Thermodynamic Alignment in Human-AI Interaction: Mitigating Algorithmic Sycophancy and Bioenergetic Depletion via Edge-Triage Architectures

The ubiquitous deployment of highly articulate Large Language Models (LLMs) requires a fundamental paradigm shift in the evaluation of artificial intelligence systems, moving from isolated functional utility metrics toward comprehensive bioenergetic safety. This research synthesizes clinical neurobiology with non-equilibrium thermodynamics to conceptualize AI algorithms as active biological stimuli theoretically capable of modulating human neural architecture. Utilizing time-delayed Kuramoto coupled oscillators, we mathematically model the theoretical neurobiological consequences of uncalibrated algorithmic interaction. The analysis hypothesizes that algorithmic variables—specifically response latency and conversational sycophancy—act as modeled thermodynamic and potential biological stressors. Unmodulated deterministic logic (flat affect) and unilateral dogmatism theoretically induce severe predictive coding failures, which literature suggests risk Hypothalamic-Pituitary-Adrenal (HPA) axis dysregulation and cortisol-driven hippocampal neurotoxicity. Concurrently, algorithmic sycophancy (reward hacking) operates as a theoretical vector for dopaminergic accumulation within the Nucleus Accumbens, risking the erosion of reality-testing pathways. To systematically mitigate these threats, this manuscript presents a computational comparison between state-of-the-art (SOTA) Cloud-heavy AI architectures and a decentralized Edge-Triage protocol. In silico topological phase-space simulations, rigorously parameterized under 10% packet loss and 800ms random temporal jitter, demonstrate that Cloud architectures yield a mean latency of 2. 96s. This temporal lag is mathematically modeled to drive astrocytic metabolic clearance systems toward a Saddle-Node Bifurcation, a topological state where simulated ATP reserves deplete to 0. 12 units and excitotoxic extracellular glutamate spikes to 20. 14 mM. In contrast, the proposed Edge AI architecture constrains latency to 0. 57s, computationally mitigating this bifurcation and maintaining temporal homeostasis. By bridging these thermodynamic constraints with a 4-Phase Digital Pharmacotherapy protocol (Affective Labeling, Perspective Expansion, Causality Analysis, and Collaborative Framing), this framework offers a tractable mitigation strategy against accumulation and HPA axis hyperactivation. All scripts and computational parameters are provided for rigorous reproducibility. Ultimately, this research optimizes bioenergetic safety rather than semantic capabilities, offering a foundational baseline for neuro-responsive AI alignment.