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LiftQuant: Continuous Bit-Width LLM via Dimensional Lifting and Projection

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arXiv:2606. 04050v1 Announce Type: new Abstract: Existing quantization methods are fundamentally limited by rigid, integer-based bit-widths (e.

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Authors: Liulu He, XuanAng Liu, Juntao Liu, Taolue Feng, Ting Lu, Chunsheng Gan, Zhiyv Peng, Yuan Du, Huanrui Yang, Yijiang Liu, Li Du
Released: 2026-06-05 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because LiftQuant: Continuous Bit-Width LLM via Dimensional Lifting and Projection overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, width, line, rate, project, control, language, model. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.04050

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