AIBullisharXiv – CS AI · Mar 267/10
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MSA: Memory Sparse Attention for Efficient End-to-End Memory Model Scaling to 100M Tokens
Researchers present Memory Sparse Attention (MSA), a new AI framework that enables language models to process up to 100 million tokens with linear complexity and less than 9% performance degradation. The technology addresses current limitations in long-term memory processing and can run 100M-token inference on just 2 GPUs, potentially revolutionizing applications like large-corpus analysis and long-history reasoning.