Twin: Playing an Unknown Game with a Test-Time Digital Twin

Instead of hand-engineering a world model for each new task, this paper has a coding agent write executable world models on the fly, purely from observing an unfamiliar game. A "twin" simulation validates each hypothesis by requiring it to reproduce every past observation before acting, using mismatches to iteratively repair the model. Tested on ARC-AGI-3 games, Twin dramatically outperforms baseline agents and even exceeds human efficiency on most levels. This approach has broad implications for general-purpose game-playing AI, robotics, and any domain requiring rapid, autonomous understanding of unknown environments. Authors: Alexy Skoutnev, Kirill Acharya, Gaston Longhitano, Madeleine Udell, Kevin Ellis, Iddo Drori Paper: https://arxiv.org/abs/2608.14490v1