Judging a game-playing AI on just a handful of noisy playthroughs? A new dataset-and-benchmark combo says that's not good enough anymore. Title: GameHorizon Suite: Multi-Horizon Data and Evaluation in Gameplay URL: GameHorizon Suite densely annotates 5,000 hours of gameplay from 21 AAA titles with short-, mid-, and long-horizon instructions, letting a single benchmark measure both execution skill and planning ability. Three highlights stand out. 🎬 Dense, multi-horizon dataset 5,000 hours across 21 titles, 411M keyboard-mouse events, and one instruction roughly every 2.63 seconds on average. Every frame is simultaneously aligned to short, medium, and long-horizon instructions. 🧩 Bottom-up annotation meets top-down evaluation Annotation builds actions bottom-up from raw inputs, while evaluation forces goal decomposition top-down. The 28.9-point accuracy gap between the two directions shows recognition and planning are genuinely different skills. 📊 Offline plus online, side by side 5,000 reproducible offline multiple-choice questions pair with an online track that resets after every failure to pinpoint where models break down. Across 44 models the mean accuracy is 64.7%, with GPT-6-Astra leading at 80.2%. What strikes me most is how clearly the numbers expose the gap between recognizing an action and actually planning ahead. #GameAI# #Benchmark#

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#

HAMIEVERSE GAMENIGHT jump in our weekly @hamieverse gamenight every thursday, same time! GIVEAWAY: 1 x Hamie NFT to those that participate! 🗓️Today, Thursday August 27th at 3pm EST/7pm UTC!