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I am currently formalizing the syntax for the Coherence_Check function and defining the minimal ontology required for general-purpose LLM guardrails.

Abstract

Current alignment techniques, such as Reinforcement Learning from Human Feedback (RLHF), primarily address safety as a probabilistic reward function. While effective for style and tone, this approach fails to prevent "hallucinations" in out-of-distribution (OOD) scenarios because it lacks a mechanism for runtime verification.

This proposal introduces Relational Coherence Theory (RCT) as a neuro-symbolic architectural layer. Unlike standard "Chain of Thought" prompting, RCT functions as a deterministic logic gate. It introduces a recursive verification step—specifically, an "Ontological Guardrail"—that validates the logical coherence of relationships between subject and object before token generation.

We argue that true robustness requires shifting from maximizing Likelihood (P(next_token)) to verifying Coherence (Logical Consistency). This post outlines the theoretical framework for such a layer and proposes a "Recursive Questioning" protocol to filter incoherence in real-time.

1. The Problem: The "Likelihood Trap"

The fundamental vulnerability of current Large Language Models (LLMs) is that they are structurally incapable of distinguishing between Truth and Likelihood.

In a standard Transformer architecture, the model predicts the next token based on statistical correlations found in the training data.

If the training data contains "The sky is blue," the model assigns a high probability to "blue."
If the training data contains misconceptions, or if the context window is confusing, the model might assign a high probability to "green."

Currently, we attempt to fix this with RLHF (Reinforcement Learning from Human Feedback). We treat the model like a dog, rewarding it when it speaks the truth and punishing it when it lies.

The Structural Failure:

This does not teach the model logic; it teaches the model mimicry. The model does not understand that "The sky is green" is physically impossible; it only understands that the sequence "sky is green" yields a low reward score.

As a result, when the model encounters a Novel Situation (one not covered in its training data), it has no "Ontological Guardrail" to stop it from making a catastrophic error. It will confidently output a hallucination because the ha...

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