Because it is.
Opus 4.6 jumps from 0.0% to 97.1% when given visual input
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?
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.
2) one of the tools is a path finding algorithm. A big improvement/crutch over a regular LLM that has no such capability.
You'd think if LLMs are intelligent they'd be able to determine that a path finding algorithm is necessary and have a sub agent code it up real quick. But apparently they just can't do that without humans stepping in to make it a standard tool for them.
Here's the paper on what they did for the Duke Harness:
ARC 3 doesn't allow that so.
>Here's the paper on what they did for the Duke Harness: https://blog.alexisfox.dev/arcagi3
Yeah, and the tools are general, not 'baked into the harness by the humans who coded it for this specific challenge.'
I guess you really want to love the current SOTA LLMs. It's a shame they're dumb af.
Have a great day.
You would need all that if you, a human wanted any chance of solving this benchmark in the format LLMs are given. The funny thing about this benchmark is that we don't even know how solvable it is, because the baseline is tested with radically different inputs.
>I guess you really want to love the current SOTA LLMs. It's a shame they're dumb af.
I guess you really don't want to think critically. Yeah good day lol.
Byebye now.
Look man, don't reply if you don't want to.
The point is whatever Francois wants it to be.
>Having a human give it more powerful tools in a harness defeats the purpose.
Why does it defeat the purpose? Restricting the tools available is an arbitrary constraint. The Duke harness is a few basic tools. What's the problem ? In what universe would any AI Agent worth its salt not have access to read, grep and bash ? If his benchmark was as great and the difference as wide as he claimed, then it simply wouldn't matter if those tools were available. Francois removed access to tools because his benchmark falls apart with them. Simple as.
>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?
I’m not upset about anything. I do not care about ARC, and I never have. I think it is a nothingburger of a benchmark: lots of grand claims about AGI, but very little predictive power or practical utility.
When models started climbing FrontierMath, that benchmark actually told us something useful: their mathematical capabilities were becoming materially stronger. And now state-of-the-art systems have helped with real research and even contributed to solving open problems. That is what a good benchmark is supposed to do.
ARC ? Has 0 utility on its own and manages to tell you nothing at the same time.
Unsaturated benchmarks matter because they help show where the state of the art actually is. The value is not “look, the score is low,” but whether the benchmark tells you something real and useful about capability. ARC has always struggled on that front, but 3 has taken that to a new level of useless.
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.