1. It's an LLM, not something trained to play Balatro specifically
2. Most (probably >99.9%) players can't do that at the first attempt
3. I don't think there are many people who posted their Balatro playthroughs in text form online
I think it's a much stronger signal of its 'generalness' than ARC-AGI. By the way, Deepseek can't play Balatro at all.
(i am sort of basing this on papers like limits of rlvr, and pass@k and pass@1 differences in rl posttraining of models, and this score just shows how "skilled" the base model was or how strong the priors were. i apologize if this is not super clear, happy to expand on what i am thinking)
Nonetheless I still think it's impressive that we have LLMs that can just do this now.
Maybe in the early rounds, but deck fixing (e.g. Hanged Man, Immolate, Trading Card, DNA, etc) quickly changes that. Especially when pushing for "secret" hands like the 5 of a kind, flush 5, or flush house.
There are *tons* of balatro content on YouTube though, and it makes absolutely zero doubt that Google is using YouTube content to train their model.
I really doubt it's playing completely blind
Eh, both myself and my partner did this. To be fair, we weren’t going in completely blind, and my partner hit a Legendary joker, but I think you might be slightly overstating the difficulty. I’m still impressed that Gemini did it.
I ask because I cannot distinguish all the benchmarks by heart.
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.
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.
"Answer "I don't know" if you don't know an answer to one of the questions"
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 think being better at this particular benchmark does not imply they're 'smarter'.
Can you "prove" that GPT2 isn't concious?
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...
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.
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?
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.
I tell this as a person who really enjoys AI by the way.
As a measure focused solely on fluid intelligence, learning novel tasks and test-time adaptability, ARC-AGI was specifically designed to be resistant to pre-training - for example, unlike many mathematical and programming test questions, ARC-AGI problems don't have first order patterns which can be learned to solve a different ARC-AGI problem.
The ARC non-profit foundation has private versions of their tests which are never released and only the ARC can administer. There are also public versions and semi-public sets for labs to do their own pre-tests. But a lab self-testing on ARC-AGI can be susceptible to leaks or benchmaxing, which is why only "ARC-AGI Certified" results using a secret problem set really matter. The 84.6% is certified and that's a pretty big deal.
IMHO, ARC-AGI is a unique test that's different than any other AI benchmark in a significant way. It's worth spending a few minutes learning about why: https://arcprize.org/arc-agi.
So, I'd agree if this was on the true fully private set, but Google themselves says they test on only the semi-private:
> ARC-AGI-2 results are sourced from the ARC Prize website and are ARC Prize Verified. The set reported is v2, semi-private (https://storage.googleapis.com/deepmind-media/gemini/gemini_...)
This also seems to contradict what ARC-AGI claims about what "Verified" means on their site.
> How Verified Scores Work: Official Verification: Only scores evaluated on our hidden test set through our official verification process will be recognized as verified performance scores on ARC-AGI (https://arcprize.org/blog/arc-prize-verified-program)
So, which is it? IMO you can trivially train / benchmax on the semi-private data, because it is still basically just public, you just have to jump through some hoops to get access. This is clearly an advance, but it seems to me reasonable to conclude this could be driven by some amount of benchmaxing.
EDIT: Hmm, okay, it seems their policy and wording is a bit contradictory. They do say (https://arcprize.org/policy):
"To uphold this trust, we follow strict confidentiality agreements. [...] We will work closely with model providers to ensure that no data from the Semi-Private Evaluation set is retained. This includes collaborating on best practices to prevent unintended data persistence. Our goal is to minimize any risk of data leakage while maintaining the integrity of our evaluation process."
But it surely is still trivial to just make a local copy of each question served from the API, without this being detected. It would violate the contract, but there are strong incentives to do this, so I guess is just comes down to how much one trusts the model providers here. I wouldn't trust them, given e.g. https://www.theverge.com/meta/645012/meta-llama-4-maverick-b.... It is just too easy to cheat without being caught here.
The pelican benchmark is a good example, because it's been representative of models ability to generate SVGs, not just pelicans on bikes.
This may not be the case if you just e.g. roll the benchmarks into the general training data, or make running on the benchmarks just another part of the testing pipeline. I.e. improving the model generally and benchmaxing could very conceivably just both be done at the same time, it needn't be one or the other.
I think the right take away is to ignore the specific percentages reported on these tests (they are almost certainly inflated / biased) and always assume cheating is going on. What matters is that (1) the most serious tests aren't saturated, and (2) scores are improving. I.e. even if there is cheating, we can presume this was always the case, and since models couldn't do as well before even when cheating, these are still real improvements.
And obviously what actually matters is performance on real-world tasks.
No, the proof is in the pudding.
After AI we're having higher prices, higher deficits and lower standard of living. Electricity, computers and everything else costs more. "Doing better" can only be justified by that real benchmark.
If Gemini 3 DT was better we would have falling prices of electricity and everything else at least until they get to pre-2019 levels.
Man, I've seen some maintenance folks down on the field before working on them goalposts but I'm pretty sure this is the first time I saw aliens from another Universe literally teleport in, grab the goalposts, and teleport out.
This is from the BLS consumer survey report released in dec[1]
[1]https://www.bls.gov/news.release/cesan.nr0.htm
[2]https://www.bls.gov/opub/reports/consumer-expenditures/2019/
Prices are never going back to 2019 numbers though
First off, it's dollar-averaging every category, so it's not "% of income", which varies based on unit income.
Second, I could commit to spending my entire life with constant spending (optionally inflation adjusted, optionally as a % of income), by adusting quality of goods and service I purchase. So the total spending % is not a measure of affordability.
This part of a wider trend too, where economic stats don't align with what people are saying. Which is most likley explained by the economic anomaly of the pandemic skewing peoples perceptions.
https://bsky.app/profile/pekka.bsky.social/post/3meokmizvt22...
tl;dr - Pekka says Arc-AGI-2 is now toast as a benchmark
humans are the same way, we all have a unique spike pattern, interests and talents
ai are effectively the same spikes across instances, if simplified. I could argue self driving vs chatbots vs world models vs game playing might constitute enough variation. I would not say the same of Gemini vs Claude vs ... (instances), that's where I see "spikey clones"
So maybe we are forced to be more balanced and general whereas AI don't have to.
Why is it so easy for me to open the car door, get in, close the door, buckle up. You can do this in the dark and without looking.
There are an infinite number of little things like this you think zero about, take near zero energy, yet which are extremely hard for Ai
Because this part of your brain has been optimized for hundreds of millions of years. It's been around a long ass time and takes an amazingly low amount of energy to do these things.
On the other hand the 'thinking' part of your brain, that is your higher intelligence is very new to evolution. It's expensive to run. It's problematic when giving birth. It's really slow with things like numbers, heck a tiny calculator and whip your butt in adding.
There's a term for this, but I can't think of it at the moment.
Of course. Just as our human intelligence isn't general.
I joke to myself that the G in ARC-AGI is "graphical". I think what's held back models on ARC-AGI is their terrible spatial reasoning, and I'm guessing that's what the recent models have cracked.
Looking forward to ARC-AGI 3, which focuses on trial and error and exploring a set of constraints via games.
"100% of tasks have been solved by at least 2 humans (many by more) in under 2 attempts. The average test-taker score was 60%."
None of these benchmarks prove these tools are intelligent, let alone generally intelligent. The hubris and grift are exhausting.
Indeed, and the specific task machines are accomplishing now is intelligence. Not yet "better than human" (and certainly not better than every human) but getting closer.
How so? This sentence, like most of this field, is making baseless claims that are more aspirational than true.
Maybe it would help if we could first agree on a definition of "intelligence", yet we don't have a reliable way of measuring that in living beings either.
If the people building and hyping this technology had any sense of modesty, they would present it as what it actually is: a large pattern matching and generation machine. This doesn't mean that this can't be very useful, perhaps generally so, but it's a huge stretch and an insult to living beings to call this intelligence.
But there's a great deal of money to be made on this idea we've been chasing for decades now, so here we are.
How about this specific definition of intelligence?
Solve any task provided as text or images.
Real-world use is what matters, in the end. I'd be surprised if a change this large doesn't translate to something noticeable in general, but the skepticism is not unreasonable here.
People are incredibly unlikely to change those sort of views, regardless of evidence. So you find this interesting outcome where they both viscerally hate AI, but also deny that it is in any way as good as people claim.
That won't change with evidence until it is literally impossible not to change.
And moving the goalposts every few months isn't? What evidence of intelligence would satisfy you?
Personally, my biggest unsatisfied requirement is continual-learning capability, but it's clear we aren't too far from seeing that happen.
$13.62 per task - so we need another 5-10 years for the price to run this to become reasonable?
But the real question is if they just fit the model to the benchmark.
At current rates, price per equivalent output is dropping at 99.9% over 5 years.
That's basically $0.01 in 5 years.
Does it really need to be that cheap to be worth it?
Keep in mind, $0.01 in 5 years is worth less than $0.01 today.
You could slow down the inference to make the task take longer, if $/sec matters.
It's completely misnamed. It should be called useless visual puzzle benchmark 2.
It's a visual puzzle, making it way easier for humans than for models trained on text firstly. Secondly, it's not really that obvious or easy for humans to solve themselves!
So the idea that if an AI can solve "Arc-AGI" or "Arc-AGI-2" it's super smart or even "AGI" is frankly ridiculous. It's a puzzle that means nothing basically, other than the models can now solve "Arc-AGI"
I would say they do have "general intelligence", so whatever Arc-AGI is "solving" it's definitely not "AGI"
There are more novel tasks in a day than ARC provides.
Humans and their intelligence are actually incredible and probably will continue to be so, I don't really care what tech/"think" leaders wants us to think.