Sessa: Selective State Space Attention
Modern sequence modeling is dominated by two families: Transformers, whose self-attention can access arbitrary elements of the visible sequence, and structured state-space models, which propagate information through an explicit recurrent state. These mechanisms face different limitations on long contexts: when attention is diffuse, the influence of individual tokens is diluted across the effective support, while recurrent state propagation can lose long-range sensitivity unless information is actively preserved. As a result, both mechanisms face challenges in preserving and selectively retrieving information over long contexts. We propose Sessa, a decoder that places attention inside a re...
| Authors | Liubomyr Horbatko |
|---|---|
| Primary category | cs.LG |
| Published | 20 April 2026 |
| Last updated at source | 21 April 2026 |
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