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cs.CL2026
LRCC: Generalizing Low-Rank Compression with Conditional Computation
Thomas Vaitses Fontanari, Maximo Eduardo Rulli, Federico Alvetreti +2
Low-rank compression reduces the cost of pretrained language models by replacing linear transformations with low-rank factorizations. However, conventional methods use a fixed rank…
cs.CL2026
DashAttention: Differentiable and Adaptive Sparse Hierarchical Attention
Yuxiang Huang, Nuno M. T. Gonçalves, Federico Alvetreti +5
Current hierarchical attention methods, such as NSA and InfLLMv2, select the top-k relevant key-value (KV) blocks based on coarse attention scores and subsequently apply fine-grain…