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

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 systems, attempt to extend this limit. However, they often suffer from severe precision degradation and rapidly
increasing latency as context length grows, an inability to dynamically modify memory content, or a lack of end-to-end optimization. These
bottlenecks impede complex scenarios like large-corpus summarization, Digital Twins, and long-history agent reasoning, while limiting memory
capacity and slowing inference. We present Memory Sparse Attention (MSA), an end-to-end trainable, efficient, and massively scalable memory
model framework. Through core innovations including scalable sparse attention and document-wise RoPE, MSA achieves linear complexity in both
training and inference while maintaining exceptional stability, exhibiting less than 9% degradation when scaling from 16K to 100M tokens.
Furthermore, KV cache compression, combined with Memory Parallel, enables 100M-token inference on 2xA800 GPUs. We also propose Memory
Interleaving to facilitate complex multi-hop reasoning across scattered memory segments. MSA significantly surpasses frontier LLMs,
state-of-the-art RAG systems, and leading memory agents in long-context benchmarks. These results demonstrate that by decoupling memory
capacity from reasoning, MSA provides a scalable foundation to endow general-purpose models with intrinsic, lifetime-scale memory.