We find mathematical structure — phase transitions, coupling dynamics, free energy landscapes — hiding inside complex systems. Then we build tools on it. Physics, network theory, information theory, dynamical systems. Whatever the problem needs.
Pick two capabilities that should be independent. Measure their coupling as a model scales. Find where coupling changes sign. That sign-flip is a phase transition — and it tells you everything about what your model can and can’t do.
Enter your model’s benchmarks. Get its phase, coupling trajectory, and what to do next.
Two papers submitted to NeurIPS 2026. More in preparation across multiple domains.
We look for mathematical structure in complex systems — drawing from physics, dynamical systems, network theory, information theory, and whatever else the problem needs. When we find structure, we build tools on it.
Current focus: AI scaling laws. We discovered that the coupling between model capabilities undergoes a phase transition at a critical scale, and that transition is predictable, measurable, and actionable.
Founded by Adil Amin. Based in Madison, WI.
Interested in collaboration, consulting, preprints, or early access?