arXiv AI
Jacobian-guided Noise Injection for Quantization Robustness in Large Language Models
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arXiv AI
Jun 4
QuBLAST: A Framework for Quantizing Large Language Models with Block-Level Compression Approach and Activation Scaling Strategy
arXiv:2606. 04620v1 Announce Type: cross Abstract: LLMs have become the state-of-the-art algorithms for solving NLP tasks.
arXiv Machine Learning
Aug 10
Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers
arXiv:2507. 07814v2 Announce Type: replace Abstract: We introduce a novel upper bound on the local Lipschitz constant of the dot-product self-attention block showing its dependence on the attention map distributions.
arXiv AI
Sep 17
HyQuant: Hybrid-Precision Quantization for LLM Attention
HyQuant introduces a hybrid-precision quantization framework for large language model (LLM) attention modules. It quantizes most attention states to low-bit formats while preserving a small set of vertical‑line tokens and local‑window states in full precision, guided by lightweight attention‑pattern signals. This design achieves near‑lossless accuracy across tasks while improving memory and hardware efficiency.
arXiv Computation and Language
Sep 22
SalQ-VLM: Fine-Grained Saliency-Guided Quantization for Vision-Language Models
arXiv:2508.03351v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated remarkable capabilities across diverse language tasks, motivating their extension to vision-la...
arXiv AI
4d ago
LeapQuant: Efficient Linear Attention with Accurate Recurrent State Quantization
arXiv:2609.38166v1 Announce Type: cross Abstract: Recent LLMs increasingly adopt hybrid designs that replace standard attention with linear attention, such as Gated DeltaNet (GDN) and Kimi Delta Atte...
arXiv Machine Learning
Jun 2
ProjQ: Project-and-Quantize for Adapter-Aware LLM Compression
arXiv:2606. 00494v1 Announce Type: new Abstract: Post-Training Quantization (PTQ) and Low-Rank Adaptation (LoRA) constitute the standard pipeline for efficient Large Language Model (LLM) deployment.