Introducing Un-0: Generating Images with Coupled Oscillators - Unconventional AI

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June 25, 2026

Model Release

Introducing Un-0: Generating Images with Coupled Oscillators

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Unconventional AI

TL;DR. Executing deep neural networks on GPUs has dominated AI for a decade, but we think the next jump in energy efficiency demands a fundamentally different computer, one where physics does the computing. We built Un-0 , an image generator powered by a simulated system of coupled oscillators, an example of an emerging physical computing substrate. On ImageNet 64×64 it reaches FID 6.74 , matching the quality of leading conventional image generation methods when they were first published. Weights, training, and ablation code are all open. Join us on an Unconventional journey!

Figure 0: A sample of trajectories of Un-0 generations over time. Each line color has an associated box of similar color that denotes the class and generated images over time.

Un-0

At Unconventional AI, we’re building a new kind of computer, one that harnesses the laws of physics to do the computing. Our goal is to run modern AI on a fraction of the energy today’s machines need, around 1,000x less. As a first step, we ask: can we train a physical dynamical system to generate images at scale?

The best AI models today are conventional deep networks with transformer backbones. However, there is also a long history of alternatives that seek energy efficiency by leveraging the dynamics of a physical system, such as the noisy, time-varying behavior of analog circuits that compute with analog voltage and current instead of conventional digitized numbers.

These physics-based alternatives include Neuromorphic Computing (Mead, 1990), Hopfield networks (Hopfield, 1982), and reservoir computing (Jaeger, 2001; Maass et al., 2002). Recently the community has also developed Hamiltonian (Greydanus et al., 2019) and Liquid (Hasani et al., 2021) networks, Neural Wave Machines (Keller & Welling, 2023), Thermodynamic Computing (Coles et al., 2023; Jelinčič, 2025), and Kuramoto Oscillators (Miyato et al., 2025; Song et al., 2025).

To exploit these alternative computing methods, the AI task needs to be mapped efficiently to the dynamics of the physical system. Un-0 validates that modern AI workloads can run more efficiently on physical substrates than on today’s hardware.

Data space trajectories of images forming for classes: Daisy, Lakeside, Agaric, Geyser, Volcano, Jellyfish.

Among a growing community building AI on physical and unconventional substrates [1–8, and others], Un-0 is, to our knowledge, the most capable image generator to date to use a simulation of a physical dynamical system. Un-0 reaches FID 6.74 on class-conditional ImageNet 64×64, though there are still opportunities to improve model performance as a function of parameter count towards the conventional frontier.

While the physical primitive we explore is not new, we scale it to a larger generative benchmark, perform an ablated analysis of the dynamics itself, and provide an interpretative analysis of the model’s behavior.

We release the model weights together with the training, evaluation, and ablation code to make it easier for anyone to experiment with models grounded in the dynamics of physical systems. We believe it is possible to quickly push beyond Un-0; it is still early in the journey to reseat modern AI on physical dynamics and reach ~1000x energy-efficiency gains.

How Un-0 works

Figure 1a: Two metronome-like oscillators exhibit three coupling regimes switched across time: 1) drift (no coupling), 2) synchronized (positive coupling) and 3) anti-phase synchronized (negative coupling).

Picture two metronomes ticking side by side (Figure 1a). Each can be described at any moment by its phase, the angle where its arm is in the swing. Place two metronomes on the same table and they will interact with each other through the shared surface. Depending on how sensitive they are to each other, i.e., coupling strength, they fall into lockstep or settle into opposition. That’s an oscillator: a primitive component with a phase that wants to rotate at its own rate, influenced by the forces of its neighbors.

Figure 1b: Illustration of the evolution of a collection of coupled oscillators.

Now scale that from two oscillators to thousands. A large population of these oscillators, each coupled to each other with their own strength, self-organizes into patterns (Figure 1b). Un-0's compute engine is a large population of oscillators where the coupling strengths between all pairs of oscillators are the primary learnable parameters of the model.

These coupled oscillators are commonly modeled as Kuramoto oscillators. Concretely, each oscillator's motion follows a single rule, applied continuously over time: it rotates at its own natural frequency, nudged by the pull of every other oscillator. The following ordinary differential equation (ODE)...