Theoria Verifies AI Reasoning with Auditable Proof Traces

Trusting AI system answers, especially in complex reasoning tasks, remains a significant challenge. While formal proof assistants offer certainty, they lack broad applicability, and scalar LLM judges provide coverage but suffer from opacity and potential coherence issues. Theoria introduces a novel verification architecture designed to overcome these limitations by providing auditable and transparent reasoning.Theoria transforms a candidate AI solution into a series of typed state transitions, each explicitly justified by a citation, computation, or problem fact. A core principle is "completeness of change," meaning every difference between consecutive states must be accounted for, thereby exposing any hidden premises or unjustified mutations. This approach yields human-readable proof traces where each step can be independently challenged. Theoria demonstrated high precision on expert problems and proved significantly more effective at catching adversarial poisoned proofs, particularly those involving hidden premises and fabricated citations, compared to holistic LLM judges.