arXiv Machine Learning By Oliver Sieberling, Bharat Runwal, David Jin, Ryan Chin, Rameswar Panda, Yoon Kim

Triadic Linear Attention: Three-Dimensional Recurrent States for Long-Context Sequence Modeling

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arXiv Machine Learning
Sep 14

RunningTensor: Generalizing Linear Attention to Higher-Order Recurrent States

RunningTensor generalizes linear attention and state‑space models by extending the recurrent memory from a second‑order tensor (matrix) to an order‑o tensor. The memory is updated via a rank‑1 outer product and read by contracting with o‑1 vector queries, with order‑2 recovering linear attention. Experiments on synthetic associative recall and real language tasks show that RunningTensor improves memory capacity from O(W²) to O(Wᵒ) and outperforms existing baselines.

By Luca Herranz-Celotti, Vincent Guigue
arXiv Machine Learning
Jun 25

ATMA: Length-Invariant Language Modeling via Polar Attention and Gated-Delta Compression Memory

arXiv:2606. 25156v1 Announce Type: new Abstract: Modern large language models based on softmax scaled-dot-product attention are constrained by their training sequence length: as the key-value sequence grows, softmax probability mass can dilute across a wider distribution, inducing activation shift and long-context performance collapse.

By Habibullah Akbar
arXiv AI
1d ago

Switching Linear Attention

Switching Linear Attention (SwiLA) is a new sequence layer that improves upon standard softmax attention by maintaining a fixed-size recurrent state while enhancing representational capacity. It derives its recurrence from a test-time regression framework, using online expectation-maximization in a mixture of linear regressions model. In various benchmarks—including associative recall, in-context language learning, and language modeling—SwiLA achieves strong performance, narrowing the gap to softmax attention and even surpassing it in some settings.

By Hyun Dong Lee, Xavier Gonzalez, Nicolas Zucchet, E. Kelly Buchanan, Emily B. Fox, Scott W. Linderman