  Learning-augmented algorithms has been extensively studied recently in the
computer-science community, due to the potential of using machine learning
predictions in order to improve the performance of algorithms. Predictions are
especially useful for online algorithms making irrevocable decisions without
knowledge of the future. Such learning-augmented algorithms aim to overcome the
limitations of classical online algorithms when the predictions are accurate,
and still perform comparably when the predictions are inaccurate.
  A common approach is to adapt existing online algorithms to the particular
advice notion employed, which often involves understanding previous
sophisticated algorithms and their analyses. However, ideally, one would simply
use previous online solutions in a black-box fashion, without much loss in the
approximation guarantees. Such clean solutions that avoid opening up
black-boxes are often rare, and may be even missed the first time around. For
example, Grigorescu et al. (NeurIPS 22) proposed a learning-augmented
algorithms for online covering linear programs, but it later turned out that
their results can be subsumed by a natural approach that switches between the
advice and an online algorithm given as a black-box, as noted in their paper.
  In this work, we introduce and analyze a simple learning-augmented algorithm
for online packing problems with linear constraints and concave objectives. We
exhibit several direct applications of our framework including online packing
linear programming, knapsack, resource management benefit, throughput
maximization, and network utility maximization. We further raise the problem of
understanding necessary and sufficient conditions for when such simple
black-box solutions may be optimal. We believe this is an important direction
of research that would unify many ad-hoc approaches from the literature.
