[famouswaffles]

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)

[daveguy]

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

[fc417fc802]

> 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.

[daveguy]

1) Pointing out what tools to use is part of the intelligence that LLMs aren't great at.

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:

https://blog.alexisfox.dev/arcagi3

[famouswaffles]

>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.

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.'

[daveguy]

Adding a path finding algorithm and environment transform tools to a supposed "AGI", sure does seem like cheating to me. Sad part is, it's a cheat that only works on environments where pathfinding is a major part. And when it doesn't have those clues it bombs on everything.

I guess you really want to love the current SOTA LLMs. It's a shame they're dumb af.

Have a great day.

[famouswaffles]

>Adding a path finding algorithm and environment transform tools to a supposed "AGI", sure does seem like cheating to me.

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.

[famouswaffles]

>The point of ARC-AGI is to test the intelligence of AI systems in novel, but simple, environments.

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.