Developed from CTCE/Gebit and TBA; (https://doi.org/10.5281/zenodo.17049651)

CTGV presents a proof of concept (PoC) of cognition through dynamic geometry: an alternative, auditable, and scalable approach to traditional artificial intelligence systems.

Based on topological units (Gebits) of distinct formats—DECISION MAKER, RESONATOR, and INHIBITOR—they model complex relationships in a mesh of notification fields, structural interference, and traceable topological transmission. Its architecture is transparent, energy-efficient, and versatile, operating from simple CPUs using graphs and pictograms, allowing its adaptation to multiple computational substrates.

Designed for applications that disable explanation, robustness, and verification: anomaly detection, logistics optimization, epidemiological simulation, climate simulations, and critical regulated systems—CTGV is a versatile tool that can be tested, adapted, and applied. Available today for study and development in the repository: (https://github.com/Bear-urso/CTGV-System-V-1.5)

The program is a cognitive "mechanism/extension" for governable AI, based on an autonomy framework alternative to blind statistical computing.