Unverified50% confidenceFactExact time
模型中不同层对量化误差敏感度差异极大，注意力层的QKV投影和输出投影通常比MLP层中间权重更敏感，首尾层比中间层更脆弱
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50%
Confidence
Long-term
Relevance
8/25/2026
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Unverified量化误差具有非均匀分布特性，注意力机制的Q/K/V矩阵和最后几层对量化更为敏感80% similarUnverified注意力层的Q、K、V投影矩阵及模型首尾层对量化噪声更敏感，而MoE架构中的专家网络冗余度更高、量化误差容忍度更高77% similarUnverified神经网络中各层对精度损失的敏感程度差异显著，patch projection等输入投影层敏感度高，中间主干的注意力与FFN模块对低精度更鲁棒76% similarUnverifiedTransformer模型中首尾几层对量化更敏感，中间层冗余度更高，注意力层和前馈网络层对量化的容忍度不同72% similarUnverifiedMPC的主要局限在于对模型精度的高度依赖，当真实环境与模型假设出现偏差时控制效果会显著下降70% similar
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