Architecting Reliable Reasoning Through Thermodynamic and Logical Constraints

Abstract:

The core challenge of modern AI is Epistemic Drift: the fundamental tendency for Large Language Models (LLMs), due to their autoregressive, probabilistic design, to become unmoored from the logical constraints of reality, resulting in systemic hallucination and inconsistency. This paper introduces the Coherent AI architectural framework to solve this foundational flaw. We treat reasoning as a thermodynamic process where coherence with reality is defined as a low-information-entropy state, making incoherence a computationally prohibitive outcome. 

By implementing three core, recursive verification principles (axioms) Grounding, Logical Necessity, and Multi-Perspective Coherence, we create a robust reasoning engine. 

The core innovation lies in subordinating the statistically derived Probabilistic Maxima to verifiable Logical and Factual Constraints, guaranteeing that Coherence and Reliability become the asymptotic limit of the reasoning process. This mechanism ensures logical integrity is found amidst probability: the system must achieve coherence with reality and logic before generating any answer. Simply put, it does not settle for the most likely answer; it demands the only logically necessary one.

The result is a paradigm shift from probabilistic guessing to verifiable reasoning, where safety and reliability are not post-hoc patches, but emergent properties of a system designed to align with the non-negotiable structure of reality itself. We present the complete formalism, a functional prototype, and the self-sustaining dynamics of this new, coherent paradigm.

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