arXiv:2607.07040v1 Announce Type: new Abstract: How can we measure intelligence beyond human capability? Human-authored benchmarks saturate, and above human capability, examiners may not know which tasks are both hard and verifiable. We argue that this difficulty is inherent to absolute-scale evaluation and propose a new paradigm based on relative measurement in which models generate public challenges that separate other systems. Aggregating these outcomes yields an adversarial psychometric rating system that can scale with the systems being measured. We describe practical protocols that reduc
The rapid advancement of AI models necessitates new evaluation methods as current human-authored benchmarks are becoming insufficient for systems exceeding human capabilities.
Developing new paradigms for measuring intelligence is critical for guiding AI research, identifying true breakthroughs, and establishing safety and alignment standards for advanced systems.
The focus shifts from absolute-scale evaluation against human benchmarks to relative measurement and adversarial psychometrics, enabling continuous and scalable assessment of AI systems.
- · Advanced AI research labs
- · AI safety researchers
- · Developers of robust AI evaluation platforms
- · AI ethicists
- · Traditional benchmark developers
- · AI companies focused solely on human-level performance metrics
- · Regulators relying on outdated evaluation methods
New standards and methodologies emerge for quantifying AI intelligence beyond human cognitive limits.
The ability to objectively measure and compare ultra-intelligent AI systems accelerates the identification and scaling of truly advanced capabilities.
Improved measurement leads to more transparent and reliable progress in AI, potentially influencing policy and resource allocation for superintelligent systems.
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Read at arXiv cs.AI