Thermodynamic Stability Analysis of Generative AI Systems via Replica Symmetry Breaking (RSB) and Parallel Tempering

[Defensive Publication / Prior Art Disclosure]

Abstract:
This paper introduces a thermodynamic framework for distinguishing stable knowledge from stochastic hallucinations in Large Language Models (LLMs). While traditional EBMs [LeCun et al., 2006] utilize the Gibbs distribution primarily for probabilistic interpretation, we apply the "Replica Trick" from Spin Glass theory to generate multiple independent inference chains (replicas) at varying thermodynamic temperatures (Parallel Tempering). We define hallucination physically as an instance of "Replica Symmetry Breaking" (RSB), where the overlap parameter q (Parisi parameter) between replicas drops below a critical threshold. Only states that maintain symmetry (high overlap) across replicas are considered valid ground truths. This method provides a rigorous, physics-based metric for AI reliability and auditability, distinct from previous probabilistic approaches.

Technical Verification:
This document has been digitally signed with a Qualified Electronic Signature (QES) in accordance with the eIDAS regulation (ID Austria). This serves as cryptographic proof of authorship and integrity of the content at the time of publication. The validity of the signature is embedded within the PDF file.