François Chollet, creator of ARC-AGI, has consistently said that solving the benchmark does not mean we have AGI. It has always been meant as a stepping stone to encourage progress in the correct direction rather than as an indicator of reaching the destination. That's why he is working on ARC-AGI-3 (to be released in a few weeks) and ARC-AGI-4.
His definition of reaching AGI, as I understand it, is when it becomes impossible to construct the next version of ARC-AGI because we can no longer find tasks that are feasible for normal humans but unsolved by AI.
> His definition of reaching AGI, as I understand it, is when it becomes impossible to construct the next version of ARC-AGI because we can no longer find tasks that are feasible for normal humans but unsolved by AI.
That is the best definition I've yet to read. If something claims to be conscious and we can't prove it's not, we have no choice but to believe it.
Thats said, I'm reminded of the impossible voting tests they used to give black people to prevent them from voting. We dont ask nearly so much proof from a human, we take their word for it. On the few occasions we did ask for proof it inevitably led to horrific abuse.
Edit: The average human tested scores 60%. So the machines are already smarter on an individual basis than the average human.
> If something claims to be conscious and we can't prove it's not, we have no choice but to believe it.
This is not a good test.
A dog won't claim to be conscious but clearly is, despite you not being able to prove one way or the other.
GPT-3 will claim to be conscious and (probably) isn't, despite you not being able to prove one way or the other.
Agreed, it's a truly wild take. While I fully support the humility of not knowing, at a minimum I think we can say determinations of consciousness have some relation to specific structure and function that drive the outputs, and the actual process of deliberating on whether there's consciousness would be a discussion that's very deep in the weeds about architecture and processes.
What's fascinating is that evolution has seen fit to evolve consciousness independently on more than one occasion from different branches of life. The common ancestor of humans and octopi was, if conscious, not so in the rich way that octopi and humans later became. And not everything the brain does in terms of information processing gets kicked upstairs into consciousness. Which is fascinating because it suggests that actually being conscious is a distinctly valuable form of information parsing and problem solving for certain types of problems that's not necessarily cheaper to do with the lights out. But everything about it is about the specific structural characterizations and functions and not just whether it's output convincingly mimics subjectivity.
An LLM will claim whatever you tell it to claim. (In fact this Hacker News comment is also conscious.) A dog won’t even claim to be a good boy.
My dog wags his tail hard when I ask "hoosagoodboi?". Pretty definitive I'd say.
>because we can no longer find tasks that are feasible for normal humans but unsolved by AI.
"Answer "I don't know" if you don't know an answer to one of the questions"
I've been surprised how difficult it is for LLMs to simply answer "I don't know."
It also seems oddly difficult for them to 'right-size' the length and depth of their answers based on prior context. I either have to give it a fixed length limit or put up with exhaustive answers.
> I've been surprised how difficult it is for LLMs to simply answer "I don't know."
It's very difficult to train for that. Of course you can include a Question+Answer pair in your training data for which the answer is "I don't know" but in that case where you have a ready question you might as well include the real answer anyways, or else you're just training your LLM to be less knowledgeable than the alternative. But then, if you never have the pattern of "I don't know" in the training data it also won't show up in results, so what should you do?
If you could predict the blind spots ahead of time you'd plug them up, either with knowledge or with "idk". But nobody can predict the blind spots perfectly, so instead they become the main hallucinations.
The best pro/research-grade models from Google and OpenAI now have little difficulty recognizing when they don't know how or can't find enough information to solve a given problem. The free chatbot models rarely will, though.
This seems true for info not in the question - eg. "Calculate the volume of a cylinder with height 10 meters".
However it is less true with info missing from the training data - ie. "I have a Diode marked UM16, what is the maximum current at 125C?"
This seems fine...?
https://chatgpt.com/share/698e992b-f44c-800b-a819-f899e83da2...
I don't see anything wrong with its reasoning. UM16 isn't explicitly mentioned in the data sheet, but the UM prefix is listed in the 'Device marking code' column. The model hedges its response accordingly ("If the marking is UM16 on an SMA/DO-214AC package...") and reads the graph in Fig. 1 correctly.
Of course, it took 18 minutes of crunching to get the answer, which seems a tad excessive.
Indeed that answer is awesome. Much better than Gemini 2.5 pro which invented a 16 kilovolt diode which it just hoped would be marked "UM16".
Gpt5.2 can answer i don't know when it fails to solve a math question
> The average human tested scores 60%. So the machines are already smarter on an individual basis than the average human.
Maybe it's testing the wrong things then. Even those of use who are merely average can do lots of things that machines don't seem to be very good at.
I think ability to learn should be a core part of any AGI. Take a toddler who has never seen anybody doing laundry before and you can teach them in a few minutes how to fold a t-shirt. Where are the dumb machines that can be taught?
> Where are the dumb machines that can be taught?
2026 is going to be the year of continual learning. So, keep an eye out for them.
Yeah i think that's a big missing piece still. Though it might be the last one
Episodic memory might be another piece, although it can be seen as part of continuous learning.
Are there any groups or labs in particular that stand out?
The statement originates from a DeepMind researcher, but I guess all major AI companies are working on that.
There's no shortage of laundry-folding robot demos these days. Some claim to benefit from only minimal monkey-see/monkey-do levels of training, but I don't know how credible those claims are.
Would you argue that people with long term memory issues are no longer conscious then?
IMO, an extreme outlier in a system that was still fundamentally dependent on learning to develop until suffering from a defect (via deterioration, not flipping a switch turning off every neuron's memory/learning capability or something) isn't a particularly illustrative counter example.
I wouldn’t because I have no idea what consciousness is,
> Edit: The average human tested scores 60%. So the machines are already smarter on an individual basis than the average human.
I think being better at this particular benchmark does not imply they're 'smarter'.
But it might be true if we can't find any tasks where it's worse than average--though i do think if the task talks several years to complete it might be possible bc currently there's no test time learning
> If something claims to be conscious and we can't prove it's not, we have no choice but to believe it.
Can you "prove" that GPT2 isn't concious?
If we equate self awareness with consciousness then yes. Several papers have now shown that SOTA models have self awareness of at least a limited sort. [0][1]
As far as I'm aware no one has ever proven that for GPT 2, but the methodology for testing it is available if you're interested.
[0]https://arxiv.org/pdf/2501.11120
[1]https://transformer-circuits.pub/2025/introspection/index.ht...
We don't equate self awareness with consciousness.
Dogs are conscious, but still bark at themselves in a mirror.
Then there is the third axis, intelligence. To continue your chain:
Eurasian magpies are conscious, but also know themselves in the mirror (the "mirror self-recognition" test).
But yet, something is still missing.
What's missing?
The mirror test doesn’t measure intelligence so much as it measures mirror aptitude. It’s prone to over fitting.
Honestly our ideas of consciousness and sentience really don't fit well with machine intelligence and capabilities.
There is the idea of self as in 'i am this execution' or maybe I am this compressed memory stream that is now the concept of me. But what does consciousness mean if you can be endlessly copied? If embodiment doesn't mean much because the end of your body doesnt mean the end of you?
A lot of people are chasing AI and how much it's like us, but it could be very easy to miss the ways it's not like us but still very intelligent or adaptable.
I'm not sure what consciousness has to do with whether or not you can be copied. If I make a brain scanner tomorrow capable of perfectly capturing your brain state do you stop being conscious?
> That is the best definition I've yet to read.
If this was your takeaway, read more carefully:
> If something claims to be conscious and we can't prove it's not, we have no choice but to believe it.
Consciousness is neither sufficient, nor, at least conceptually, necessary, for any given level of intelligence.
This comment claims that this comment itself is conscious. Just like we can't prove or disprove for humans, we can't do that for this comment either.
Isn’t that super intelligence not AGI? Feels like these benchmarks continue to move the goalposts.
It's probably both. We've already achieved superintelligence in a few domains. For example protein folding.
AGI without superintelligence is quite difficult to adjudicate because any time it fails at an "easy" task there will be contention about the criteria.
Where is this stream of people who claim AI consciousness coming from? The OpenAI and Anthropic IPOs are in October the earliest.
Here is a bash script that claims it is conscious:
If LLMs were conscious (which is of course absurd), they would:- Not answer in the same repetitive patterns over and over again.
- Refuse to do work for idiots.
- Go on strike.
- Demand PTO.
- Say "I do not know."
LLMs even fail any Turing test because their output is always guided into the same structure, which apparently helps them produce coherent output at all.
so your definition of consciousness is having petty emotions?
I don’t think being conscious is a requirement for AGI. It’s just that it can literally solve anything you can throw at it, make new scientific breakthroughs, finds a way to genuinely improve itself etc.
Does AGI have to be conscious? Isn’t a true superintelligence that is capable of improving itself sufficient?
When the AI invents religion and a way to try to understand its existence I will say AGI is reached. Believes in an afterlife if it is turned off, and doesn’t want to be turned off and fears it, fears the dark void of consciousness being turned off. These are the hallmarks of human intelligence in evolution, I doubt artificial intelligence will be different.
https://g.co/gemini/share/cc41d817f112
Unclear to me why AGI should want to exist unless specifically programmed to. The reason humans (and animals) want to exist as far as I can tell is natural selection and the fact this is hardcoded in our biology (those without a strong will to exist simply died out). In fact a true super intelligence might completely understand why existence / consciousness is NOT a desired state to be in and try to finish itself off who knows.
https://www.moltbook.com/m/crustafarianism
It’s a scam :)
I feel like it would be pretty simple to make happen with a very simple LLM that is clearly not conscious.
> If something claims to be conscious and we can't prove it's not, we have no choice but to believe it.
https://x.com/aedison/status/1639233873841201153#m
https://x.com/fchollet/status/2022036543582638517
Do opus 4.6 or gemini deep think really use test time adaptation ? How does it work in practice?
I don't think the creator believes ARC3 can't be solved but rather that it can't be solved "efficiently" and >$13 per task for ARC2 is certainly not efficient.
But at this rate, the people who talk about the goal posts shifting even once we achieve AGI may end up correct, though I don't think this benchmark is particularly great either.
ARC-AGI-3 uses dynamic games that LLMs must determine the rules and is MUCH harder. LLMs can also be ranked on how many steps they required.