Author

Xu Han

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#artificial intelligence Jun 2026

LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization

LC-QAT is proposed, a 2-bit weight-only VQ-QAT framework that represents quantized weights via a learned affine mapping over discrete vectors, which yields a high-quality PTQ initialization and enables fully differentiable end-to-end optimization without explicit codebook lookup in the training forward pass.

Hao-Yu Wang, Xingyu Yu, Hai-Yan Zhao et al. · arXiv.org · 2 citations

Preprint Jul 2026

UltraX: Refining Pre-Training Data at Scale with Adaptive Programmatic Editing

UltraX is proposed, a function-calling refinement framework for large-scale pre-training data that completes the editing function space by introducing insertion in addition to deletion and modification, enabling fine-grained instance-level editing.

Xinlong Zhao, Dongsheng Liu, Hengyu Zhao et al. · 0 citations