MHIL exists to answer a question the AI industry has avoided: how intelligent are these systems, really? Not how much they know. Not how fast they respond. How well they actually think.
A rigorous cognitive evaluation framework for artificial intelligence systems — designed to measure genuine understanding, not knowledge recall.
Through science-grounded cognitive testing, independent evaluation, human-calibrated scoring, and transparent methodology.
Because deployment decisions, regulations, and public trust depend on accurate understanding of what AI systems can and cannot do.
Eight measurable dimensions of intelligence, grounded in established cognitive science, applicable to any AI system regardless of architecture or vendor.
Third-party assessment with no vendor influence. Systems will be tested by MHIL, not by their creators. Results will be published publicly.
Every score is contextualized against human developmental stages. MHIL-100 equals average adult cognitive performance. No arbitrary percentages.
Benchmarks evolve as AI evolves. Dynamic test generation prevents memorization. The standard adapts to remain meaningful.
No funding from AI vendors. No commercial pressure on results. The integrity of the standard is non-negotiable.
Grounded in cognitive science, psychometrics, and established measurement theory. Peer-reviewed methodology.
Open methodology, published criteria, reproducible results. The standard belongs to the field, not to us.
As AI capabilities evolve, our frameworks evolve with them. Static benchmarks become obsolete; living standards endure.
Any system will be able to be evaluated. Any researcher can contribute. The standard aims to serve everyone, not just leading labs.
We are building measurement infrastructure for the next fifty years, not the next product cycle.