By this definition, even most humans would not qualify as having AGI though.
Some problems presented needs a very large context and some are not much solvable (e.g. trading) since market responds to traders' actions, as well, making it effectively an oracle problem (of computation).
On the other hand, we must be aware that these models are static, and they indeed stop when nobody asks something or requests an action from them. However, brains in nature never stops. Wonder, daydream, sleep, self-evolve, clean up and eliminate memories and views and much more.
The hardware of human intelligence is not a singular thing that is uniform throughout. You cannot take the prefrontal cortex white matter out of someone's head and say you are holding a person. Much of the parts of our brains that enable much of our intelligence, is made of different specialized stuff. The visual cortex and sensorimotor regions aren't only there for input and output, they are used by the more thinky parts of the brain to do visualization and spatial reasoning. The cerebellum contains billions of neurons making little oscillator circuits and PID-like self-regulation machines that help make muscles do what they're supposed to, but also provide attention and time perception.
Heck, our brains contain language models, that train themselves up based on a glut of data over a span of about 10 years, and then they become more or less set in stone for the rest of our lives. Of course we can learn languages, but the "Critical Period" is a very real thing that produces a permanent architecture for some grammatical structures, or things like the ability to partition a lexicon by gender for faster lexical access which cannot be learned as an adult if your native language did not have gender.
I'm not trying to make a direct analogy, the point is that the language model doesn't need to be fully "generally intelligent" all on its own for there to exist a general intelligence, because the language model can be part of a generally intelligent system, which can do things like form, recall, and manage memories which are by now a standard feature in basically every chatbot.
Same with instruments. I started to play instruments at an early age, but started to play guitar around that age. Well, I'm not a virtuoso, but can play and more importantly can improve.
These AI systems we built are static things. We generally try to make them more intelligent by augmenting the context they can see, but the model doesn't evolve in every turn, for example.
Intelligence is a multi-faceted and multi-input construct, that's true, and GPT-6 may do amazing things w.r.t. other models, I didn't try it yet. OTOH, my main call is to remember that these are still static algorithms fed with enormous amount of data. They are more rooted on statistics rather than fixed inputs. In short, they are still fitting to the frame of "advanced search".
In general, I'm not against the tech, but the hype. I have other gripes about how AI is being built, but that's not subject of this comment.
The parent commenter noted:
"if a harness alone can help the LLM saturate the benchmark with a near perfect score then the combination of the two must still be effectively AGI"
Harnesses absolutely can enable models to continue thinking about things. And LLMs do wonder and explore weird ideas like daydreams when you allow them to do this.
Nature doesn't work that way.
Most intelligence researchers would agree that people seem to have a genetic cap on their intelligence. While someone can underperform their intellectual potential with an upbringing that doesn't adequately enrich their minds, it's near-impossible for humans to become more intelligent through reading, studying, etc.
When humans learn we gain knowledge, not intelligence.
I think the only real difference is that we humans are born lacking a lot of initial knowledge/data which means we have to go through a decade or more of education to reach our potential intelligence. LLMs on the other hand come pre-loaded with that knowledge.
Passed this point, wherever knowledge is passed in as context or stored in the neural net I don't think is that significant personally. I'm of course not suggesting we're exactly the same as LLMs and there is no noteable difference, I just don't think continual learning is as important as some suggest it is – at least assuming a model is deployed with adequate training such that it reaches its potential given it's size + architecture.
Machines aimed at running AGI are provided that, but the money spent there could as well go towards enhancing humans.