This arXiv preprint (not yet peer-reviewed) introduces Attentive Neural Processes for interpolating sparse, multi-band light curves from simulated transients under realistic Vera C. Rubin Observatory cadences. The study meta-trains on diverse simulated events across 15 classes, then performs amortized inference that handles all bands simultaneously without per-object kernel fitting or class labels. Methodology relies entirely on synthetic data with no real observations tested, limiting claims about domain shift. Compared to Gaussian Processes, which require individual fits and struggle with cross-band correlations, the model delivers superior regression, feature recovery, and calibrated uncertainties while running over 10,000 times faster. A key omission in the source is discussion of training-data biases that could degrade performance on rare or novel transients not represented in simulations. Related work on neural processes for meta-learning (Garnelo et al., 2018) and LSST alert-stream requirements (Ivezić et al., 2019) shows this approach directly addresses the nightly data volume bottleneck that standard GPs cannot scale to meet. The result is a concrete bridge from deep-learning advances to real-time multimessenger follow-up, though deployment will still require validation on early LSST commissioning data.