divergence as signal

Our founding thesis: when multiple AI models are asked the same question and they disagree, the disagreement itself is the most valuable signal. Not the answer from any single model, but the pattern of divergence across all of them.

the method

QORPUS runs prompts through multiple foundation models simultaneously, comparing their outputs structurally. Where models converge, confidence is high. Where they diverge, human attention is needed. This transforms AI from a black box into a transparent decision-support system.

publication

The full whitepaper is in preparation. When published, it will be available here and on arxiv.org. If you would like to be notified when it drops, get in touch via the contact page.