Researchers have introduced a new architecture for large language models that dynamically generates and adapts weights from live data rather than relying on a fixed parameter count. This approach enables theoretically infinite-parameter LLMs capable of continuously updating their capabilities in real-time. By bypassing traditional static weight storage, the technique aims to significantly enhance model adaptability while overcoming conventional memory constraints.