Test-time world model inference—where an AI builds and validates its own simulation of unknown environments—dramatically improves task performance (from 7.8% to 93.3%) and shows that learning game rules is easier than inferring goals.
Twin is a system where an AI agent builds executable world models to solve puzzle games by observing and simulating gameplay. Rather than hand-coding rules for each game, the system learns the game's mechanics through trial and error, validates predictions against actual outcomes, and fixes mismatches. It solves 97.8% of test games more efficiently than humans.