Imagine an AI that seamlessly integrates new knowledge, like a human learning from daily experiences. No more retraining from scratch or bulky external memory banks—just continuous, on-the-fly learning. That's the promise of SELF-PARAM, a groundbreaking technique that lets large language models (LLMs) absorb new information directly into their core parameters. But how does it work? Traditional LLMs struggle to retain new knowledge without overwriting old memories. Continual learning methods often lead to 'catastrophic forgetting,' where the model loses previously learned information as it acquires new data. Other methods rely on external storage, which can become unwieldy and inefficient. SELF-PARAM tackles this challenge by using a clever trick: it minimizes the difference between the predictions of an original model (with access to new information) and a target model (without that access). By doing this, it essentially 'teaches' the target model to internalize the new knowledge. Think of it like a student studying with a teacher. The teacher (the original model) has all the answers, while the student (the target model) learns by trying to match the teacher's responses. Through this process, the student eventually absorbs the knowledge and can answer questions independently. This 'knowledge distillation' approach allows SELF-PARAM to update the LLM in real-time, without relying on extra memory or retraining. Researchers tested SELF-PARAM on various tasks, including question answering and conversational recommendations. In each case, SELF-PARAM outperformed existing methods, showcasing its ability to learn and retain knowledge effectively. Imagine the possibilities: LLMs that continuously adapt to evolving information, personalize interactions based on individual conversations, and even learn new skills on the fly. SELF-PARAM brings us closer to this vision of truly intelligent, adaptable AI.