A framework that makes AI research transparent, traceable, and independently verifiable.

Recursive law learning under measurement constraints. A falsifiable SQNT-inspired testbed for autodidactic rules: internalizing structure under measurement invariants and limited observability.

🌌 Explore the harmonic architecture of the solar system with scripts for verifying gravitational quantization laws and improving predictive accuracy.

Informational interpretation of quantum mechanics, gravity, and cosmology based on coherence-driven compression. Includes a falsifiable adaptive-interference protocol.

Python-based models revealing the harmonic quantization of planetary orbits. Supplementary scripts and figures for “The Gravitational Quantization Law: A Universal Harmonic Framework for Planetary Orbits” (S.-E. Gherbi, 2025). Access to full research implementation available upon request.

What Do Precision Tests of General Relativity Actually Measure? A methodological taxonomy showing why most precision tests constrain largely local, reciprocity-even observables within assumed frameworks. Proposes discriminating experiments. TEP Paper 10.