Controlling specific behaviors in large language models while preserving their general capabilities is a central challenge for safe and reliable artificial intelligence deployment. Current steering methods, such as vector addition and directional ablation, are constrained within a two-dimensional subspace defined by the activation and feature direction, making them sensitive to chosen parameters and potentially affecting unrelated features due to unintended interactions in activation space. We introduce Angular Steering, a novel and flexible method for behavior modulation that operates by rotating activations within a fixed two-dimensional subspace. By formulating steering as a geometric rotation toward or away from a target behavior direction, Angular Steering provides continuous, fine-grained control over behaviors such as refusal and compliance. We demonstrate this method using refusal steering emotion steering as use cases. Additionally, we propose Adaptive Angular Steering, a selective variant that rotates only activations aligned with the target feature, further enhancing stability and coherence. Angular Steering generalizes existing addition and orthogonalization techniques under a unified geometric rotation framework, simplifying parameter selection and maintaining model stability across a broader range of adjustments. Experiments across multiple model families and sizes show that Angular Steering achieves robust behavioral control while maintaining general language modeling performance, underscoring its flexibility, generalization, and robustness compared to prior approaches. Code and artifacts are available at https://github.com/lone17/angular-steering/.
Activation Steering Induces Emergent Misalignment: Steering Vector Construction and Comprehensive Evaluation
Nanyang Technological University（南洋理工大学）；Sun Yat-sen University（中山大学）；University of Science and Technology of China（中国科学技术大学）；National University of Singapore（新加坡国立大学）
Minimizing Collateral Damage in Activation Steering
Department of Electrical \& Computer Engineering, Rice University, Houston, USA；Department of Computational；Applied Mathematics, Rice University, Houston, USA；Department of Electrical \& Computer Engineering, The University of Texas at Austin, Austin, USA
Interpretable Steering of Large Language Models with Feature Guided Activation Additions
Raffles Science Institute（莱佛士科学学院）；Raffles Institution（莱佛士书院）；Nous Research（努斯研究机构）；Centre for Frontier AI Research (CFAR)（前沿人工智能研究中心）；Agency for Science Technology and Research (A*STAR)（新加坡科技研究局）
Graph-Regularized Sparse Autoencoders for LLM Safety Steering
ELLIS Institute Tübingen（图宾根ELLIS研究所）；Max Planck Institute for Intelligent Systems（智能系统马克斯·普朗克研究所）；Intesa Sanpaolo（Intesa Sanpaolo公司）；University of Southern California（南加州大学）
ODESteer: A Unified ODE-Based Steering Framework for LLM Alignment
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Northwestern University（西北大学）；William & Mary（威廉与玛丽学院）；University of Washington（华盛顿大学）；Stanford University（斯坦福大学）
MLLMEraser: Achieving Test-Time Unlearning in Multimodal Large Language Models through Activation Steering
University of Science and Technology of China（中国科学技术大学）；National University of Singapore（新加坡国立大学）；The Hong Kong Polytechnic University（香港理工大学）；Huawei（华为）
Nullify: Null-Space Activation Steering for Training-Free LLM Unlearning
College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机与人工智能学院）；BEDI Cloud（BEDI云公司）；School of Electrical and Information Engineering, Tianjin University（天津大学电气与信息工程学院）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；School of Software Technology, Zhejiang University（浙江大学软件学院）；School of Transnational Law, Peking University（北京大学国际法学院）
