Break Through the Compression Bottleneck: From Theory to Practice
As language model parameter sizes grow, effective compression is essential to reduce computational and memory overhead. Existing methods suffer from performance degradation at high compression ratios. This paper provides the first mathematical proof that low-rank decomposition and quantization are non-orthogonal—their combination causes significant performance loss. The authors propose Diagonal Adhesive Method (DAM) to effectively combine both techniques and mitigate the loss.
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[Submitted on 11 May 2026]
Title:Break Through the Compression Bottleneck: From Theory to Practice
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Abstract:As the parameter size of language models continues to grow, effective model compression is required to reduce their computational and memory overhead. Existing compression methods suffer from bottleneck issues: when the compression ratio is increased, performance degrades significantly. Low-rank decomposition and quantization are two prominent compression methods that have been proven to significantly reduce the computational and memory requirements of Large Language Models (LLMs) while maintaining model accuracy. Evidently, combining these two methods will break through the existing compression bottleneck. However, how these two methods interact when combined remains a critical question for developers, as many assume they are orthogonal, meaning their combination would not introduce additional errors beyond those independently introduced by each method. This paper provides the first mathematical proof that low-rank decomposition and quantization are non-orthogonal. We validate these findings through a series of experiments on large language models. Our results demonstrate that these methods are non-orthogonal, and their combination leads to significant performance degradation. Importantly, we propose a novel approach Diagonal Adhesive Method (DAM), which can effectively combine the two methods and mitigate the performance loss. Our research provides deep insights into model compression and lays a solid theoretical and experimental foundation for future related studies.
Comments: 18 pages, 3 figures,
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.20434 [cs.CL]
(or arXiv:2607.20434v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.20434
arXiv-issued DOI via DataCite
Submission history
From: Xiusheng Huang [view email] [v1] Mon, 11 May 2026 10:50:26 UTC (10,280 KB)
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