AIQuiet 40d · day 40
Researchers introduce 'recirculation' technique for Transformer working memory
A new method gives pretrained language models inference-time memory without retraining by feeding context back through earlier layers.
What to know
- Recirculation is a new inference-time technique that adds working memory to existing pretrained Transformers without retraining.
- The method prevents useful context from being lost in deeper layers by recycling information back through earlier layers during processing.
- The approach appears designed to improve model reasoning and context retention without computational cost of full retraining.
@askalphaxiv AI researcher
The record 2 articles and posts · last 30 days
What people are saying 1 voices from 1 site · best of 2 · verbatim
- Aug 19
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"Recirculation" Recirculation gives a pretrained Transformer a kind of working memory at inference time, without retraining. So instead of letting useful context get buried in deeper layers, it feeds a small part of that information back into earlier layers as the model reads