AI BriefingSessa for Long Context
Key point
Sessa is a decoder architecture for long context that puts attention into a recurrent feedback path.
Details
Sessa is a decoder architecture for long-context LLMs that places attention not as a single read operation but inside a recurrent feedback path.
The core idea is to make attention part of the memory dynamics, forming multiple attention-mediated paths across the time axis.
Under explicit assumptions and in a matched regime, compared to Transformer and Mamba-style baselines, it suggests:
- slower memory decay
- more flexible selective retrieval
- and under some conditions, even effectively non-decaying influence profiles
It proposes a structure that can improve memory retention and selective retrieval, which are important in long-context processing, and both the arXiv paper and GitHub code have been released together.
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