A Clinician’s Proposal for Detecting and Correcting Meta-Logical Failure Modes in LLMs**

Author: Seo Young-eun

Position: Chief Psychiatrist (Forensic Psychiatry), National Forensic Hospital,

Ministry of Justice, Republic of Korea

Co-writing disclosure: The conceptual model in this post emerged from many hours of conversation with Gemini 3 Pro and GPT-5.1, with full transparency about AI-assisted writing.

1. Motivation

This post argues for a specific architectural layer—

AMPA (Autonomous Meta-Process Architecture)—

whose purpose is to detect, constrain, and correct meta-logical vulnerabilities in advanced language models.

The core claim:

Current LLMs generate divergent outputs because they lack a shared, formalized, top-level meta-logic.

But when a human supplies a stable meta-logical frame and verifies outputs across two independent frontier models, the final conclusions converge.

This observation did not emerge from a laboratory setting.

It emerged from an unusual naturalistic experiment: a psychiatrist specializing in forensic reasoning engaging two different frontier models across dozens of unrelated domains, while repeatedly performing human-guided meta-logical correction.

Surprisingly, when the meta-logic was held constant by the human operator, the two models consistently converged—even when the surface content differed.

This suggests that future AGI-safety work should include not only alignment of values, but also alignment of meta-logical structure.

2. Background Assumptions

The argument rests on three premises:

Human reasoning is limited by domain knowledge but strong in causal meta-logic.
Especially in psychiatry and forensic evaluation, detecting faulty causal structure is core to the profession.
LLMs are strong in pattern retrieval but weak in top-down causality checks.
When a human supplies an explicit meta-logical structure, two independent LLMs can be forced to converge to the same final reasoning chain.

This is the empirical seed from which AMPA is proposed.

3. Behavioral Evidence From Two Models

The conversations included 37 heterogeneous prompts across history, neuroscience, geopolitics, literature, exercise physiology, economics, and more.

3.1 Gemini 3 Pro — Quantitative Stability

Gemini provided a u...

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