提出统一秩分配方法，突破低秩分解压缩LLM的瓶颈，兼顾效率与性能。
arXiv:2606.21847v1 Announce Type: cross Abstract: Low-rank decomposition serves as a promising compression paradigm for large language models, however…
新方法Swift-SVD实现理论最优性与实际效率兼得，专为低秩大模型压缩而生，ICML 2026收录。
arXiv:2604.01609v2 Announce Type: replace Abstract: The deployment of Large Language Models is constrained by the memory and bandwidth demands of stat…
仅靠排序即可实现大模型张量化，比传统分解方法更简洁高效，是LLM压缩与加速的新范式
arXiv:2606.08565v1 Announce Type: new Abstract: Tensor networks provide efficient representations for compressing large neural networks. By carefully …
用SVD低秩分解加学习缩放矩阵，实现高效LLM压缩，精准降维保性能。
arXiv:2606.07098v1 Announce Type: cross Abstract: We present SigmaScale, a method for learning auxiliary scaling matrices $S$ to aid truncated Singula…
提出输入输出白化SVD方法，实现自适应秩的大语言模型压缩，提升推理效率。
arXiv:2605.15626v1 Announce Type: new Abstract: Large language models deliver strong performance across language and reasoning tasks, but their storag…