MiniMax dropped a new attention architecture. [N]

It contains something interesting about context windows. They’re natively scaling to 1M tokens with MiniMax Sparse Attention (MSA) , bypassing standard quadratic complexity by completely restructuring the memory access patterns at the operator level. Instead of relying on typical sparse approximations that degrade recall, MSA utilizes a clean " KV outer gather Q " approach. By treating KV blocks as the outer loop to aggregate hit queries, hardware memory reads remain strictly contiguous, and each block is fetched exactly once. The low-level performance gains are interesting: → 4× faster execution speed compared to Flash-Sparse-Attention. → Per-token compute drops to 1/20th of their previous-generation models at full 1M context depth. → 9× speedup in prefilling and a 15× speedup in decoding phases. Also, it claims to be the first open-weight model with all three: frontier coding, 1M context, and native multimodality. Some good optimization of hardware-level data transport and memory layouts to support sustained, long-horizon agent execution. Thoughts? submitted by /u/superintelligence03 [link] [留言]