MARR (LLM quantization): current frontier — recent, not yet superseded in the knowledge base. 0 paper(s) critique it, 0 beat it on benchmarks — not ranked as a superseded baseline. Sub-problem: cluster led by GPTAQ. Newer alternatives in the same sub-problem include SplitQ, MARR, Quantization-aware Integrated Gradients (QIG), SPEED-Q, Quant-dLLM.

LLM quantization

MARR

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization

On the frontierfirst seen May 18, 2026

Current frontier — recent, not yet superseded in the knowledge base

0 papers critique it · 0 beat it on benchmarks

What to use instead

Recent methods in the same sub-problem, not yet superseded in the knowledge base — arXiv benchmark leaders, not vetted production recommendations.

SplitQBreaking Modality Heterogeneity in Low-Bit Quantization for Large Vision-Language Models
May 19, 2026
MARRMARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization
May 18, 2026
Quantization-aware Integrated Gradients (QIG)Fine-Grained Post-Training Quantization for Large Vision Language Models with Quantization-Aware Integrated Gradients
Mar 18, 2026
SPEED-QSPEED-Q: Staged Processing with Enhanced Distillation towards Efficient Low-bit On-device VLM Quantization
Nov 12, 2025
Quant-dLLMQuant-dLLM: Post-Training Extreme Low-Bit Quantization for Diffusion Large Language Models
Sep 27, 2025