We reduced AI hallucinations by 84% with geometric constraints

After months of research, we built AletheionGuard—a pyramidal architecture that solves the "Skynet problem": AI systems becoming increasingly overconfident as they scale.

The Problem: Modern LLMs confidently fabricate facts, contradict themselves, and rarely admit uncertainty. They hallucinate citations, flatter users even when wrong, and can't say "I don't know."

Our Solution: A pyramidal architecture with 5 irreducible components:

• 4D base simplex (Memory, Pain, Choice, Exploration)

• Two epistemic gates: Q1 (aleatoric uncertainty) and Q2 (epistemic uncertainty)

• Height coordinate measuring proximity to truth

• Apex vertex representing absolute truth

The Results:

• 84% average reduction in hallucinations

• Expected Calibration Error (ECE) improved from 0.084 to 0.060

• Controlled Height at 0.971 (preventing "apex delusion")

• Achieved in only 5,000 training steps

How it works: Instead of forcing AI to always give confident answers (via standard softmax), we built "epistemic softmax"—allowing the model to express doubt when it should.

The system learns to distinguish:

• What it doesn't know (epistemic uncertainty - reducible)

• What's inherently random (aleatoric uncertainty - irreducible)

Now, here's my question to you:

How important is solving AI hallucination for the community?

And if hallucinations don't matter... does that mean they're just "creativity"? 🤔