Imagine being able to enhance the abilities of a large AI model by simply transferring knowledge from a smaller, more specialized one. This is the intriguing idea behind ConTrans, a novel framework that facilitates "weak-to-strong alignment transfer via concept transplantation." Think of it like a brain transplant for AI, where specific concepts, like "honesty" or "toxicity awareness," are extracted from a smaller model and implanted into a larger one. This process works by refining concept vectors within the smaller model using a set of positive and negative examples. These vectors are then transformed to match the larger model's architecture and inserted into its residual stream, effectively influencing its output preferences. Experiments show that ConTrans successfully transfers various concepts between different models, even across different model families. Remarkably, in some cases, ConTrans outperforms models trained with traditional instruction tuning, particularly in generating truthful responses. This approach offers a more efficient way to align large language models with human values, potentially reducing the need for extensive training data and computational resources. While ConTrans currently focuses on single concept transfer, future research could explore the simultaneous transplantation of multiple concepts, opening up exciting possibilities for more sophisticated and aligned AI models. This innovative approach to AI development could pave the way for more robust, ethical, and efficient large language models, capable of understanding and responding to complex concepts with greater accuracy and safety.