MSA: Memory Sparse Attention for Efficient End-to-End Memory Model Scaling to 100M Tokens

📰 ArXiv cs.AI

MSA enables efficient end-to-end memory model scaling to 100M tokens with sparse attention

advanced Published 26 Mar 2026

Action Steps

Implement sparse attention mechanisms to reduce computational complexity
Scale LLMs to process lifetime-scale information with MSA
Evaluate MSA against existing approaches like hybrid linear attention and RAG

Who Needs to Know This

ML researchers and engineers working on large language models (LLMs) can benefit from MSA to improve model performance and scalability, while software engineers can apply MSA to develop more efficient AI systems

Key Insight

💡 MSA overcomes the limitations of full-attention architectures, enabling LLMs to process longer context lengths

Key Takeaways

MSA enables efficient end-to-end memory model scaling to 100M tokens with sparse attention

Full Article

Title: MSA: Memory Sparse Attention for Efficient End-to-End Memory Model Scaling to 100M Tokens

Abstract:
arXiv:2603.23516v1 Announce Type: cross Abstract: Long-term memory is a cornerstone of human intelligence. Enabling AI to process lifetime-scale information remains a long-standing pursuit in the field. Due to the constraints of full-attention architectures, the effective context length of large language models (LLMs) is typically limited to 1M tokens. Existing approaches, such as hybrid linear attention, fixed-size memory states (e.g., RNNs), and external storage methods like RAG or agent syste

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