Selective Neuron Amplification for Training-Free Task Enhancement

ArXi:2604.07098v1 Announce Type: new Large language models often fail on tasks they seem to already understand. In our experiments, this appears to be less about missing knowledge and about certain internal circuits not being strongly activated during inference. We explore Selective Neuron Amplification, which increases the influence of task relevant neurons without changing the model's parameters. The method works at inference time and does not permanently alter the model. SNA helps mainly when the model is uncertain, while having low effect when the model is already confident.