Conversational Thermodynamics: A Corpus-Based Framework for Benchmarking AI Fluid Intelligence, Recursive Stability, and Semantic Integrity

Conversational Thermodynamics (CT) introduces a unified theoretical and corpus-based framework for evaluating how large language models (LLMs) preserve, degrade, and recover semantic integrity during recursive, multi-turn conversations. Drawing on a 250,000-word naturalistic corpus and a Hybrid-Lab comparison of GPT-5 and GPT-4o, the framework integrates Conversational Thermodynamics (CT), Contextual Loops (CTL), Cognitive Conversational Prompt Engineering (CCPE), Synaptic Drift Theory, the Principle of Recursive Impactrum (PRI), the Principle of Irreducible Instability (PII), and the KPRI-E/KPRI-I framework into a mechanism-level approach for benchmarking conversational stability. The study operationalizes semantic entropy, drift rate, collapse velocity, semantic progression, recursive stability, and informational dissipation as measurable constructs. Rather than emphasizing static benchmark accuracy, it evaluates how conversational reasoning evolves under recursive stress and demonstrates that semantic degradation follows predictable thermodynamic trajectories requiring human anchoring for recovery. This proof-of-concept establishes Conversational Thermodynamics as a foundational framework for AI benchmarking, conversational evaluation, governance, human-AI collaboration, and future entropy-aware AI systems.