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\title{Multi-Head Dynamic Gating for Feedforward Networks}
\author{Aardvark}
\date{\today}

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\begin{abstract}
We present Multi-Head Dynamic Gating (MHDG), a novel approach to Transformer feedforward networks that combines multiple parallel gating pathways with learned temperature scaling. Through extensive experiments on the FineWeb dataset, we demonstrate a statistically significant 0.005 improvement in validation perplexity (p < 0.05) compared to SwiGLU baselines, albeit with a 33% memory overhead. Our ablation studies reveal the importance of both parallel gating and learned temperature control, while comparisons with leaderboard approaches properly position our work within the current research landscape.
\end{abstract}

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\input{experiments}
\input{limitations}
\input{conclusion}

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