ARC Prize: Strong correlation among the three ARC-AGI benchmark scores
AI summary
After plotting ARC-AGI model scores, ARC Prize found a strong association among the three benchmarks: the power curve v2 = v1^4.53 can explain about 98% of the variation in ARC-AGI-2 scores, and models that are on par on ARC-AGI-1 may still differ by more than 20 percentage points on ARC-AGI-2. A similar trend appears on ARC-AGI-3, but the low-reasoning tier of Gemini 4 Argon is a clear exception: only 42.1% on ARC-AGI-2, yet 97.5% on ARC-AGI-3.
Body
Plotting ARC-AGI model scores reveals a strong relationship across all three benchmarks.
For example, the power curve v2 = v1^4.53 explains about 98% of the variation in ARC-AGI-2 scores. Models tied on ARC-AGI-1 can still differ by over 20 percentage points on ARC-AGI-2. https://t.co/7uG7hNXAwo