QSLAW (LLM quantization): superseded — cited as a baseline and beaten by newer methods. 2 paper(s) critique it, 0 beat it on benchmarks — #48 of 80 most-superseded. 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.

Superseded baseline#48 of 80 most-superseded

QSLAW

LLM quantization

superseded — cited as a baseline and beaten by newer methods

2 papers critique it · 0 beat it on benchmarks

What papers say

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

“these methods either require expensive parameter fine-tuning xie2024qslaw, specialized manipulation at inference time yu2025mquant, or rely on a suboptimal grid search li2025mbq, failing to offer an efficient and effective solution for both calibration and inference”
— VLMQ: Efficient Post-Training Quantization for Large Vision-Language Models via Hessian Augmentation
“only quantizes the language component to 4-bit and leaves the vision module at its original precision (i.e., FP16).”
— SPEED-Q: Staged Processing with Enhanced Distillation towards Efficient Low-bit On-device VLM Quantization

Newer alternatives

Recent methods in the same sub-problem, not yet superseded in the knowledge base.