arXiv:2606. 30461v1 Announce Type: new Abstract: State space models (SSMs) have emerged as efficient linear-time alternatives to attention for long-sequence modeling.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2606. 12364v1 Announce Type: new Abstract: Transformers dominate modern sequence modeling, but their quadratic attention incurs substantial computational cost.
arXiv:2503. 18970v4 Announce Type: replace Abstract: Structured State Space Models (SSMs) have become a prominent class of sequence models, developed against two long-standing difficulties: the sequential computation and gradient propagation limits of Recurrent Neural Networks (RNNs), and the quadratic time and memory cost of self-attention in Transformers.
arXiv:2609.36529v1 Announce Type: new Abstract: Recurrent neural networks (RNNs) compress the historical context into a memory state of fixed size, thus allowing for constant-time inference. The memo...
arXiv:2506. 05678v3 Announce Type: replace Abstract: The evolution of sequence modeling architectures, from recurrent neural networks and convolutional models to Transformers and structured state-space models, reflects ongoing efforts to address the diverse temporal dependencies inherent in sequential data.
arXiv:2609.16540v1 Announce Type: cross Abstract: State Space Models (SSMs) have emerged as a compelling alternative to Transformers, enabling sequence modeling with constant memory and linear comput...
The paper proposes two extensions to State Space Models (SSMs) to reduce memory usage and improve performance. First, it introduces depth recurrence, allowing a looped SSM with fewer parameters to match the performance of a larger, non-recurrent model. Second, it advocates using a fixed time granularity across tasks by reshaping input sequences, which enhances how information is presented to the model. Both techniques consistently benefit four representative SSM architectures (LRU, S5, LinOSS, LrcSSM).