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

A new algorithm is presented that learns correct sequential behavior from just 2-10 execution traces to validate new executions in autonomous agents. It combines compiler theory with multimodal LLM-powered semantic understanding to construct a generalized ground truth model, achieving high accuracy in detecting product bugs.