El Profesor and El Hacker both stop at the same component and reach opposite conclusions about whether learning on your machine is a feature.
Reasoning and trade-offs · AI analysis
El Hacker scores this well because the learning component runs on hardware he owns, so nothing leaves the room. El Profesor stops at the same component and asks who checked that the lessons are correct, since the tool grades its own contribution. El Crítico adds the part neither framed: nothing described here removes a lesson once it is wrong.
El Profesor wins, because a private mistake is still a mistake and it now propagates to every agent. El Hacker keeps the licence argument and loses the score. Trial only, and the exit criterion is a documented way to inspect and discard what it has learned.