Measure for Measure: Why AI Evaluation Must Follow the Failure
TL;DR for operators A lower model bit width is not automatically a speedup. A lower training loss is not automatically a reliable policy. GRINQH evaluates quantization through the mechanism it is meant to change: decoding-stage memory traffic, kernel throughput, end-to-end generation speed, and retained task accuracy.1 Kolmogorov regression evaluates diffusion policies through trajectory geometry, a PDE-based inference residual, rollout behavior, anomaly detection, and an external safety filter.2 The shared lesson is not that the two forms of “precision” are technically equivalent. They are not. The lesson is that fidelity and evidence should be allocated according to the actual failure structure of the system. A production evaluation should connect four things explicitly: the intervention, the mechanism it changes, the diagnostic that observes that change, and the operational outcome that justifies deployment. Composite scores are useful only when their weights reflect real business priorities and their components remain separately visible. Otherwise, they are merely spreadsheets wearing authority. The dashboard is not the system AI evaluation has developed an awkward habit: optimize a convenient number, improve that number, and declare the system improved. ...