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LACE-SVD first estimates the calibration negative-log-likelihood increase induced by candidate layer-wise compression ratios and solves a budget-co","title":"LACE-SVD: Loss-Aware SVD with Cumulative Error Correction for LLM Compression","url":"https://arxiv.org/abs/2607.03057","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.03057v1 Announce Type: cross \nAbstract: The rapid growth in the parameter scale of large language models (LLMs) has created a strong demand for efficient compression techniques. As a hardware-agnostic and highly compatible approach, low-rank compression has been widely adopted to reduce both memory footprint and computational cost. 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