Getting 50% (SoTA) on Arc-AGI with GPT-4o

(ARC Prize co-founder here).

Ryan's work is legitimately interesting and novel "LLM reasoning" research! The core idea:

> get GPT-4o to generate around 8,000 python programs which attempt to implement the transformation, select a program which is right on all the examples (usually there are 3 examples), and then submit the output this function produces when applied to the additional test input(s)

Roughly, he's implemented an outer loop and using 4o to sample reasoning traces/programs from training data and test. Hybrid DL + program synthesis approaches are solutions we'd love to see more of.

A couple important notes:

1. this result is on the public eval set vs private set (ARC Prize $).

2. the current private set SOTA ~35% solution also performed ~50% on the public set. so this new result might be SOTA but hasn't been validated or scrutinized yet.

All said, I do expect verified public set results to flow down to the private set over time. We'll be publishing all the SOTA scores and open source reproductions here once available: https://arcprize.org/leaderboard

EDIT: also, congrats and kudos to Ryan for achieving this and putting the effort in to document and share his approach. we hope to inspire more frontier AI research sharing like this

Ah, give it a rest. That's not "frontier AI research", neither is it any kind of reasoning. It's the dumbest of the dumb possible generate-and-test approach that spams a fire hose of Python programs until it hits one that works. And still it gets only 50% on the public eval.

How many thousands of Python programs does a human need to solve a single ARC task? That's what you get with reasoning: you don't need oodles of compute and boodles of sampling.

And I'm sorry to be so mean, but ARC is a farce. It's supposed to be a test for AGI but its only defense from a big data approach (what Francois calls "memorisation") is that there are few examples provided. That doesn't make the tasks hard to solve with memorisation it just makes it hard for a human researcher to find enough examples to solve with memorisation. Like almost every other AI-IQ test before it, ARC is testing for the wrong thing, with the wrong assumptions. See the Winograd Schema Challenge (but not yet the Bongard problems).