RIGen: Beyond Action Following with Reciprocal Interaction in Generative Gameplay

Generative gameplay requires more than immediate responses to player actions. Actions should have persistent consequences, while the generated world should act according to its own state and rules, shaping the player's situation and subsequent choices. Video generation captures local dynamics, but limited history, partially observed game state, and the need for contextual behavior decisions make such interactions difficult to sustain through audiovisual prediction alone. We introduce \rig (Reciprocally Interactive Generation), a framework for reciprocal interaction in generative gameplay. \rig combines persistent state and configurable rules, contextual interaction reasoning, and action-conditioned audiovisual generation. Together, these components make interaction consequences and actor behavior jointly determine generated content while maintaining consistency between game state and visible outcomes. We construct a Xonotic gameplay dataset, train the corresponding audiovisual generator, and develop \rigarena, a playable generative first-person shooter (FPS) game in a fixed arena environment, through post-training. Experiments demonstrate more reliable combat outcomes and reciprocal interaction between the player and the generated world. We will release all models, datasets, code, and data creation pipelines as open source.