Matrix-Game 3.0: Interactive World Model

• Matrix-Game 3.0 is a memory-augmented interactive world model that integrates real-time high-resolution video generation with stable minute-scale consistency.
• It employs an industrial-scale data pipeline that fuses synthetic, game-captured, and real-world video corpora into a unified quadruplet format for robust training.
• The framework advances model architecture and long-horizon memory via a bidirectional Diffusion Transformer augmented with error-awareness and camera-aware retrieval.

Matrix-Game 3.0 is a memory-augmented interactive world model and video generation framework designed for real-time, high-resolution, longform outputs with stable minute-scale consistency. It introduces systematic advances in data collection, model architecture, long-horizon memory, inference efficiency, and training methodology, targeting current limitations of streaming interactive world modeling and video synthesis (Wang et al., 10 Apr 2026). The term also has historic antecedents in matrix multiplication games (MMGs), which explore alternating matrix product games with growth-rate objectives (Asarin et al., 2015).

1. Historical and Conceptual Foundations

Matrix-Game 3.0 builds upon two strands of research: the theory of matrix multiplication games—formally defined zero-sum games in which two players alternately select matrices from finite sets, with the objective of minimizing or maximizing the spectral (growth) rate of their infinite product (Asarin et al., 2015)—and the development of interactive, temporally-coherent video world models. While classical MMGs are notable for their undecidability and deep connections to entropy games and IRU-set minimax theorems, practical interactive world models have evolved out of diffusion-based generative models, encountering challenges such as breakdown of long-horizon temporal consistency, lack of explicit memory, and compounding inference errors in autoregressive pipelines (Asarin et al., 2015, Wang et al., 10 Apr 2026).

2. Data Pipeline: Scale and Structure

Matrix-Game 3.0 employs an industrial-scale, heterogeneous data pipeline that fuses synthetic, in-game, and real-world sources into harmonized quadruplet format for each training sample:

• Unreal Engine–based Synthetic Data: Over 1,000 custom UE5 scenes using Nanite geometry and Lumen lighting, where NavMesh-RL hybrid agents generate diverse and tick-synchronized action, pose, and camera tracks (>10^8 character variants).
• AAA Game Capture: A four-layer capture stack connects plugins in titles such as GTA V, RDR2, and Cyberpunk 2077 to navigation agents, OBS-coordinated video, and per-frame state/action pose, producing tightly synchronized video-action-pose CSV pairs.
• Real-World Video Corpora: Large public datasets (DL3DV-10K, RealEstate10K, OmniWorld, SpatialVid-HD) are uniformly re-annotated (e.g., using ViPE), with quality filtering by reprojection error, perceptual score, and motion anomaly detection.
• Quadruplet Construction: Each sample comprises a frame sequence $I_{t-k:t+N}$, latent embeddings $x^{1:k}$, $x^{k+1:N}$, discrete action vector $a_t$, and a prompt generated via the InternVL3.5-8B model. Augmentations further diversify weather, time, and geometry (Wang et al., 10 Apr 2026).

3. Model Architecture and Long-Horizon Memory

The core generative model is a bidirectional Diffusion Transformer (DiT), augmented with error-awareness and explicit camera-aware memory retrieval:

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