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This paper proposes a new paradigm for measuring intelligence beyond human capability using adversarial psychometric rating systems, where models generate challenges to separate other systems, enabling evaluation that scales with AI capabilities.
The paper proposes an attack-agnostic robustness metric based on the spectral norm of the Fisher Information Matrix, providing theoretical bounds and scalable evaluation methods for deep neural networks.