most citedAPTQ: Attention-aware Post-Training Mixed-Precision Quantization for Large Language Models

26 citations · 26 across the 4 of their papers we have counts for

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cs.LG2026

M3-AD: Reflection-aware Multi-modal, Multi-category, and Multi-dimensional Benchmark and Framework for Industrial Anomaly Detection

Chao Huang, Yanhui Li, Yunkang Cao +5

Although multimodal large language models (MLLMs) have advanced industrial anomaly detection toward a zero-shot paradigm, they still tend to produce high-confidence yet unreliable…

cs.LG2024★ 26 cited

APTQ: Attention-aware Post-Training Mixed-Precision Quantization for Large Language Models

Ziyi Guan, Hantao Huang, Yupeng Su +3

Large Language Models (LLMs) have greatly advanced the natural language processing paradigm. However, the high computational load and huge model sizes pose a grand challenge for de…