Measuring Agents With Interactive Evaluations

In this monologue, Greg Kamradt from ARC Prize Foundation discusses how to measure frontier AI using interactive benchmarks. He defines intelligence as skill acquisition efficiency and introduces ARC AGI-3, a benchmark consisting of 150 open-sourced video game environments designed to test an agent's ability to adapt to novel situations. Greg emphasizes the importance of "action efficiency," which measures how directly an agent achieves a goal, and highlights the "human-AI gap," which represents the difference between human and AI learning efficiency. He argues that interactive benchmarks and action efficiency provide a more comprehensive way to evaluate AI performance, moving beyond static benchmarks and traditional accuracy metrics. He also invites listeners to try out the preview games and use the API to test their agents.