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It is shown, both geometrically and empirically, that high input--output cosine similarity implies low effective rank, and rank-preserving fine-tuning (RPFT) is proposed, which adapts only a small subset of retained singular components so that the allocated rank stays bounded without re-decomposition.
WIDE is presented, the first end-to-end differentiable token-level dynamic width pruning framework designed for both prefill and decode scenarios, and a pruning--kernel co-design framework that decomposes dynamic sparsity acceleration into mask reordering, hardware-agnostic block-level skipping, and hardware-dependent...
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