Aletheion AGI

Aletheion AGI

Research Colab

About

We're solving AI's biggest problem: overconfidence. Modern AI systems hallucinate facts, contradict themselves, and rarely admit uncertainty. As they scale, they become MORE confident but LESS reliable—the "Skynet phenomenon." AletheionAGI developed a pyramidal architecture with epistemic gates that: - Reduces hallucinations by 84% on average - Distinguishes what AI knows from what it guesses - Provides explicit uncertainty quantification - Prevents "apex delusion" (believing it's omniscient) Our mission: Build AI systems that remain calibrated as they scale—not by limiting capability, but by encoding humility architecturally. Critical for Healthcare, Justice, Scientific Research, and any domain where wrong answers cost lives or money. Open research. Practical solutions. Honest AI.

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Tastemaker
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Gone streaking
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Maker History

  • AletheionGuard
    AletheionGuardDetect LLM hallucinations before your users do
    Nov 2025
  • 🎉
    Joined Product HuntNovember 12th, 2025

Forums

Hey ProductHunt! 👋 Founder here. Ask me anything!

Your LLM just told a user that Mars has a capital city. By the time your monitoring tool alerts you, 10,000 users saw it. We built the circuit breaker that prevents this.
What we do:

Real-time epistemic analysis BEFORE outputs reach users.

When GPT-4 is guessing (high Q2 = epistemic uncertainty), we block it.

When it knows (low Q2), we allow it.

Aletheion AGI

2mo ago

We reduced AI hallucinations by 84% with geometric constraints

After months of research, we built AletheionAGI a solution to 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 can't say "I don't know."

Our Solution:

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

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