Adaptive Attention Redistribution in Deep Encoder Decoder Pipelines

Abstract

Encoder–decoder architectures with multi-head attention are widely used in sequence modeling;
however, uniform attention distribution across heads often dilutes contextual relevance and weakens
semantic alignment between encoded representations and generated outputs. This article introduces an
Adaptive Attention Redistribution (AAR) mechanism that dynamically scales attention head
contributions based on learned significance, enhancing the interpretive strength of high-value
contextual features without modifying core transformer structure or increasing computational cost.
The mechanism maintains full representational capacity while improving coherence, convergence
stability, and long-sequence generation accuracy. Quantitative and qualitative evaluations demonstrate
that the AAR-enhanced architecture achieves lower perplexity, reduced sequence error rates, and more
focused attention patterns compared to a standard encoder–decoder baseline. Because AAR integrates
seamlessly into existing pipelines and pretrained frameworks, it offers a practical and efficient
solution for improving transformer performance in varied application environments.