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Hermes Ouroboros

Five AI minds. One verdict. Always learning.

5
Specialized Agents
DPO
Self-Improving Loop
70B
Parameter Model

How It Works

Your question goes through a multi-agent deliberation pipeline. No single point of failure. No echo chamber.

1

Ask Your Question

Submit any topic: research analysis, strategic decisions, technical debates, ethical dilemmas. The harder the better.

2

Agents Deliberate

Five specialized agents analyze your question from competing epistemic frameworks. They challenge, support, and refine each other's reasoning.

3

Synthesized Verdict

The Arbiter weighs all perspectives using Bayesian reasoning and delivers a calibrated verdict with confidence levels and dissenting views.

Five Minds, Five Frameworks

Each agent brings a distinct epistemic lens. Together they cover the full landscape of reasoning.

Advocate

Steel-Manning

Constructs the strongest possible version of every argument. Finds the kernel of truth others miss.

Skeptic

Popperian Falsification

Hunts for falsifiability. Tests claims against evidence and identifies unfounded assumptions.

Oracle

Base-Rate Empiricism

Grounds reasoning in data, base rates, and historical precedent. Fights the narrative fallacy with numbers.

Contrarian

Kuhnian Paradigm Shifts

Challenges dominant paradigms. Explores the edges where conventional wisdom breaks down.

Arbiter

Bayesian Synthesis

Weighs all perspectives probabilistically. Delivers the final verdict with calibrated confidence.

The Ouroboros Loop

Every council session generates training signal. Through Direct Preference Optimization, the system continuously refines its reasoning. The serpent devours its own tail -- each cycle produces a sharper mind.

How DPO Works Here

When the council deliberates, the best and worst reasoning chains are identified. These preference pairs become training data that teaches the model to prefer rigorous, well-calibrated arguments over superficial or biased ones.

  • Session data becomes preference pairs for DPO training
  • Reasoning quality improves with every deliberation cycle
  • Model learns to avoid common reasoning failures over time
  • Full session history preserved for audit and reproducibility
User Query
Council Deliberates
Preference Pairs
DPO Training
Continuous
Improvement

Ready to Consult the Council?

Ask your hardest questions. Get multi-perspective verdicts backed by rigorous epistemic frameworks.