That's a misconception. Context window of humans is measured in seconds and limited to single-digit amount of entities. Human attention is truly ephemeral, with a ridiculously short span. What you're talking about cannot be emulated by putting everything into the context, you need models with deeper cognitive capabilities, and the longer your context the smarter it needs to be. It can be done by increasing the depth and the size of the model, and by training it better. Test-time scaling does work (longer reasoning, agentic compression, ICL, etc), but hits the wall rather quickly if not assisted by better cognition.

> Human attention is truly ephemeral, with a ridiculously short span.

I do not believe this at all. I think you'd have to have a very limited experience working with other human beings to be able to believe this.

> and by training it better.

"Oh yea, just do it _better_. That's your problem." Perhaps some people operate without any context but most of us find the experience lacking.

That's a simple neurologic fact, there's nothing to believe. You can use a trivial experiment to verify that you can't keep details in your sliding attention window for more than a few seconds or focus on more than a few things simultaneously. Human memory and cognition is layered and this immediate layer is what resembles the model's context the most.

You're possibly mixing it up with long-term memory which doesn't keep immediate facts and details, it's for heavily processed and compressed summaries, for the lack of a better analogy from the LLM world. You aren't keeping the entire codebase in your memory, just its highly processed and conceptualized version. This conceptualization can be somewhat emulated as an agentic loop, but it can only go so far, current models quickly lose coherency and aren't good enough to predict what's important.

Models don't need to remember more details, they need stronger processing of what they already remember.

>"Oh yea, just do it _better_. That's your problem."

I think we're talking about different things. Models can be trained better to cram more intelligence into the same amount of parameters, that's what I mean. Similarly to how your ability to learn (and perform) math depends on your prior math training.

> You can use a trivial experiment to verify that you can't keep details in your sliding attention window for more than a few seconds or focus on more than a few things simultaneously.

I said "context window" not "attention window." Of course spans of attention are limited. Knowledge is not. Knowledge is often highly specific.

> You're possibly mixing it up

Not really. You've simply failed to verify your understanding of my argument and instead created something of a strawman.

> You aren't keeping the entire codebase in your memory, just its highly processed and conceptualized version

And what is your basis for this claim? Why a codebase? You don't think I can't remember an entire function? Yet actors can remember entire sets of lines for a scene? Is that just a highly processed and abstracted version in their minds? And they just run some cognitive loop to recreate dialog in real time?

> they need stronger processing of what they already remember.

What is "stronger?" More time? More memory? More compute? And how is that put to use? Why is it, when given certain prompts, that LLMs reproduce 100s of pages of directly copied and copyrighted work?

> Models can be trained better to cram more intelligence into the same amount of parameters, that's what I mean.

Cool. _How_? What is the limit of this training? How efficient is it? How many resources do you need on the input for a given increase in output? Otherwise it's just a ton of hand waving going on here.

If there's an actual neurological attention window that you're referring to, that's as ephemeral as you say, it is not analogous to the context window of an LLM.

When I'm working on a problem, I can explicitly apply knowledge I gained decades ago to that problem. Not just "oh, it's somehow encoded in the training data"; not "oh, it's all mixed in there somewhere"; I can call up the memories, understand how to apply that knowledge to the problem at hand, and do so.

Except in certain very superficial ways, and despite many misguided LLM proponents' strident insistence to the contrary, LLMs do not work much like human brains.

Yes, that's my entire point and I'm well aware about LLM internals. Reread the comment above, I'm responding to a person who suggested to increase the context window of an LLM to make it less different, saying it will never work.

I suppose that's one read of themafia's comment.

My read of it was "this is why humans are fundamentally different from LLMs", not "this is how you fix LLMs to be like humans".

It's still hard to see how you can argue that humans' "context window", looked at in even a vaguely similar manner to that of LLMs, is tiny.

Is this true, though? AIs already interact with way more data than their context window, they can make queries to external data sources, and they can make context references to external data that can be pulled in as needed.

The context window is the working memory, which is not ‘years’ for humans either.

Okay. Can you take _all_ the data you have out of your mind and put into a computerized format? Is there nothing intangible in your mind?

The human context window includes actual _context_ not just _data_.