arXiv Machine Learning
Activation Denoising: A Robustness View on Parallel vs Sequential LLM Quantization
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arXiv AI
Jun 9
Joint Structural Pruning and Mixed-Precision Quantization for LLM Compression
arXiv:2606. 07819v1 Announce Type: new Abstract: Recently, the efficiency of Large Language Models (LLMs) deployment has become a critical concern in practical applications.
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
Jul 10
KronQ: LLM Quantization via Kronecker-Factored Hessian
arXiv:2607. 07964v1 Announce Type: new Abstract: Post-training quantization (PTQ) is a widely adopted technique for compressing large language models (LLMs) without retraining.
arXiv Machine Learning
Aug 28
Activation Outliers Matter: Robust Recovery for Quantized Multimodal LLMs
The paper investigates low‑bit quantization for Multimodal Large Language Models (MLLMs), showing that MXFP8 retains near‑lossless performance while 4‑bit formats like MXFP4 and HiF4 cause significant degradation. It identifies activation quantization as the main source of this loss and introduces Residual Fallback Quantization (RFQ), a lightweight framework that adds a quantized residual pathway to improve activation fidelity without architectural changes. Experiments on Wan2.2 and Qwen3‑VL demonstrate that RFQ recovers much of the performance gap to BF16 baselines across generation and reasoning tasks.
arXiv Machine Learning
Jun 4
STaR-Quant: State-Time Consistent Post-Training Quantization for Diffusion Large Language Models
arXiv:2606. 04945v1 Announce Type: new Abstract: Diffusion large language models (DLLMs) have recently emerged as a promising alternative to autoregressive LLMs by generating text through iterative masked denoising with bidirectional context.
arXiv AI
Jul 21
LaCache: Exact Caching and Precision-Adaptive Inference for Diffusion Large Language Models
arXiv:2607. 16339v1 Announce Type: new Abstract: Diffusion-based Large Language Models(DLLMs) enable parallel generation via Semi-Autoregressive (SAR) decoding in text generation.