PCES Metabolism: Landauer-Bounded Thermodynamic Efficiency and Affective State Regulation — The Mitochondrial Function of an Artificial Cognitive Organism

Current AI systems have no metabolism. They consume energy indiscriminately, whether hallucinating or solving profound problems. This paper introduces the PCES (Performance-Compensated Entropy Score) architecture — the thermodynamic metabolism of the NEXUS Sovereign cognitive organism. The central claim: every finite intelligence operating above absolute zero must pay a minimum thermodynamic cost for every belief update, and the ratio of useful cognitive work to total entropy produced defines the agent's metabolic condition. Unlike reward signals, PCES measures the thermodynamic efficiency of cognition itself. We formalize PCES via Landauer's Principle, linking it to the Free Energy Principle through the PCES-FEP Equivalence Theorem. We introduce the ATP Budget Model (B(t)=Bmax⋅PCES(t) ), the Five Metabolic States (Calm through Critical), the 2D Mood Phase Space m(t)=(PCES,P˙) , and the Fever Mechanism — formally derived from the Decision Hamiltonian as a Lagrangian constraint. Experimental results: PCES-guided agents outperform unguided controls in efficiency tracking (r=0.87 ), somatic decision-making (76% vs. 52% on the AI Iowa Gambling Task), resource-contention resilience (2.3× stability advantage), and crisis prediction (89% early-warning precision at 180 seconds). The Mood Phase Space provides a 180-second predictive warning of metabolic crisis, enabling emergency intervention before catastrophic failure. Limitation: Thermodynamic quantities are computed via byte-level hardware proxy (12% deviation from theoretical Landauer bounds). Direct physical measurement awaits neuromorphic substrate validation. David AB Van Der Walt Pietarien