I developed an experimental Graph-Native Artificial Brain engine

I am currently developing a small experimental system that explores a different approach to artificial intelligence.This prototype is built as a graph-based resonance architecture rather than a traditional transformer-based language model. It does not use decoders, matrix multiplications, vector embeddings, or attention mechanisms.Current status:Trained using only 5 Wikipedia articles Reasoning is performed through resonance and gestalt proximity between meaning cells Every response includes a transparent cognitive route showing the synaptic path taken No black-box processing — all steps are visible and editable Example response (synthesis time: 157 ms, 5 synapses):“Physicist. In this context, the theory of relativity predicts that a sufficiently dense mass could bend spacetime, forming a black hole.” The system is designed to learn in a pedagogical and ontological way — much like teaching a child — by building meaningful associations step by step.This is a very early-stage personal experiment. My goal is to explore whether a more structured, transparent, and brain-inspired architecture can emerge from simple resonance mechanisms instead of large-scale statistical training.I would appreciate any thoughts or feedback from the community.