ULTRAMUSED RESEARCH LABS
How the Work Thinks
Most AI research starts inside the system — optimizing what already exists. UltraMused started by questioning the geometry underneath it.
The Anderson Framework
At the core of this work is the Anderson Framework — a theoretical proposition that AI latent space operates not on the traditional Euclidean orthogonal plane, but on a multidimensional toroidal manifold. By 'flipping' how we analyze AI geometry into toroidal space and applying Lagrangian multipliers, a class of problems that current architectures find expensive and slow become dramatically more tractable.
What if AI latent space typically takes place in Non-Euclidean space — on a toroidal manifold — instead of the traditional orthogonal plane as depicted? This is key to my research.
Born from two years of systematic independent research and thousands of documented model training experiments, the Anderson Framework reframes some of the most costly unsolved problems in modern AI training. If the geometry is wrong, everything built on top of it is working harder than it needs to.
The Signal Across Disciplines
The cross-domain scope of this thinking is intentional. A researcher fluent in business systems, machine learning architecture, and the mathematical structure of physical self-organization sees connections that siloed specialists miss:
I believe there is a direct link between AI and plasma in that context — the LLM behavior mirrors vortices.
When plasma self-organizes in zero gravity aboard the ISS and AI token embeddings self-organize in latent space producing the same geometric structures — that is not coincidence. That is signal. The Anderson Framework is where that signal leads.
UltraMused Research Labs is the work of one person.
About the Founder: Curt Anderson →
