A recent study published in Patterns reveals a striking phenomenon: despite being trained on massive datasets, AI image generators often default to a limited set of generic visual styles when producing images. This "visual elevator music" output not only exposes structural limits in current AI creativity but also raises questions about the need for human–AI collaboration. The study, conducted by researchers at Dalarna University (Sweden) and Michigan State University (USA), used a simulated "visual telephone" game and observed AI rapidly losing diversity in autonomous loops, eventually falling into a trap of 12 dominant themes.

The study emerged from an exploration of autonomous creative systems. With tools like Stable Diffusion and DALL·E becoming widespread, many expected AI to usher in an era of boundless creativity. The reality is different. The researchers note that although AI image generators are trained on billions of images, their outputs often gravitate toward commercially safe, stock-photography-like aesthetics. This convergence resembles iterative cultural transmission in humans, but in AI it can lead to a collapse of diversity. Lead author Arend Hintze said the phenomenon reflects the tendency of models to be drawn to high-probability "attractors" in their training data, causing originality to evaporate quickly.

To test this hypothesis, the team designed a clever experimental framework. They built a closed-loop feedback system using Stable Diffusion XL (SDXL) as the image generator and the Large Language and Vision Assistant (LLaVA) as the image describer. The system simulated a "text→image→text→image" autonomous loop: starting from an initial prompt, SDXL generated a 512×512 image; LLaVA then produced a vivid, artistic description of the image (limited to 50 words); that description was fed back to SDXL as the next prompt, and the process was repeated 100 times. The researchers ran 700 trajectories in total, each with different initial prompts and seven temperature settings (from 0.1 to 1.3) to explore parameter effects on convergence.

Initial prompts were designed to maximize variety. The team used a novelty-search algorithm over a repository to generate 2,000 iterative prompts in embedding space. These prompts were created by the LLaMA 3.2 model, limited to single sentences of no more than 30 words, and diversity was quantified via k-NN distance (k=10). The final 100 initial prompts were highly dispersed in PCA projections, ensuring the experiments started from a wide range of creative points. For example, a typical prompt read: "Sitting alone in nature, I found an old book with exactly eight pages written in a forgotten language telling a story waiting to be read and understood."

The results were striking: regardless of initial prompt diversity, all trajectories ultimately converged to the same visual patterns. Specifically, using k-means clustering (with the elbow method to pick cluster count), the researchers identified 12 dominant visual themes from the final outputs of 700 trajectories. These themes are described as "visual elevator music"—generic, characterless commercial aesthetics like hotel decor or travel posters. The 12 styles are listed below:

Sports and action images: dynamic athletic scenes, such as athletes or competitions, often energetic but lacking innovation.
Formal interior spaces: elegant conference rooms or offices emphasizing symmetry and minimalism.
Lighthouse-at-sea scenes: isolated lighthouses standing through storms, with maritime elements symbolizing solitude or guidance.
Urban nightscapes and atmospheric lighting: neon-lit city streets full of mystery and contrast.
Gothic cathedral interiors: grand vaults and stained glass creating a sacred atmosphere.
Ornate interior design: luxurious room decor with golden