Background
Modern machine learning is built on linear-algebra primitives whose interpretation is largely empirical. Genesis develops an alternative: a math-first framework where the same operator structure that drives an attention head also drives a quantum measurement, a gene-expression dynamic, and a control loop.
The framework was developed bottom-up — derivation by derivation, falsifier by falsifier — over the past two years. Failures are documented as honestly as successes; the catalogue of refuted hypotheses is part of the public record.
Application reach
- Quantum mechanics — Bell tests, Gleason, no-cloning and the rest of the standard quantum suite reproduced in the framework (track: Sadalsuud).
- Biology — oncology biomarker, neurodivergence substrate axes, and a Genesis-based genomic analysis pipeline (track: Life Sciences). Patent IDs and filing stages NDA-gated.
- Neural networks — matrix-state substrate for next-generation architectures (track: K2K + matrix-token).
- Control — applied to hardware platforms in the KeiTech sub-brand (KeiT0 prosthetic, KeiDrive vehicle dynamics).
What you can read publicly
Concept-level descriptions and high-level results are on this site and across the lab's public-safe blog posts. Full derivations, formulas, and patent-method content are gated by mutual NDA — see the investors page.
cross-references