Lost in the Middle at Birth: An Exact Theory of Transformer Position Bias

Researchers discover that the 'Lost in the Middle' phenomenon in transformer models - where AI performs poorly on middle context but well on beginning and end content - is an inherent architectural property present even before training begins. The U-shaped performance bias stems from the mathematical structure of causal decoders with residual connections, creating a 'factorial dead zone' in middle positions.