Real-World AI Evaluation: How FRAME Generates Systematic Evidence to Resolve the Decision-Maker's Dilemma

ArXi:2603.13294v1 Announce Type: cross The rapid expansion of AI deployments has put organizational leaders in a decision maker's dilemma: they must govern these technologies without systematic evidence of how systems behave in their own environments. Predominant evaluation methods generate scalable, abstract measures of model capabilities but smooth over the heterogeneity of real world use, while user focused testing reveals rich contextual detail yet remains small in scale and loosely coupled to the mechanisms that shape model behavior.