← Back to all news
arXiv Machine Learning By Yixiao Qian, Song Chen, Jiaxu Liu, Shengze Cai, Chao Xu

DSSMs: State Space Models with Explicit Memory via Delay Differential Equations

Read the original on arXiv Machine Learning →

arXiv:2607. 10244v1 Announce Type: new Abstract: State Space Models (SSMs) have emerged as a powerful paradigm for efficient long-sequence modeling, offering parallel training and fast linear-time recurrent inference.

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.

Related stories

arXiv Machine Learning
Aug 11

Advancing Intelligent Sequence Modeling: Evolution, Trade-offs, and Applications of State-Space Architectures from S4 to Mamba

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.

By Shriyank Somvanshi, Md Monzurul Islam, Mahmuda Sultana Mimi, Sazzad Bin Bashar Polock, Gaurab Chhetri, Anandi Dutta, Amir Rafe, Subasish Das
llms
More like this →
arXiv Machine Learning
Jun 25

Frequency Domain Reservoir Computing

arXiv:2606. 24969v1 Announce Type: new Abstract: While the quadratic sequence-length bottleneck of transformers has fueled a resurgence in recurrent models, effectively capturing complex dynamics requires architectures that balance efficient training with highly expressive latent states.

By Klaus Schertler, Xiomara Runge, Andrea Ceni, David Kappel, Claudio Gallicchio
llmsragbenchmarks
More like this →
arXiv AI
2d ago

Reshape and Recur: Improving SSMs with Input Reshaping and Depth Recurrence

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).

By M\'onika Farsang, Ramin Hasani, Daniela Rus, Radu Grosu
llmsragefficiency
More like this →
arXiv Machine Learning
Jul 28

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions

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.

By Haotian Jiang, Zeyu Bao, Shida Wang, Qianxiao Li
llmsefficiencybenchmarks
More like this →