3 papers
cs.CV2026
RPFQ-ViT: Rotated Phase-Frame Quantization for Extremely Low-Bit Weights in Vision Transformers
Mengyuan Fan, Bokai Huang, JiaMing Pan +4
Vision Transformers (ViTs) achieve strong performance on image recognition and mobile vision applications, but their high-dimensional linear projections and attention computations…
cs.LG2026
Fairy2i: Training Complex LLMs from Real LLMs with All Parameters in
Feiyu Wang, Xinyu Tan, Bokai Huang +4
Large language models (LLMs) have revolutionized artificial intelligence, yet their massive memory and computational demands necessitate aggressive quantization, increasingly pushi…
cs.LG2025
iFairy: the First 2-bit Complex LLM with All Parameters in
Feiyu Wang, Guoan Wang, Yihao Zhang +7
Quantization-Aware Training (QAT) integrates quantization into the training loop, enabling LLMs to learn robust low-bit representations, and is widely recognized as one of the most…