We demonstrated the chemistry-, format-and cycling procedure-agnostic ARCANA framework, and its ability to reliably monitor battery life and SOH by utilizing multitask learning with an attention mechanism. ARCANA excelled across three predictive settings, demonstrating that augmenting the model with diverse knowledge streams enhances its generalization across virtually all variations possible in batteries such as anode, cathode, electrolyte and shuttle ion chemistry and format. The ARCANA model integrates uncertainty quantification and attention mechanisms for each and every cycle to elucidate the model's focus for each prediction and is essential for uncovering complex patterns associated across multiple factors. Further evaluation involves saliency and sensitivity assessments, allowing us to understand the impact of perturbation of input parameter on output predictions. By examining whether saliency and attention are directly correlated or orthogonal to each other, we gain a comprehensive understanding of input-output relationships, increasing the model's explainability and reliability in extrapolation. Incorporating raw data and failed experiments, as suggested in prior studies is a deliberate strategy to teach our models to recognize variations across similar cell types and manufactures. This inclusion not only enables uncertainties to be quantified more accurately but also deepens reliability insights, reduces bias, and offers a more meaningful understanding of the data. A conceptually straightforward extension to this work would be to incorporate additional features, such as the rate of change of voltage with respect to capacity (dQ/dV) , and leverage different characterization methods like spectroscopy, to enhance the predictive power of the models.This will not only enhance multi-feature predictions, but also deepen the understanding of degradation processes .