A research paper proposes Infinite-Parameter LLMs, which use a compact hypernetwork to generate model weights dynamically from live interaction data rather than storing fixed parameters. This approach enables models to learn and adapt from user-supplied information during deployment while maintaining a constant memory footprint, potentially improving upon traditional in-context learning methods.