发表机构
Princeton（普林斯顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究如何测量超越人类能力的智能，提出基于相对测量的新范式，模型生成公开挑战，汇总结果得对抗性心理测量评级系统，还描述实用协议，在不同领域实例化框架，实现对超越人类前沿系统的测量。
AI 中文摘要
我们如何测量超越人类能力的智能？人工编写的基准已达到饱和，在超越人类能力的情况下，考官可能不知道哪些任务既困难又可验证。我们认为这种困难是绝对尺度评估所固有的，并提出了一种基于相对测量的新范式，其中模型生成区分其他系统的公开挑战。汇总这些结果会产生一个对抗性心理测量评级系统，该系统可以随着被测量的系统扩展。我们描述了减少私人信息攻击动机、支持无评判裁决并自然地随智能体能力扩展的实用协议。我们在可验证和开放式、不可验证的领域中实例化了该框架，说明了模型生成的评估如何能够继续测量超越人类前沿的系统。
英文摘要
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 reduce incentives for private-information attacks, support judge-free adjudication, and naturally scale with agent capabilities. We instantiate the framework across verifiable and open-ended, non-verifiable domains, illustrating how model-generated evaluation can continue to measure systems beyond the human frontier.
