- Human baseline is "defined as the second-best first-run human by action count". Your "regular people" are people who signed up for puzzle solving and you don't compare the score against a human average but against the second best human solution
- The scoring doesn't tell you how many levels the models completed, but how efficiently they completed them compared to humans. It uses squared efficiency, meaning if a human took 10 steps to solve it and the model 100 steps then the model gets a score of 1% ((10/100)^2)
- 100% just means that all levels are solvable. The 1% number uses uses completely different and extremely skewed scoring based on the 2nd best human score on each level individually. They said that the typical level is solvable by 6 out of 10 people who took the test, so let's just assume that the median human solves about 60% of puzzles (ik not quite right). If the median human takes 1.5x more steps than your 2nd fastest solver, then the median score is 0.6 * (1/1.5)^2 = 26.7%. Now take the bottom 10% guy, who maybe solves 30% of levels, but they take 3x more steps to solve it. this guy would get a score of 3%
- The scoring is designed so that even if AI performs on a human level it will score below 100%
- No harness at all and very simplistic prompt
- Models can't use more than 5X the steps that a human used
- Notice how they also gave higher weight to later levels? The benchmark was designed to detect the continual learning breakthrough. When it happens in a year or so they will say "LOOK OUR BENCHMARK SHOWED THAT. WE WERE THE ONLY ONES"
TBF, that's basically what the kaggle competition is for. Take whatever they do, plug in a SotA LLM and it should do better than whatever people can do with limited GPUs and open models.
If you are trying to measure GENERAL intelligence then it needs to be general.
We tested ~500 humans over 90 minute sessions in SF, with $115-$140 show up fee (then +$5/game solved). A large fraction of testers were unemployed or under-employed. It's not like we tested Stanford grad students. Many AI benchmarks use experts with Ph.D.s as their baseline -- we hire regular folks as our testers.
Each game was seen by 10 people. They were fully solved (all levels cleared) by 2-8 of them, most of the time 5+. Our human baseline is the second best action count, which is considerably less than an optimal first-play (even the #1 human action count is much less than optimal). It is very achievable, and most people on this board would significantly outperform it.
Try the games yourself if you want to get a sense of the difficulty.
> Models can't use more than 5X the steps that a human used
These aren't "steps" but in-game actions. The model can use as much compute or tools as it wants behind the API. Given that models are scored on efficiency compared to humans, the cutoff makes basically no difference on the final score. The cutoff only exists because these runs are incredibly expensive.
> No harness at all and very simplistic prompt
This is explained in the paper. Quoting: "We see general intelligence as the ability to deal with problems that the system was not specifically designed or trained for. This means that the official leaderboard will seek to discount score increases that come from direct targeting of ARC-AGI-3, to the extent possible."
...
"We know that by injecting a high amount of human instructions into a harness, or even hand-crafting harness configuration choices such as which tools to use, it is possible to artificially increase performance on ARC-AGI-3 (without improving performance on any other domain). The purpose of ARC-AGI-3 is not to measure the amount of human intelligence that went into designing an ARC-AGI-3 specific system, but rather to measure the general intelligence of frontier AI systems.
...
"Therefore, we will focus on reporting the performance of systems that have not been specially prepared for ARC-AGI-3, served behind a general-purpose API (representing developer-aware generalization on a new domain as per (8)). This is similar to looking at the performance of a human test-taker walking into our testing center for the first time, with no prior knowledge of ARC-AGI-3. We know such test takers can indeed solve ARC-AGI-3 environments upon first contact, without prior training, without being briefed on solving strategies, and without using external tools."
If it's AGI, it doesn't need human intervention to adapt to a new task. If a harness is needed, it can make its own. If tools are needed, it can chose to bring out these tools.
Back in the 90's, Scientific American had an article on AI - I believe this was around the time Deep Blue beat Kasparov at chess.
One AI researcher's quote stood out to me:
"It's silly to say airplanes don't fly because they don't flap their wings the way birds do."
He was saying this with regards to the Turing test, but I think the sentiment is equally valid here. Just because a human can do X and the LLM can't doesn't negate the LLM's "intelligence", any more than an LLM doing a task better than a human negates the human's intelligence.
Don't read the statement as a human dunk on LLMs, or even as philosophy.
The gap is important because of its special and devastating economic consequences. When the gap becomes truly zero, all human knowledge work is replaceable. From there, with robots, its a short step to all work is replaceable.
What's worse, the condition is sufficient but not even necessary. Just as planes can fly without flapping, the economy can be destroyed without full AGI.
> even Alan M. Turing allowed himself to be drawn into the discussion of the question whether computers can think. The question is just as relevant and just as meaningful as the question whether submarines can swim.
(I am of the opinion that the thinking question is in fact a bit more relevant than the swimming one, but I understand where these are coming from.)
There are very valid reasons to measure that. You wouldn’t ask a plane to drive you to the neighbor or to buy you groceries at the supermarket. It’s not general mobile as you are, but it increases your mobility
But the arc-agi competitions are cool. Just to see where we stand, and have some months where the benchmarks aren't fully saturated. And, as someone else noted elswhere in the thread, some of these games are not exactly trivial, at least until you "get" the meta they're looking for.
It also doesn't actually matter much, as ultimately the utility of it's outputs is what determines it's worth.
There is the moral question of consciousness though, a test for which it seems humans will not be able to solve in the near future, which morally leads to a default position that we should assume the AI is conscious until we can prove it's not. But man, people really, really hate that conclusion.
Despite so many claims an LLM has never done any interesting task better than a human. I could claim that cat is better than humans at writing text, but the non-specificity of my language here makes that statement simultaneously meaningless and incorrect. Another meaningless and incorrect (but less incorrect than most pro AI sttements) "git clone" is better at producing correct and feature rich c compiler code than $20,000 worth of Claude tokens.
I really wonder why so many people fight against this. We know that AI is useful, we know that AI is researchful, but we want to know if they are what we vaguely define as intelligence.
I’ve read the airplanes don’t use wings, or submarines don’t swim. Yes, but this is is not the question. I suggest everyone coming up with these comparisons to check their biases, because this is about Artificial General Intelligence.
General is the keyword here, this is what ARC is trying to measure. If it’s useful or not. Isn’t the point. If AI after testing is useful or not isn’t the point either.
This so far has been the best test.
And I also recommend people to ask AI about specialized questions deep in your job you know the answer to and see how often the solution is wrong. I would guess it’s more likely that we perceive knowledge as intelligence than missing intelligence. Probably commom amongst humans as well.
LLM are way past us at languages for instance. Calculators passed us at calculating, etc.
I would imagine if you simply encoded the game in textual format and asked an LLM to come up with a series of moves, it would beat humans.
The problem here is more around perception than anything.
If model creators are willing to teach their llms to play computer games through text it's gonna be solved in one minor bump of the model version. But honestly, I don't think they are gonna bother because it's just too stilly and they won't expect their models are going to learn anything useful from that.
Especially since there are already models that can learn how to play 8-bit games.
It feels like ARC-AGI jumped the shark. But who knows, maybe people who train models for robots are going to take it in stride.
- Take a person who grew up playing video games. They'll pass these tests 100% without even breaking a sweat.
- BUT, put a grandmother who has never used a computer in front of this game, and she'll most likely fail completely. Just like an LLM.
As soon as models are "natively" trained on a massive dataset of these types of games, they'll easily adapt and start crushing these challenges.
This is not AGI at all.
My main criticism would be that it doesn’t seem like this test allows online learning, which is what humans do (over the scale of days to years). So in practice it may still collapse to what you point out, but not because the task is unsuited to showing AGI.
I've been a gamer for just about 40 years. Gaming is my "thing"
I found the challenges fun, but easy. Coming back and reading comments from people struggling with the games, my first thought was - yup definitely not a gamer.
My approach was to poke at the controls to suss the rules, then the actual solutions were really straightforward.
fwiw, I'm pretty dumb generally, but these kinds of puzzles are my jam.
- open book, you have access to nearly the whole Internet and resources out of it, e.g. torrents of nearly all books, research paper, etc including the history of all previous tests include those similar to this one
- arguably basically no time limit as it's done at a scale of threads to parallelize access through caching ridiculously
- no shame in submitting a very large amount of wrong answers until you get the "right" one
... so I'm not saying it makes it "easy" but I can definitely say it's not the typical way I used to try to pass tests.
I met a guy who, for fun, started working on ARC2, and as he got the number to go up in the eval, a novel way to more efficiently move a robotic arm emerged. All that to say: chasing evals per se can have tangible real world benefits.
Talking to the ARC folks tonight, it sounds like there will be an ARC-4,5,6,etc. I mean of course there will be.
But with them will be an increasing expectation that these models can eventually figure things out with zero context, and zero pretraining; you drop a brain into any problem and it'll figure out how to dig its way out.
That's really exciting.
This measures the ability of a LLM to succeed in a certain class of games. Sure, that could be a valuable metric on how powerful (or even generally powerful) a LLM is.
Humans may or may not be good at the same class of games.
We know there exists a class of games (including most human games like checkers/chess/go) that computers (not LLMs!) already vastly outpace humans.
So the argument for whether a LLM is "AGI" or not should not be whether a LLM does well on any given class of games, but whether that class of games is representative of "AGI" (however you define that.)
Seems unlikely that this set of games is a definition meaningful for any practical, philosophical or business application?
So there is a business application, but no practical or philosophical one.
I really like these puzzles. There’s a lot to them both in design and scoring — models trained to do well on these are going to be genuinely much more useful, so I’m excited about it. As opposed to -1 and -2, to do well at these, you need to be able to do:
- Visual reasoning
- Path planning (and some fairly long paths)
- Mouse/screen interaction
- color and shape analysis
- cross-context learning/remembering
Probably more, I only did like five or six of these. We really want models that are good at all this; it covers a lot of what current agentic loops are super weak at. So I hope M. Chollet is successful at getting frontier labs to put a billion or so into training for these.
It feels like it should be about having no ARC-AGI-3-specific tools, not "no not-built-in-tool"...
If you've played Wordle you might've solved the game in a minute once before as well. And if you've played a bunch then you've perhaps also taken the entire day to solve it.
So why is it that today’s puzzle was so intuitive but next month’s new puzzle shared here could be impossible. A more satisfying explanation than luck and the obvious “different things are different” (even though… Yeah different things are different)
I don't know if this is how we want to measure AGI.
In general I believe the we should probably stop this pursuit for human equivalent intelligence that encourages people to think of these models as human replacements. LLMs are clearly good at a lot of things, lets focus on how we can augment and empower the existing workforce.
That is a nice sentiment but not what the AI companies are out to do; they want your job.
Surprised at the comments here re. not figuring it. Simple game. Super annoying though lmao.
Maybe the internet will briefly go back to a place mainly populated with outliers.
Without a big jump, we're just going to boil the frog (ourselves).
seriously. lmao. if you aint, I dunno what to say.
I still don't quite understand the exact mirroring rules at play.
CRAZY 0.1% in average lmao
So if a model can solve every question but takes 10x as many steps as the second best human it will get a score of 1%.
If the AI has to control a body to sit on a couch and play this game on a laptop that would be a step in the right direction.
It is a simple game with simple rules that solvers have an incredibly difficult time solving compared to humans at a certain level. Solutions are easy to validate but hard to find.
Given how hard even pure v2 was for modern LLMs, I'm not surprised to see v3 crush them. But that wouldn't last.
There's world state that you can change. Not just place pixel.
Here's v2:
Once the AIs solve this, there will be another ARC-AGI. And so on until we can't find any more problems that can be solved by humans and not AI. And that's when we'll know we have AGI.
It's a "let's find a task humans are decent at, but modern AIs are still very bad at" kind of adversarial benchmark.
The exact coverage of this one is: spatial reasoning across multiple turns, agentic explore/exploit with rule inference and preplanning. Directly targeted against the current generation of LLMs.
It used to be easy to build these tests. I suspect it’s getting harder and harder.
But if we run out of ideas for tests that are easy for humans but impossible for models, it doesn’t mean none exist. Perhaps that’s when we turn to models to design candidate tests, and have humans be the subjects to try them out ad nauseam until no more are ever uncovered? That sounds like a lovely future…
Anyway, from the article:
> As long as there is a gap between AI and human learning, we do not have AGI.
This seems like a reasonable requirement. Something I think about a lot with vibe coding is that unlike humans, individual models do not get better within a codebase over time, they get worse.
By updating the tests specifically in areas AI has trouble with, it creates a progressive feedback loop against which AI development can be moved forward. There's no known threshold or well defined capability or particular skill that anyone can point to and say "that! That's AGI!". The best we can do right now is a direction. Solving an ARC-AGI test moves the capabilities of that AI some increment closer to the AGI threshold. There's no good indication as to whether solving a particular test means it's 15% closer to AGI or .000015%.
It's more or less a best effort empiricist approach, since we lack a theory of intelligence that provides useful direction (as opposed to a formalization like AIXI which is way too broad to be useful in the context of developing AGI.)
Edit: Having messed around with it now (and read the .pdf), it seems like they've left behind their original principle of making tests that are easy for humans and hard for machines. I'm still not convinced that a model that's good at these sorts of puzzles is necessarily better at reasoning in the real world, but am open to being convinced otherwise.
If you mess around a little bit, you will figure it out. There are only a few rules.
Apparently those games supposed to be hard.
Barely any of them break 0% on any of the demo tasks, with Claude Opus 4.6 coming out on top with a few <3% scores, Gemini 3.1 Pro getting two nonzero scores, and the others (GPT-5.4 and Grok 4.20) getting all 0%
Yes, we get that LLMs are really bad when you give them contrived visual puzzles or pseudo games to solve... Well great, we already knew this.
The "hype" around the ARC-AGI benchmarks makes me laugh, especially the idea we would have AGI when ARC-AGI-1 was solved... then we got 2, and now we're on 3.
Shall we start saying that these benchmarks have nothing to do with AGI yet? Are we going to get an ARC-AGI-10 where we have LLMs try and beat Myst or Riven? Will we have AGI then?
This isn't the right tool for measuring "AGI", and honestly I'm not sure what it's measuring except the foundation labs benchmaxxing on it.
I believe the CEO of ARC has said they expect us to get to ARC-AGI-7 before declaring AGI.
They'll specifically work to pass the next version of ARC-AGI, by evaluating what kind of dataset is missing that if they trained on would have their model pass the new version.
They ideally don't directly train on the ARC-AGI itself, but they can train in similar problems/datasets to hope to learn the skills that than transfer to also solving for the real ARC-AGI.
The point is that, a new version of ARC-AGI should help the next model be smarter.
LLMs weren’t supposed to solve 1, they did, so we got 2 and it really wasn’t supposed to be solvable by LLMs. It was, and as soon as it started creeping up we start hearing about 3: It’s Really AGI This Time.
I don’t know what Francois’ underlying story is, other than he hasn’t told it yet.
One of a few moments that confirmed it for me was when he was Just Asking Questions re: if Anthropic still used SaaS a month ago, which was an odd conflation of a hyperbolic reading of a hyperbolic stonk market bro narrative (SaaS is dead) and low-info on LLMs (Claude’s not the only one that can code) and addressing the wrong audience (if you follow Francois, you’re likely neither of those poles)
At this point I’d be more interested in a write up from Francois about where he is intellectually than an LLM that got 100% on this. It’s like when Yann would repeat endlessly that LLMs are definitionally dumber than housecats. Maybe, in some specific way that makes sense to you. You’re brilliant. But there’s a translation gap between Mount Olympus and us plebes, and you’re brilliant enough to know that too. So it comes across as trolling and boring.
