An article published on September 24, 2026 presents linear-complexity layers with expressive hidden states, updated through self-supervised learning.
Published on September 24, 2026, the article presents a layer framework for sequence modeling. Its proposal treats the hidden state as a machine-learning model and updates it through self-supervised learning. The text highlights linear complexity and expressive hidden states.
The approach is explored as an alternative to conventional attention for sequences with long context. To assess the scope of the findings, consult the original article and check its methods, experimental conditions, and supporting evidence; the available summary does not provide specific metrics or comparative results.