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cs.CL2026
Break Through the Compression Bottleneck: From Theory to Practice
Xiusheng Huang, Lu Wang, Yequan Wang +2
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…
cs.LG2026
Theory-optimal Quantization Based on Flatness
Xiusheng Huang, Zhe Li, Xuanwu Yin +5
Post-training quantization has emerged as a widely adopted technique for compressing and accelerating the inference of Large Language Models (LLMs). The primary challenges in LLMs…
cs.CV2026
Mutual Enhancement Between Global Tokens and Patch Tokens: From Theory to Practice
Xiusheng Huang, Xin Jiang, Jun Zhao +2
Accurate and effective discrete image tokenization is crucial for long image sequence processing. However, current methods rigidly compress all content at a fixed rate, ignoring th…