Associative Recurrent Memory Transformer
Authors: name: Ivan Rodkin, name: Yuri Kuratov, name: Aydar Bulatov, name: Mikhail Burtsev

This paper addresses the challenge of creating a neural architecture for very
long sequences that requires constant time for processing new information at
each time step. Our approach, Associative Recurrent Memory Transformer (ARMT),
is based on transformer self-attention for local context and segment-level
recurrence for storage of task specific information distributed over a long
context. We demonstrate that ARMT outperfors existing alternatives in
associative retrieval tasks and sets a new performance record in the recent
BABILong multi-task long-context benchmark by answering single-fact questions
over 50 million tokens with an accuracy of 79.9%. The source code for training
and evaluation is available on github.