ICML 2026 spotlight: Universal Aesthetic Alignment Narrows Artistic Expression \[R]

I wanted to share an ICML 2026 spotlight position paper on a failure mode in image-generation alignment: aesthetic preference optimization may override explicit user intent when the requested output is anti-aesthetic or outside mainstream visual taste. The paper frames this as \*\*reversed alignment\*\*. Instead of the model aligning to the user's stated preference, the output is pulled back toward the model's learned aesthetic prior. We test generation and reward models on prompts asking for blurry, distorted, low-fidelity, negative-emotion, and other anti-aesthetic images. GitHub repo: [https://github.com/weathon/icml2026\_position](https://github.com/weathon/icml2026_position) Paper: [https://arxiv.org/abs/2512.11883](https://arxiv.org/abs/2512.11883) OpenReview: [https://openreview.net/forum?id=1gQ4zc1Q8I](https://openreview.net/forum?id=1gQ4zc1Q8I) I would be interested in feedback on the framing and on evaluation designs for separating prompt understanding from preference override.