Abstract
arXiv:2602.16837v2 Announce Type: replace Abstract: Transformer models systematically favor certain token positions, yet the architectural origins of this position bias remain poorly understood. This bias is closely connected to the Lost-in-the-Middle phenomenon, where models underutilize information placed in the middle of the context. We show that Lost-in-the-Middle-type behavior can arise from the architecture of causal Transformers itself. To do so, we develop a structural theory of position bias based on residual-aware cumulative attention rollout. At finite depth, causal masking and residual connections induce broad, often U-shaped, influence profiles. At infinite depth, our framework resolves a discrepancy between prior attention-only collapse theory and practical Transformer behavior: residual connections fundamentally change cumulative attention dynamics. Empirically, the predicted profiles closely match measured input-token influence in pretrained language models.