The Structural Origin of Attention Sink: Variance Discrepancy, Super Neurons, and Dimension Disparity
Why language models obsess over the first word and how to fix it
Large language models tend to give disproportionate attention to initial tokens—a problem called "attention sink"—because of how they aggregate information and process data through their internal layers. Researchers traced this to a specific structural imbalance: early neurons create inconsistent signal strengths that force the model to anchor attention to the first token as a stabilizing mechanism. They proved this causal chain by deliberately triggering attention sinks at different positions, then tested a simple architectural fix that balanced the signals during training and sped up model convergence.
Attention sinks waste computational resources and can degrade model performance by forcing the network to concentrate on irrelevant tokens. Understanding the root cause opens the door to cleaner, more efficient models—the architectural tweak the researchers tested could reduce training time and improve how language models process information, with potential benefits for speed and accuracy in real applications.