Learning Correct Behavior from Examples: Validating Sequential Execution in Autonomous Agents

arXiv:2605.03159v1 Announce Type: new Abstract: As autonomous agents become increasingly sophisticated, validating their sequential behavior presents a significant challenge. Traditional testing approaches require manual specification, exact sequence matching, or thousands of training examples. We present a novel algorithm that automatically learns correct behavior from just 2-10 passing execution traces and validates new executions against this learned model. Our approach combines dominator ana