Comment on "Deep Regression Learning with Optimal Loss Function" (2509.03702v1)

Abstract: OpenReview benefits the peer-review system by promoting transparency, openness, and collaboration. By making reviews, comments, and author responses publicly accessible, the platform encourages constructive feedback, reduces bias, and allows the research community to engage directly in the review process. This level of openness fosters higher-quality reviews, greater accountability, and continuous improvement in scholarly communication. In the statistics community, such a transparent and open review system has not traditionally existed. This lack of transparency has contributed to significant variation in the quality of published papers, even in leading journals, with some containing substantial errors in both proofs and numerical analyses. To illustrate this issue, this note examines several results from Wang, Zhou and Lin (2025) [arXiv:2309.12872; https://doi.org/10.1080/01621459.2024.2412364] and highlights potential errors in their proofs, some of which are strikingly obvious. This raises a critical question: how important are mathematical proofs in statistical journals, and how should they be rigorously verified? Addressing this question is essential not only for maintaining academic rigor but also for fostering the right attitudes toward scholarship and quality assurance in the field. A plausible approach would be for arXiv to provide an anonymous discussion section, allowing readers-whether anonymous or not-to post comments, while also giving authors the opportunity to respond.