MASA recasts sparse-attention selection as matrix approximation, lifting long-context accuracy across three sparse frameworks at no extra budget
The authors argue that existing sparse-attention selectors keep large or high-mass entries of the attention matrix even though Transformers use that matrix through multiplication with value vectors, and propose MASA, which replaces attention-mass ranking with a closed-form score measuring each sparse unit's reduction in matrix-product approximation error; plugged into MInference, SeerAttention, and FlexPrefill without changing their sparse kernels, candidate units, or budgets, it yields consistent accuracy gains on RULER, InfiniteBench, and LongBench with negligible latency overhead.