N2UQ (LLM quantization): superseded — cited as a baseline and beaten by newer methods. 1 paper(s) critique it, 1 beat it on benchmarks — #42 of 80 most-superseded. Sub-problem: cluster led by PACT. Newer alternatives in the same sub-problem include GABFusion, MEC-Quant.

LLM quantization

N2UQ

Superseded baseline#42 of 80 most-superseded

Superseded — cited as a baseline and beaten by newer methods

1 papers critique it · 1 beat it on benchmarks

What papers say

Verbatim critique sentences, each from a paper that cites N2UQ as a baseline.

even with the most powerful non-uniform quantization method N2UQ, the quantized model still suffers a 3.8% accuracy loss

— GABFusion: Rethinking Feature Fusion for Low-Bit Quantization of Multi-Task Networks

Beaten on benchmarks

Head-to-head results where a newer method reports beating N2UQ. Values are copied from the source paper's tables — verify against the cited paper.

+ Ours beats N2UQ
78.0 vs 75.5
mAP · [YOLOv5s W3A3]
GABFusion: Rethinking Feature Fusion for Low-Bit Quantization of Multi-Task Networks

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Recent methods in the same sub-problem, not yet superseded in the knowledge base — arXiv benchmark leaders, not vetted production recommendations.

GABFusionGABFusion: Rethinking Feature Fusion for Low-Bit Quantization of Multi-Task Networks
Nov 8, 2025
MEC-QuantMEC-Quant: Maximum Entropy Coding for Extremely Low Bit Quantization-Aware Training
Sep 19, 2025