Researchers have developed an efficient and scalable way to extract linear representations of general concepts in large AI models, such as language models, visual-language models, and reasoning models with sizes ranging from 8 to 90 billion parameters[1][2]. These representations enable models to be controlled, thereby revealing vulnerabilities, mitigating misbehavior, and improving model capabilities beyond standard prompting[1][2]. Quantitative analysis of hundreds of concepts has shown that newer and larger models are more controllable[1][2]. Concept representations are transferable between human languages and can be combined to manage multiple concepts at once[1][2]. The method also serves to monitor incorrect content, such as hallucinations or toxic content, while predictive models based on representations are more accurate than direct assessment of outputs[1][2]. The concept identification and control process takes less than a minute on a single NVIDIA A100 GPU with less than 500 training samples[1].