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ARC-AGI-3

arcprize.org

341–350 of 394 posts

Re: ARC-AGI-3

#341

Same question I have for all these benchmarks: What's going to stop e.g. OpenAI from hiring a bunch of teenagers to play these games non-stop for a month and annotate the game with their logic for deriving the rules, generate a data set based on those playthroughs and fine tuning the next version of chatgpt on all those playthroughs?

They would score much worse on the private set than the public set. And they haven't done this for any of the other ARC-AGI benchmarks, so why would they do it for this one?

Re: ARC-AGI-3

#342

Earlier quoted context omitted.

This counterpoint doesn't address the issue, and I would argue that it is partially bad faith. Yes, making it to the test center is significantly harder, but in fact the humans could have solved it from their home PC instead, and performed the exact same. However, if they were given the same test as the LLMs, forbidden from input beyond JSON, they would have failed. And although buying robots to do the test is unfeas…

I just realized that this also means that the benchmark is in practice unverified by third parties, as all tasks are not verified to be solvable through the JSON interface. Essentially there is no guarantee that it is even possible to understand how to complete every task optimally through the JSON interface alone. I assume you did not develop the puzzles by visualizing JSON yourselves, and so there might be non obvi…

Oookay. I actually tried the harness myself, and there was a visual option. It is unclear to me if that is what the models are using on the official benchmark, but it probably is. This probably means that much of my critique is invalid. However, in the process of fiddling with the harness, building a live viewer to see what was happening, and playing through the agent API myself, I might have found 3-4 bugs with the default harness/API. Dunno where to post it, so of all places I am documenting the process on HN.

Bug 1: The visual mode "diff" image is always black, even if the model clicked on an interactive element and there was a change. Codex fixed it in one shot, the problem was in the main session loop at agent.py (line 458).

Bug 2: Claude and Chatgpt can't see the 128x128 pixel images clearly, and cannot or accurately place clicks on them either. Scaling up the images to 1028x1028 pixels gave the best results, claude dropped off hard at 2048 for some reason. Here are the full test results when models were asked to hit specific (manually labeled) elements on the "vc 33" level 1 (upper blue square, lower blue square, upper yellow rectangle, lower yellow rectangle):

Model | 128 | 256 | 512 | 1024 | 2048

claude-opus-4-6 | 1/10 | 1/10 | 9/10 | 10/10 | 0/10

gemini-3-1-pro-preview | 10/10 | 10/10 | 10/10 | 10/10 | 10/10

gpt-5.4-medium | 4/10 | 8/10 | 9/10 | 10/10 | 8/10

Bug 3: "vc 33" level 4 is impossible to complete via the API. At least it was when I made a web-viewer to navigate the games from the API side. The "canal lock" required two clicks instead of one to transfer the "boat" when water level were equilibriated, and after that any action whatsoever would spontaneously pop the boat back to the first column, so you could never progress.

"Bug" 4: This is more of a complaint on the models behalf. A major issue is that the models never get to know where they clicked. This is truly a bit unfair since humans get a live update of the position of their cursor at no extra cost (even a preview of the square their cursor highlights in the human version), but models if models fuck up on the coordinates they often think they hit their intended targets even though they whiffed the coordinates. So if that happens they note down "I hit the blue square but I guess nothing happened", and for the rest of the run they are fucked because they conclude the element is not interactive even though they got it right on the first try. The combination of an intermediary harness layer that let the models "preview" their cursor position before the "confirmed" their action and the 1024x1024 resolution caused a major improvement in their intended action "I want to click the blue square" actually resulting in that action. However, even then unintended miss-clicks often spell the end of a run (Claude 4.6 made it the furthest, which means level 2 of the "vc 33" stages, and got stuck when it missed a button and spent too much time hitting other things)

After I tried to fix all of the above issues, and tried to set up an optimal environment for models to get a fair shake, the models still mostly did very badly even when they identified the right interactive elements...except for Claude 4.6 Opus! Claude had at least one run where it made it to level 4 on "vc 33", but then got stuck because the blue squares it had to hit became too small, and it just couldn't get the cursor in the right spot even with the cursor preview functionality (the guiding pixel likely became too small for it to see clearly). When you read through the reasoning for the previous stages though, it didn't truly fully understand the underlying logic of the game, although it was almost there.

Re: ARC-AGI-3

#343

Earlier quoted context omitted.

> For example, in a variant of environment TR87, Opus 4.6 scores 0.0% with no harness and 97.1% with the Duke harness (12), yet in environment BP35, Opus 4.6 scores 0.0% under both configuration This is with a harness that has been designed to tackle "a small set of public environments: ls20, ft09, and vc33" (of the arc-agi-3 challenge), yet it looks like it does not solve the full arc-agi-3 benchmark, just some of t…

The harness was designed with the preview, but no it was still tested on the full public set in that environment. You can run the benchmark in different 'environments' though it's unclear what the difference between them is. >We then tested the harnesses on the full public set (which researchers did not have access to at the time)

It may have been tested on the full set, but the score you quote is for a single game environment. Not the full public set. That fact is verbatim in what you responded to and vbarrielle quoted. It scored 97% in one game, and 0% in another game. The full prelude to what vbarrielle quoted, the last sentence of which you left out, was:

> We then tested the harnesses on the full public set (which researchers did not have access to at the time). We found extreme bimodal performance across the two sets, controlling for the same frontier model...

The harness only transfers to like-environments and the intelligence for those specific games is baked into the harness by the humans who coded it for this specific challenge.

The point of ARC-AGI is to test the intelligence of AI systems in novel, but simple, environments. Having a human give it more powerful tools in a harness defeats the purpose. You should go back and read the original ARC-AGI paper to see what this is about+. Are you upset about the benchmark because frontier LLM models do so poorly exhibiting the ability to generalize when the benchmarks are released?

+ https://arxiv.org/abs/1911.01547

Re: ARC-AGI-3

#344

Without reading the .pdf, I tried the first game it gave me, at https://arcprize.org/tasks/ls20 , and I couldn't begin to guess what I was supposed to do. Not sure what this benchmark is supposed to prove. 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…

If you tried for a few more minutes you would have figured it out.

Re: ARC-AGI-3

#345
post #310

Earlier quoted context omitted.

>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. Quintessential goal post moving...

If you read the charter of the eval (or any eval, really), this statement is pretty silly. The whole point of each eval version is to identify a chunk of challenges that humans do well that AI can't. When AI gets to ~80, you move to the next chunk. When you run out of challenges, you have AGI.

HN occasionally devolves into “supremely pedantic and nitpicky” mode. Today is one of those days.

Re: ARC-AGI-3

#346

Earlier quoted context omitted.

Not true. We don't have a good definition for intelligence - it's very much an I'll know it when I see it sort of thing. Frontier models are reliably providing high undergraduate to low graduate level customized explanations of highly technical topics at this point. Yet I regularly catch them making errors that a human never would and which betray a fatal lack of any sort of mental model. What are we supposed to make…

> Yet I regularly catch them making errors that a human never would I have yet to see a "error" that modern frontier models make that I could not imagine a human making - average humans are way more error prone than the kind of person who posts here thinks, because the social sorting effects of intelligence are so strong you almost never actually interact with people more than a half standard deviation away. (The one…

> I have yet to see a "error" that modern frontier models make that I could not imagine a human making

I mostly agree if "a human" is just any person we pluck of the street. What I still see with some regularity is the models (right now, primarily Opus 4.6 through Claude Code) making mistakes that humans:

- working in the same field/area as me (nothing particularly exotic, subfield of CS, not theory)

- with even a fraction of the declarative knowledge about the field as the LLM

- with even a fraction of frontier LLM abilities suggested by their perf in mathematical/informatics Olympiads

would never make. Basically, errors I'd never expect to see from a human coworker (or myself). I don't yet consider myself an expert in my subfield, and I'll almost certainly never be a top expert in it. Often the errors seem to present to me as just "really atrocious intuition." If the LLM ran with some of them they would cause huge problems.

In many regards the models are clearly superhuman already.

Re: ARC-AGI-3

#347

Earlier quoted context omitted.

The harness was designed with the preview, but no it was still tested on the full public set in that environment. You can run the benchmark in different 'environments' though it's unclear what the difference between them is. >We then tested the harnesses on the full public set (which researchers did not have access to at the time)

It may have been tested on the full set, but the score you quote is for a single game environment. Not the full public set. That fact is verbatim in what you responded to and vbarrielle quoted. It scored 97% in one game , and 0% in another game. The full prelude to what vbarrielle quoted, the last sentence of which you left out, was: > We then tested the harnesses on the full public set (which researchers did not hav…

> intelligence for those specific games is baked into the harness

This is your claim but the other commenter claims the harness consists only of generic tools. What's the reality?

I also encountered confusion about this exact issue in another subthread. I had thought that generic tooling was allowed but others believed the benchmark to be limited to ingesting the raw text directly from the API without access to any agent environment however generic it might be.

Re: ARC-AGI-3

#348

Earlier quoted context omitted.

Blind people do function within the context of a human-centric world, though, so they would qualify as intelligent.

Yes, but they use various "harnesses" to do so (dog guides, text to speech software, assistance of other humans when needed..). Why can't AI?

Assistance of other humans? You do realise we're talking about an intelligence test right, at that point what are you even testing for. I'm sure you've taken exams where you couldn't bring your own notes, use Google or get help from someone, even though real life doesn't have those constraints

Re: ARC-AGI-3

#349

Earlier quoted context omitted.

We don't call a calculator intelligent. A calculator is extremely useful, but it is not intelligent. A computer is extremely useful, but it is not intelligent. Airplanes don't have wings, but they're damn sure useful, and also not intelligent. If LLMs cannot learn to beat not-that-difficult of games better than young teens, they are not intelligent. They are extremely useful. But they are not AGI. Words matter.

So your definition of intelligence would be exactly equal to a human or some subset of them you choose? Could a dog solve ARC-AGI? Probably not. I would not say they lack intelligence. Same with a fruit fly. What if the calculator is powered by actual living neurons? I think you need to know where you actually think the difference between organic machine and intelligence is before making blanket statements. A modern…

Dogs are just pattern matching based on their training set which is evolution /s
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