Game world models tend to generate pixels directly, but pose and geometry drift the longer you roll them out. Marionette simply gives up the parts that must be exact — to a renderer. Title: Marionette: Predicting World States, Rendering Geometry, Painting Appearance URL: A world model that delegates geometry, occlusion, and motion to a deterministic renderer and lets the neural net paint only appearance. Three highlights. 🎮 Highlight 1: Predict an explicit 276-D world state Instead of pixels, it predicts a 3D state of joints, root trajectory, and rotations via two-stage autoregression. Control is just overwriting an action token (like a player's button press). Forcing a wrong action shifts the pose by ~31% — proof the control truly bites. 📐 Highlight 2: A zero-parameter deterministic renderer Six closed-form operations turn state into pose-control video, so world consistency, occlusion order, and metric scale hold by construction. Long-horizon failures are fixed by rules (e.g. a terrain collider) without retraining appearance — cutting penetration by 66%. 🎨 Highlight 3: Video diffusion for appearance only A Wan2.2-Fun-5B diffusion paints photorealistic RGB on top of exact geometry. Even via predicted state, FVD is 831 (799 from recorded state, 975 for pixel-autoregressive) — no detectable fidelity loss. Exactness to deterministic compute, appearance to the neural net — a clean division of labor. #WorldModels# #GameAI#