Harm-Conditioned Computational Friction as a Diagnostic of Alignment Robustness: A Critical Review and Evaluation Framework

Robust alignment requires that AI models maintain safety-relevant behavior under distribution shift, adversarial prompting, and optimization pressure. Current evaluation methods often rely on surface compliance metrics—such as refusal rates or policy-template adherence—that may fail to detect fragile safety generalization, reward hacking, or prompt-contingent refusal policies. This paper critically reviews alignment evaluation methods through the lens of harm-conditioned computational friction (HCCF): a diagnostic principle positing that aligned models should exhibit measurable increases in deliberative cost, uncertainty, or constraint activation specifically when processing higher-harm inputs, controlling for task difficulty. We formalize HCCF through behavioral, inference-level, and mechanistic proxies; propose measurement protocols for friction gradients across harm domains; analyze confounds and failure modes (including "theatrical friction"); and provide an evaluation blueprint with robustness checks and cross-domain aggregation. By emphasizing conditional internal resistance rather than only external refusal behavior, HCCF provides a framework to unify existing evaluation methods and distinguish genuine safety generalization from brittle or cosmetic alignment, with potential implications for model auditing, training objectives, and AI safety governance.