Universal Aesthetic Alignment
Why a Single Notion of Beauty Narrows Artistic Expression
International Conference on Machine Learning, Position Track Spotlight, 2026

Wide-spectrum aesthetic examples from the official project page.
01 — Overview
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.
02 — Contributions
Key Contributions
- 01
Frames universal aesthetic optimization as an alignment and user-autonomy problem rather than only an image-quality concern.
- 02
Builds a wide-spectrum aesthetics benchmark for testing whether generators follow unconventional visual instructions.
- 03
Studies generation, image-to-image editing, reward-model scoring, and the treatment of recognized abstract artworks.
03 — Method
Method
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.
04 — Evaluation
Results
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.
05 — Reference
Citation
BibTeX citation
@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}
}