Researchers introduced HLA-WM, a hybrid linear attention architecture for long-horizon video world models that combines softmax attention's full-history KV cache with recurrent linear attention's fixed-size state compression to cut memory use. The method targets persistent scene consistency over extended rollouts, where softmax attention's growing KV cache and linear attention's compressed fixed-size states each pose trade-offs.
Long-horizon video world models require persistent memory to preserve scene consistency over extended rollouts. Softmax attention retains the full generation history through a growing KV cache, whereas recurrent linear attention compresses history into fixed-size states with substantially lower memo
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