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Existing deep learning models, typically based on Convolutional Neural Networks (CNNs), often regress a deterministic score and struggle to surpass a performance plateau. In this work, we propose the Holo-Attentive Relational Network (H-ARN), a novel architecture designed to overcome these fundamental limitations. H-ARN makes two primary contributions: (1) It pioneers the use of distribution learning by predicting the mean and variance of beauty scores, employing a Gaussian Negative Log-Likelihood loss to robustly model the subjectivity and ambiguity in training data. (2) It introduces a relational self-attention module atop a powerful Vision Transformer (ViT) backbone, explicitly learning the complex, long-range relationships between facial components that define aesthetic harmony. Evaluated on the challenging SCUT-FBP5500 benchmark, our proposed H-ARN achieves a new state-of-the-art Pearson Correlation of 0.9333, significantly outperforming previous methods. The code will be made publicly available upon publication\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/DjameleddineBoukhari\/H-ARN\" ext-link-type=\"uri\">https:\/\/github.com\/DjameleddineBoukhari\/H-ARN<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1007\/s13735-026-00401-2","type":"journal-article","created":{"date-parts":[[2026,6,21]],"date-time":"2026-06-21T14:12:44Z","timestamp":1782051164000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["H-ARN: A holo-attentive relational network for holistic facial beauty prediction via distribution learning"],"prefix":"10.1007","volume":"15","author":[{"given":"D. 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