Split Before You Scale: Why Useful AI Starts by Sorting the Mess
TL;DR for operators AI systems fail less dramatically when they stop treating every messy signal as the same kind of mess. The three papers in this cluster look unrelated at first: one generates graphs, one studies exploration in restless bandits, and one improves reinforcement-learning generalisation from formal task specifications. Under the surface, they make a shared operational point: before scaling an AI system, separate the structure that must be preserved, the uncertainty that should guide action, and the supervision signal stable enough to train on. ...