.52% over 50 steps on simulated DLO trajectories while preserving physical constraints.
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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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ABMAMBA: Multimodal Large Language Model with Aligned Hierarchical Bidirectional Scan for Efficient Video Captioning
ABMamba uses Mamba-based linear-complexity processing plus a novel Aligned Hierarchical Bidirectional Scan to deliver competitive video captioning on VATEX and MSR-VTT at roughly 3x higher throughput than typical Transformer MLLMs.
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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.
- Advancing Intelligent Sequence Modeling: Evolution, Trade-offs, and Applications of State-Space Architectures from S4 to Mamba