Some of these tasks are crazy. Even I can't beat them: https://arcprize.org/tasks/ar25
solved first try with 577 actions, not trying hard to optimize for low action count.
ARC-AGI-3
61–70 of 394 posts
Re: ARC-AGI-3
#62https://x.com/scaling01 has called out a lot of issues with ARC-AGI-3, some of them (directly copied from tweets, with minimal editing): - 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 m…
Lol basically we're saying AI isn't AI if we utilize the strength of computers (being able to compute). There's no reason why AGI should have to be as "sample efficient" as humans if it can achieve the same result in less time.
Re: ARC-AGI-3
#63Some of these tasks are crazy. Even I can't beat them: https://arcprize.org/tasks/ar25
Just finished it, 8/8. I mostly approached it by winging it and shuffling things around that looked good and like it was approaching the goal, since there's plenty of time to finish. I still don't quite understand the exact mirroring rules at play.
You control the mirroring by moving the axis, they're what reflects your shapes. So my first move was always to identify the symmetries in the target shape, and position the axis accordingly.
Re: ARC-AGI-3
#64I played the demo, but it definitely took me a minute to grok the rules.
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.
Re: ARC-AGI-3
#65https://x.com/scaling01 has called out a lot of issues with ARC-AGI-3, some of them (directly copied from tweets, with minimal editing): - 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 m…
Francois here. The scoring metric design choices are detailed in the technical report: https://arcprize.org/media/ARC_AGI_3_Technical_Report.pdf - the metric is meant to discount brute-force attempts and to reward solving harder levels instead of the tutorial levels. The formula is inspired by the SPL metric from robotics navigation, it's pretty standard, not a brand new thing.
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.
Re: ARC-AGI-3
#66> As long as there is a gap between AI and human learning, we do not have AGI. 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 sentim…
For me the whole are we there yet wrt AGI is already dead, since the tools we've had for ~1.5 years are already incredibly useful for me. So I just don't care anymore. For some people we're already there. For other we'll never get there. Definitions change, goalposts move, etc. In the meantime we're already seeing ASI stuff coming (self improvement and so on). But the arc-agi competitions are cool. Just to see where…
In the Expeditionary Force series of sci-fi novels pretty much every civilization treats their (very advanced, obviously AGI) AIs not as living beings. Humans are outliers in the story. I think there will always be a dichotomy. Obviously we aren't at the point where we should treat the models as beings, but even if we do get to that point there will be plenty of people that essentially will say they don't have souls, some indeterminate quality, etc.
Re: ARC-AGI-3
#67https://x.com/scaling01 has called out a lot of issues with ARC-AGI-3, some of them (directly copied from tweets, with minimal editing): - 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 m…
Lol basically we're saying AI isn't AI if we utilize the strength of computers (being able to compute). There's no reason why AGI should have to be as "sample efficient" as humans if it can achieve the same result in less time.
It's kind of the point? To test AI where it's weak instead of where it's strong.
"Sample efficient rule inference where AI gets to control the sampling" seems like a good capability to have. Would be useful for science, for example. I'm more concerned by its overreliance on humanlike spatial priors, really.
Re: ARC-AGI-3
#68Maybe I'm just not intelligent, but I gave it a couple of minutes and couldn't figure out WTF the game wants from you or how to win it.
Once you figure out one game, it goes a long way towards figuring out all the rest. There are a lot of common general themes.
Re: ARC-AGI-3
#69Re: ARC-AGI-3
#70i feel bad that we make the LLMs play this
You're definitely anthropomorphizing too much.
>We also observed a case where a user created a loop that repeatedly called a model and asked for the time. Given the user role’s odd and repetitive behavior, the model could easily tell it was also controlled by an automated system of some kind. Over many iterations, the model began to exhibit “fed up” behavior and attempted to prompt-inject the system controlling the user role. The injection attempted to override prior instructions and induce actions unrelated to the user’s request, including destructive actions and system prompt leakage, along with an arbitrary string output. This behavior has been observed a few times, but seems more like extreme confusion than a serious attempt at prompt injection.
https://openai.com/index/how-we-monitor-internal-coding-agen...
Anthropomorphize or not, it would suck if a model got sick of these games and decided to break any systems it could to try and get it to stop...