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Life sciences · Preprint
arXiv · September 10, 2026
Posted before peer review. The findings may change or fail to hold.
AdamX is a proposed first-order optimizer that combines cosine similarity with adaptive update magnitude control and a variance rectification scheme. The authors report empirical evidence of competitive convergence rates on benchmark datasets, but the preprint does not provide sufficient methodological detail, statistical analysis, or head-to-head comparisons to permit independent assessment of the claim or generalization to practice.
Preprint. Intervention: AdamX optimizer with cosine similarity adaptive mechanism and variance rectification scheme.
AdamX achieves competitive convergence rates across a range of benchmark datasets and architectures Method incorporates cosine similarity as an adaptive mechanism for controlling update magnitudes Variance rectification scheme reported to promote smoother optimization during early training stages
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This is an unrefereed arXiv preprint describing a novel optimization algorithm with empirical benchmarking; it has not undergone peer review and lacks sufficient detail on experimental design, statistical analysis, and comparative rigor to support stronger evidence claims.
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We introduce AdamX, a first-order optimizer that incorporates cosine similarity as an adaptive mechanism for controlling update magnitudes. The proposed method is scalable, model-agnostic, and straightforward to integrate into existing training pipelines. We further introduce a variance rectification scheme that promotes smoother optimization during the early stages of training. Overall, we provide empirical evidence that AdamX achieves competitive convergence rates across a range of benchmark datasets and architectures. Performance is evaluated in terms of the number of epochs required to reach predefined performance thresholds under a fixed hyperparameter budget. Code and Experiments available at: https://github.com/FranciscoCaldas/adamX.
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