Accurate stock return forecasting remains a central challenge in quantitative finance, as it directly informs the construction of portfolios and the management of risk. Although traditional static factor models are widely used, they are limited by manual factor selection and fixed weight assignments, which makes them vulnerable to evolving market conditions and regime shifts. To overcome these limitations, we introduce Market Regime Aware-Augmented Attention GRU (MRA-AGRU), an automated dynamic factor gating framework that adaptively reweights factors in response to market regime signals. By integrating an attention-enhanced GRU network, MRA-AGRU effectively suppresses obsolete or noisy factors while amplifying those most relevant to the prevailing environment, thereby capturing nuanced temporal and cross-factor dependencies. Extensive experiments on the CSI 300 and NASDAQ 100 demonstrate the superior performance of MRA-AGRU, highlighting machine-driven factor modulation's role in improving robustness to structural breaks and reducing bias in factor engineering.