We are abandoning the statistical path. Our methodology is grounded in physics, recursion, and the fundamental limits of computation.
Current LLMs are mimics. We build systems that discover the "source code" of reality. By searching for the shortest program that explains data, we achieve true causality.
Intelligence requires a body. We train agents in high-fidelity 3D physics engines. They learn gravity, friction, and persistence before processing a single token of text.
Transformers scale quadratically. We are developing O(N) complexity architectures that allow for effectively infinite context windows and lifelong learning without retraining.
Neural networks are universal function approximators. They fit a curve to data points. This works for interpolation but fails at extrapolation (new situations).
Our Recursive Discovery Engine does not fit curves. It searches for algorithms. It creates small, modular programs that explain the observed data. When it sees a falling apple, it doesn't memorize "apples fall"; it derives F = ma.
Human children learn 90% of their common sense before they can speak. They learn object permanence, stability, and cause-and-effect by playing with blocks.
We replicate this "Silent Childhood" for our AI. Before an agent reads Wikipedia, it spends 1,000,000 years in our physics engine, learning how to manipulate the world. This grounds its language in physical reality, preventing hallucinations.
The Transformer architecture (the "T" in GPT) has a fatal flaw: its memory cost grows quadratically. Doubling the text length requires 4x the compute.
Mateza uses a novel State-Space Model (SSM). Our memory cost is linear. We can ingest entire codebases, legal archives, or genetic sequences in a single pass without running out of memory. This enables "Lifelong Learning"—the model never needs to be rebooted.