The Landauer Context: A Physics-Grounded Energy Basis for Large Language Model Orchestration

System: LID-LIFT Orchestrator v1.4
Series: Part 2 of the LID-LIFT Technical Suite

Abstract:
As Large Language Model (LLM) agents approach autonomy, their energy consumption becomes a critical limiting factor. Current 'Green AI' metrics rely on fluctuating estimates of kilowatt-hours (kWh) or carbon intensity, which fail to provide a stable baseline for algorithmic efficiency. This paper proposes the Landauer Context, a reporting framework that normalizes agentic energy expenditure against the theoretical Landauer limit (kT ln 2). We introduce the Thermodynamic Efficiency Ratio (eta_therm) as a standardized metric for the LID-LIFT Orchestrator, allowing for precise arbitration between high-accuracy/high-energy models and heuristic approximations based on fundamental physical limits.

TECHNICAL KEYWORDS:
Green AI, Sustainable Computing, Landauer Limit, Thermodynamics, LLM Efficiency, Energy Metrics, Super Learner Arbitration.