Temporal. I had a research project where the LLM had no concept about preventing data from the future to leak in. I eventually had to create a wall clock and an agent that would step through every line of code and ensure by writing that lines logic and why there is no future of the wall clock data leaking.
Spatial. I created a canvas for rendering thinking model's attention and feedforward layers for data visualization animations. It was having a hard time working with it until I pointed Opus 4.7 to some ancient JavaScript code [0] about projecting 3d to 2d and after searching Github repositories. It worked perfect with pan zoom in one shot after that.
No matter how hard I tried I couldn't get it to stack all the layers correctly. It must have remembered all the parts for projecting 3d to 2d because it could not figure out how to position the layers.
There is a ton of information burnt into the weights during training but it can not reason about it. When it does work well with spatial and temporal it is more slight of hand than being able to generalize.
People say, why not just do reinforcement learning? That can't generalize in the same way a LLM can. I'm thinking about doing the Rubik's Cube because if people can solve that it might open up solutions for working temporal and spatial problems.
[0] https://jakesgordon.com/writing/javascript-racer-v1-straight...