ConSA: Controllable Sparsity in Hybrid Attention via Learnable Allocation

arXiv:2606.18056v1 Announce Type: new Abstract: Hybrid architectures combining full attention (FA) and sliding-window attention (SWA) are a promising paradigm for efficient LLM inference. However, existing methods typically rely on hand-crafted rules or simple post-hoc heuristics for FA/SWA allocation and offer limited analysis of the attention behaviors underlying these designs. We propose Controllable Sparsity in Hybrid Attention (ConSA), a framework that learns optimal FA/SWA assignment under a user-specified sparsity target. ConSA employs L0 regularization to learn binary masks selecting b

Source: arXiv cs.CL — read the full report at the original publisher.

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