DEVLOG #40 - What happens if the model runs out of room?

SL-LLM-R is meant to keep learning over time, which creates a long-term problem if a fixed-size model eventually runs out of useful capacity. My current idea is a stable core, fast memory, small learnable parts, and possibly extra specialist modules only when the existing capacity is not enough. The project cannot do that yet, so this is still future research.