

I saw several papers about LLM safety (for example Alignment faking in large language models) that show some “hidden” self preserving behaviour in LLMs. But as I know, no-one understands whether this behaviour is just trained and does mean nothing or it emerged from the model complexity.
Also, I do not use the ChatGPT app, but doesn’t it have a live chat feature where it continuously listens to user and reacts to it? It can even take pictures. So the continuity isn’t a huge problem. And LLMs are able to interact with tools, so creating a tool that moves a robot hand shouldn’t be that complicated.

As I said in another comment, doesn’t the ChatGPT app allow a live converation with a user? I do not use it, but I saw that it can continuously listen to the user and react live to it, even use a camera. There is a problem with the growing context, since this limited. But I saw in some places that the context can be replaced with LLM generated chat summary. So I do not think the continuity is a obstacle. Unless you want unlimited history with all details preserved.