Shared Memory Reduces Memory Use in Recurrent Transformers
Radar: an article on sharing memory across recursions in recurrent Transformers, reporting a 76–79% reduction in context memory for models from 150M to 1B parameters, compared with standard Transformers.
Radar briefing: the article studies memory sharing across recursions in recurrent Transformers. According to the source, the publication reports a 76–79% reduction in context memory and quality gains over standard Transformers, in models from 150M to 1B parameters. The source is dated 5 October 2026.
Relevance: the item may interest engineers working on model architecture who evaluate ways to cut the memory cost of recurrent Transformers. Rota Nacional does not implement this architecture and does not claim to reproduce these results. To verify, consult the original article cited in the source and review its methodology, evaluation sets and experimental conditions before drawing conclusions.