Mind-Studio: Executable World Models with Lookahead Evaluation for Partially Observable Games
For game AI and world-model synthesis, it provides a method to produce fully executable models with improved fidelity over prior approaches.
Mind-Studio synthesizes executable pygame-style world models from interaction trajectories using LLMs, achieving 48.7% next-state prediction accuracy on Montezuma's Revenge (vs. 0.3% for PoE-World) and verifying 5 of 8 subgoals.
World-model synthesis aims to turn interaction experience into an internal model of environment dynamics. Existing symbolic approaches often fit observed transitions or mixtures of local rules, but they do not produce a complete executable program that can run independently of the real environment. We present Mind-Studio, a framework that synthesizes executable pygame-style world models from state-action-next-state trajectories using large language models. Mind-Studio combines entropy-selected traces with a lightweight game skill file containing object, action, and static scene information extracted from screenshots. We evaluate synthesis quality with a K-step lookahead fidelity protocol that compares generated world-model rollouts against Real-ALE rollouts from the same state. On Montezuma's Revenge, Mind-Studio improves chosen-action next-state prediction from 0.3% for PoE-World to 48.7% while verifying 5 of 8 subgoals; across Alien, Assault, and Skiing, it achieves stronger branch-level fidelity than prior learned lookahead sources.