Universal Aesthetic Alignment

I. Overview

Image-generation systems are commonly optimized toward a broad, average notion of visual appeal. This paper argues that the same preference can conflict with user intent when a request deliberately calls for abstraction, discomfort, visual roughness, or other non-mainstream aesthetics.

The authors call this reversed alignment: instead of adapting to the user’s stated aesthetic goal, the system steers the output back toward the developer’s preferred visual norm.

II. Key Contributions

• Frames universal aesthetic optimization as an alignment and user-autonomy problem rather than only an image-quality concern.
• Builds a wide-spectrum aesthetics benchmark for testing whether generators follow unconventional visual instructions.
• Studies generation, image-to-image editing, reward-model scoring, and the treatment of recognized abstract artworks.

III. Methodology

The study expands ordinary image descriptions with controlled wide-spectrum aesthetic attributes, compares generated outputs against those requests, and evaluates how aesthetic reward models score prompt-following but conventionally unattractive images. It also tests image editing and real artworks to separate prompt adherence from generic beauty preference.

IV. Main Findings

Across the evaluated generators and reward models, the study finds a recurring preference for conventionally polished imagery. Systems often beautify or sanitize deliberately unconventional requests, while reward models can penalize outputs that follow those requests more faithfully.

@InProceedings{Guo_2026_AestheticAlignment,
  author    = {Guo, Wenqi Marshall and Qian, Qingyun and Hasan, Khalad and Du, Shan},
  title     = {Position: Universal Aesthetic Alignment Narrows Artistic Expression},
  booktitle = {Proceedings of the 43rd International Conference on Machine Learning},
  year      = {2026},
  note      = {Position Track Spotlight},
  url       = {https://openreview.net/forum?id=1gQ4zc1Q8I}
}