This kind of context management is not that hard, even when building LLMs. Especially when you have huge windows like we do today. Look at how ChatGPT can remember things permanently after you said them once using a function call to edit the permanent memory section inside the context. You can also see that in Anthropic's latest post on Claude 4 where it learns to play Pokemon. The only remaining issue here is maybe how to diffuse explicit knowledge from the stored context into the weights. Andrej Karpathy wrote a good piece on this recently. But personally I believe this might not even be necessary if you can manage your context well enough and see it more like RAM while the LLM is the CPU. For your example you can then always just fetch such information from a permanent storage like a VDB and load it into context once you enter an area in the real world.

Big context windows are a poor substitute for updating the weights. Its like keeping a journal because your memory is failing.

Having used vectorDBs before, we're very much not there yet. We don't have any appreciable amounts of context for any reasonable real-life memory. It works if that is the most recent thing you did. Have you talked to an LLM for a day? Stuff is gone before the first hour. You have to use every trick currently in the book, treat context like it's your precious pet

> This kind of context management is not that hard, even when building LLMs.

It is, at least if you wish to be in the meatspace, that's my point. Every day has 86400 seconds during which a human brain constantly adapts to and learns from external input - either directly as it's being awake or indirectly during nighttime cleanup processes.

On top of that, humans have built-in filters for training. Basically, we see some drunkard shouting about the Hollow Earth on the sidewalk... our brain knows that this is a drunkard and that Hollow Earth is absolutely crackpot material, so if it stores anything at all then the fact that there is a drunkard on that street and one might take another route next time, but the drunkard's rambling is forgotten maybe five minutes later.

AI, in contrast, needs to be hand-held by humans during training that annotate, "grade" or weigh information during the compilation of the training dataset, in order that the AI knows what is written in "Mein Kampf" so it can answer questions upon it, but that it also knows (or at least: won't openly regurgitate) that the solution to economic problems isn't to just deport Jews.

And huge context windows aren't the answer either. My wife says me, she would like to have a fruit cake for her next birthday. I'll probably remember that piece of information (or at the very least I'll write it down)... but an AI butler? I'd be really surprised if this is still in its context space in a year, and even if it is, I would not be surprised if it weren't able to recall that fact.

And the final thing is prompts... also not the answer. We've seen it just a few days ago with Grok - someone messed with the system prompt so it randomly interjected "white genocide" claims into completely unrelated conversation [1] despite hopefully being trained on a ... more civilised dataset, and to the contrary, we've also seen Grok reply to Twitter questions in a way that suggest that it is aware its training data is biased.

[1] https://www.reuters.com/business/musks-xai-updates-grok-chat...