<p>As a cornerstone of Transformer architectures, Attention has transformed the landscape of machine learning, due to its ability to capture contextual relevance in sequential data. However, prior research has raised concerns that Attention may be too general to effectively capture the inductive biases inherently present in task-specific data. In this study, we propose a hybrid learning framework to embed Attention with task-specific inductive biases. In particular, we first introduce a novel weighting scheme named Cumulative Importance Weighting (CIW) to capture recency effects, a form of inductive bias that favors recent information in prediction. We then incorporate CIW with Attention to construct a hybrid framework that leverages both contextual relevance and temporal weighting. We apply it to two distinct tasks: stock time-series prediction and machine translation, where the presence of recency effects is hypothesized to test. Our experiments reveal that the hybrid framework yields substantial performance gains in the stock prediction task but insignificant improvements in the machine translation task. These results are consistent with well-established studies in finance and linguistics, suggesting that the hybrid framework effectively learns the complementary strengths of each component. Our findings support the broader principle that integrating domain knowledge into learned representations enhances model effectiveness. They also point to a promising direction for the application of hybrid learning framework, where each component is deliberately designed to capture meaningful, domain-specific data patterns, and their integration has the potential to yield superior performance.</p>