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arXiv · 2602.16837

A Structural Theory of Position Bias in Transformers

Hanna Herasimchyk·Robin Labryga·Tomislav Prusina·Sören Laue
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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.

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Hanna Herasimchyk, Robin Labryga, Tomislav Prusina, Sören Laue. 2026-02-18. A Structural Theory of Position Bias in Transformers. https://arxiv.org/abs/2602.16837

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