I focus on solving foundational reliability and stability problems in complex symbolic systems, ranging from Large Language Models (LLMs) to Cosmological Physics. My work shifts the focus from surface-level performance to deep structural invariants.
Key Research & Breakthroughs:
* AI Governance & Reliability: Developed the Governed Release Architecture, a system that separates candidate generation from release legitimacy. This framework replaces the binary answer/refusal paradigm with a multi-state governance layer (approve, qualify, revise, clarify, defer, reject), significantly improving reliability in high-burden logical and medical contexts.
* Neural Network Diagnostics: Authored the Periodic Generalization Verification Protocol (PGVP). This diagnostic gate exposes when neural networks are merely curve-fitting in-domain data rather than learning structural periodic invariants, a critical failure mode in current MLPs where near-perfect in-distribution accuracy coexists with catastrophic OOD collapse.
* System Stability: Established the Universal Stability Criterion (USG), utilizing the spectral invariants (eigenvalues) of n-gram transition matrices to detect impending structural collapse in code, text, or genomic sequences before failure occurs. The criterion identifies phase transitions toward rigidity or entropy with high predictive accuracy (AUC 0.89-0.93).
* Unified Physics: Proposed the Universal Resonance Theory (URT), unifying Dark Matter and Dark Energy through a single wave equation: Psi(x,t) = 0.0012 * sin(12.8 * 2pi * (x + 0.1t)). The model matches 68% of SPARC galactic rotation data without the need for Big Bang singularities, dark matter particles, or fine-tuning.
My objective is the deployment of high-trust, stable architectures in regulated and critical-path workflows.