Block-Sparse FlashAttention Versus Gated Sparse Attention for Cross-Document Synthesis in 100k-Token Contexts

Modern large language models increasingly require long contexts for reasoning and multi-document tasks, but attention's quadratic complexity creates a severe computational bottleneck. We present Block-Sparse FlashAttention (BSFA), a drop-in replacement that accelerates long-context inference while preserving model quality. Unlike methods that predict importance before computing scores, BSFA computes exact query-key similarities to select the top-k most important value blocks for each query. By comparing per-block maximum scores against calibrated thresholds, we skip approximately 50\% of the co

Research goal: What is the impact of Block-Sparse FlashAttention versus Gated Sparse Attention on answer accuracy for queries requiring cross-document synthesis in contexts exceeding 100k tokens?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.6/10.