The goal is to test for AGI where G stands for general, that means ability to act in any environments, ideally solving novel tasks using novel tools we’ve never seen before in the world. If a specific prompt or tool design lifts a model’s score it’s a sign the model is overfitting to a particular modus operandi, therefore not general.

I think in this age where models are heavily RL-ed on acting in specific harnesses, this type of benchmark is more important than ever, to make sure they’re not in fact moving further away from general intelligence.

It’s because of inductive bias. Harnesses will massively skew results towards working solutions. You might think that’s a good thing but what it might mean that sometimes it becomes enough to run brute force search or a simple parameter search over the harness. Creating the harness is the actual work, because you’re selecting what are the levers to pull. There were some attempts of LLMs generating harnesses on the fly in ARC 2, but they were all mostly based on one handcrafted DSL that was copied over and over again. As it stands harnesses are not allowed because they’re simply not a meaningful measure. What you’d like is to measure how the model performs if it saw this benchmark for the very first time… but then again everyone knows the game is rigged, millions are at stake, and the AI companies fine tune and cheat on the benchmarks any way they can.

You could argue that if you allowed a harness, and that harness was specific for ARC, then you don’t have AGI, you have something that is definitely not general.

iirc, a harness isn't allowed, but if the LLM wants to write its own tools to solve things that is allowed.

The value of a harness is more about developer workflows, I don’t think it really improves the model output.

Harness can totally change capability of the model