  Freely Long-Thinking Transformer (FraiLT) is an improved transformer model
designed to enhance processing capabilities without scaling up size. It
utilizes a recursive approach, iterating over a subset of layers multiple
times, and introduces iteration encodings to maintain awareness across these
cycles. Iteration encoding allows FraiLT to achieve the interpretive depth of
larger models in a compact form. When evaluated on a synthetic story dataset,
FraiLT outperformed larger models, showcasing its ability to deliver
high-quality performance while reducing memory demands. This model represents a
step forward towards more efficient and accessible language models.
