Like suppose there were only two tasks, each with a baseline score of solving in 100 steps. You come along and you solve one in only 50 steps, and the other in 200 steps. You might hope that since you solved one twice as quickly as the baseline, but the other twice as slowly, those would balance out and you'd get full credit. Instead, your scores are 1.0 for the first task, and 0.25 (scoring is quadratic) for the second task, and your total benchmark score is a mere 0.625.
I'm guessing you did not pass the human testers JSON blobs to work with, and suspect they would also score 0% without the eyesight and visual cortex harness to their reasoning ability.
(This version of the benchmark would be several orders of magnitude harder wrt current capabilities...)
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 unfeasible, giving LLMs a screenshot is easy.
Without visual input for LLMs in a benchmark that humans are asked to solve visually, you are not comparing apples to apples. In fact, LLMs are given a different and significantly harder task, and in a benchmark that is so heavily weighted against the top human baseline, the benchmark starts to mean something extremely different. Essentially, if LLMs eventually match human performance on this benchmark, this will mean that they in fact exceed human performance by some unknown factor, seeing as human JSON performance is not measured.
Personally, this hugely decreased my enthusiasm for the benchmark. If your benchmark is to be a North star to AGI, labs should not be steered towards optimizing superhuman JSON parsing skills. It is much more interesting to steer them towards visual understanding, which is what will actually lead the models out into the world.
I assume you did not develop the puzzles by visualizing JSON yourselves, and so there might be non obvious information that is lost in translation to JSON. Until humans optimally solve all the puzzles without ever having seen the visual version, there is no guarantee that this is even possible to do.
I think the only viable solution here is to release a version of the benchmark with a vision only harness. Otherwise it is impossible to interpret what LLM progress on this benchmark actually means.
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.
Denying proper eyesight harness is like trying to construct speech-to-text model that makes transcripts from air pressure values measured 16k times per second, while human ear does frequency-power measurement and frequency binning due to it's physical construction.
I guess it could be interesting to provide alternative versions that made available various representations of the same data. Still, I'd expect any AGI to be capable of ingesting more or less any plaintext representation interchangeably.
But by all means, give the agents access to an API that returns pixel data. However I fully expect that would reduce performance rather than increase it.
However, if it can't figure out to render the json to a visual on its own does it really qualify as AGI? I'd still say the benchmark is doing its job here. Granted it's not a perfectly even playing field in that case but I think the goal is to test for progress towards AGI as opposed to hosting a fair tournament.
Can you render serialized JSON text blob to a visual with your brain only? The model can't do anything better than this - no harness means no tool at all, no way to e.g. implement a visualizer in whatever programming language and run it.
Why don't human testers receive the same JSON text blob and no visualizers? It's like giving human testers a harness (a playable visualizer), but deliberately cripples it for the model.
Also, if it makes that big of a difference, then make a renderer for your agent that looks like the web page and have it solve them in the graphical interface and funnel the results to the API. I guarantee you won't get better performance, because the AGI is going to have to "understand" the raw data can be represented as a 2D matrix regardless of whether it gets a 2D matrix of pixels or a 2D matrix of enumeration in JSON. If anything, that makes it a more difficult problem for a AI system that "speaks" in tokens.
This is already a solved benchmark. That's why scoring is so convoluted and a self proclaimed Agent benchmark won't allow basic agent tools. ARC has always been a bit of a nothing burger of a benchmark but this takes the cake.
[1] https://arcprize.org/media/ARC_AGI_3_Technical_Report.pdf
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 them.
>We then tested the harnesses on the full public set (which researchers did not have access to at the time)
> 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?
Do you have a source for this? I would be very curious to see how top models do with vision.
Nit: I didn't see a final score of how many actions I took to complete 7 levels. Also didn't see a place to sign in to see the leaderboard (I did see the sign in prompt).
If I understand correctly the model can carry only very limited memory among tests, so it looks like it's not really possible for the model to self specialize itself under this assumptions.
My reading of that part in the technical report (models "could be using their own tools behind the model’s API, which is a blackbox"), is that there's no way to prevent it.
But from fchollet's comment here, using tools and harnesses is encouraged, as long as they are generic and not arc-agi specific. In that case, the models should be benchmarked by prompting through claude code and codex, rather than the through API (as from the api we only expect raw LLM output, and no tool use).
A theoretical text-only superintelligent LLM could prove the Riemann hypothesis but fail ARC-AGI-3 and won't even be AGI according to this benchmark...
If you were phrasing things to quantify intelligence, you would have a visual intelligence pillar. And they would not pass that pillar. It doesn't make them dysfunctional or stupid, but visual intelligence is a key part of human intelligence.
Or perhaps the view is that any gains are good gains? Like studying for a test by leaning on brute memorization is still a non-zero positive gain.