  The potential for Large Language Models (LLMs) to generate new information
offers a potential step change for research and innovation. This is challenging
to assert as it can be difficult to determine what an LLM has previously seen
during training, making "newness" difficult to substantiate. In this paper we
observe that LLMs are able to perform sophisticated reasoning on problems with
a spatial dimension, that they are unlikely to have previously directly
encountered. While not perfect, this points to a significant level of
understanding that state-of-the-art LLMs can now achieve, supporting the
proposition that LLMs are able to yield significant emergent properties. In
particular, Claude 3 is found to perform well in this regard.
