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MSA: Memory Sparse Attention

Trainable latent-memory framework for 100M-token contexts

MSA, or Memory Sparse Attention, is a research framework for scaling language-model memory to extremely long contexts. It replaces full attention over all tokens with sparse selection of compressed latent memory states. Document-wise rotary position encoding and top-k routing keep training and inference close to linear complexity. A tiered KV-cache design stores routing keys on GPU while larger content states can remain on CPU.

Downloads: 0 This Week

Last Update: 2026-09-18
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