Activation-Informed Pareto-Guided Low-Rank Compression for Efficient LLM/VLM

• URL: http://arxiv.org/abs/2510.05544v1
• Date: Tue, 07 Oct 2025 03:07:47 GMT
• Title: Activation-Informed Pareto-Guided Low-Rank Compression for Efficient LLM/VLM
• Authors: Ryan Solgi, Parsa Madinei, Jiayi Tian, Rupak Swaminathan, Jing Liu, Nathan Susanj, Zheng Zhang,
• Abstract summary: Large language models (LLM) and vision-language models (VLM) have achieved state-of-the-art performance, but they impose significant memory and computing challenges in deployment.<n>We present a novel low-rank compression framework to address this challenge.
• Score: 11.762499172999886
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLM) and vision-language models (VLM) have achieved state-of-the-art performance, but they impose significant memory and computing challenges in deployment. We present a novel low-rank compression framework to address this challenge. First, we upper bound the change of network loss via layer-wise activation-based compression errors, filling a theoretical gap in the literature. We then formulate low-rank model compression as a bi-objective optimization and prove that a single uniform tolerance yields surrogate Pareto-optimal heterogeneous ranks. Based on our theoretical insights, we propose Pareto-Guided Singular Value Decomposition (PGSVD), a zero-shot pipeline that improves activation-aware compression via Pareto-guided rank selection and alternating least-squares implementation. We apply PGSVD to both LLM and VLM, showing better accuracy at the same compression levels and inference speedup.

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