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HLA-WM: Hybrid Linear Attention for Long-Horizon Video World Models

HLA-WM is a hybrid linear-attention framework designed to improve long-horizon video world models by combining geometry-guided retrieval with recurrent linear-state computation. It enhances scene consistency and camera control metrics without additional training, achieving a 0.74 dB PSNR gain and reducing rotation error by 28.5% on the SANA-WM-Bench dataset.

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PublishedOctober 5, 2026Zhuokun Chen, Feng Chen, Xi Lin
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HLA-WM enhances long-horizon video world models by combining geometry-guided retrieval with recurrent linear-state computation, improving scene consistency and camera control metrics without additional training.

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