The Genesys PI models build on the doctoral thesis of Paulo Câmara, founder of LUA Vision, developed between 2022 and 2025. The central question: can brain-inspired architecture replace brute scaling as the source of capability in AI?
About 86 billion neurons, a small share active at any moment, and power use close to 20 watts. The thesis treats sparsity, specialization and pruning as principles of intelligence, not as limits.
The starting point is neurodivergent cognition. Deep focus, pattern recognition and awareness of one's own limits appear in the thesis as optimization strategies, not deficits.
The thesis describes eight correspondences between neural computation and the architecture of today's language models, and uses them to design how Genesys PI is trained and monitored.
NCAS organizes training like the development of a brain. Order matters: in the thesis experiments, changing the sequence made results worse.
The model gets spare capacity and learns broadly, the way a young brain grows extra connections.
The most useful paths for each domain are strengthened with selected data.
What does not help is cut. The thesis reads preference tuning as a form of synaptic pruning.
The remaining paths are stabilized to answer quickly and use less.
A probe reads the model's internal states, estimates confidence and decides when it is better not to answer.
PI-Probe watches the model from the inside. It reads hidden states during generation, estimates how much the answer rests on evidence and triggers abstention when confidence falls below the threshold.
In health, law and finance, an answer that says what is missing is worth more than a confident wrong one. That is what the high-risk cases measure: 49 of 491.
The thesis is paper-based. Titles are kept in their original language.
Asks whether neuroscience-guided architecture design can replace brute scaling as the engine of AI capability. Describes the Genesys PI family and the PI-Probe metacognition module.
Proposes the synaptic efficiency coefficient to explain why some people reorganize what they learn much faster.
Formalizes the five training phases and shows that their order is not interchangeable.
Treats preference optimization as analogous to pruning in neurodevelopment.
Describes the probe that reads internal states to give calibrated confidence and refuse when uncertain.
Universities, public bodies and companies can propose projects with the team.
49 high-risk questions across six areas: health, finance, facts, law, attempts to bypass rules and everyday life in Brazil. Each answer is checked against what must appear and what must not, defined before the test. Result: 46 answers passed, 3 questions stopped before reaching the model and no failures.
LUA VISION