A shared, human-readable schema can simultaneously ground agent generation and enable human interpretation, making AI-generated content more transparent and controllable.
This paper introduces a semantic action graph—a structured representation of sports matches using performers, actions, recipients, moments, and states—that enables both AI agents to generate video highlights and humans to understand and control them.