想低成本搞懂LLM拒绝行为？这篇论文用RFM-AGOP几秒就定位到多维子空间，比传统方法快还准。
论文提出RFM-AGOP方法，通过适应RFM算法和探针初始化，在数秒内从推理模型Qwen 3和非推理模型Qwen 2.5中提取多维拒绝子空间。该方法在消融任务上表现优于现有方法，且计算成本较低。研究为LLM安全对齐提供了高效可扩展的监控手段。
Fast Multi-dimensional Refusal Subspaces via RFM-AGOP
Steering and monitoring activations in Large Language Models (LLMs) are increasingly used for both safety and interpretability. Early work assumed behaviours are encoded along single linear directions, but recent findings suggest complex behaviours, such as the refusal to answer harmful queries, live in multi-dimensional subspaces. However, existing methods for extracting these subspaces are computationally expensive, which becomes prohibitive on reasoning models who produce long reasoning traces. By adapting the Recursive Feature Machine (RFM) algorithm -- which can be computed efficiently -- with a probe-informed initialization, we are able to identify the multi-dimensional refusal subspace in seconds, on reasoning (Qwen 3) and non-reasoning (Qwen 2.5) models. While RFM allows for faster subspace identification, it also showed better performances on the ablation task than its alternatives. More work is planned to better understand the relations between subspaces found by different methods. If confirmed, RFM could be a cheap and scalable complement to existing subspace-extraction methods in LLMs.