Direct quote from the ARC-AGI blog:
“SO IS IT AGI?
ARC-AGI serves as a critical benchmark for detecting such breakthroughs, highlighting generalization power in a way that saturated or less demanding benchmarks cannot. However, it is important to note that ARC-AGI is not an acid test for AGI – as we've repeated dozens of times this year. It's a research tool designed to focus attention on the most challenging unsolved problems in AI, a role it has fulfilled well over the past five years.
Passing ARC-AGI does not equate achieving AGI, and, as a matter of fact, I don't think o3 is AGI yet. o3 still fails on some very easy tasks, indicating fundamental differences with human intelligence.
Furthermore, early data points suggest that the upcoming ARC-AGI-2 benchmark will still pose a significant challenge to o3, potentially reducing its score to under 30% even at high compute (while a smart human would still be able to score over 95% with no training). This demonstrates the continued possibility of creating challenging, unsaturated benchmarks without having to rely on expert domain knowledge. You'll know AGI is here when the exercise of creating tasks that are easy for regular humans but hard for AI becomes simply impossible.”
The high compute variant sounds like it costed around *$350,000* which is kinda wild. Lol the blog post specifically mentioned how OpenAPI asked ARC-AGI to not disclose the exact cost for the high compute version.
Also, 1 odd thing I noticed is that the graph in their blog post shows the top 2 scores as “tuned” (this was not displayed in the live demo graph). This suggest in those cases that the model was trained to better handle these types of questions, so I do wonder about data / answer contamination in those cases…