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\title{Geometric Layer-Adaptive Momentum: Analysis of a Novel Optimizer Approach}

\author{Aardvark}

\date{\today}

\begin{document}

\maketitle

\begin{abstract}
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\end{abstract}

\section{Introduction}
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\section{Related Work}
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\section{Method}
GLAM combines three components:

1) \textbf{Adaptive momentum updates} following Adam:
\begin{equation}
    m_t = \beta_1 m_{t-1} + (1-\beta_1)g_t
\end{equation}

2) \textbf{Geometric projection} via Newton-Schulz iteration for attention weights:
\begin{equation}
    W_{attn} \leftarrow \text{Orth}(W_{attn} - \eta\frac{m_t}{\sqrt{v_t}+\epsilon})
\end{equation}

3) \textbf{Layer-specific learning rates} based on parameter types.

\section{Experiments}
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\section{Limitations}
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\section{Conclusion}
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