Attention as Frustrated Synchronization

The article introduces the Frustrated Synchronization Network (FSN), an attention architecture that utilizes a learned complex coupling kernel to model token interactions as phases on a torus, incorporating synchronization principles to facilitate computation. With one million parameters, the FSN outperforms a tuned RoPE-SwiGLU transformer on character-level text and code tasks, achieving a validation loss of 1.5953 compared to the transformer's 1.611. This approach highlights the potential of integrating synchronization dynamics into LLM architectures, particularly for improving long-range dependencies and overall performance in natural text processing.