The ARCANA model is augmented with additional features such as the attention mechanism, which provides insight into the decision-making process of the model. This feature distinguishes between predictions based on underlying patterns and those arising from stochastic variability. Saliency analysis is additionally performed to emphasize the relative importance of each parameter through a computation of the absolute gradient of the model output relative to the input of the test set. It quantifies the sensitivity of the input parameters, revealing how minor variations significantly alter the output results , thus aligning the internal logic of the model with domain-specific knowledge. Adding another layer of robustness is uncertainty quantification, which is valuable not only for understanding the reliability of cycling protocols, but also for assessing material performance across different battery chemistries.