Paper page - Light Interaction: Training-Free Inference Acceleration for Interactive Video World Models

Source: https://huggingface.co/papers/2605.31158

Abstract

Light Interaction accelerates interactive video world models through adaptive computation strategies and optimized attention mechanisms without requiring model retraining.

Interactive videoworld models generate video chunk by chunk in response to user-controlled camera movements, enabling applications such as real-time game simulation, virtual scene navigation, and embodied AI training. However, scaling to long interactive trajectories is prohibitively expensive due to growing context memory, quadratic attention complexity, and repeated denoising steps. We present Light Interaction, a training-free inference acceleration framework forinteractive videoworld models. Our key insight is that interaction naturally enablestrajectory-dependent adaptive computation: retrievedspatial memorycan be discarded during novel exploration,temporal contextcan be adjusted according to local latent dynamics, and early-step model outputs can be reused when the camera revisits familiar regions. Based on this insight, Light Interaction combines adaptive context management,denoising cache acceleration, and hardware-software co-designed3D block sparse attentionwith fusedTriton kernels. Evaluated on HY-WorldPlay and Matrix-Game-3.0, Light Interaction achieves up to 2.59x speedup without model retraining while maintaining competitive visual quality.

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