AI RESEARCH
Advancing Intelligent Sequence Modeling: Evolution, Trade-offs, and Applications of State- Space Architectures from S4 to Mamba
arXiv CS.LG
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ArXi:2503.18970v3 Announce Type: replace 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.