Inverse Rubric Optimization: A testbed for agent science

Researchers at Fulcrum have introduced Inverse Rubric Optimization, a new framework designed to serve as a testbed for studying agent behavior and alignment. The system allows developers to define complex behavioral criteria through rubrics, which agents then learn to optimize in reverse, enabling controlled experimentation with goal-directed AI systems. This approach aims to provide a more rigorous and interpretable method for evaluating how AI agents pursue specified objectives.