3 papers
cs.LG2026
IntraSlice: Towards High-Performance Structural Pruning with Block-Intra PCA for LLMs
Meng Li, Peisong Wang, Yuantian Shao +5
Large Language Models (LLMs) achieve strong performance across diverse tasks but face deployment challenges due to their massive size. Structured pruning offers acceleration benefi…
cs.LG2025
Block Rotation is All You Need for MXFP4 Quantization
Yuantian Shao, Peisong Wang, Yuanteng Chen +3
Large language models (LLMs) have achieved remarkable success, but their rapidly growing scale imposes prohibitive costs in memory, computation, and energy. Post-training quantizat…
cs.LG2025
DartQuant: Efficient Rotational Distribution Calibration for LLM Quantization
Yuantian Shao, Yuanteng Chen, Peisong Wang +5
Quantization plays a crucial role in accelerating the inference of large-scale models, and rotational matrices have been shown to effectively improve quantization performance by sm…