Every autonomous AI agent has three problems: it contradicts itself, it can't decide, and it says things confidently that aren't true. Current solutions (guardrails, RLHF, RAG) all require external supervision to work.
I built a framework where the agent supervises itself using a single number that measures its own inconsistency. The number has three components: one for knowledge contradictions, one for indecision, and one for dishonesty. The agent minimizes this number through the same gradient descent used to train neural networks, except there's no training data and no human feedback. The agent improves because internal consistency is the only mathematically stable state.
The two obvious failure modes (deleting all knowledge to avoid contradictions, or becoming a confident liar) are solved by evidence anchoring: the agent's beliefs must be periodically verified against external reality. Unverified beliefs carry an uncertainty penalty. High confidence on unverified claims is penalized. The only way to reach zero inconsistency is to actually be right, decisive, and honest.
I proved this as a theorem, not a heuristic. Under the evidence anchoring mechanism, the only stable fixed points of the objective function are states where the agent is internally consistent, externally grounded, and expressing appropriate confidence.
The system runs on my own hardware (desktop with multiple GPUs and a Surface Pro laptop) with local LLMs. No cloud dependency.
The interesting part: the same three-term objective function that fixes AI hallucination also appears in theoretical physics, where it recovers thermodynamics, quantum measurement, and general relativity as its three fixed-point conditions. Whether that's a coincidence or something deeper is an open question.
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