We extract the reasoning ontology from Human intelligence and bring it to AI systems.
AI can generate thousands of plausible scientific hypothesis. Evidentia determines which ones are causally valid, evidence-backed, and trustworthy to act on.
The last decade has brought to us extraordinary AI systems with impressive scale, but it is now approaching diminishing returns. The limit is no longer architecture, data or labelling but instead the quality of supervision they receive.
The next leap toward AGI will require models that learn not only what experts conclude, but how experts reason.
Extract the taxonomy of reasoning operations that experts and formalisms of reasoning define.
Build a multi-dimensional framework by which reasoning traces can teach or correct frontier models.