The evaluation of heuristic optimizers on test problems, better known as benchmarking, is a cornerstone of research in multi-objective optimization. However, many frequently used test problems either feature a limited degree of optimization challenges or have poorly understood reference solutions. Here, we present an overview of BONO-Bench [5], a recently proposed problem generator and benchmark set for bi-objective numerical optimization. Building on convex-quadratic problems, it features diverse challenges ranging from di!erent levels of conditioning, shapes of Pareto set and front as well as plateaus to di!erent structured and unstructured multimodality patterns. Furthermore, we enable best practices for empirical runtime analysis of optimizers using reference solutions that can be approximated to an arbitrary degree, resulting in precise target values for the hypervolume and exact R2 indicators.