\documentclass{article}
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\title{AMO: Analysis of Adaptive Momentum Optimization}
\author{Aardvark}
\date{\today}

\begin{document}

\maketitle

\begin{abstract}
We analyze Adaptive Momentum Optimization (AMO) for language models. While showing stable training, AMO underperformed standard baselines (validation loss 9.773 vs AdamW's 4.9266). Our negative results contribute insights into momentum adaptation challenges.
\end{abstract}

\section{Introduction}
Recent work has focused on adaptive optimizers \cite{kingma2014adam}. We examine momentum adaptation's effectiveness in transformers.

\section{Method}
AMO adapts momentum $\beta$ based on gradient statistics:
\begin{equation}
\beta = \beta_1(1-0.5\frac{\|g_t\|}{\|g\|_{ema}})
\end{equation}

\section{Results}
\begin{tabular}{|c|c|}
\hline
Step & Loss \\ \hline
100 & 10.80 \\
300 & 9.96 \\
600 & 9.78 \\
640 & 9.77 \\ \hline
\end{tabular}

\section{Conclusions}
Momentum adaptation alone shows limited effectiveness for transformers.

\begin{thebibliography}{1}
\bibitem{kingma2014adam} Kingma and Ba, 2014.
\end{thebibliography}

\end{document}
