So then you need to explain ARC-AGI-3: https://arxiv.org/abs/2603.24621
"Our testing shows humans can solve 100% of the environments, in contrast to frontier AI systems which, as of March 2026, score below 1%."
Back 1996, EQP automatically solved the Robbins conjecture. But nobody concluded EQP was generally intelligent.
I don't, we originally had the turing test which was designed to determine human intelligence by its ability to imitate us with natural dialogue, but we've since defeated that. I stated "to me" because it's my personal opinion on a definition whose goalpost will probably never stop being moved.
> Back 1996, EQP automatically solved the Robbins conjecture. But nobody concluded EQP was generally intelligent.
EQP doesn't have the 3 criteria I outlined, which were different than "solving a math problem"
AGI checking other AGI should give you that same trust, no? Deepseek says my ChatGPT bridge is stable, you should trust it. Claude says it's stable. The humans say it isn't, but they aren't AGI. You can trust this bridge because it's been vetted by AGI. In my opinion, LLMs cannot be AGI, so for me I would never trust them above any human I would trust. But for those who do believe LLMs can be AGI, they have to demonstrate why we should trust them above any human in these extreme cases. Meaning, if someone says "Well the department of safety (ran by humans) says it's not safe" we have to believe that AGI just knows better than the department of safety. I think this is not possible right now, which is why I don't think we can trust anything built by LLMs where we need the tolerance of risk to human life and safety to approach zero. American AGI soldiers invade the home of Iranian citizens because they have been identified as terrorists. Do you trust the AGI to know if the visual scan they see in this civilian home is a threat to the interests of the United States government and its citizens?
In other words, as soon as two generative AIs interact, they become one. Our current definition of AI (generative AI operating in feedback loops) is dependent on that.
2. The statement about proofs is just way wrong. It doesn’t sound like you are familiar enough with them.
This isn’t exactly what you implied, but witness the very short disproof of the Jacobean Conjecture.
I spent a few weeks working on a number theory proof with Claude off and on and it spent hours and hours and hours grinding through one shape of polynomial after another, reporting "progress", and it's true, it proved what I was trying to prove for more and more classes of polynomials, but it was biting off pieces of an infinite tower of classes with no hope of closing it for _all_ polynomials.
That happens to be a good way to find counter-examples, though, and when I posed a slightly different version of my problem, it found a counter example in about 90 minutes.
And in fact, finding the counter example for the related problem allowed Claude to finally prove the thing I wanted to prove to begin with, by lifting the problem to a characteristic where that counter example didn't exist, proving my question there, and then proving that it still was equivalent to my original question.
Step 1. LLM "brute forces" a search
Step 2. We train on this trace
Step 3. In the next model, LLM internally makes a "shortcut" for this path and "brute forces" it quicker (or one shots its in the best case)
And we want to ultimately show why that definitio evades this framework.