Despite some limitations in repetitive environments, MaaG marks a significant advancement in addressing long-standing consistency issues in generative games.

Looking ahead, plans include expanding MaaG into more complex 2D and 3D environments and improving strategies for spatial consistency, paving the way for more playable and logically coherent AI-generated game worlds.

The framework comprises a numerical module, LogicNet, which manages game logic and score updates without performing arithmetic directly, and a spatial module, External Map, which maintains a memory of previously explored areas to ensure visual continuity.

Researchers from Microsoft and collaborating institutions have introduced a new framework called Model as a Game (MaaG), aimed at enhancing consistency in AI-generated games.

MaaG tackles two primary types of inconsistencies: numerical consistency, which ensures accurate score updates, and spatial consistency, which preserves visual coherence in the game environment.

The framework's modular design allows developers to adjust game logic and spatial mapping, providing greater control over gameplay environments compared to previous systems like GameGAN.

Generative games, which create environments frame by frame using neural networks, often face issues such as disappearing elements after player actions, highlighting the need for improved consistency.

To test the framework, the team developed a minimalist 2D game called Traveler, which revealed limitations in existing generative models while also showcasing numerical and spatial challenges.

MaaG demonstrated its capabilities through examples from Traveler, Pong, and Pac-Man, showing enhanced visual consistency and more reliable score tracking compared to traditional models.