A recent paper published on arXiv addresses the ongoing challenges in achieving compositional generalization within machine learning, underscoring the necessity for improved evaluation methods. The authors highlight significant limitations in current benchmarks, which often fail to comprehensively assess models' abilities to generalize compositionally. Additionally, the paper points out that many existing models prioritize efficiency over thoroughness, a focus that may impede meaningful progress in the field. This emphasis on efficiency potentially restricts the development of more robust and generalizable machine learning systems. The discussion aligns with broader concerns in the AI research community about balancing performance metrics with deeper understanding and evaluation rigor. By drawing attention to these issues, the paper contributes to ongoing efforts to refine both model design and assessment strategies. Such advancements are critical for enhancing the reliability and applicability of machine learning technologies.