Energy-Efficient Safety Alignment of Large Foundation Models via Selective Reasoning Path Regulation (International Journal of Artificial Intelligence Engineering and Systems, 2026)
The alignment of large foundation models with human values and safety constraints is a computationally and energetically expensive undertaking, as it often involves exhaustive reinforcement learning procedures, repeated safety audits, or auxiliary shielding models that substantially increase the carbon footprint of both training and inference. This paper presents a system-oriented analysis of energy-efficient safety alignment through selective reasoning path regulation, a paradigm in which internal chain-of-thought trajectories are dynamically modulated to suppress unsafe reasoning while avoiding the full computational overhead of post-hoc safety filtering or alignment fine-tuning. We examine the structural trade-offs involved in designing gating mechanisms that intercept reasoning paths at the token, layer, or trajectory level and evaluate the implications for latency, throughput, and energy consumption. The discussion spans architecture design, deployment infrastructure, and the interplay between sparse activation optimization, early exiting, and energy-proportional hardware. Robustness and fairness considerations are addressed, including the risk that selective path interventions may inadvertently introduce distributional biases or be circumvented by adversarially crafted reasoning shortcuts. The paper further extends into governance and policy dimensions, arguing that sustainability reporting for foundation model providers should encompass the energy costs of safety alignment regimes and that auditability of reasoning path interventions is essential for regulatory compliance. By framing selective reasoning path regulation as a cross-layer systems challenge, we contribute a multi-perspective analysis that connects sustainable AI, safety engineering, and institutional accountability.
Keywords: foundation models, safety alignment, energy efficiency, reasoning paths, selective regulation, sustainable AI, system architecture