New sparse attention methods target long-context LLM inference efficiency

Recent research introduces four distinct sparse attention architectures—Recall Before You Rank, CoSA, GLIDE, and RIS-Kernel—designed to reduce the quadratic computational cost and KV cache memory overhead of long-context LLM inference. These approaches aim to bypass standard full self-attention bottlenecks, enabling more efficient processing of extended document sequences.