What I've noticed with current AI is that it acts like a people pleaser. What I mean is that it gives an answer without checking to see if it fits the question first. Humans do this a lot too. We give a quick answer and only after do we check...
Request for Comment 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...
What I've noticed with current AI is that it acts like a people pleaser. What I mean is that it gives an answer without checking to see if it fits the question first. Humans do this a lot too. We give a quick answer and only after do we check in with ourselves to see if that answer was truly aligned with how we feel.
For me, it's a very different experience when I hear a question and I stop to feel into my answer before replying. This is called recursive thinking and it teaches a person, or LLM, how to check in before giving an answer.
The basic idea is, "Does this answer... (read more)
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... (read 963 more words →)