When agents in a network each see partial data and pass only predictions forward, information loss follows a precise mathematical pattern: errors stay constant until the network depth reaches M², then shrink proportionally to M²/D.
This paper solves a theoretical problem about how information degrades when passed through networks of AI agents. Each agent sees only partial data and passes predictions forward, like a game of telephone.