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.
Kinda crazy that Yudkowsky and all those rationalists and enthusiasts spent over a decade obsessing over this stuff, and we've had almost 80 years of elite academics pondering on it, and none of them could come up with a meaningful, operational theory of intelligence. The best we can do is "closer to AGI" as a measurement, and even then, it's not 100% certain, because a model might have some cheap tricks implicit to the architecture that don't actually map to a meaningful difference in capabilities.
Gotta love the field of AI.
It doesn't prove anything of the sort. ARC-AGI has always been nothing special in that regard but this one really takes the cake. A 'human baseline' that isn't really a baseline and a scoring so convoluted a model could beat every game in reasonable time and still score well below 100. Really what are we doing here ?
That Francois had to do all this nonsense should tell you the state of where we are right now.
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…
Thats not intelligence though. Even if it may appear to be. Does it matter? Thats another question. But certaintly is not a representation of intelligence.
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.
But vibe coding also tends to produce somewhat poor architecture, lots of redundant and intermingled bits that should be refactored. I think the model is worse the worse code it has to work with, which I presume is only in part because it's fundamentally harder to work with bad code, but also in part because its context is filled with bad code.
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.)
I think one major disconnect, is that for most people, AGI is when interacting with an AI is basically in every way like interacting with a human, including in failure modes. And likely, that this human would be the smartest most knowledgeable human you can imagine, like the top expert in all domains, with the utmost charisma and humor, etc.
This is why the "goal post" appears to be always moving, because the non-commoners who are involved with making AGI and what not never want to accept that definition, which to be fair seems too subjective, and instead like to approach AGI like something different, it can solve some problems human's can't, when it doesn't fail, it behaves like an expert human, etc.
Even if an AI could do any intellectual task about as well as a highly competent human could, I believe most people would not consider it AGI, if it lacks the inherent opinion, personality, character, inquiries, failure patterns, of a human.
And I think that goes so far as, a text only model can never meet this bar. If it cannot react in equal time to subtle facial queues, sounds, if answering you and the flow of conversation is slower than it would be with a human, etc. All these are also required for what I consider the commoner accepting AGI as having been achieved.
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%
Not like it's a big lead as of yet. I expect to see more action within the next few months, as people tune the harnesses and better models roll in.
This is far more of a "VLA" task than it is an "LLM" task at its core, but I guess ARC-AGI-3 is making an argument that human intelligence is VLA-shaped.
When running, the grids are represented in JSON, so the visual component is nullified but it still requires pretty heavy spatial understanding to parse a big old JSON array of cell values. Given Gemini's image understanding I do wonder if it would perform better with a harness that renders the grid visually.
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.
These 'tests' are not labeled AGI by magic but because they are designed specificly for testing certain things a question answer test cant solve.
Gemini and OpenAI are at 80-90% at ARC-AGI-2 and its quite interesting to see the difference of challange between 2 and 3.
AGI progress means btw. general. So every additional dimension an agent can solve pushes that agent to be more general.
When you run out of such tests then it's evidence that you have reached AGI. The point of these tests is to define AGI objectively as the inability to devise tests that humans have superiority on.
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.