AdamX: Cosine similarity meets gradient descent

AdamX is a new first‑order optimizer that uses cosine similarity to adaptively control update magnitudes, making it scalable, model‑agnostic, and easy to add to existing training pipelines. It also includes a variance rectification scheme that smooths optimization early in training. Empirical results show AdamX achieves competitive convergence rates across various benchmark datasets and architectures, measured by the number of epochs needed to hit predefined performance thresholds under a fixed hyperparameter budget.

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