neuron-editing

该论文提出 SEDATE（选择性检测与定向编辑），一种基于检测-编辑-验证策略的免训练方法，通过定位跨语言共享的“攻击性”神经元来提升大模型的安全性，并在7种语言和4个模型家族上验证了其优于基线的效果。

This paper investigates whether repetition loops in long factual enumeration tasks by Gemma 4 models can be fixed by editing a single neuron. It finds that targeted weight edits on a small set of MLP neurons can significantly reduce loop failures, though not completely eliminate doom looping in larger models.