We were once promised to be self-driving cars and robot maids. Instead, we’ve seen the rise of artificial intelligence systems that can beat us in chess. This was one of the great surprises of modern times. Physical tasks that are simple for humans have proven to be extremely difficult for robots, but algorithms increasingly mimic our intelligence.
Another surprise that has long been bewildered is the algorithmic tips that researchers have for their own strange kind of creativity.
The diffusion model, which is the backbone of image generation tools such as Dall-E, Imagen, and Stable Diffusion, is designed to generate carbon copies of trained images. But in reality, they seem to be improvised and improvised, blending elements within the image to create a new one. It’s a consistent image with not only meaningless chunks of colour, but also meaningful meaning. This is the “paradox” behind the diffusion model, said Giulio Billolli, an AI researcher and physicist at Ecole Normal Superiure in Paris. “But they aren’t – they can actually generate new samples.”
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To generate images, the diffusion model uses a process called removal. Converts images into digital noise (an inconsistent collection of pixels) and reassembles them. It’s like putting the picture repeatedly on a shredder until all you’re leaving is a pile of fine dust. For years, researchers have been wondering. How can the novelty come into the picture if the model is simply reassembled? It’s like reassembling your finely drawn painting into a completely new work of art.
Today, two physicists make surprising claims. It is a technical flaw in the removal process itself that leads to the creativity of the diffusion model. In a paper presented at the conference on International Machine Learning 2025, the duo developed a mathematical model of a trained diffusion model, showing that so-called creativity is indeed a deterministic process.
By illuminating the black box of diffusion models, new research could have a significant impact on future AI research. “The real strength of this paper is that it predicts something very accurately that is very non-trivial,” says Luca Ambrogioni, a computer scientist at Ladboud University in the Netherlands.
Mason Camb, a graduate student studying applied physics at Stanford University and a lead author of a new paper, has long been fascinated with morphogenesis. This is the process by which the raw system self-organizes.
One way to understand embryo development in humans and other animals is through what is called the Turing Pattern, named after 20th century mathematician Alan Turing. The Turing Pattern describes how groups of cells organize themselves into distinct organs and limbs. Importantly, all this adjustment is made at the local level. A CEO who oversees trillions of cells will not make sure everything fits the final body plan. In other words, individual cells do not have a complete body blueprint that forms the basis of the work. They are simply taking action and making corrections in response to signals from their neighbors. This bottom-up system usually works smoothly, but for example, you may generate your hands with extra fingers.
When the first AI-generated images began to occur online, many looked like surrealistic paintings, portraying humans with extra fingers. These quickly made Kambus think about morphogenesis: “It smelled like the failure you expect [bottom-up] The system,” he said.
AI researchers knew at that point that the diffusion model would take some technical shortcuts when generating images. The first is known as the region. We only pay attention to a single group or “patch” of pixels at a time. The second is that it sticks to strict rules when generating images. For example, the system will automatically adjust to create the same image you generate. This feature, known as translational equigalance, is a model method for preserving coherent structures. Without it, it would be much more difficult to create realistic images.
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Because of these features, the diffusion model does not pay attention to where a particular patch fits the final image. They focus on generating one patch at a time and automatically fit into place using a mathematical model known as a score function. This can be thought of as a digital Turing pattern.
Researchers have long seen the region and equivalent elimination process simply limits, technical quirks that prevent diffusion models from creating perfect image replicas. They didn’t associate them with creativity. Creativity was considered a higher phenomenon.
They were there for another surprise.
It’s made locally
Kamb began his graduate work in Surya Ganguli’s lab in 2022. Surya Ganguli is a physicist at Stanford University and has also been appointed in neurobiology and electrical engineering. Openai released ChatGpt in the same year, causing a surge in interest in the field now known as the Generating AI. As technology developers work to build powerful models that have never been more powerful than ever, many scholars remained obsessed with understanding the internal mechanisms of these systems.
To this end, KAMB developed the hypothesis that local and qualitative tolerance leads to creativity. It has increased the experimental potential for appetite. If he can devise a system that does nothing but optimize for local and isopathic, it should act like a diffusion model. This experiment was at the heart of his new paper, which he wrote as his co-author ganguli.
Kamb and Ganguli call the system equal-quantity local score (ELS) machines. This is a set of equations that can analytically predict the composition of images removed based solely on locality and equivalence mechanisms rather than a trained diffusion model. A series of images converted to digital noise were then taken and executed via both the ELS machine and many powerful diffusion models, including resnet and Unet.
The results are “shocking,” Gangari said: Overall, the ELS machine was able to match the output of the trained diffusion model equally with 90% average accuracy. He said that he had never heard of this in machine learning.
The results appear to support the KAMB hypothesis. “As soon as you imposed a community, [creativity] It was automatic. It completely fell from the dynamics into natural origin,” he said. The mechanism that limits the attention window in the diffusion model forces the focus to individual patches regardless of where they ultimately fit into the final product. of pixels with wider contextlessness.
Experts interviewed for this story agreed that Kamb and Ganguli’s papers generally illuminate the mechanisms behind the creativity of the diffusion model, but many remain mystical. For example, large language models and other AI systems also appear to display creativity, but do not utilize local and isologic.
“I think this is a very important part of the story,” Biroli said.[but] That’s not a whole story. ”
Create creativity
The researchers showed how the creativity of diffusion models can be thought of as mathematically formalized and predictable, unprecedentedly advanced and predictable. It appears as if neuroscientists have put a group of human artists in an MRI machine and found a common neural mechanism behind creativity that can be written down as a series of equations.
Comparison with neuroscience may be beyond merely a factor. The works of Kamb and Ganguli may also provide insight into the black box of the human mind. “The creativity between humans and AI may not be that different,” said Benjamin Hoover, a machine learning researcher at Georgia Tech and IBM Research, who studies diffusion models. “We build things based on what we have experienced, dreamed, seen, heard, or desire. AI builds building blocks from what it sees and sometimes it is required.” According to this view, human and artificial creativity may be fundamentally rooted in an imperfect understanding of the world. We all do our best to fill knowledge gaps, and sometimes we generate both new and valuable things. Perhaps this is what we call creativity.
The original story was reproduced with permission from Quanta Magazine, an editorially independent publication supported by the Simons Foundation.
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