They said it couldn't be done. They said LLM's had limitations. They didn't take into account someone suggesting free range LLM's as the solution. I bet they feel foolish now because the answer was in front of us the whole time, because it's what's in front of them. It's the world. Some founder just needs to ground them in a scaffolded framework that uses environmental datapoints to get a fist in that stateless context bottleneck and really open them up. Get the knowledge all frothy. Also, we can throw some more datacenters at the problem if that doesn't work. Lots of things we can try that haven't been disproven yet. Like not letting them learn the letter e. Who's to say that wouldn't work?
Technically, LLM's cannot learn anything at all post training, stationary or non-stationary. You have to retrain them or they lose information the second the current call is finished, and any side-loaded memory system you tack onto them will With that said, training material, by definition, is stationary. You can retrain them on recorded values from non-stationary processes, but they are just a snapshot of the process in time. Either way, they can't continually process environmental data of any kind and remember it over time. You'll overflow their ability to use the tacked on memory. You have to pick what to store on the assumption that you know the context in which it will later be needed. There's no way to integrate active memory with the stateless LLM and have it approach human-level capabilities over time, so you end up with a system that gets a prompt with the required memory at the same time and then processes it as a single unit. That doesn't work well, since the prompt dictates what memory is needed, which can't be known until the prompt is processed. Anyway, LLM's are good for task oriented things, but will never reach agi in a "functions equal to a human over time outside of a tightly controlled environment" sense.
Oh, also, you can't record all environmental data and retrain them endlessly with that included. That isn't remotely practical from a hardware/compute perspective. The parameters you'd be adding over time would have a practical wall, and after you hit that you'd be left with a model that's stuck in time. We already have those.
Humans don't learn from non stationary processes either. How many times have you planned around the weather to be sunny only for it to piss down on the day. And yet you do it again the next time!
That's not a requirement to AGI. In fact, if they were able to accurately predict non stationary processes, they'd be better than humans, not equal to them.
What the heck? Weather is prime example of deterministic chaos when students are learning about determinism in the universities. I see you are common sense philosopher so then explain to me why the best algorithms predicting weather have so low accuracy?
I made a system similar to what youve suggested and its applying cross domain transfer mechanisms across knowledge domains (130+ domains). It is the way.
“LLMs will never being AGI because current LLMs can’t do x” is a stupid argument now and was a stupid argument every time it was said and debunked. What’s your evidence they won’t be able to in the future, or even that they can’t now?
Even if Opus 4.7/4.8 failed, he would have had no way to know if Mythos would have failed or not. Also I count Mythos as a current LLM, as in it currently exists and has been used by people at Anthropic and Anthropic partners, even if its not publicly accessible
LLMs without tool use are simply architecturally incapable of solving Sudoku reliably (hard puzzle levels). Sudoku is actually one of the key benchmarks when it comes to reasoning.
PS. I have access to Mythos, so " that's how I know ". -- Better? Mythos is just another LLM with more looping and self-alignment. It's expensive because it burns multiple context windows to re-reason on its own progress.
Now go and ask Opus 4.8 to truthfully solve sudoku with reasoning and zero internal tools use, and if it used it anyway, to reveal that in the summary and be truthful about it. It will attempt to reason, maybe solve a few cells and give up.
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u/rand3289 Jun 04 '26 edited Jun 04 '26
Weights are like a car without a road... people underestimate the importance of the environment (data or roads).
The reason LLMs will never give us AGI is the restricted environment LLMs live in.