  While Transformers have revolutionized deep learning, their quadratic
attention complexity hinders their ability to process infinitely long inputs.
We propose Feedback Attention Memory (FAM), a novel Transformer architecture
that leverages a feedback loop to enable the network to attend to its own
latent representations. This design fosters the emergence of working memory
within the Transformer, allowing it to process indefinitely long sequences.
TransformerFAM requires no additional weights, enabling seamless integration
with pre-trained models. Our experiments show that TransformerFAM significantly
improves Transformer performance on long-context tasks across various model
sizes (1B, 8B, and 24B). These results showcase the potential to empower Large
Language Models (LLMs) to process sequences of unlimited length.
