Multiverse Mechanica: a testbed for learning game mechanics via counterfactual worlds

Conference item

We study how generative world models trained on video games can go beyond mere reproduction of gameplay visuals to learning game mechanics—the modular rules that causally govern gameplay. We introduce a formalization of the concept of game mechanics that operationalizes mechanic-learning as a causal counterfactual inference task and uses the causal consistency principle to address the challenge of generating gameplay with world models that do not violate game rules. We present Multiverse Mechanica, a playable video game testbed that implements a set of ground truth game mechanics based on our causal formalism. The game natively emits training data, where each training example is paired with a set of causal DAGs that encode causality, consistency, and counterfactual dependence specific to the mechanic that is in play—these provide additional artifacts that could be leveraged in mechanic-learning experiments. We provide a proofof-concept that demonstrates fine-tuning a pre-trained model that targets mechanic learning. Multiverse Mechanica is a testbed that provides a reproducible, low-cost path for studying and comparing methods that aim to learn game mechanics—not just pixels.

Authors: Ness, R; Cannizzaro, R; Wu, Y; Kunze, L

• Publication status: Accepted
• Peer review status: Peer reviewed
• Publisher: OpenReview
• Host title: Proceedings of the 14th International Conference on Learning Representations (ICLR 2026)
• Article number: 19865
• Publication date: 2026-04-23
• Acceptance date: 2026-01-26
• Event title: 14th International Conference on Learning Representations (ICLR 2026)
• Event location: Rio de Janeiro, Brazil
• Event website: https://iclr.cc/Conferences/2026
• Event start date: 2026-04-23
• Event end date: 2026-04-27
• Language: English