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arXiv · 2512.11883

Position: Universal Aesthetic Alignment Narrows Artistic Expression

Wenqi Marshall Guo·Qingyun Qian·Khalad Hasan·Shan Du
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Abstract

Over-aligning image generation models to a generalized aesthetic preference conflicts with user intent, particularly when "anti-aesthetic" outputs are requested for artistic or critical purposes. This adherence prioritizes developer-centered values, compromising user autonomy and aesthetic pluralism. We test this bias by constructing a wide-spectrum aesthetics dataset and evaluating state-of-the-art generation and reward models. This position paper finds that aesthetic-aligned generation models frequently default to conventionally beautiful outputs, failing to respect instructions for low-quality or negative imagery. Crucially, reward models penalize anti-aesthetic images even when they perfectly match the explicit user prompt. We confirm this systemic bias through image-to-image editing and evaluation against real abstract artworks. Our code, fine-tuned models, and datasets are available on our meta-expression intentionally anti-aesthetics webpage: https://weathon.github.io/icml2026_position/.

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Wenqi Marshall Guo, Qingyun Qian, Khalad Hasan, Shan Du. 2025-12-09. Position: Universal Aesthetic Alignment Narrows Artistic Expression. https://arxiv.org/abs/2512.11883

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